Infrastructures appear increasingly strained by myriad environmental factors including emerging and disruptive technologies, climate change, cybertechnologies, political polarization, and ageing assets. At the same time, our critical systems appear rooted in designs and operational principles that emerged in the last century, that are insufficient for today’s challenges. While ample knowledge exists about more desirable infrastructure futures, we appear unable to change these critical systems at pace and scale with their changing environments. This signals that fundamental knowledge and competencies are needed to transform critical systems in the face of growing complexity, and this knowledge may be missing from our current infrastructure approaches. New strategies are needed to navigate infrastructures through accelerating, volatile, and uncertain conditions.

This workshop will create a space for dialogue to identify grand challenges for the infrastructure community, and novel models for confronting the increasingly complex conditions of the Anthropocene. Past workshops by ISSST members have resulted in an Infrastructure Misfits community, a group of innovative infrastructure thinkers dedicated to navigating infrastructure through increasingly complex environments. The Infrastructure Misfits – an (un)society – community has introduced a series of workshops, podcasts, books, and articles to facilitate dialogues around how infrastructures – as technologies, and socio-governmental processes – should transform into the future, including and perhaps most importantly, the barriers to these transformations.

Should a clear set of visions emerge then participants will be invited to submit these visions as articles to a special collection in Environmental Research: Infrastructure and Sustainability (ERIS). ERIS has offered to support the special collection and is willing to consider from the workshop commentary and perspective type articles.

More broadly, the goal of the workshop is to create a community of infrastructure leaders that are capable of navigating systems through future complexity. This community will need to be capable of recognizing the increasingly complex nature of infrastructures themselves as well as their environments. They will recognize that technocentric solutions are limited, and that dynamics between socio-governmental and ecological processes are not only relevant but also leverage points in affecting change. Those who participate in the workshop will be brought into the Infrastructure Misfits network and encouraged to continue discussions after ISSST.

1. Is it open to the community or special invite only? Open to community
2. How many people do you expect? 30
3. Will it be full-day? Yes

Each year the ACLCA/SETAC working group on "Life Cycle Assessment of Emerging Technologies" meets for a full day preceding the ISSST conference. This year, we are proposing to meet on Sunday, June 15 from approximately 9am-5pm. Additional information about the working group is available here: https://aclca.org/setac-aclcawgemergingtechnology/

This is a working meeting in which we recap past work by the group, advance ongoing work to provide guidance for the LCA community, and plan for future topics of interest that the group will explore. These meetings generally serve as the launching point for an academic manuscript co-authored by a wide range of interested parties.

Past topics have included: general guidance on LCA of emerging technologies as supplemental advice beyond the ISO 14040 and 14044 standards; discussion of contentious issues such as treatment of uncertainty or appropriate use of LCA (currently undergoing peer-review); and exploration of data quality indicators and what they say about the 'depth' of the LCA study with reference to the special requirements of pre-commercial technologies or lab-scale technologies.

Exact topic and schedule for 2025 has not yet been determined, but all members of the ISSST community are welcome to join for any part of the session that may interest them. An exact schedule will be posted closer to the conference.

(Please note that the listed authors simply indicate the convenors of this meeting and does not attempt a full listing of the working group leadership or membership).

The Climate Fresk (https://climatefresk.org/world/) is a collaborative workshop that employs the serious game method to educate adults on the scientific facts of climate change based on the IPCC report. The workshop is facilitated by The Climate Fresk french non-profit and has reached 2+ million participants in 162 countries. Using a snowball system of participants becoming facilitators and then facilitation trainers as well, it has a goal to 'fresk' 3% of the US population in the next years.

I will be offering to do the 3 hour workshop covering causal relationships of climate change, emotions, and actions. The workshop is aimed at adults and level of knowledge depends on participants background. It is suitable for everyone, including experts. Afterwards participants can join the Fresker community, and are elegible to take a facilitator training themselves eventually bringing the workshop to their students and communities.

Over the past three decades, life cycle assessment has proliferated as a sustainability tool. Most LCA are conducted on specific products, although a greater number have started to attempt to harmonize and generalize LCA findings across a particular product category or industry sector. There have been few, if any, attempts to translate the overall learnings from LCA research into major guiding principles to broadly assist in environmental decison-making. Nevertheless, there is a wealth of environmental impact wisdom and rules-of-thumb that has been developed within our community that are largely unknown to those outside the environmental community. There are any number of insights that our community finds mundane that would be surprising to members of the general public.

This workshop will invite participants to step outside the boundaries of product-specific LCA and take a broader view of what foundational knowledge and guidance we have learned after conducting thousands of LCAs.

This workshop will ask participants to propose and debate a series of high-level statements on major environmental lessons we have learned. For example, do we typically expect upstream supply chain transportation to dominate an LCA? How important is the use phase vs. end-of-life in consumer-facing products? Are there specific types of emissions or tradeoffs that we typically observe in certain categories of products that may be important? What inputs typically have outsized contributions to a product's impact? What are some broad life cycle principles that we would like the public to know?

At the conclusion of the workshop, we will determine if there is a consensus on any statements or principles and discuss potential future steps to disseminate those consensus statements more broadly.

At the conference you'll have many opportunities to share ideas and explain your LCA and sustainability work during hallway conversations, poster sessions, lightening talks, or formal presentations. What's the best way to make your point? How can you be succinct and informative?

Through a combination of lecture and practice, this workshop will help you become a more effective and strategic communicator. Workshop participants will learn and apply techniques for being clear, compelling, and better understood. You will be introduced to several concepts and practice applying them to topics of your choice. You will work solo and have the opportunity to volunteer or partner to test out new language.

Join your colleagues to learn communication approaches that will help you make the most of the 2025 conference and build your reputation in your field of specialty.

You'll learn how to:

Extract the essence of your ideas and findings

Craft a main takeaway message and support points

Structure content with appropriate detail and technical depth

Who Should Attend
This workshop is ideal for students and anyone who wants to upskill their ability to communicate with clarity and impact.

Duration 90 minutes

Instructor

Amy Aines

Educator, Author, Keynote Speaker, Communication Coach
CEO Damianakes Communications
Author Championing Science – Communicating Your Ideas to Decision Makers University of California Press

Amy Aines builds STEM career success skills. She is a communication strategist on a mission to help scientists speak and listen so they can make a bigger impact. Co-author of the ‘how to’ book: Championing Science: Communicating Your Ideas to Decision Makers, Amy honed her expertise directing corporate, media, and public policy communications for a $10B telecommunications company and consulting to more than 50 companies from start-ups to global giants in technology, healthcare, and biotech. Amy provides insights into effective communication through customized skill building experiences for graduate students, early career professionals, researchers, and faculty for universities, research development centers. Since 2019 she has delivered workshops for cohorts at the CanCO2Re Inititiative, DOE RECS program, JPL, Stanford, Princeton, Center for BioEnergy Innovation, Florida International University, Northeastern Network Science Institute, UCLA, and Lawrence Livermore National Laboratory. Amy gives of her time as an advisor to the SAi Collective, Quest Science Center, and Beyond the PhD.

Sustainable aviation fuel (SAF) is a drop-in fossil fuel alternative made from various biological feedstocks, including those that can be naturally cultivated like corn, soybean, and algae. SAF has the potential to greatly reduce emissions generated by the aviation industry, which accounts for 2.5% of global emissions and is projected to grow. However, the economics of decarbonizing the aviation sector through SAF are not well understood at a local resolution, and precise models estimating domestic SAF production are lacking.

To foster success in the deployment of new infrastructure and land-use changes to meet the demands for domestic SAF, this study uses process modeling and techno-economic analysis (TEA) to study the economic feasibility of SAF production compared to standard jet fuel. A discounted cash flow rate of return method was used to evaluate the internal rate of return (IRR) of the SAF systems when the net present value of cash flows becomes zero. The TEA model integrates the mass, energy, and financial inputs required for SAF production systems and outputs accurate operational and capital expenditures (OPEX, CAPEX) over the system’s life cycle. A corn-to-SAF pathway was studied and an engineering process model including corn cultivation, starch fermentation, and fuel upgrading was developed. A second pathway that uses miscanthus as a SAF feedstock was also modeled. In this pathway, miscanthus undergoes a Fischer-Tropsch gasification process that produces a biocrude, which is then processed into various liquid fuels and upgraded into SAF.

Historical data was utilized to inform the process model, enabling geo-specific inputs of the South-Central region of the U.S. A comparative analysis was then conducted between the corn-to-SAF and miscanthus-to-SAF pathways to identify key contributors to the Minimum Fuel Selling Price (MFSP) of SAF from either feedstock. Results show that the corn-to-SAF process consistently portrayed greater values in terms of OPEX, CAPEX, and MFSP over miscanthus-to-SAF. For instance, the average MFSP for the corn-derived SAF in the studied agricultural districts in Kansas was found to be $5.62 per gallon of gasoline equivalent (GGE) compared to $3.06 for miscanthus-based SAF.

Future work will include the modeling of multiple feedstock pathways in the same agricultural districts and counties to further compare MFSP and economic feasibility of SAF production in the South-Central U.S. region. Additionally, Monte Carlo methods will be utilized to portray variability in the model, as depicted by historical and projective data sources of biomass availability.

Food waste (FW) management is a significant environmental issue in the United States, with 38% of ~ 96.8 million metric tons annually ending up in landfills, causing substantial greenhouse gas emissions and resource loss. This study fills knowledge gaps by conducting an environmental impact assessment of FW valorization in Wastewater Resource Recovery Facilities (WRRFs), using static Material Flow Analysis (MFA) and Life Cycle Impact Assessment (LCIA) at the county level in the U.S. The evaluated scenarios include (1) traditional landfilling, (2) co-digestion of FW in a conventional activated sludge (CAS) WRRF with anaerobic digestion + struvite fertilizer production, and (3) co-digestion in an anaerobic membrane bioreactor (AnMBR) WRRF + struvite fertilizer production. For Gwinnett County, Georgia, MFA showed that 104,870 tons of FW are landfilled annually, with only 17,240 tons per year processed in WRRFs. LCIA results indicated that landfilling FW has the highest Global Warming Potential (GWP) at 68.55 Kg CO₂ eq per ton of FW due to uncontrolled methane emissions. Conversely, CAS and AnMBR WRRF scenarios reduced GWP to 2.8 E-4Kg CO₂ eq per ton of FW+ wastewater (WW) and 1.12 E-3 Kg CO₂ eq per ton of FW + WW, respectively. Valorization of FW only at CAS and AnMBR WRRFs contributed only 1.05 E-08 and 1.68 E-08 CO₂ eq per ton of FW, respectively. Sensitivity and uncertainty analyses confirmed these findings, showing that co-digestion of FW does not affect effluent quality or regulatory compliance. This study is innovative in its integrated approach, using real-scale WRRF process modeling with MFA and LCIA, providing actionable insights for policymakers and industry stakeholders. The results support a sustainable transition in FW management practices, demonstrating that WRRFs can convert FW into valuable resources, reduce emissions by over 99% compared to landfilling, and contribute to a circular economy while protecting watershed health.

The production and consumption of eggs have grown rapidly in the United States and across the world due to its popularity as a versatile cooking ingredient for many types of cuisine, as well as being one of the more affordable sources of high-quality animal protein. While eggs are also considered to be environmentally friendlier compared to many livestock products, the rapid and sustained growth of egg demand has prompted interest in reducing its environmental footprint to prepare the industry for a net zero future. One of the promising ways to potentially improve the sustainability of the egg industry is to utilize grocery wastes destined for the landfills as a feed ingredient for layer hens. Food waste in the landfill generates potent greenhouse gases such as methane and is widely recognized as a serious climate change concern. Therefore, the food waste to feed valorization approach offers a multifaceted solution that can reduce the large climate change burden of landfilling food wastes, improves circularity of nutrients, and reduces the environmental burden of egg production all at the same time. Despite its potential, there currently exists a lack of studies in the literature that investigate food waste to feed valorization, particularly at a commercial scale in western countries. Therefore, to contribute towards filling the knowledge gap, this study investigates the environmental footprint of a commercial scale grocery waste to poultry feed manufacturer based in Pennsylvania through Life Cycle Assessment (LCA). This study also investigates the environmental impacts and benefits of incorporating this feed input for conventional egg production in the US. It was found that a climate burden reduction of at least 8.5% could be achieved compared to conventional egg production when the layer feed ingredients were substituted at a rate of only 5% by weight with the valorized product. Improvements in equipment energy use efficiency, efficient transportation, and incorporating renewable energy sources can maximize the environmental benefits.

The steel sector contributes 11% of industrial emissions in the U.S., primarily driven by automotive demand and carbon-intensive upstream metal production. Decarbonizing the U.S. automotive steel sector is critical for meeting the IPCC's 2050 emission reduction targets. Most automotive demand goes into light-duty vehicle (LDV) manufacturing, in which alloyed steel sheet accounts for over 70% of steel-based semi-components. Manufacturing these sheet alloys is challenging with secondary steelmaking processes due to the target alloys' intolerance to copper contamination in scrap streams, leading to a reliance on primary steelmaking in the U.S., which predominantly follows the blast furnace route.

This presentation aims to propose a decarbonization roadmap for the U.S. Automotive steel consumption by examining material efficiency pathways (lightweighting, yield improvements, and recycling), advanced steelmaking and low-carbon technologies (CCUS, hydrogen-based methods, and electrolysis), and adopting cleaner electrical grids (supported by institutional efforts such as the Inflation Reduction Act and national goal of 100% carbon-free electricity by 2035). This presentation will highlight a dynamic vehicle fleet model for estimating future sales and end-of-life vehicle volumes, which we used to project the annual steel consumption for light-duty vehicles from 2023 to 2050. These time-varying trajectories were combined with vehicle weights for 10 powertrains across 3 light-duty vehicle classes (Cars, SUVs, and Light-duty trucks) to predict the annual demand of steel for LDVs and the mass of end-of-life scrap generated. Then, using a steel sheet model developed in collaboration with Ford Motor Company, Nucor, and U.S. Steel, along with trade statistics from UN Comtrade and USGS, we calculated the energy consumption and global warming potential under business-as-usual (BAU) conditions. Subsequently, we evaluated 4,455 decarbonization pathways under varying consumption, trade, and technological scenarios through techno-economics and life-cycle assessments. Results have revealed significant emission reduction potential via direct reduction technologies and electrolysis while highlighting the substantial carbon abatement from hydrogen-based pathways, albeit at higher costs. Government incentives such as hydrogen production tax credits were shown to dramatically reduce the economic burden of transitioning to hydrogen technologies. Based on these findings, this presentation will explore decision-making insights and provide actionable guidance to stakeholders and policymakers on decarbonizing the automotive industry through the production of low-carbon steel.

Over the past half-century, the manufacturing and extraction sectors in the United States have experienced a steady decline. However, bipartisan investments and private contributions signal a potential reversal of this trend. Spurred by geopolitical tensions, supply chain vulnerabilities exposed during the pandemic, and the economic potential of clean and emerging technologies, Biden-era policies directed approximately $2 billion toward the development of domestic production, supplemented by $614 billion in private investments.

While this shift promises extensive economic opportunities, the potential social implications vested in this transition require consideration, many of which may be disproportionate to certain US populations. Racial and economic-based environmental inequality has been tied to exposure to industrial pollution since the 1980s, propagating negative health outcomes throughout marginalized US communities (Salazar et al., 2019). As the US ushers in a new era of industrial advancement, an opportunity exists to harness development to address long-standing inequities, using production growth as a catalyst for reducing racial and economic disparities. Equitable development — where individuals or groups receive tailored resources or experiences to achieve true fairness and accessibility — is essential to realizing key United Nations Development Goals, including Gender Equality; Reduced Inequalities; Peace, Justice, and Strong Institutions; and Partnerships for the Goals (Romero-Lenko & Nobler, 2018).

The type 1 Social Life Cycle Assessment (SLCA) framework offers a comprehensive tool to evaluate the social performance and equitable achievements of industrial operations. However, its application in the US is limited by a lack standardized methods and procedures accounting for regionally variabilities, such as the US. This critical review aims to enhance SLCA methodologies by developing a data decision tree addressing data availability, interdisciplinary data collection methods, US specific performance reference points (PRPs) to assess inventory data and interpretation through the lens of systemic equity. We aim to demonstrate this methodology using a case study on the US manufacturing sector.

Using the data decision tree in future SLCA studies will help identify indicators for which unavailable data inhibits the generation of performance reference points. Unavailable data highlighted through this matriculation can then be populated through stakeholder interviews, focus groups, or questionnaires, where guiding questions are informed by case study analysis and literature review. Subsequently, inventory data will then be scored according to the most geographically specific available data, which can then be compared with relevant performance reference points. Finally, this study recommends interpretation according to the Systemic Equity Framework proposed by Bozeman et al. (2022) (i.e., distributive, procedural, and recognitional equity).

This integrated and standardized approach enhances the replicability of SLCA studies while increasing the relevance of their findings in the US and other similarly heterogenous regions, such as the European Union. Broader adoption of these methods can expand data availability, significantly elevate corporate social responsibility (CSR) performance, and contribute to advancing all 17 Sustainable Development Goals (SDGs).

Article 6 was established under the Paris Agreement to facilitate the goal of holding “the increase in global average temperature to well below 2°C”. It allows countries to engage cooperatively to achieve their climate pledges either directly or through carbon markets. Article 6 enables cooperating parties to have greater ambition while diverting fewer resources than would have been required had the parties acted independently. The intent is to enhance mitigation ambition by utilizing efficiency gains from trading. Such international emissions trading forms the bedrock to mobilize public and private sector investment flows to meet ambitious climate and sustainable development goals (SDGs). However, a significant body of research concludes that there are important links between mitigation and other societal objectives, such as the SDGs. The aggregate success of such global cooperation and emissions trading mechanisms will then depend on the critical co-benefits and tradeoffs on some of the SDGs. This raises an important and unexplored question of how global emissions trading may enable or hinder energy justice and how different countries might evaluate their participation in such markets.

In this paper, using a global integrated assessment model (GCAM: Global Change Analysis Model,), we demonstrate that spatial and temporal distributions of the influence of Article 6 emissions markets on a subset of the broader SDGs may differ. We use a subset of sustainability metrics related to the energy equity and justice issues. Our analysis of these metrics tracks the interconnected nature of human and earth systems under different emission market designs for 20 key geographical regions (including USA, EU, China, India, Japan, Brazil, Russia, Australia, Sub-Saharan Africa, South Asia, Indonesia & Latin America) from 2030 to 2050, under a consistent integrated framework. This allows us to assess the local implications of emissions market design on different dimensions of energy justice such: energy access, residential energy prices (affordability), clean energy shares (sustainability), & energy imports (security). We show the effects of redistribution and international financial transfers and demonstrate these effects between the Global North & South for different national and global mitigation scenarios and alternative market designs and pricing mechanisms: the Glasgow Pledges, a net zero 2050 and an equity-oriented net zero pathway.

Our results imply that global cooperation in markets can be altered if interactions between mitigation and local effects on the energy justice dimensions were accounted for. Furthermore, we demonstrate that the extent to which these distributions differ depends on market design and pricing of nature-based mitigation options. Our analysis provides a foundation for assessing how global emission market schemes under Article 6 could be better understood in the local developmental contexts of energy justice & equity.

Since countries view their own climate mitigation efforts through a more comprehensive lens than mere emissions reduction, and the links with societal outcomes would influence their consideration of comparability and participation in emissions trading markets, the success of such global cooperation mechanisms would depend on perceptions of the relationships of mitigation with local and regional societal goals. The degree of congruence between these relationships demonstrated in this paper could influence future climate negotiations and carbon market design.

A future powered by clean energy and a circular economy could transform how industry and society approach resources and waste. However, there are few resources for analyzing the community impacts of emerging technologies in these fields. This talk will introduce an environmental justice framework that uses a series of metrics, questions, and actionable guidelines to empower experts and nonexperts to evaluate the broader implications of their solutions. Through a series of case studies related to plastic circularity and chemical decarbonization, we will showcase how early consideration of community impacts can inspire innovative research that minimizes and mitigates harm to the environment and humanity.

Direct air capture (DAC) of CO2 is seen as an important technology for reaching net zero by mitigating hard-to-abate emissions and, in the long term, as a strategy for removing built-up anthropogenic CO2 for a net-negative carbon economy. To spur development in DAC, the DOE is funding the development of four DAC hubs that will capture 1 million tonnes of CO2 annually [1]. DAC processes may require a combination of heat and electricity, but an all-electric plant is generally preferred, so that the plant itself does not produce CO2 and can be run using low-carbon electricity sources.

Most proposed all-electric DAC processes require around 1.2 megawatt-hours of electricity [2]. This creates a major barrier for a 1 million-tonne DAC process, as it would require 1.2 terawatt-hours of electricity, which is equivalent to roughly 10% of the energy generated by Maine [3]. A load of this size would require significant upgrades to the electrical grid, such as enhanced substations and transmission lines. Further, the increased electrical load may lead to unforeseen increases in energy costs and create reliability issues for nearby consumers. Here, we utilize methods provided by Regional Transmission Organizations (RTOs) to analyze these costs for a variety of prospective DAC projects [4].

From an environmental perspective, large DAC loads have the potential to siphon large portions of low-carbon electricity from the grid, increasing their consequential emissions and reducing their efficacy. Depending on how the low-carbon electricity is replaced, this could fully negate the carbon removal potential of a DAC hub. To assess these impacts, this work will use data from the Energy Information Administration to calculate the consequential emissions associated with reducing renewable energy availability for the grid. In combination, the environmental and economic impacts will be used to evaluate existing analyses of DAC to determine how these factors affect the cost of carbon removal.

1. US Department of Energy, U.S. Department of Energy Announces $52.5 Million to Catalyze Commercial Carbon Dioxide Removal Technology, 2024, https://www.energy.gov/fecm/articles/us-department-energy-announces-525-million-catalyze-commercial-carbon-dioxide-0.

2. Herzog, H., et al., Getting Real about Capturing Carbon from the Air, One Earth, 2024.

3. Energy Information Administration, Electricity data browser - Net Generation for all sectors 2023.

4. Midcontinent Independent System Operator, Transmission and Substation Project Cost Estimation Guide for MTEP 2018, https://cdn.misoenergy.org/Transmission-and-Substation-Project-Cost-Estimation-Guide-for-MTEP-2018144804.pdf.

Background and Motivation

In the U.S., approximately 3% of the electricity load is consumed by municipal wastewater treatment processes aimed at removing organic matter and nutrients. Despite this energy expenditure, the significant chemical energy embedded in these organic materials and nutrients remains largely unrecovered due to current technological limitations. Therefore, the paradigm for wastewater treatment has shifted to water resource recovery that enables water reuse, nutrient recovery, and energy recovery.

Methods

To enhance the environmental performance of emerging water resource recovery technologies, prospective life cycle assessment (LCA) is a suitable method that can be utilized(1,2). Prospective LCA involves analyzing emerging technologies at early development stages, where environmental insights can guide significant modifications(3). In this study, we evaluated the environmental impacts and the cost of an intelligent water resource recovery system with prospective LCA and technoeconomic analysis (TEA). The intelligent water resource recovery system integrates solar steam generation to concentrate water that undergoes coupled aerobic-anoxic nitrous decomposition operation with phosphorus removal (CANDO+P). This process recovers energy and nutrients. Meanwhile, evaporated steam condenses and is recovered. Sensors monitor system performance in real-time, feeding data to machine learning algorithms for process control and optimization. We gather data from experiments, including operational parameters, material inputs, energy usage, and economic inputs, to build detailed models for LCA and TEA. Prospective LCA evaluates the environmental impacts of photothermal material production, the benefits of adopting CANDO+P, the footprint of sensor production and deployment, and energy usage, with potential reductions in the system's overall environmental impacts through improved monitoring and control. Similarly, TEA assesses the cost-effectiveness of photothermal materials, quantifies the economic value of energy and nutrient recovery, and analyzes the cost savings from improved monitoring and control.

Significance

By leveraging the combined strengths of LCA and TEA tools, it helps to enable a holistic perspective on innovation, resulting in the creation of systems that outperform conventional wastewater treatment systems in both environmental impact and cost-effectiveness. This ultimately supports global efforts to address water resource challenges and transition toward a sustainable development.

Reference:

(1) Hellweg, S.; Milà I Canals, L. Emerging Approaches, Challenges and Opportunities in Life Cycle Assessment. Science 2014, 344 (6188), 1109–1113. https://doi.org/10.1126/science.1248361.

(2) Bergerson, J. A.; Brandt, A.; Cresko, J.; Carbajales‐Dale, M.; MacLean, H. L.; Matthews, H. S.; McCoy, S.; McManus, M.; Miller, S. A.; Morrow, W. R.; Posen, I. D.; Seager, T.; Skone, T.; Sleep, S. Life Cycle Assessment of Emerging Technologies: Evaluation Techniques at Different Stages of Market and Technical Maturity. J of Industrial Ecology 2020, 24 (1), 11–25. https://doi.org/10.1111/jiec.12954.

(3) Arvidsson, R.; Tillman, A.; Sandén, B. A.; Janssen, M.; Nordelöf, A.; Kushnir, D.; Molander, S. Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA. J of Industrial Ecology 2018, 22 (6), 1286–1294. https://doi.org/10.1111/jiec.12690.

The design for circularity (DfC) strategy reduces a product's environmental footprint by adopting environmentally benign design and material choices. The transition from aluminum to steel in photovoltaic (PV) is being increasingly adopted as an environmentally beneficial DfC strategy, as primary steel has an 82% lower climate footprint than primary aluminum. The transition to steel also alleviates material constraints for the PV industry as the DOE defines aluminum as a critical material due to an increasing demand, declining domestic production, and a complete reliance on imports for bauxite. However, there has been no research on whether the DfC strategy will generate environmental benefits if aluminum and steel secondary sources, which contribute to 55 to 80% of the overall aluminum and steel produced in the US, are used instead of primary sources in PV frames.

This is the first study to adopt a comprehensive lifecycle assessment (LCA) approach to evaluate the environmental tradeoffs between four material choices for PV frames – primary aluminum, secondary aluminum, primary steel, and secondary steel. For the primary supply chain, we account for 98 smelting plants, 15 frame manufacturing facilities, 36 module manufacturing facilities, and 4500 utility-scale PV installation sites. For the secondary supply chain, we account for 4500 PV decommissioning sites, 30 PV recycling plants, 2300 scrap collection facilities, 250 secondary smelting and refining facilities, and 15 frame manufacturing facilities. We also account for the inventory requirements, transportation distances, and electricity mixes used in the different processes in the primary and secondary supply chain (listed above).

Excluding transportation distances, the analysis reveals that incorporating DfC strategies can lower the Greenhouse Gas (GHG) footprint of PV manufacturing by 35% to 50%, only when steel replaces primary Al. However, the DfC strategy increases the GHG footprint of PV manufacturing by 4% to 31% when steel replaces secondary Al.

The inclusion of transportation distances across the primary and secondary supply chains significantly changes the relative environmental preferences of the four material choices. The impact of the transportation distances is depicted visually through an environmental preference graph, which identifies cut-off points and bounded regions wherein a material is preferable to the other three alternatives. Using a geographic information system (GIS) analysis, we depict the environmentally preferable material alternative to manufacture frames for PV modules to be installed in utility plants across the US map based on the geospatial spread of the supply chain process for the four material alternatives. Furthermore, we demonstrate how DfC strategies have a policy implication by quantifying how the US-manufactured PV modules with frames made from the four-material alternatives either meet or fail to meet the EPA-recommended ultralow carbon PV standards defined specifically for US PV manufacturers.

The building sector contributes nearly 20% of global greenhouse gas (GHG) emissions, driven by the extensive use of conventional HVAC systems for heating and cooling. Networked Geothermal Systems (Net-Geos) offer an innovative, low-carbon alternative by utilizing underground thermal energy for efficient heating and cooling. By connecting multiple buildings, boreholes, and heat pumps through a shared underground loop, Net-Geos allow buildings to exchange excess heat, balance energy loads, and reduce installation and operational costs.

Despite their potential, the sustainability of Net-Geos remains underexplored, particularly regarding their environmental and economic impacts in urban settings Existing research focuses mainly on standalone Ground Source Heat Pumps (GSHPs), leaving a critical gap in understanding the performance and scalability of shared geothermal networks like Net-Geos. This study introduces a novel application of Life Cycle Assessment (LCA) to evaluate Net-Geos across diverse building types, including single-family, multi-family, and commercial properties, over a 25-year period. Furthermore, this research uniquely incorporates the distribution system of Net-Geos and the gas pipeline infrastructure of conventional HVAC systems into the model, ensuring a comprehensive comparison of their lifecycle impacts.

The methodology systematically evaluates lifecycle stages—manufacturing, installation, operation, and end-of-life—using primary data from National Grid’s design specifications and secondary data from Ecoinvent and the International Energy Conservation Code (IECC). The functional unit is defined as 1 MJ of energy supplied for heating and cooling, ensuring consistent comparison between the systems.

Preliminary results from the Lowell geothermal network for single-family homes indicate that Net-Geos can reduce lifecycle GHG emissions by 26%, with a 72% reduction in operational emissions due to their reliance on electricity rather than natural gas. While manufacturing and installation phases show slightly higher emissions—attributed to material-intensive components like geothermal boreholes and HDPE piping—these trade-offs are outweighed by long-term operational benefits. These findings show Net-Geos' potential to provide clean and cost-effective heating and cooling for single-family buildings while advancing energy equity.

This study is innovative in addressing critical knowledge gaps regarding shared geothermal networks. Broader implications include enabling access to affordable and sustainable energy for marginalized communities, reducing reliance on fossil fuels, and supporting a just transition to renewable energy. By offering scalable solutions for urban and suburban areas, particularly environmental justice communities, this study provides a framework for integrating Net-Geos into urban energy strategies, contributing to climate resilience and energy justice.

Zero-emission vehicles are a key component of California’s clean energy strategy. Currently, transportation accounts for 30-40% of California’s greenhouse gas emissions. As ZEVs do not require the use of fuels that carry high emissions, the transition towards ZEVs has the potential to drastically decrease these hazardous emissions (Current California GHG Emission Inventory Data | California Air Resources Board). To help accelerate the transition, California enacted executive order N-79-20, which aims for 100% of new passenger vehicle sales to be zero-emission vehicles by 2035 - now just a decade away. This is an ambitious goal that will require a better understanding of the intricacies involved in the consumer mindset and market surrounding ZEV uptake. To this end, we are conducting a set of interviews with prominent ZEV planners across the twelve transit districts in California. The interviewees include local community organizers, representatives of transportation authorities, authors of ZEV adoption plans, and more. The goal of these interviews is to identify the major factors - be that policy, community organizing, financial incentives, access, infrastructure availability, community mindset, etc. - that have helped enhance or limit ZEV adoption across different communities in California. To identify which communities upon which to focus, we used ZEV registration and vehicle population data along with CalEnviro Screen data to identify over- and under-performing zip codes. We classified under and over-performing zip codes as locations with higher (or lower) than expected uptake of ZEVs based on factors such as income, education level, housing burden, and poverty levels. Additionally, we are continuing to research the influence of factors such as linguistic isolation and local air pollution health impacts on ZEV uptake. Moving forward we plan to continue our interviews and surveys with additional county and city representatives, as well as planning and non-profit organizations that collaborate on ZEV plans. We expect that influencing factors and responses will differ based on the community, with certain strategies being more effective in smaller, more remote communities and other strategies being more effective in larger, more urban communities. Ultimately, we anticipate that this research will provide further insight into the planning and implementation of incentives, rules, policies, and practices that positively influence the uptake of zero-emission vehicles in pursuit of state and federal climate goals.

This study examines the implications of policy requirements on the three-pillar methods of greenhouse gas (GHG) analysis in the context of low-carbon hydrogen production in the USA. The three-pillar incremental generation, geographical matching, and temporary matching approach provide a comprehensive framework for establishing Energy Attributes Certificates - EAC facilities for hydrogen production. Recent policy developments, including tax incentives and regulatory standards, are analyzed to understand their impact on the adoption and optimization of low-carbon hydrogen technologies. The findings highlight the critical role of policy in shaping the GHG emissions profile of hydrogen production while leveraging available resources and technologies to ensure sustainable and economically viable hydrogen solutions are adopted. This research contributes to providing insights concerning the Clean Hydrogen Production Reduction Act (45V) for the three pillars of H2 production, and a broader discourse on hydrogen decarbonization pathways, and the transition to a low-carbon energy future.

Lithium is designated as a critical mineral by global actors, including the United States Geological Survey based on its scarcity and importance for lithium-ion energy storage. Demand for lithium (Li) is projected to increase in the coming decades and a push for near- and on-shoring critical lithium sources is driving innovation in lithium extraction. In the Salton Sea region of Southern California, direct lithium extraction (DLE) methods have been proposed to extract lithium from Li-rich geothermal brine. This brine is used to produce energy at existing geothermal power plants and could be processed using DLE before reinjection into geothermal wells. Introducing DLE processes at existing geothermal power plants in this region will impact water consumption, which is an important issue for the region. The Salton Sea region’s freshwater is sourced from the Colorado River, which is facing historic drought. Changes to water consumption in this region can have cascading effects on agriculture, human health, community wellbeing, indigenous communities, and ecology.

Most studies do not empirically quantify water consumption for DLE or the supplementary processes required for its implementation (i.e., pretreatment). As a result, models that attempt to quantify the environmental, social, and economic impacts of DLE are not representative of true conditions. We have previously estimated that water use for DLE requires ~3-4 time the freshwater of geothermal energy production, based on incomplete data that also overlooks pretreatment. In this work, we address these two crucial data gaps to improve our understanding of whole-process water requirements for implementing DLE.

To experimentally assess the water use requirements for DLE, lithium adsorption materials being used in the region were synthesized. Bench-scale kinetic and adsorption isotherms were completed and fit to appropriate models to evaluate kinetics and adsorption capacity. Water consumption was measured during multiple stages: adsorbent synthesis and conditioning (based on established methods in the literature); brine pretreatment; adsorption; desorption; and adsorbent regeneration. This data was compared to the water use data provided by industry partners who have proposed pilot plants in the Salton Sea region.

To assess the required pretreatment steps, a systematic literature review was conducted using the PRISMA method. Keywords were chosen to capture papers relating to pretreatment, lithium, DLE, and relevant brine sources. In the initial screening step, papers were excluded if they did not discuss pretreatment or DLE, or if they focused on non-brine lithium sources. The second screening for relevant data identified that pretreatment processes include water softening, nanofiltration, solvent extraction or precipitation of competing ions, and pre-concentration of lithium. Pretreatment steps specific to DLE from Salton Sea geothermal brines were determined based on the chemical composition, salinity, pH, and temperature of this brine.

In this presentation, we will discuss these new data and insights on water use and how it fits into the assessment of water consumption and impact on the region. These assessments can be used to inform policy makers, local citizens, and DLE industry partners on how to best implement DLE for minimal impact moving forward.

The transition from fossil to renewable energy sources is a key lever to mitigate greenhouse gas (GHG) emissions and restrict global temperature to below 1.5C above pre-industrial levels. Yet growth trajectory and predicted ultimate scale could lead to PV becoming one of the largest global industries with enormous annual GHG emissions from manufacturing, even while lowering carbon emissions compared to the incumbent energy sources they displace. Herein we will demonstrate how low carbon manufacturing, even for an already low carbon energy source like PV, can yield significant additional climate benefits, as measured by reduced future temperature change.

We use the absolute global warming potential (AGTP) metric to quantify the temperature increase resulting from GHG emissions. The AGTP metric reveals that the temperature-increase caused by a GHG emission pulse is directly proportional to the lifetime of the GHG in the atmosphere. Consequently, emission of a GHG occurring earlier in a timeframe will remain longer within that timeframe and, thereby, induce a greater temperature increase than an equal mass of GHG emitted later in that timeframe. If a societal goal is to reduce future temperature increase, earlier GHG emission reductions (i.e., in the manufacturing stage of PV) are of higher value than later. We apply the AGTP metric to quantify temperature mitigation potential of low carbon manufacturing of PV systems considering the net impact of when GHGs are emitted and avoided over their life cycle. We explore the potential for decarbonization over the PV supply chain by accounting for various manufacturing sites, two PV technologies (silicon and CdTe), six high and low-carbon sources of electricity (used in manufacturing) and eight high and low-carbon pathways to produce multi- and mono-crystalline silicon feedstock.

Whereas we demonstrate that all PV provides net AGTP benefits over their 30-year lifetime, shifting from high to low-carbon PV manufacturing increases the net AGTP benefit by up to 208% points under US-average grid and solar insolation conditions. Decreasing electricity intensity of PV manufacturing and shifting to less CO2 intensive sources of electricity will generate significant increases in the net AGTP benefit, even if pursued alone. While low-carbon CdTe generates 170% greater net AGTP benefit than high-carbon mono-Si PV, this benefit diminishes to just 1% when Si PV manufacturers use less CO2-intensive sources of electricity. Based on these findings, we recommend strategies for PV manufactures to most effectively decarbonize their supply chain even if they are located in geographies which rely on high-carbon electricity. Additionally, we identify strategies to address the most important gaps in the lifecycle inventory (LCI) and increase the data transparency over the PV supply chain. The increased data transparency will enable LCA practitioners to more robustly quantify the climate benefit of a transition to terawatt of PV, quantify the impact of different material choices and energy mixes across various manufacturing pathways and assess the ability of PV modules with various designs to meet policy goals defined in the ultra-low carbon PV standard for the US market.

Vehicle electrification is a cornerstone strategy for decarbonizing the light duty vehicle (LDV) fleet. Aggressive fleet electrification targets aligning with a net-zero trajectory will place an increasing strain on associated battery supply chains [1], [2]. Despite the current overcapacity in battery supply[3], researchers have raised concerns over the feasibility of scaling the supply of batteries to keep pace with the projected global demand of the LDV sector, especially in light of geopolitical risks over securing critical minerals[2] and competing demand for batteries from other sectors[4] (e.g. stationary storage, electronics). As uncertainties are large[1], [3], strategic allocation of battery capacity is essential to address any possible future battery supply chain risks as well as reduce the socio-environmental impacts of battery production[5], [6].

This study determines strategies for deployment of a limited battery capacity in the U.S. LDV sector to afford the largest reduction in life cycle greenhouse gas (GHG) emissions by model-year. The study evaluates the prospective life cycle GHG emissions of the U.S. LDV fleet using a bottom-up approach, accounting for often-neglected characteristics such as vehicle classes, powertrain architectures, regional differences impacting energy consumption (e.g., drive-cycle, ambient temperatures) and background modified inventories impacting the production characteristics of sectors that feed into the LDV life cycle such as electricity, fuel and steel. The study focuses on three powertrain technologies (hybrid, plug-in hybrid, and battery electric vehicles), different ranges and powertrain architectures (e.g. series, parallel, power-split) and five vehicle classes (from compact car up to pickup truck) and four model-years (2023, 2030, 2040 and 2050). The study integrates the latest U.S. vehicle characteristics as obtained from the 2023 vehicle technology assessment[7] by Argonne National Laboratory as well as other relevant attributes (drive cycle, electricity grid) in the Carculator lifecycle assessment model[8]. The modeling leverages the Premise[9] background modified inventories to evaluate the impacts per vehicle type under different policy scenarios (business-as-usual, or 66% chance of remaining within 2oC and 1.5oC above pre-industrial levels). The vehicle lifecycle modeling is then coupled with a linear optimization model to determine how different prospective amounts of battery capacity supply should be optimally allocated across new LDV sales in each county to minimize the resulting life cycle GHG emissions per model-year.

The results reveal considerable tradeoffs between efficient battery allocation and the GHG mitigation potential of electrified powertrains across vehicle, temporal and spatial markets. The average midsize BEV with a 300-mile range requires 6 times the battery capacity of a plugin hybrid vehicle with a 35-mile range, and 70 times the battery capacity of a parallel hybrid electric vehicle, to mitigate only 1.5 and 3 times the emissions mitigated by these powertrains compared to a gasoline internal combustion engine vehicle (ICEVG). The spatial analysis highlights large variations of these tradeoffs across U.S. counties and market segments.

The optimization results establish that if only 30% of the battery capacity required for full fleet electrification of a model-year fleet is available, hybrid and plugin hybrid electric vehicles should be prioritized over BEV deployment as a way to remove the largest number of ICEVGs from the fleet. With such strategies in place, half of the 2023 and 2050 model-year life cycle GHG emissions would be mitigated. If a larger supply of batteries is available for each model year, BEV deployment becomes more suitable and necessary to minimize model-year GHG emissions, starting from urban counties with clean electricity grids. These findings challenge the efficacy of uniform BEV-focused policies, advocating for more flexible, adaptive fleet electrification strategies.

References

[1] C. Xu, Q. Dai, L. Gaines, M. Hu, A. Tukker, and B. Steubing, “Future material demand for automotive lithium-based batteries,” Commun. Mater., vol. 1, no. 1, Art. no. 1, Dec. 2020, doi: 10.1038/s43246-020-00095-x.

[2] International Energy Agency, “Energy Technology Perspectives 2023,” Energy Technol. Perspect., 2023.

[3] “BNEF Electric Vehicles Outlook 2024.” Accessed: Aug. 08, 2024. [Online]. Available: https://assets.bbhub.io/professional/sites/24/847354_BNEF_EVO2024_ExecutiveSummary.pdf

[4] D. Gohlke et al., “Quantification of Commercially Planned Battery Component Supply in North America through 2035,” ANL--24/14, 2319242, 187735, Mar. 2024. doi: 10.2172/2319242.

[5] B. Tarabay, A. Milovanoff, A. F. N. Abdul-Manan, J. McKechnie, H. L. MacLean, and I. D. Posen, “New cathodes now, recycling later: Dynamic scenarios to reduce battery material use and greenhouse gas emissions from U.S. light-duty electric vehicle fleet,” Resour. Conserv. Recycl., vol. 196, p. 107028, Sep. 2023, doi: 10.1016/j.resconrec.2023.107028.

[6] F. Degen, M. Mitterfellner, and A. Kampker, “Comparative life cycle assessment of lithium-ion, sodium-ion, and solid-state battery cells for electric vehicles,” J. Ind. Ecol., vol. 1, no. 16, 2024, doi: 10.1111/jiec.13594.

[7] E. Islam et al., “Detailed Simulation Study to Evaluate Future Transportation Decarbonization Potential,” ANL/TAPS--23/3, 2279172, 186057, Oct. 2023. doi: 10.2172/2279172.

[8] R. Sacchi and C. Mutel, Carculator: prospective environmental and economic life cycle assessment of vehicles. (Dec. 18, 2019). Zenodo. doi: 10.5281/ZENODO.3778259.

[9] R. Sacchi et al., “PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models,” Renew. Sustain. Energy Rev., vol. 160, p. 112311, May 2022, doi: 10.1016/j.rser.2022.112311.

Human activities, particularly food production and consumption, contribute significantly to environmental degradation. Footprint indicators are generally used to understand the extent of impacts on the environment. However, relying on individual footprint indicators often leads to conflicting sustainability outcomes, as the reduction of one impact may exacerbate another. This paper applies a framework to holistically evaluate the environmental impacts of food products. By integrating multiple indicators using Multi-Criteria Decision-Making (MCDM) methods, we aim to rank and develop a Relative Aggregate Footprint (RAF) for food products. Considering the uncertainties in the footprints of the food products, we applied a Monte Carlo-based uncertainty analysis to the evaluate the robustness of the results. Sensitivity analysis was conducted to determine the influence of individual indicators on the RAFs. Our findings suggest that this framework offers a reliable approach to evaluating food products, supporting sustainable decision-making by revealing potential trade-offs and providing insights into the variability of environmental impacts. This framework aids policymakers and industry stakeholders in promoting sustainable consumption and production practices aligned with global environmental goals.

Artificial intelligence (AI) is an emerging technology with recent research utilizing large language models (LLMs) to advance climate change. One of the strengths of LLMs in climate research is providing predictions based on probabilistic modeling. However, the potential of AI in predicting embodied carbon in the early building design phase hasn't been explored. This study investigates AI as a prediction tool in early building LCA using existing data sets. We quantitatively analyzed buildings' carbon emissions in the early building designs by comparing various LLMs' scenarios.

Existing literature indicates that the early conceptual design phase affects LCA more than the late stage of design since there is more flexibility to change. Our research emphasizes the timeliness of LCA in early building design to mitigate uncertainty and provides predictions of different design stages. A methodology was developed to predict the potential embodied carbon reduction depending on when the LCA can be implemented. Determining what information needs to be provided at each stage is the next critical step for our process-driven research. Several important decisions are made during the early design stage, such as size and height, shape, materials, and structural system, and those were defined as time frames.

Our knowledge-intensive process provides embodied carbon predictions at each defined time frame. Moreover, the process can suggest different actions representing embodied carbon reduction strategies at the prediction point. These predictions provide insights into customized strategies for early building projects with accumulated data. Following this process, designers are guided in implementing optimized early building designs. This approach reduces reliance on specialized sustainability consultants and streamlines the building system choices and design process. By addressing both the timing and amount of embodied carbon reduction strategies, this study explores the prediction capabilities of AI during the early LCA process. This information can eventually lead to optimized building designs during the early design process.

Digital technology continues to pervade all aspects of modern life, from the way we socially interact, our defense industries, how we find & cure diseases, how we power and heat our homes, and how we transport ourselves in society. The increasing pervasiveness of digital technology also fuels concerns related to the environmental impacts of semiconductor production and design. This paper explores challenges associated with understanding the total environmental impact of digital technologies across their life cycle. The holistic approach addresses difficulties in understanding for instance: (a) the environmental "footprints" created during the production phase and use of digital technologies; (b) the environmental "handprints" from digital technologies as these technologies could help reduce emissions in other industries; (c) uncertainties and difficulties in performing LCA and handprint analysis for digital technologies; and (d) the inherent difficulties in understanding what the future, and specifically, new innovations may bring.

Climate change poses a formidable challenge to humanity, manifesting through both gradual stresses and extreme disasters that threaten efforts to achieve and foster sustainable development. Among its many impacts, average temperature increases and heatwaves are particularly evident examples of stresses and shocks. These phenomena claim tens of thousands of lives annually, even in developed nations, while placing immense strain on energy systems and infrastructure. Moreover, they can exacerbate and trigger other disasters, such as wildfires, amplifying the need for resilience-focused planning and design. Conventional design and assessment approaches often lack the capacity to address these challenges, particularly at the high temporal and spatial resolutions required for robust analyses. Furthermore, existing temperature records and models often fail to capture the impacts of climate change and extreme heatwaves, limiting their utility for long-term resilience and sustainability assessments.

To address these critical gaps, we have developed a suite of tools for generating synthetic temperature data scenarios. These tools, based on 30 years of monthly data averages and 19 years of daily data averages, enable the creation of high-resolution temperature datasets that are both realistic and flexible. The tools can generate synthetic daily temperature scenarios spanning decades, from present-day conditions to the end of the century, while accounting for gradual temperature increases derived from observed or projected climate change scenarios. Additionally, they incorporate the capacity to simulate heatwaves at chosen rates or randomly, with probabilities and durations increasing over time in alignment with climate change models. This novel integration of stochastic processes ensures realistic variations while aligning with observed data, providing a robust foundation for resilience assessments. The methodology assumes that monthly temperature distributions follow a normal pattern, with summer maximums and winter minimums situated at two standard deviations from the mean, in their respective seasons. Randomly generated monthly averages are compared with reference averages derived from daily temperature records, and daily averages are then adjusted to ensure alignment. This process allows the tools to produce datasets that are representative of real-world conditions while maintaining sufficient variability for meaningful scenario generation. The average temperatures align with real data, and the day-to-day and year-to-year variations remain meaningful and realistic.

This innovative approach enables the assessment of resilience and sustainability across diverse scales, such as pavements, the energy-water nexus, and buildings. Applications include forecasting future demand under various scenarios, testing the flexibility and preparedness of infrastructure, and enhancing disaster planning and operational practices. Moreover, the tools’ potential for further refinement, including higher temporal resolution (e.g., hourly data) and the incorporation of spatial dimensions, enhances their relevance for urban climate studies. This could support investigations into urban heat islands, albedo effects, and the interplay between urban design and infrastructure. By bridging the gap between climate data generation and practical resilience assessments, this work represents a significant advancement in climate adaptation tools. The ability to generate synthetic yet representative temperature data with high temporal resolution addresses critical needs for planning sustainable and resilient systems in the face of climate change.

There are numerous initiatives towards envisioning better cities for the future. Three intertwined initiatives are sustainability, resilience, and smart cities. Sustainability means the ability to sustain; the goal of sustainability is maintaining a state at a certain level. Resilience has been typically defined in technological domains (such as engineering and disaster response) as robustness, rapid recovery and resourcefulness – where a system should withstand the force of a disturbance and, should it fail, return to its original performance levels quickly with readily available resources. And, the smart city is deeply rooted in the usage of information and communication technology in urban policies to solve diverse problems (e.g., energy, parking, safety, etc.). Oftentimes, research on these initiatives will reference one or two of the other initiatives. Therefore, we set out to describe how are sustainability, resilience, and smart city concepts converging (or not). We conducted a scoping literature review to clarify the relationships between sustainability, resilience, and smart cities in urban systems. We followed the process suggested by Pham et al. (2014) and refined our literature pool using the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). For each article, we coded the title, author, year, and keywords. This coding was conducted by each of the three authors independently and then validated through consensus. Combining this literature review and coding, we established an integrated framework for sustainability, resilience, and smart cities in urban systems.

Unsurprisingly, the topics of sustainability, resilience, and smart cities show alignments and contradictions. It appears that resilience is a key component of both urban sustainability and smart cities, but there are not agreed upon approaches to this assimilation. Both resilience and smart city concepts are distinctive yet can share similar goals of having sustainable development. We discuss and map these interconnections to help navigate the concepts. The review led to a few interesting discussion points, including the complexity of urban systems and how this complexity can lead to lock-in and create difficulties in pursuing physical or institutional adaptation and transformation within any of these three initiatives, the increasing amount of information generated in interconnected systems and its exchange can bring opportunities and challenges (such as demand on the environment for data storage, AI, etc.), and the role of the citizen within the urban system. There is not a silver bullet for urban planning to address sustainability, resilience, and smart cities. By understanding how these three topics interrelate, decision-makers can avoid over-emphasizing one topic over another in the design process. By understanding how each topic impacts the built environment, they can also strategically place resources based on their local context.

Road-stream crossings (RSCs) are critical infrastructures for stream ecosystems and transportation networks. Many RSCs are aging, in disrepair, and/or undersized, threatening the resilience of freshwater habitats and transportation systems. Given the limited resources of state agencies and municipalities that manage these assets, it is essential to prioritize these RSCs for replacement effectively. However, there is a significant lack of coordination among stakeholders. This research develops a multi-objective optimization (MOO) framework that incorporates the interests of multiple stakeholders and compares its results with conventional scoring and ranking (S&R) methods. We employed the non-dominated sorting genetic algorithm (NSGA-II) to optimize environmental, transportation, and replacement cost objectives, achieving optimal solutions at the watershed scale. To determine the optimal population size, initialization method, and termination criterion, we utilized the modified inverted generational distance (IGD+) performance indicator. Our custom-seeded initialization method demonstrated superior performance and faster convergence compared to other initialization methods. Compared to S&R methods, MOO consistently resulted in higher scores for environmental and transportation objectives across various budget limitations, with increases of at least 19.57% and 37.68%, respectively. The frequent selection of certain RSCs among Pareto optimal solutions highlights the significant impact of their replacements. Analysis of this selection frequency in relation to RSC characteristics revealed that structural condition had the highest correlation (Pearson correlation of 0.60), indicating it as the most significant factor. This systematic approach promotes more comprehensive and effective infrastructure management by aligning multiple objectives and addressing the diverse priorities of stakeholders.

Wetlands are essential for humanity’s continued existence. They offer significant “ecosystem services”, including freshwater supply and climate change mitigation. They are also among the most biologically diverse ecosystems on Earth, supporting a myriad of plant, animal, and microbial life. However, the Ramsar Convention of Wetlands reports that since 1970, approximately 35% of wetland area has been lost, disappearing almost three times faster than forested areas. The loss of these areas of global importance is valued at almost $50 trillion a year. This economic toll directly translates into diminished health and well-being for billions who rely on wetlands for water, food, and livelihoods, hampering progress toward critical Sustainable Development Goals (SDGs) - including those focused on clean water, zero hunger, and climate resilience.

Africa has 431 Ramsar sites covering approximately 1,000,000 km2 of Africa’s 30,000,000 km2, the largest combined area of sites in the world. The Ramsar Convention, in their 2021 Global Outlook Report, notes that integrated water resource management will aid in safeguarding freshwater ecosystems. However, the World Meteorological Organization estimates that by 2030, almost 80% of Africa will not have an integrated water resource management plan. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change notes that Africa’s lack of progress towards adopting integrated water resource management plans is hindered by the lack of data.

In this study, we leverage remote sensing products to provide a holistic characterization of the spatial and temporal changes of Ramsar-listed wetlands across Africa, specifically between 2003 and 2022. Recognizing that gradual and abrupt change can occur simultaneously, we focus on the following change definitions:

1. Identifying monotonic trends in the surface water extent of water bodies over the 20-year period.

2. Examining altered seasonal patterns in surface water extent over time.

3. Detecting evidence of changes in the underlying data-generating processes that drive surface water dynamics.

4. Assessing whether extreme occurrences in surface water extent - both high and low—have become more frequent or severe.

We perform our analysis for each change definition on each Ramsar listed site and aggregate by basin and then by country to report the loss or gain in surface area over time. We also anticipate providing a priority list of Ramsar sites from most concerning (necessitating closer monitoring) to least concerning using multi-criteria decision analysis.

Our study extends beyond the conventional approaches of investigating long-term inter-annual and intra-annual dynamics of surface water to explore surface water dynamics through three additional change definitions. Furthermore, we anticipate the insights gleaned from our study will help guide targeted interventions – guiding investments in infrastructure and conservation efforts, informing regional water resource management policies, and aiding in evaluating the effectiveness of water management strategies, enabling policymakers to assess whether existing strategies have mitigated risks or exacerbated challenges.

Legacy forms of electricity generation developed in the way they did according to the demand that civilization presented to them in terms of both magnitude and in changes to that magnitude at various time scales and they did so with essentially no energy storage involved. Wind and solar electricity generation could at least in theory supply all of a region’s electricity needs under business-as-usual conditions. However, because the available power from those forms of primary generation varies with the weather and with natural annual and daily cycles, it would be necessary to incorporate energy storage in some form and this presents a challenge when considering energy generation regimes that depend primarily or entirely on these fuel-less forms of generation. In this presentation, an all-solar-photovoltaic generation regime servicing a North American region of approximately 150,000km2 and a population of approximately 11M persons is imagined. Realistic proxies for an electricity demand time series and an insolation time series are established for the same one-calendar-year period. The proxied demand time series is scaled such that the integral of the scaled timed series is equal to the actual annual demand for the region for that year and based upon that result, the proxied insolation time series is scaled given the need to oversupply to compensate for transmission and distribution losses and for losses within a hypothetical energy storage system with the capacity to make up the difference between electricity supply and demand at any given moment. The presentation then characterizes the solar generation system that would be required and the associated storage system that would be required in two ways: as an electrochemical battery apparatus and as an electromechanical apparatus that uses in situ material as an inertial mass. In so doing, the presentation aims to communicate the enormity of the storage problem associated with high-proportion or total solar electricity generation regimes and the targets society must set for itself to achieve such a regime.

For many consumer products, the use phase dominates the environmental impacts of the product. Nevertheless, many LCA do not rigorously take into account consumer behaviors and characteristics when estimating use phase emissions. This talk argues that the LCA community exhibits techno-bias that oversimplifies and underemphasizes user behavior, potentially creating misleading results due to its focus on technical system parameters. Evidence from published case studies will demonstrate how user behavior has the potential to change the directionality of an analysis, and advocates for greater integration of user behavior in a more rigorous way. The talk will provide a framework of six major ways that consumer characteristics and behavior can influence an LCA and review which of these factors are commonly incorporated in LCA, discussing which factors may be important yet are often overlooked. The framework will also provide categorization of different consumer-facing products, indicating which of the six major consumer characteristic and behavior factors may be most important to include in different kinds of products.

Consumer engagement in sustainable behaviors, such as repair, reuse, and sharing, is essential if we are to realize a sustainable future; however, substantial barriers to these behavior changes remain. This is partly due to the transition being viewed as uncertain in terms of what it entails, including the impacts on the quality of life of individuals in their role as e.g., product users. Existing transition narratives also tend to focus on “sacrifices”, such as decreased convenience and unpleasant aesthetics.

Counter such a gloomy outlook, research on Circular Economy (CE) Behaviors, such as reuse, sharing, and reductions, indicate opportunities for enhanced well-being outcomes for product users, such as when engaging in sharing pools, conducting at-home repairs, and practicing voluntary simplicity. However, these potential well-being outcomes have not been systematically studied. This entails lost opportunities for decision-makers involved in the CE transition, such as businesses, community organizations, and policymakers, to strategically and comprehensively ensure positive quality of life outcomes for their customers, community members, and constituents. Moreover, this gap leaves us without the important chance to rewrite the narrative of what a future with sustainable consumption could entail, namely one of enhanced thriving. All we need are the insights and tools to imagine and implement it.

This presentation constitutes a summary of multiple research outputs conducted using conceptual frameworks and models, verified and refined through Delphi surveys with two expert groups in CE and Quality of Life Science.

Our research reveals a first contribution: that the product user’s experience with CE Behaviors (e.g., sharing and reuse) can be regarded as consisting of different areas, such as effort, social, and social status. These areas come with their respective frictions and rewards that determine whether the experience is perceived positively or negatively. Widening the perspective, our second contribution consists of a comprehensive product user well-being (C-PUWB) framework; it clearly delineates the implications from the CE Behavior engagement as it affects different areas of the experience, such as social and moral. It also considers the indirect effect from the engagement on other areas of life, unrelated to the CE Behavior engagement, through the expenditure of (or gains to) the individual’s finite resources (i.e., time, effort, and money). Moreover, the C-PUWB Framework accounts for how product user well-being consists of levels: (1) affective outcomes (i.e., more transient pleasures and pains); (2) cognitive outcomes (i.e., assessment of events and life overall against standards), and; (3) developmental outcomes (e.g., more enduring sense of meaning, growth, and belonging). Considering this notion of product user well-being, we then identified several “key aspects” of the CE Behavior engagement as having a significant influence on product user well-being outcomes, such as the nature of social norms and the availability of support to engage in activities successfully.

These findings demonstrate a theoretical pathway towards systematically considering product user well-being in the CE transition, as well as sustainable consumption more broadly. For practitioners, it highlights specific goals (i.e., the key aspects) for ensuring higher quality of life outcomes.

Energy efficiency is a key component of decarbonization and sustainability efforts, yet gaps persist in adopting energy-saving interventions in industry despite their profitability. This has been partially attributed to the lack of perceived strategic links between energy efficiency and the primary functions of firms. Multiple benefits to energy efficiency projects beyond energy savings have been identified. Still, potential linkages are not well understood, although preliminary work has been conducted to categorize them.

We conducted a survey of U.S. manufacturing executives to better understand the linkages between strategy and energy efficiency. The survey focused on three key areas: stakeholder mapping related to energy efficiency, the relationship between sustainability goals and energy efficiency decisions, and the use of non-energy benefits to justify energy efficiency interventions. Qualitative analysis was performed to understand the effects of company and facility size on these parameters.

Preliminary findings highlight the challenges of engaging industry on energy efficiency. First, the results indicate that stakeholder mapping needs to extend beyond internal actors to include external constituents such as utilities and consultants, due to the limited emphasis on energy efficiency within companies. Second, while sustainability goals were broad among the survey population, greenhouse gas emissions targets—most closely linked to energy efficiency—were prevalent primarily among the largest firms. Likewise, larger firms sometimes demonstarted alternative processes for funding sustainability and energy efficiency projects. Third, the most commonly listed non-energy benefits were safety, improved work environment, production and quality improvements. Notably, these benefits are closely linked to the primary functions of firms.

This research contributes to deep understanding of decision-making processes around energy efficiency in U.S. manufacturing firms. Future work will include additional panel responses and interviews to further enhance our insights into this critical area. Ultimately, this is part of a wider research thrust investigating how aligning energy efficiency measures more closely with corporate strategy might increase their adoption rate.

Argan oil holds historical and cultural importance within Morocco, particularly for the Amazigh people. The argan tree is endemic to Morocco, where the climate provides ideal conditions for the tree to thrive. It is customary for women in rural Moroccan villages within the Argan tree's range to collect fallen affiyache (argan fruit) from their surrounding forests and produces both edible and cosmetic Argan oil by hand. For decades, these women have performed each stage of the production process (collection, dehusking, cracking, roasting, grinding, and pressing), making it a significant aspect of their social life within a predominantly patriarchal society.

As cosmetic argan oil has reached international markets, the increasing demand has led to the mechanization of certain processes to improve production efficiency. However, as demand grows, it is essential to consider the social impacts on the women who have long been central to this production process, alongside environmental concerns.

This study conducts a Social Life Cycle Assessment (S-LCA), analyzing the social impacts of argan oil production on rural Amazigh women in Morocco. The functional unit is one liter of argan oil, with a cradle-to-gate system boundary encompassing all labor stages from the collection of the affiyache to the packaging of the extracted oil. The assessment follows the United Nations Environment Programme's (UNEP) 2020 guidelines for S-LCA, employing a framework with stakeholder categories, impact categories, subcategories, inventory indicators, and inventory data. The stakeholders chosen for this study were: workers (with impact subcategories: working hours, health and safety, and technology), cooperatives (technology, local employment), and rural communities (access to material resources, access to immaterial resources, local employment).

The primary data collection methods included in-country interviews conducted through household questionnaires, cooperative interviews, and discussions with key informants. The 2024 research team focused on examining the location and frequency of injuries associated with Argan oil production. During fieldwork in Morocco, injury data for each production step were collected from 30 individuals, documenting injury types at the different process stages. Examples of prevalent injuries during different processes include back pain, finger injuries, heat discomfort, and wrist pain with ranging frequencies. The study also explored ergonomics in traditional production methods, and the use of protective policies and procedures within cooperatives. Age was considered as a factor in injury frequency as only women older than 45 recorded higher than 12 injuries.

Overall, the findings from 2024 expand on the impacts related to health and safety for those working traditionally in argan oil production, and in cooperatives. These results highlight the need for more safety practices and realizing worker needs during certain steps. Future work is recommended to expand the sample size and gather more data on workers involved in mechanized steps.

The energy demands of the retail sector are influenced by multiple factors, yet traditional energy models primarily focus on economic and technological variables, often neglecting the role of social behavior changes. The shift from in-store shopping to online shopping represents a significant societal transition that directly affects the demand for retail space and, consequently, energy consumption. It is widely believed that online shopping reduces the need for retail stores and lowers energy demand in retail building sector, but there is no quantitative or empirical study about it yet. This study addresses this gap by examining how changes in shopping behavior impact retail building space and energy demand in the United States.

This paper develops a regression model to predict retail building space based on in-store shopping time, which serves as a proxy for the demand for physical retail infrastructure to accommodate consumer behavior. The historical retail space data is derived by integrating information from the CoStar database and the Commercial Buildings Energy Consumption Survey (CBECS), and the shopping time data is developed based on the American Time Use Survey (ATUS). The study employs three approaches to project future in-store shopping time using historical shopping behavior data from 2003 to 2023. The first approach directly predicts in-store shopping time using historical trends, providing stable projections. The second approach derives in-store shopping time from overall shopping time, integrating interactions between in-store, online, and other shopping modes. The third approach uses online shopping time to derive in-store shopping time, employing two sub-models: one based on historical online shopping growth from 2003 to 2023 and another focusing on the rapid growth phase from 2015 to 2023. By linking these projections to retail space demand and energy intensity (energy consumption per unit area of building space), the study quantifies the cascading effects of online shopping on retail building energy demand.

Results from all three approaches indicate a consistent decline in in-store shopping time, leading to reduced retail building energy demand. Approach 1 projects a steady decline, with energy demand decreasing from 660 trillion BTU in 2023 to 588 trillion BTU in 2030, representing a 10.9% reduction. Approach 2 accounts for the dynamic interplay between different shopping behaviors, yielding projections with moderate uncertainty. By 2030, the upper bound for energy demand decreases to 605 trillion BTU, while the lower bound reduces to 581 trillion BTU. Approach 3, particularly its rapid-growth sub-model, predicts the sharpest declines. The upper bound drops to 279 trillion BTU in 2030, while the lower bound decreases significantly to 80 trillion BTU, emphasizing the transformative impact of rapid e-commerce growth. Across all models, the findings underscore the significant role of online shopping in reshaping retail energy demand, with the potential for rapid e-commerce growth to accelerate reductions in energy consumption. This study highlights the importance of integrating social behavior changes into energy demand models. The results provide insights for policymakers, urban planners, and energy managers, emphasizing the evolving retail landscape and its implications for sustainability.

Effective road-stream crossing (RSC) management is crucial for maintaining transportation connectivity, ecological health, and flood resilience. Existing frameworks often overlook the diverse priorities of stakeholders, such as structural safety for transportation agencies and ecological impacts for environmental groups. This study developed a stakeholder-informed framework to align priorities and improve RSC management’s effectiveness and equity. We convened a technical advisory committee of transportation and environmental state agencies, regional planning commissions, and nonprofit organizations to co-develop an inclusive framework considering eight management aspects: flood vulnerability, wildlife conservation and restoration, environmental quality, structural risk, road criticality, economic impact, community support and readiness, and environmental justice. Building on this framework, this study examined stakeholder preferences, collaboration dynamics, and non-financial challenges for effective RSC management.

We conducted a co-designed online survey in New Hampshire to gather data on the perceived importance of the eight management aspects, stakeholders’ collaborators, and non-financial challenges. Before launching the survey, extensive outreach via webinars and meetings ensured robust and informed participation. Survey responses were analyzed using the Kruskal-Wallis (KW) test to detect significant variations in ratings among stakeholder groups. Flood vulnerability emerged as the most important aspect of consensus among all stakeholder groups. While wildlife conservation, environmental quality, road criticality, and structural risk also received high ratings, KW test results revealed significant differences among stakeholder ratings (p < 0.001). These findings underscore the importance of balancing conflicting priorities for these aspects. In contrast, environmental justice was ranked as the least important aspect, with no significant differences among stakeholder groups (H = 13.62, p = 0.255 in the KW test), highlighting gaps in integrating equity into RSC prioritization frameworks.

Network analysis evaluated stakeholder collaboration dynamics and identified the New Hampshire Departments of Transportation and Environmental Services as central actors in the cross-agency collaboration network. Additionally, content analysis of qualitative responses identified key non-financial challenges, such as regulatory barriers, project complexities, and the lack of prioritization frameworks. These findings highlight opportunities for state agencies and decision-makers to address issues that do not require financial investment, such as updating regulations, streamlining processes, and enhancing stakeholder collaboration in RSC management. This study provides actionable insights for effective, equitable, and coordinated RSC management by highlighting key priorities, stakeholder conflicts, collaboration dynamics, and non-financial challenges.

Cities are the major driver of construction consumption emissions globally1, and growing demand for infrastructure and housing will drive this construction to unprecedent levels in the coming decades. Despite the urgent need to reduce construction emissions to meet climate goals, understanding of past emissions and future plans remain vague. There is a lack of work exploring the total amount of GHG that can be emitted by future construction, especially at the city level. To address this, we estimate historic construction consumption emissions and develop GHG budgets (the amount of cumulative future GHG that can be emitted within climate limits) for over 1000 cities with more than 1.2 billion people in developed and developing countries across the world. We compare these budgets to future housing growth in over a dozen cities to determine how construction must change to stay within global climate limits.

We estimate city-level construction emissions using a top-down approach. Starting with the Exiobase input-output model, we use national statistical data for over 40 countries to disaggregate Exiobase capital and calculate construction asset investment between 2000-2022. We then down-allocate capital investments and emissions to the city level using a regularized regression on economic proxy variables (e.g. employment, housing starts) for each city. Next, we set a budget on construction emissions for cities by extending blended sharing principles previously used in country-level budgeting. We explore how fairness, geographic definition of a city, and population dynamics change the GHG budgets. Finally, we compare the budgets to the amount of GHG that future construction could emit using published material intensity and LCA building data.

Our final emissions estimates encompass 1.4 ± 0.1 GtCO2e of construction consumption across 1000+ cities in 2020. We find that approximately 1/3rd of quantified emissions are attributed to 30 large global metropolitan areas, mainly in high-income countries. 2/3rd of emissions are attributed to middle and small sized cities which are not part of frameworks for reducing consumption emissions. City construction emissions must reduce to net-zero levels by 2040-2070 to meet a well-below 2°C global carbon budget, depending on assumptions made while budgeting. Adjusting for future population growth or the historic failure of high-income countries to meet an equitable budget reduces the budget of large cities in North America and Europe.

Regardless of assumptions, it is not possible to stay within GHG budgets and meet future construction demand with current practices in most cities. Staying within global climate limits requires radical, city-level changes in planning and building. Examples include reducing floor area to less than 25m2 per capita in future buildings, shifting towards small, multi-unit buildings, and increasing budget allocation to construction at the expense of decarbonizing other sectors up to 5-10 years quicker. Our results provide policy makers and designers with key information on trade-offs in near-term decarbonization planning.

Decarbonizing the road transport sector requires concerted efforts in clean energy production and the adoption of low-carbon powertrain technologies. Achieving a net-zero road transport sector necessitates not only the utilization of renewable energy sources for clean fuels and electricity generation but also substantial investments in advanced passenger vehicles. While battery electric vehicles (BEVs) are often regarded as the principal powertrain to achieve carbon mitigation targets in numerous countries, their optimality in decarbonizing passenger vehicles remains debatable when compared to other powertrains such as hybrid electric vehicles (HEVs) and plug-in hybrid vehicles (PHEVs) in terms of life-cycle greenhouse gas (GHG) emission reduction and total cost of ownership (TCO) across various regions. Furthermore, regional BEV promotion strategies must consider the varying levels of fuel economy, grid electricity emissions, and energy prices.

In this study, we developed a life-cycle GHG emissions and TCO model to assess the carbon emissions and abatement costs for BEVs and HEVs in comparison to internal combustion engine vehicles (ICEVs) at the U.S. state level and Chinese provincial level. The results indicate that, while BEVs have a greater potential for reducing life-cycle GHGs than HEVs, the average TCO of BEVs remains higher than that of HEVs in most U.S. states, with GHG abatement costs ranging from $134 to $2526 per metric ton CO2eq. In states such as Kentucky, West Virginia, and Wyoming, where grid electricity is more carbon-intensive, BEVs perform worse than HEVs in reducing GHG emissions. HEVs offer dual benefits by reducing both GHG emissions and TCO, resulting in negative abatement costs. Conversely, BEVs are more cost-effective than HEVs in most Chinese provinces, except for Beijing, Shanghai, Zhejiang, and Fujian. Additionally, the GHG emission intensities and abatement costs of BEVs exhibit significant regional variation, influenced by factors such as fuel economy, travel distance, energy prices, and grid carbon intensity. This analysis suggests that the U.S. should seek solutions to lower the vehicle costs of BEVs to enhance their economic competitiveness with HEVs, while China should continue decarbonizing grid electricity to promote BEV adoption for achieving a net-zero emission transport sector. The model presented is a valuable tool for researchers and industrial stakeholders to better comprehend the trade-offs in GHG emissions and TCO across regions when adopting alternative powertrain technologies.

Considering the emission-intensive materials consumption and their associated environmental impacts, the building industry is a priority sector for reducing its environmental impacts and meeting greenhouse gas (GHG) emissions targets globally. The embodied carbon of building structures, substructures, and enclosures is responsible for 11% of global GHG emissions and 28% of global building sector emissions. Thus, various strategies are gradually adopted to reduce the emissions from buildings, including the early adoption of lifecycle assessment (LCA) tool during the design stage (new construction) to select the less emissive materials, sustainable strategies during the retrofitting process, and demolition/ deconstruction process including the sustainable materials management at the end-of-life of buildings. Numerous efforts have been devoted to developing such tools for conducting building LCA. This study aimed to critically analyze the existing tools to understand their implications and adoption by users through pre-defined criteria such as geographic coverage, construction applications, targeted users, distinct features and specificity, adopted databases and methods, etc. Two groups of tools dedicated to whole building LCA (e.g., TallyLCA, Athena, One Click, GREET Building LCA Module, LCAbyg, and EVAMED), and building materials/ elements (e.g., EC3, TallyCAT, WoodWorks Carbon Calculator, BEAM Estimator, C.Scale, and BEES) were selected, and their adoption in the existing literature was explored. The study found that most of the tools are regional with specific applications though some of them are adopted globally. To enhance the accuracy of the assessment, local/regional tools with databases are preferred in the scientific community. Some of the tools are based on carbon emission only, where the Environmental Product Declaration (EPD) was used as a database. The use of such tools (as standalone) may be inappropriate for whole building analysis due to limited impact indicators and also a lack of transportation of materials modeling. Additionally, while some tools incorporate waste management and partial recycling, most fail to comprehensively model end-of-life scenarios, particularly recycling and reuse aligned with circular economy (CE) principles. Therefore, a methodological framework is proposed to strengthen the existing ones for developing adaptive building LCA tools for promoting CE, particularly for whole building LCA. The outcomes of this study would facilitate the users to select the most suitable tool for their specific applications comprehensively and accurately while promoting CE adoption in the industry.

The resilience of the electric vehicle charging infrastructure is a notable challenge as the transportation sector transitions toward electrification, driven by the need to mitigate climate change and reduce greenhouse gas emissions. This paper examines the risks posed by natural hazards and power outages to electric vehicle charging stations in the United States, with a focus on understanding how these risks impact charging infrastructure. We analyze correlations between national risk index scores and the deployment of Level 2 and DC fast charging stations, utilizing national risk index score, geospatial charging infrastructure data, and records of electric disturbance events for 2023. Our findings reveal weak but statistically significant correlations between the distribution of chargers and national risk index score, with severe weather and natural hazards emerging as primary causes of power outages that disrupt charging operations, particularly in high-risk states like Texas. The risk threshold analysis in Texas identifies a critical tipping point, a risk index score greater than 97.18, where the probability of power outages due to natural hazards increases significantly, providing a clear decision-making guideline to prioritize infrastructure fortification in high-risk and hazard-prone regions.

Many hard-to-decarbonize sectors of the economy, e.g., heavy duty transportation and industrial processing, can utilize hydrogen to reduce emissions, particularly when electrification is problematic. The Department of Energy (DOE) National Hydrogen Strategy projects the use of hydrogen to grow in the United States by up to 500% by 2050, equal to 500 million tonnes of demand per year. The hydrogen ecosystem build-out (production, conditioning, delivery, storage, and end-use) is complex and faces many challenges such as the cost of clean hydrogen (e.g., renewable and nuclear sources), technology readiness, facility siting and community acceptance, incumbent equipment replacement, and private/public sector climate policy targets. The objective of this research is to develop a fuzzy cognitive map (FCM) of a hydrogen ecosystem that informs a set of principles to address this complexity and serves to guide industry and government investment and deployment.

Currently, 95% of hydrogen production in the United States comes from steam methane reforming (SMR) of natural gas, which emits 9-11 kg CO2e/kg H2. Through the Bipartisan Infrastructure Law, the federal government has invested $8 billion in regional hubs toward demonstration and deployment of technologies for low carbon hydrogen production (less than 4 kg CO2e/kg H2). A key aim of the framework is to align technology, policy, market, and behavior drivers to accelerate sustainable hydrogen deployment. Through workshops, feedback, and engagement with over 100 stakeholders, we mapped out three main components of a hydrogen ecosystem FCM: system drivers, parameters/constraints, and sustainability performance metrics. The drivers of the system represent different variables of ecosystem design that reflect perspectives ranging from state policy to technological readiness to community engagement. The system parameters imposed on these drivers help to constrain the ecosystem network, e.g., the physical conditions of hydrogen along the supply chain, spatial variability in siting, temporal changes in demand, state/federal regulations, etc. The metrics within the FCM are objectives for the rollout of a sustainable and just ecosystem e.g., emissions abatement cost, total cost of ownership, levelized cost of hydrogen, land use, and social impact, which are minimized to help inform the creation of 12 core principles based on the relative impact of the system drivers. The FCM is mapped to all five stages of the ecosystem supply chain, resulting in a near term focus on transportation applications of green hydrogen in southeast Michigan. This work will characterize the key drivers that shape near term and long term deployment decisions in the state, and their impact on the performance metrics that measure the sustainability of the system. The principles serve as a guide for stakeholders to assess their own deployment strategy as the ecosystem continues to develop.

Rebar selection plays a critical role in the sustainability of construction projects, given its impact on durability and environmental performance. This study evaluates the environmental impacts of five commonly used rebar types: thermoplastic, thermoset, regular steel, stainless steel, and epoxy-coated steel. The research aims to identify the most sustainable material options through a comprehensive life cycle assessment in line with ISO 14040 and ISO 14044 standards.

The analysis is performed at three levels: (1) cradle-to-gate assessment of unidirectional fiber-reinforced polymer tapes used in thermoplastic rebars, focusing on input materials and energy consumption; (2) mass-based comparison of environmental impacts across all rebar types, highlighting differences in material properties and lifecycle emissions; and (3) a broader evaluation of rebar impacts within bridge deck assemblies to understand their real-world implications. Key environmental impact categories, including global warming potential, resource depletion, and toxicity, are quantified using TRACI v2.1 and IPCC 2021 frameworks.

Preliminary results indicate that thermoplastic and thermoset composite rebars offer significant advantages due to their corrosion resistance, longevity, and lower embodied impacts during production. However, they face challenges in terms of field adaptability and recyclability. Traditional steel rebars are highly recyclable but are prone to corrosion, potentially shortening their lifespan in aggressive environments. Stainless steel rebars, while durable and corrosion-resistant, incur higher production costs and environmental impacts. Epoxy-coated rebars provide moderate corrosion resistance but are susceptible to damage if the coating integrity is compromised.

This research underscores the trade-offs between durability, cost, and environmental performance among rebar types. By integrating multi-level analysis, it aims to guide material selection for bridge construction, promoting the adoption of sustainable practices in the infrastructure sector. The findings are expected to provide actionable insights for engineers, policymakers, and material manufacturers, driving greener and more resilient infrastructure development.

Over a billion metric tons of waste and biomass are projected to be available in a future mature market in the United States. These resources represent an opportunity to decouple chemical production from conventional fossil fuel feedstocks, but such a broad solution space can also make for challenging decision-making. This talk will showcase how key analysis tools such as techno-economic analysis, life cycle assessment, material flow analysis, and multi-criteria decision analysis can be used to benchmark the costs, environmental impacts, and circularity of new innovations as well as to identify opportunities for prioritization and optimization. Using a series of examples related to chemical manufacturing, we will explore how analysis can guide where and how to leverage waste carbon in supply chains towards a future circular economy.

There have been many studies of how to decarbonize road transportation, but comparatively few on the best path for rail decarbonization in the United States. US railroads rely on diesel and have been resistant to electrification with catenary. Hydrogen, batteries, and biofuels are under consideration as options for decarbonization. The route from the Port of Los Angeles to Barstow (the Cajon Sub) is a good case study because of its high utilization and its uneven topography.

This analysis considered the net present cost over the lifetime of a locomotive of five technologies for traction along the Cajon Sub: Diesel, the current mainstay; biodiesel, a drop-in replacement with lower carbon intensity; hydrogen, a widely considered option for decarbonization of heavy freight; batteries, which are also widely used for transportation electrification; and catenary, a widely used but capital intensive way to decarbonize rail systems. It considered the net present value in three methods of calculation: private cost to a company without considering policies; the credit cost, meaning the private cost after considering the sale of credits under California’s low-carbon fuel standard and tax credits for hydrogen under the Inflation Reduction Act; and the social cost, considering the total cost to society of air pollution and greenhouse gas emissions. It conducted a Monte Carlo simulation which deployed wide ranges of values for inputs to produce a range of outputs for all fifteen NPV cost calculations. It also conducted analysis of the same route with only one train per day, to simulate options for lightly utilized railways.

The ranges for costs of all options at least partially overlapped, but clear trends were visible. The analysis found that for all three methods of calculation, on average, electrification with catenary is the lowest cost option, with battery electrification as a close second. The use of hydrogen fuel cells is the third lowest cost option, but with noticeably higher cost than the electric options. This is true even when ignoring externalities, and even more the case when considering them. When considering the full social cost of operation, diesel is the highest cost option, while when considering only the private cost, with or without tax credits and LCFS credits, biodiesel is the highest cost option while diesel is the second highest.

In the simulation with only one train per day, battery electric operation was the lowest cost option, for all three methods of calculation. Catenary was by far the highest cost option in this simulation, though if extremely low cost of catenary can be achieved, it could be competitive with other options. The choice between the other three depends on the method of calculation.

This study finds that over the lifespan of a typical locomotive, electrification through batteries or catenary is typically the most cost-effective option for rail operation, in most cases by a substantial margin. This holds true for both highly and lightly utilized rail corridors, and whether or not policies to reduce emissions are considered. It bolsters the case for electrification of land transportation.

As the world advances, the demand for middle distillates, particularly jet fuel and diesel grows rapidly, driven by factors such as rising global demand for transportation and energy fuels. This demand translates to an increase in CO2 emissions as the current fuels’ sources are fossils. Notably, hard-to-abate sectors such as aviation, shipping, and heavy hauling transport are challenging to transition away from fossil fuels since the current option, being electrification is challenging in these sectors. Therefore, an alternative solution such as biofuels can be a sustainable substitute for fossil fuels to decarbonize these sectors. Renewable feedstocks like lignocellulosic biomass can be transformed into low-carbon fuels to partially meet the existing demands of fuels and energy for hard-to-decarbonize sectors. In the last 30 years, a plethora of conversion pathways and catalytic upgrading alternatives for lignocellulosic materials have been discovered. However, a crucial decision in chemical process design, which involves the selection of methods for process representation, simulation, and optimization in a cost-effective and sustainable manner becomes challenging. Typical approaches such as detailed simulations or lifecycle analyses struggle to efficiently explore all possible alternatives because of the huge design space.

Hence, to fill this gap, in this research, we developed a novel superstructure-based optimization framework and mathematical models to study the diverse pathways for middle distillates production. Our superstructure contains 500 catalysts, 165 biomass upgrading chemistries and 320 individual biofuels. Notably, our superstructure allows three optimal decisions: the choice of catalysts, biomass upgrading chemistries, and blends of middle distillates. We use machine learning like the graph neural networks to parameterize the properties of biofuels that have not been tested experimentally as jet fuels or diesel. Then, the rationally designed blends of the transformed biomass molecules, identified via optimization, can be used as novel fuel products, such as diesel and jet fuels, which could match or outperform existing fossil fuel counterparts. We formulate this integrated process and product design as a multi-objective mixed integer non-linear programming problem. Our multi-objectives are aimed at identifying the minimum selling price of our desired fuels and their minimum greenhouse gas emissions (CO2 equivalent).

Using the developed framework, we examine the tradeoffs between the economics (cost) and environmental sustainability of different biofuel designs. We propose a good compromise between these competing objectives ensuring profitability, lower emissions, and resilience of our biofuel design solutions. This research aims to inform policymakers and industries on novel product blends which are cost-effective and environmentally sustainable for decarbonizing the transportation and energy sector, contributing to a net-zero carbon economy.

With generation capacity of electricity systems in Canada projected to grow up to 116% by 2050 to accommodate electrification and population growth, power system planners face a growing challenge in implementing new generation capacity which retains system reliability while addressing public concerns of affordability, greenhouse gas emissions, and other socio-environmental impacts.

While optimizing for reliable and affordable electricity is the core focus of power system planning, planners are increasingly leveraging policies such as emission caps or moratoriums to address political and social pressure to limit social and environmental impacts of new electrical infrastructure. To establish policies which robustly support objectives despite limited knowledge of the future, contemporary methods use established uncertainty analysis tools such as stochastic optimization or Monte Carlo simulations but remain limited in modeling uncertainties with no underlying probability distribution and often have limited consideration for subjective or intangible socio-environmental impacts. This study addresses this gap by introducing a chronological myopic modelling approach to Monte Carlo uncertainty analysis where uncertainties are modeled as random walks, and socio-environmental outcomes are emphasized. This method is of particular interest for systems with high exposure to uncertainty and is thus applied to a case study in Yukon, Canada for its small and electrically isolated grid, but is broadly applicable to centrally planned grids.

For this study, we modeled the Yukon electricity system and measured the effects of introducing policy instruments to constrain power system planning decisions between the years 2022 and 2050. Policies evaluated include emissions limits, restrictions on imported fuels, and moratoriums on highly polluting or impactful generators. We let uncertain parameters take many random walks through time to form many hypothetical futures. For each future, we sequentially and myopically solve for cost-optimal capacity expansion with a multi-year lookahead given expected or forecasted values for each parameter. Uncertainties are resolved as the model progresses chronologically, solving for dispatch decisions of generation capacity built from prior model periods. The result is a distribution of system performance with measured outputs including costs, greenhouse gas emissions, aggregated Likert scoring of community and ecological impacts, and proxy measurements for system reliability and stability.

Early results indicate that regardless of policy, Yukon’s electricity system is most vulnerable to changing hydrological conditions, demand increases, and a peakier load profile, with all measured outputs exhibiting sensitivity to these parameters. In a pair-wise comparison of futures under each policy, systems planned without new fossil generators had on average 8.5% less generation costs and 99% less emissions than in the absence of policy but exhibited 42% higher ecological impacts owing to higher proportions of hydroelectric development. Additionally, we present a decomposition of uncertain parameters and correlate positive outcomes in the presence or absence of individual generators, revealing robust generation assets.

As the climate crisis intensifies, switching to renewable energy remains a critical piece of the solution to ensure rapid decarbonization. However, renewable energy generation is highly reliant on the ambient environmental conditions, making it difficult to estimate the long-term generation—a task that is likely to get more difficult under climate change. Accounting for the impact of climate change is particularly difficult, as there remains uncertainty related to the magnitude of climate change within the mid- and long-term in addition to the relatively unknown impacts of climate change on generation of renewable energy technologies. In this work, we aim to fill this gap by leveraging machine learning to investigate the impact of climate change on state-level renewable energy generation across the US. Using data from the Energy Information Administration (EIA), we project the solar, wind, and hydropower generation across multiple US states under two key climate change scenarios. Our goal is to answer two key questions: (1) How will climate change impact renewable energy generation; and (2) Do these impacts differ across states? To answer these questions, we leveraged several machine learning techniques, as well as an ensemble of models, to first model the observed relationship between renewable energy generation and the surrounding weather and climate. Then, we used those same models to project the changes to the system, given the most recent IPCC climate change scenarios. Here, we will present the results from the projection analysis across multiple US states, including the states of California, New York, Florida, and Georgia, which contain some of the largest electric utilities in the country. The results indicate significant changes across different states and seasons, which could impact grid management and planning. Ultimately, the results will provide critical insights into the sustainability of renewable energy technologies over the long-term, given the reality of climate change.

Utility-scale solar photovoltaic (PV) is pivotal to decarbonizing the power sector, driven by declining costs, supportive policies, and its scalability. The Midwest, with its vast agricultural lands, is rapidly emerging as a key region for solar expansion. However, existing studies on the economic impact of solar have predominantly focused on local benefits, overlooking the opportunity costs of converting agricultural lands into solar installations. In addition, traditional power planning research has yet to integrate economic impact assessments directly into capacity models for guiding optimal utility-scale siting decisions.

This study bridges these gaps by endogenously integrating local economic metrics into a power system planning model to assess the effects of economic impacts on utility-scale solar siting. We analyze all counties within the Great Lakes region—comprising Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin—to develop localized supply and marginal benefit curves. The supply curves capture the costs of capacity additions while accounting for zoning regulations, land exclusions, and parcel-level constraints on agricultural lands. Marginal benefit curves quantify the economic contribution per county from project lifecycle expenditures that support interconnected local industries, as well as accounting for the opportunity cost of agricultural land conversion. These curves feed into a multi-objective power system planning model aimed at minimizing system costs and maximizing local economic benefits for solar investments through 2040.

Results reveal that counties with larger economies and below-average crop productivity deliver the highest value-added per megawatt (MW) of installed capacity. For example, large counties (population above 50,000) in Minnesota generate up to $38,400 per MW, whereas this value declines by 31% in smaller counties (population below 15,000). Moreover, the conversion of agricultural land in large Minnesota counties results in annual reductions of 12% ($4,900 per MW) for low crop productivity counties versus 16% ($6,000 per MW) in counties with high crop productivity. A scenario that reallocates solar investments towards counties offering higher economic benefits indicates potential shifts of up to 53% ($860 million) in Ohio, with a corresponding decrease of 47% ($500 million) in Wisconsin, all without increasing overall system costs.

Our findings underscore the necessity of integrating economic considerations into utility-scale solar planning to balance decarbonization with rural economic development. By discussing the trade-offs and synergies between cost minimization and community benefits, this research provides actionable insights for policymakers and energy planners to promote equitable and sustainable solar deployment.

Energy and water systems are deeply interconnected, leading to complex interdependencies that change in magnitude with changing climate, socioeconomic, and policy landscapes. Energy systems rely on water at every stage of transformation--directly, for activities like cooling power plants or as a “feedstock” for hydropower and electrolysis, and indirectly, for mining primary fuels or cultivating biomass. Similarly, water systems require energy for a range of applications, such as groundwater extraction, reservoir operations, and water conveyance and treatment. Concurrent management of these interdependent and often competing energy and water flows is crucial for sustaining key societal functions, which require understanding their relative magnitudes and interdependencies across a range of futures to enable informed planning and management. Yet, such forward-looking, internally-consistent evaluations are missing, which hampers evidence-based planning of integrated energy-water systems. Consistent accounting of future energy, water, and combined energy-water flows, while incorporating multisector feedbacks among energy, water, and land systems under climate, socioeconomic, and technological change is a daunting task. To address this, we have used the GCAM-USA version of the Global Change Analysis Model, which includes the necessary sectoral, spatial, and technological detail to enable quantification of future energy-water flows under global change drivers through an internally consistent framework. We have evaluated a suite of scenarios that span a range of plausible climate and socioeconomic futures for the US. The modeling outputs have been leveraged to populate an open-source tool developed at PNNL to visualize complex sectoral and spatial dynamics through dynamic Sankey diagrams. These diagrams are an effective tool to illustrate the flow of key resources, fuels, and commodities from source to end use, capturing the impacts of scenario constraints on technological transformations and resource allocation decisions. Energy-water flows exhibit significant change across climate and socioeconomic futures over the 21st century. In futures with lower radiative forcing, the amount of electricity needed grows over time to simultaneously meet the dual challenges of supporting a growing population and addressing climate change . The role of renewable technologies becomes more pronounced in the future, with transformation pathways showing a switch from dominantly used natural gas to wind and solar resources for electricity production. Similarly, tradeoffs between competing water uses become prominent as large magnitudes of water needed for thermoelectric cooling of power plants get amplified by irrigation requirements to grow biomass to meet electrification targets. Such relationships and relative dependence between energy and water use, especially for emerging technologies such as hyperscaler data centers, suggest that strategies for sustainable transitions would require flexibility in the production, transformation, and consumption of resources, technologies, and commodities. To ensure sustainable transitions, special attention will be needed to manage and minimize the grave and simultaneous pulls on both energy and water resources in the future.

India is the third-largest producer of electricity globally which is undergoing a significant transformation towards a renewable energy (RE)-dominated power system. At present, thermal power constitutes 61% of the nation’s installed capacity and 75% of its electricity generation, positioning India as the second-largest coal consumer and the third-largest emitter of CO2 worldwide. Aligning with global commitments to mitigate greenhouse gas emissions, India has set ambitious targets of achieving 450 GW RE installed capacity by 2030, with aspirations to transform into a RE-dominated system by 2050. This transition marks a fundamental shift from a conventional, firm power system to one characterized by intermittent, variable, and uncertainty associated with renewable energy system. Such a transformation poses significant challenges, including the need for balancing variable supply with variable demand, managing the economic and social impacts of stranded thermal power assets, addressing resource constraints driven by weather variability, and adapt to the rigidity of existing conventional power infrastructure. Addressing these challenges requires a comprehensive and integrated approach. One promising solution lies in the integration of supply-side management (SSM) and demand-side management (DSM) interventions in transition planning. SSM focuses on optimizing the generation and supply of electricity to match the demand, while DSM emphasizes strategies to reshape and manage consumer energy consumption patterns.

This study, using mathematical modelling approach, explores the effectiveness of integration of SSM and DSM strategies in mitigating the variabilities introduced by renewable energy sources. A linear programming-based mathematical model is developed to optimally match supply and demand by adopting economically attractive solutions. On the supply side, we have minimised the total cost of the electricity system by optimising the power generation mix of the system. On the demand side, interventions such as load shifting are employed to reshape load curves, enabling better alignment with variable RE generation. DSM options, including load curtailment and shifting, are analyzed under both incentive-based and penalty-based pricing strategies to encourage consumer participation. The Karnataka electricity system, a high renewable energy-rich state in India, is used as a case study for model implementation and validation. The results demonstrate that the integrated model effectively moderates demand variability, reduces high storage costs, and enhances the utilization of renewable energy capacities. Additional benefits include deferred capacity additions, improved utilization of existing infrastructure, minimized reliance on thermal power plants, and a reduction in overall demand variability. This study underscores the potential of integrated SSM and DSM interventions in facilitating a cost-efficient and sustainable transition to a RE-dominated electricity system.

Behind the meter rooftop solar plus storage (PVESS) has the potential to benefit the hosting customers by providing affordability, environmental, and reliability and resilience value. Whereas the bill reduction and environmental benefits of PVESS are well studied, its monetary resilience benefits are less understood. The increasing trend of power interruptions driven by extreme weather events heightens the need to understand these benefits. This study leverages various publicly available datasets to perform a cost benefit analysis of adding to determine the resilience value of PVESS for a typical single family home in each county in the continental U.S. We find that PVESS is very effective to technically mitigate interruptions across the country. However, the monetary benefits in the base case only cover about 14% of battery costs, with no county exceeding 60%. This is somewhat expected, given that PVESS provide other monetary benefits that are not part of this analysis. Through sensitivities, we find that higher frequency of extreme weather events roughly triples the resilience value of PVESS and that higher values of lost load double the same metric. Our sensitivity analysis shows that the benefit cost ratio of PVESS for customers living in areas with higher-than-average frequency of long duration interruptions and value of lost load is already above one even without considering other value streams. We conclude with recommendations that regulators and utilities could implement to enable customers to calculate and capture the resilience value of PVESS more efficiently.

Abstract

The UNEP/LCI-funded Biogenic Carbon Project was launched to harmonize biogenic carbon accounting in Life Cycle Assessment (LCA) by addressing inconsistencies in current practices across sectors and standards. Between March and November 2024, a consortium of multidisciplinary stakeholders from around the globe were organized into four distinct working groups: Temporal Aspects, End-of-Life, Reuse and Recycling, System Boundaries, and Standards and Adoption Barriers. These groups engaged in periodic meetings, workshops, and analyses to identify key challenges and propose preliminary solutions. The findings from this collaborative effort have been consolidated into a progress report marking the project’s Phase 1. This report serves as an open invitation for global feedback to refine and guide the development of consistent and best practices for biogenic carbon accounting. This session will share the key insights from the project to date, highlighting challenges, proposed solutions, and their implications for global LCA practices. Participants will engage in interactive discussions to provide input, helping to expand the perspective of the project as it transitions into Phase 2, which aims to develop actionable guidance for consistent application across sectors and practitioners of all experience levels. The session aligns with the ISSST 2025 thematic area of Agroecology, Industrial Ecology, and the Bioeconomy, offering critical understanding into biogenic carbon flows in agriculture, forestry, and bio-based systems. It also contributes to Innovative Tools and Methods in LCA and Sustainability Analysis by addressing advancements in dynamic carbon inventories and counterfactual scenarios.

Session Structure

The session will begin with an overview of the UNEP/LCI Biogenic Carbon Project, including its objectives, stakeholder engagement, and the scope of its work. Key findings from Phase 1, such as identified gaps and inconsistencies in current practices, will be presented. The discussion will then shift to the proposed best practices and their implications, exploring options for harmonizing accounting methods and offering practical insights on implementation challenges across sectors. An interactive feedback session will follow, providing an open dialogue to gather participants input on the proposals, focusing on addressing ambiguities and improving cross-sector consistency. The session will conclude with a discussion of the project’s next steps and future collaboration opportunities.

Target Audience

This session is designed for sustainability researchers, LCA practitioners, policymakers, and industry professionals focused on bioeconomy and climate change mitigation. Attendees will contribute to advancing global standards for biogenic carbon accounting, ensuring their applicability and relevance across diverse contexts.

Expected Outcomes

1. Refinement of proposed best practices based on participant feedback.

2. Increased stakeholder awareness and engagement in the UNEP/LCI project.

3. Strengthened collaboration toward harmonized LCA guidelines for biogenic carbon.

The USEPA has developed and maintained the Waste Reduction Model (WARM), a process-based life cycle assessment (LCA) decision support tool for waste management, since 1998. WARM estimates greenhouse gas (GHG) emissions, energy use, labor hours, wages, and taxes associated with the handling of non-hazardous solid waste. WARM covers 61 material categories of what are traditionally considered Municipal Solid Wastes (MSW) and Construction and Demolition Debris (C&D) and 6 waste management pathways: recycling, landfilling, combustion, composting, anaerobic digestion, and source reduction. WARM serves as a solid waste management planning tool that can be used to perform high-level comparisons of the environmental and economic impacts of material-specific waste management decisions. WARM is likely the most widely-used tool for estimating GHGs associated with MSW materials end-of-life management, although official statistics are not kept.

Much of the data and calculations underlying WARM are documented but are not available for public review or use. WARM includes intrinsic estimates of key data inputs to waste management processes that are static (e.g. electricity and transportation intensities of activities and associated emissions factors) and similarly it provides intrinsic models that are used to compute offsets of recovery activities like material production and soil carbon storage that are not based on the most current available models and many do not include full life cycle accounting. USEPA is evaluating ways in which modeling can be improved and made more transparent, data quality can be disclosed, additional impacts can be considered, and how WARM can draw on existing data and models that represent the best data sources that are actively maintained. This effort to restructure WARM to achieve these objectives is referred to as WARMer. The purpose of this special session is to solicit public input on WARMer. This session will begin with a presentation on the technical requirements for WARM and describe potential modifications. The presentation will be followed by an interactive session to gather insights from participants. We will use interactive QR-code based polling, as well as moderated, open discussion to solicit input from participants.

Disclaimer:

The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.

The New York State (NYS) Climate Act calls for research to support low-carbon products and a climate-focused bioeconomy, including methods and tools for measuring and verifying carbon sequestration and greenhouse gas (GHG) emissions, which are essential for creating appropriate procurement guidelines and incentives. In the building industry, there is limited data available to support Environmental Product Declarations (EPDs) of wood building materials representing the northeast United States. This data gap prevents informed decision-making regarding the environmental impact of these materials produced in New York compared to traditional, energy-intensive products like concrete and steel.

Here, we introduce the Tool for Assessing Carbon Storing Materials (TACSMA), a life cycle accounting tool designed to assess the GHG impacts of carbon-storing materials, such as lumber, CLT, glulam, etc. This tool calculates cradle-to-gate life cycle GHG emissions based on user-provided data and other relevant regionally relevant data. TACSMA evaluates factors such as forest biomass operations, and transportation distances, enabling more localized decision-making. By providing region-specific calculated results, TACSMA will help reduce carbon emissions and align with the goals of the Climate Leadership and Community Protection Act (CLCPA).

TACSMA’s modular design addresses sustainable forest management, from seedlings to extraction and processing, while accounting for multifunctionality issues in life cycle assessments of these products. The tool integrates spatial and temporal data to facilitate the development of accurate EPDs, thus enhancing transparency. Ultimately, TACSMA will provide decision-makers with detailed insights into the environmental impact of carbon-storing materials, fostering sustainable forest operation and wood processing and reducing the environmental footprint of building materials.

Globally, the cement industry contributes over 6% of global greenhouse gas (GHG) emissions. Multiple research studies have confirmed that strategies such as fuel switching, the use of supplementary cementitious materials (SCMs), energy efficiency improvements, and carbon capture, utilization, and storage (CCUS) have the potential to reduce more than 80% of the carbon intensity of cement (g CO₂eq/kg-cement). Despite these pathways being commercially tested and demonstrating technical feasibility, they have seen limited adoption across various regions. In the Canadian cement industry, for instance, fossil fuel substitution increased marginally from 3.5% to 8%, while SCM substitution rose modestly from 11% to 18% over the past two decades. While high costs of decarbonization technologies and insufficient policy support are often cited as critical barriers, the persistent slow progress suggests deeper structural, economic, and institutional challenges (e.g., building codes) that merit further investigation. This study aims to examine these barriers by systematically analyzing cement industry reports and related government policies to provide some insights for accelerating decarbonization.

Methods: We are currently analyzing 42 cement industry documents published between 1993 and 2024, including roadmaps, environmental performance assessments, benchmarking studies, case studies, and net-zero strategy documents. These reports are primarily sourced from the Portland Cement Association (PCA), the Cement Association of Canada (CAC), the European Cement Research Academy (ECRA), the U.S. Department of Energy (DOE), the U.S. Environmental Protection Agency (EPA), and the World Business Council for Sustainable Development (WBCSD). We categorized decarbonization-related information from these reports into three areas: Technology, Policy, and Other Barriers/incentives. In the technology category, we recorded energy efficiency measures, SCM substitution targets, fuel-switching goals, CCUS initiatives, and electrification innovations. In the policy category, we identified industry priorities, national GHG reduction policies, implementation mechanisms, and government financial support. Within the other barriers/incentives category, we noted any technological limitations, economic constraints, supply chain challenges, investment hesitancy, and market demand dynamics.

Our preliminary retrospective analysis of the Canadian industry identifies two key challenges in the slow progress of cement sector decarbonization: less ambitious 1990s decarbonization targets compared to today’s net-zero goals and the disconnect between decarbonization targets and supply chain constraints. Energy efficiency was emphasized as a central focus in the early 2000s, targeting a return to 1990 emission levels, but these goals were largely voluntary and did not involve binding commitments. Also, the rising costs of alternative fuels, coupled with the limited availability of natural gas, led to coal becoming a primary fuel source, resulting in a 24% increase in emissions from 1990 to 2002. These findings suggest that short-term economic priorities have significantly influenced decision-making, often at the expense of long-term decarbonization efforts. We plan to expand this analysis further to identify structural, economic, and policy-driven barriers that hinder decarbonization and provide some recommendations for addressing these challenges.

Over the past few years, California has significantly reduced its solar incentives, including lowering the valuation and crediting of exported solar generation and cutting support for its emerging community solar initiatives. These changes have slowed solar deployment across the state, a state known to spearhead national benchmarks, in a phenomenon branded “the California effect.” Recently, California Governor Newsom vetoed Senate Bill 1374, a bill that would have allowed schools and multi-family properties to generate electricity on one meter to offset usage on another and obtain credits for on-site solar generation equivalent to those available for single-family homes. This raises two critical questions that this paper addresses: Are we fully tapping into the potential of schools for solar deployment? Is solar adoption distributed equitably across all communities?

This research project employs a two-stage analysis to investigate these questions: 1) solar detection/segmentation and 2) socioeconomic analysis. In the first stage, we utilized DeepSolar, a machine-learning framework developed at Stanford University, to analyze satellite imagery to detect and locate solar panels across all K-12 schools in California based on their GIS locations. We have adapted DeepSolar to meet the specific needs of our project, including the ability to automatically process thousands of images and store detection/segmentation results. California leads the nation in solar adoption within K-12 schools, with 2,815 schools reportedly equipped with solar panels as of 2023, according to Generation180. We aim to use DeepSolar’s machine learning with Google Satellite images to validate and refine this number, providing a more detailed and accurate assessment of solar adoption in California schools.

In the second stage, we will integrate GIS-based socioeconomic data and employ various data analysis methods to explore the relationships between a school's socioeconomic characteristics and the level of solar adoption. We aim to identify if socioeconomic divisions may dramatically impact solar adoption across different communities, creating disparate access to state subsidies and clean energy, and resulting in higher utility rates for some. Examining solar installation through the lens of energy equity provides insights into broader patterns of adoption, usage, and the distributional effects of policy-driven legislation. Previous national-scale research indicates that solar installation rates in disadvantaged communities are approximately 30% lower than in non-disadvantaged communities. Building on these findings, we aim to assess whether a similar or even wider gap exists in California. We hypothesize that targeted legislation could significantly enhance solar adoption in disadvantaged communities, reducing this equity gap and fostering more inclusive access to clean energy solutions.

Biosand filter is being used as a low-cost, point-of-use, sustainable drinking water treatment technique globally. However, due to the limitations of biosand filter in achieving complete decontamination of drinking water, modification of the biosand filters were proposed which have increased the environmental burden of filter production. This study aims to compare the environmental impact, cost, and performance of the biosand filter (BSF), the physically modified biosand filter with biochar addition (MBSF biochar), and the chemically modified biosand filter (MBSF) by coating the gravel with iron oxide (MBSF IOCG).

To achieve this goal, BSF was constructed and modified with biochar and iron oxide coated gravel (IOCG) to collect life cycle inventory. The life cycle assessment was then applied using the SimaPro version 9.1.17 to highlight the most significant impact of this process with ReCiPe 2016 endpoint (E). Sensitivity analysis was also made to assess the robustness of the result by changing some input variables on emission reduction.

It was found that natural aggregate extraction has the most impact on BSF and MBSFs construction. The total environmental impact of filters was; MBSF IOCG (675.8898 pt), > MBSF biochar (674.9818 pt), > BSF (672.5751 pt). Modification of the BSFs increased the cost and environmental burden of MBSF production. When the modification process in the MBSFs was compared, it was found that the production of biochar was less environmentally friendly (3.34 pt) than the Iron oxide coating of Gravel (IOCG) (3.14 pt). The estimated cost of constructing the filters was; MBSF (IOCG) ($ 37.28) > MBSF (Biochar) ($ 20.99) > BSF ($ 18.49). Although modifications increased the cost and environmental impact of filters, the performance of filters slightly increased for the removal of the tested pollutants. For instance, MBSF (IOCG) showed a removal percentage of 99.27% for Cu, 99.1% for Zn, 95.28% for Fe, 98.6% for Ni, and 95.33% for total coliform. On the other hand, MBSF (biochar) showed 99.2% for Cu, 99% for Zn, 78% for Fe, 75.4% for Ni, and 92.8% for Total coliform removals while the BSF gave a removal percentage of 99% for Cu, 90% for Zn, 60.1% for Fe, 73% for Ni and 93.89% for Total coliform.

The result demonstrates the high cost and environmental impact generated when MBSF is modified with iron oxide coated gravel (IOCG), in comparison to biochar, which can be mitigated by the replacement of IOCG with other greener alternatives such as iron rich sand or greener production routes of IOCG. Accordingly, recommendations were proposed for a more sustainable BSF and its modification. And future studies should focus of the development of cost-effective environmentally friendly MBSFs with high treatment efficiency for production of quality drinking water to regions with water issue.

In 2022, steel production accounts for over 2% of total U.S. emissions, with Indiana contributing about half of this total. Decarbonizing Indiana’s steelmaking is thus imperative to achieving national climate goals. As the leading steel-producing state in the U.S., Indiana operates 10 steel plants, utilizing both Electric Arc Furnace (EAF) and Blast Furnace-Basic Oxygen Furnace (BF-BOF) technologies.

The BF-BOF route is highly emission-intensive, primarily due to the reliance on carbonaceous reducing agents and energy sources in blast furnace. In 2022, Indiana’s blast furnace plants emit 1.7 (1.1 – 2.1) tons of CO2e per ton steel. By contrast, the EAF route, often considered less carbon intensive, becomes more sustainable when integrated with hydrogen-based direct reduced iron (H-DRI). This method replaces carbon-based reductants with hydrogen derived from electrolysis, significantly reducing process emissions. The H-DRI process involves pre-heating iron-ore pellets, reducing them in a hydrogen-fed shaft, and feeding the direct reduced iron into an EAF. Remaining emissions stem from flux materials, carbonaceous inputs, and EAF electrode degradation.

In our analysis of direct emissions at a H-DRI plant, we analyze a range of input values to reflect the variation in plants and a specific value to present the typical practice. The emissions are calculated using a mass-based approach and depend on the type of steel produced. The approach is similar to that used by EPA when estimating annual emissions from existing EAF facilities. The flux ranges within 25-140 kg/tLS as limestone, and the typical value is 93 kg/tLS, which is a combination of 57 kg/tLS dolomite and 36 kg/tLS limestone; 5.4 (1.5-31.2) kg/tLS as carbon; 2 (2-6) kg carbon/tLS as the EAF degradation rate; and 0.18% (0.05-1.04%) as the carbon grade. By incorporating the H-DRI, the EAF gets rid of most of the process emissions and achieves the direct carbon emissions 0.0675 (0.0220-0.1599) t CO2e/tLS.

We apply NREL’s Regional Energy Deployment System (ReEDS) to model the Midcontinent Independent System Operator (MISO) grid region to investigate electricity emissions and scenario analysis to determine the optimized grid operation. We suppose the Indiana steel industry transitioned instantaneously or gradually in the next 10 years to a fully electrified system. Five grid cases are developed as mid, renewables, solar, wind, and nuclear cases. The added grid load is set as no extra load, median load, or 95th percentile extra load. Combining each item from the three categories forms a scenario, thus multiple scenarios established. The minimized total cumulative emissions through 2050 are 198.06 (182.66 - 233.44) million metric tons of CO2e as the results of instantaneous transition and 95th percentile extra load of the renewables grid. The normalized emission rate considering both direct and grid emissions are 0.36 (0.30 – 0.48) t CO2e per ton steel. As such, this pathway presents a transformative opportunity for low-carbon steelmaking.

Fast pyrolysis offers a renewable pathway for bio-oil, syngas, and biochar production. Drawing from existing literature, this review examines the techno-economic assessment (TEA) of fast pyrolysis systems for biofuel production from lignocellulosic biomass, driven by the growing global demand for sustainable energy. The Minimum Selling Price (MSP) of raw and upgraded bio-oil revealed significant price disparities due to the additional refining processes required for upgraded bio-oil. Furthermore, the analysis examines the influence of feedstock type (residues, grown feedstocks, and blended feedstocks) on the MSP, identifying the logistical and quality control challenges associated with residue-based feedstocks. Sensitivity analyses reveal that bio-oil yield and feedstock costs are the most influential parameters on the MSP among the articles that were included in the review. Furthermore, the integration of machine learning into process optimization and predictive modeling emerges as a transformative approach to enhance the economic feasibility of pyrolysis-based biorefineries, offering potential for cost reduction and operational efficiency improvements.

The transition to a low-carbon energy system depends on both critical minerals and bulk materials, each playing distinct roles in decarbonization pathways. Bulk materials, such as steel, cement, and aluminum, are responsible for a significant portion of the embodied carbon in energy technologies and supporting energy infrastructure, while critical minerals face unique geopolitical, ethical, market, and environmental challenges. This study develops an integrated framework that combines the energy system optimization model, TEMOA, with industrial ecology principles to assess the role of both material and infrastructure factors in energy system planning. A core focus of the research is the impact of infrastructure—such as CO2 pipelines, transmission and distribution networks, and hydrogen infrastructure—on embodied carbon and operational emissions across sectors.

The first part of the study will explore how these infrastructures, alongside material constraints, influence decarbonization pathways. By running capacity expansion scenarios with and without embodied carbon constraints, the study aims to adapt TEMOA to incorporate both operational and embodied emissions, particularly in sectors where infrastructure plays a significant role in emissions profiles. The research will prioritize bulk materials, given their substantial contribution to embodied carbon in energy technologies, while providing a foundation for addressing critical mineral supply and infrastructure considerations in future energy system analyses.

Transitioning from animal-based protein to plant-based alternatives has emerged as a promising strategy to address global challenges such as climate change, economic instability, and food insecurity faced by the agri-food systems. Evaluating the overall sustainability performance of plant-based protein production can help scientists prioritize research efforts by maximizing financial gains, improving resource efficiency, and minimizing environmental burdens. Life cycle sustainability assessment (LCSA) has been proposed as an effective quantitative method for evaluating overall sustainability across a product or system’s life cycle, however, its application in plant-based proteins remains limited and inconsistent. This study aims to systematically review the existing literature from 2011 to 2024 to 1) evaluate the state-of-art status of the LCSA framework in general and its specific application in the agri-food sector, and 2) identify opportunities and limitations for applying life cycle sustainability assessments to plant-based proteins. We find that LCSA results can be evaluated independently or integrated into a single-score method, such as multi-criteria decision analysis. Independently, to capture the complexities of agri-food systems, environmental indicators often focus on resource depletion, air pollution, and climate changes; economically, key cost metrics are quantified from profitability and feasibility analysis of specific processes. For social sustainability, incorporating different stakeholder perspectives and identifying social hotspots are critical to improving social impact evaluations. Among all stakeholders considered in social LCA, workers, society, and local communities are frequently considered major stakeholders in the agri-food sector. In addition, we find that the alignment of the core scope of an LCSA study is the foundation for ensuring consistency and completeness of a robust sustainability result. Assessing the trade-offs and synergies among three sustainability dimensions (environment, economic, and social) also provides valuable insights to diverse stakeholders, especially decision-makers along the agri-food supply chain. Developing a dedicated and standardized LCSA framework for agri-food systems enables comprehensive evaluations that can effectively assess the overall sustainability of the agri-food system and support the sector’s alignment with global sustainability goals.

Typical material flow analysis (MFA) and supply chain analyses are static and do not project how material demands could shift in the future. In this study, we plan on creating framework to expand on existing methodologies to generate material demands for a commonly used building material (i.e. concrete) for general facility types used at our case study sites. Building on the material flow analysis of concrete, our team will assess at points throughout each key raw material's supply chain where potential supply vulnerabilities exist. Our MFA will include all material precursors, additives, and other elements that contribute to the manufacturing and supply of concrete. We will include supply details specific to the case study sites, such as regional sources of supply and specific design requirements. The results from this study can be used to model material demands by facility type, track material flows across supply chains for key building materials, identify supply chain vulnerabilities that could impact project costs and timelines, and create scenarios for reducing those impacts. The methods in this study can be modified to analyze other materials with high demand and significant vulnerabilities (e.g., materials with high import reliance).

The control methods applied in drinking water treatment are often simple feedback loops that allow operators to select set points in response to demand. The time-varying carbon intensity of operation is typically not considered as human operators schedule pumping and treatment to meet demand. This work takes a model predictive control (MPC) approach to minimize total emissions and satisfy demand while maintaining critical system states. This work develops a switched discrete-time linear state-space system as a mathematical model of a treatment plant and its distribution dynamics. This model is then used in a mixed-integer linear-quadratic optimization problem that determines optimal plant control inputs over a prediction horizon. This presentation will discuss the model and controller formulation, numerical simulation results, and findings from deployment on a physical testbed.

Material availability is a significant concern for sustainable development, particularly in achieving energy transition and digital development goals. According to the International Energy Agency (IEA, 2024), solar photovoltaic (PV) technology is projected to become the leading source of renewable electricity by 2030. Additionally, advancements in communication technology have shifted from copper to fibre optic cables, offering a more sustainable and faster method of data transmission. However, this transition presents challenges in securing the necessary resources for manufacturing PV panels and fibre optic cables, as well as managing waste from end-of-life products classified as waste from electrical and electronic equipment (WEEE).

The generation of solar photovoltaic (PV) panel waste was 0.6 billion kg in 2022 and according to the Global E-waste Monitor (2024) this amount is expected to increase fourfold by 2030, reaching 2.4 billion kg annually. Additionally, small IT and telecommunication equipment, which includes cables, contributed to 5 billion kg of waste in 2022. As the waste stream continues to grow, it is crucial to take proactive measures to mitigate its impact on the environment and create effective recycling systems which address all important elements.

Current WEEE recycling practices overlook several critical raw materials (CRMs) such as Indium, Gallium, and Germanium, which are typically found in small concentrations. These materials are critical to the European Union’s (EU) economy and typically are obtained as by-products in the extraction of zinc and aluminium. Although the EU has refining capacity for primary raw materials, it lacks a functional infrastructure for recycling these elements, making recovery economically unfeasible.

This research aims to investigate the feasibility of using Ireland's current mining and materials processing infrastructure to establish a system that is both economically and environmentally sustainable in the recovery of these materials from end of life products. As Ireland is a leading producer of zinc ore, it provides an opportunity to collaborate with mining enterprises on circular economy and in particular in how Germanium, which is produced as a by-product from zine ores, could be recycled as part of the primary production system.

The current work is focusing on Cadmium, Indium, Gallium, and Selenide (CIGS) photovoltaic panels and bend-intensive fibre optic cables, often used in data centres. It aims to develop processes for recovering critical raw materials (CRMs) from these products at end of life. It will also explore regulatory issues that might arise and address logistical considerations for their integration into mining production. Additionally, an analysis of the legal framework will be carried out, regarding the use of recovered materials in primary production and cross-border transportation regulations.

Through an interdisciplinary approach, it is expected to conduct a comprehensive analysis of the feasibility of connecting two different systems processes: WEEE recycling and mining production. This approach leverages the currently installed infrastructure for these systems to improve the circularity of critical raw materials, reduce the environmental impacts during the PV panel and fibre optic cable life cycle, and reduce the reliance on materials supplied from the main market outside the EU.

Renewable energy plants, including solar, wind, and hydroelectric facilities, are all climate dependent. The intensity of solar radiation, the magnitude of wind, and the amount of precipitation all affect the efficiencies of renewable energy infrastructure. Identifying optimal locations for these power plants is critical to their success, as energy efficiencies vary regionally based on climate conditions. Over the past decade, anthropogenic climate change has continued to exacerbate climate variability, but its effects are not uniform. Some regions experience hotter, more arid conditions, while others face triple the normal rainfall. This variability adds uncertainty to planning renewable energy infrastructure. To address this challenge, we leverage a machine learning approach to analyze regional climate changes in the U.S. from 2011 to 2022 and model the regional output efficiencies of renewable energy power plants with capacities of 1 MW or larger. Using monthly climate indicators from the North American Regional Reanalysis (NARR), we apply Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning (REDCAP) to segment the U.S. into climate clusters for each year. By fixing the number of clusters annually and averaging them over the 12-year period, we establish standardized regional climate trajectories. Additionally, we track monthly power plant outputs using data from the U.S. Energy Information Administration (EIA). Rather than directly modeling energy generation, we calculate the Capacity Utilization Factor (CUF) to normalize generation and strengthen dependencies on NARR climate variables. Random forest models then predict CUF within each REDCAP cluster, illustrating the relationships between renewable energy performance and regional climate conditions. Ultimately, this analysis provides insights into regional climate trajectories and highlights how large power plants have experienced changes in output due to shifting climate patterns. These findings offer valuable information for optimizing future renewable energy infrastructure in the context of a changing climate.

Space heating is a significant contributor to residential heating emissions, and many policies have leveraged subsidies for heat pumps as a step toward building electrification and broader decarbonization goals. Massachusetts aims to reduce residential heating emissions to 0.8 MtCO2eq per year by 2050, offering a $10,000 subsidy for whole-home heat pumps in addition to the $2,000 tax credit awarded from the Inflation Reduction Act. This study evaluates the impact of various state-level subsidy scenarios on adoption rates, emission reductions, and associated co-benefits in Massachusetts and the region. Using GLIMPSE, a graphical interface for the integrated assessment model GCAM-USA, the analysis explores state-level scenarios including current subsidy levels, 75% and 100% subsidy rates, as well as forced adoption, to predict future emissions and heat pump usage. Results indicate that under the current subsidy rates, 2050 residential heating emissions in Massachusetts would exceed the state’s target by a factor of six. Even under the most aggressive forced-adoption scenario, heat pump usage reached only 88% by 2050, with emissions remaining 62% above the target. The impact on emissions and electricity demand on neighboring states was minimal, with less than 1% increase in PM2.5 and greenhouse gas emissions. This is attributed to initiatives such as the Regional Greenhouse Gas Initiative and state-level renewable energy programs that limit emissions in the region. A public benefit-cost analysis was conducted using the Co-Benefits Risk Assessment Screening Model (COBRA) and the social cost of greenhouse gases for Massachusetts and the region from 2030-2050. The analysis found the highest subsidy cost-effectiveness ratio, 1.8, under the current scenario, with diminishing returns as subsidy levels increased. The findings of this study suggest that the state’s reliance on fossil fuel heating and lengthy lifespan (>20 years) of current heating technology limit heat pump adoption. As a result, financial subsidies alone may not be sufficient to encourage adoption, and additional decarbonization strategies are needed to meet emission targets.

Vehicle electrification has become a cornerstone strategy for decarbonizing the light duty vehicle (LDV) fleet in many countries. Potential risks in battery supply chains, the environmental impacts of battery production and the varying battery requirements for different electrified vehicles drive the need for strategic deployment of battery capacity to maximize the mitigation of greenhouse gas (GHG) emissions.

Our recent work[1] reveals considerable variation in GHG mitigated per kWh of battery deployed across different vehicle (e.g., powertrain and size), temporal (e.g., model year) and spatial (e.g., state/county) markets. The most efficient use of battery capacity also changes under various deep uncertainties related to technological advancements, manufacturing conditions, political and regulatory frameworks, and the deployment and usage patterns of electrified vehicles. This renders scenario-based analysis, commonly associated with prospective life cycle assessments, insufficient for long-term transportation planning.

In this study, we utilize the robust decision-making (RDM) approach to determine robust strategies for allocating limited battery capacity across the U.S. LDV fleet from 2023 to 2050. The RDM approach models proposed strategies under a large number of plausible futures, accounting for different combinations of uncertainties. Using statistical analyses and visualization on the large resulting datasets, the characteristics of futures where a proposed strategy performs well or poorly can be assessed.

Specifically, we test the robustness of five stylized strategies:

1. By 2035, all new vehicle sales are plug-in hybrid electric vehicles (PHEVs).

2. By 2035, all new sales are battery electric vehicles (BEVs).

3. By 2035, all new sales are zero-emission vehicles (BEVs and PHEVs).

4. From 2035, all new car sales are BEVs, and all light truck sales are PHEVs.

5. From 2035, county archetypes with urban and combined drive-cycles adopt BEVs, while archetypes with rural drive-cycles adopt PHEVs.

We apply Monte Carlo simulations to evaluate the impact of proposed strategies on model-year fleet life cycle GHG emissions across a broad range of plausible future scenarios. This modeling results in a large dataset containing the values of the stochastic parameters modeled in each run, as well as the resulting GHG emissions. We then apply the Patient Rule Induction Method (PRIM)[3], a scenario discovery algorithm, on the resulting dataset to identify clusters of uncertain conditions where the proposed strategies perform poorly. The ‘failure’ of these strategies is defined with respect to a percent reduction in model-year emissions, compared to the base-case results.

Our results identify which of the five strategies perform better under conditions of deep uncertainty, including limited availability of battery capacity along with variations in technological progress, shifts in vehicle market shares by class, powertrain architectures, and changes in energy carrier GHG intensity, among others. The study highlights the most critical factors to prioritize in LDV policies and advocates for a novel approach for integrating life cycle assessment into policymaking.

References

[1] N. Alzaghrini, D. Liang, A. F. N. Abdul-Manan, J. McKechnie, I. D. Posen, and H. L. MacLean, “Battery supply constraints in the light-duty vehicles sector - a barrier for fleet electrification or an opportunity for more efficient battery use?” Submission in progress.

[2] R. J. Lempert, S. W. Popper, D. G. Groves, N. Kalra, J. R. Fischbach, and S. C. Bankes, Making Good Decisions Without Predictions: Robust Decision Making for Planning Under Deep Uncertainty. RAND Corporation, 2013. doi: 10.7249/RB9701.

[3] Project-Platypus/PRIM. (Oct. 02, 2024). Python. Project Platypus. Accessed: Oct. 30, 2024. [Online]. Available: https://github.com/Project-Platypus/PRIM

With growing environmental challenges, integrating sustainable technologies into communities is increasingly critical. Sustainable development requires technologies that meet human needs while minimizing environmental harm. Life Cycle Assessment (LCA) helps evaluate ecological performance. Still, true sustainability also needs to consider social and economic factors, as trade-offs often exist among these pillars. New technologies can drive job creation, infrastructure improvements, and community development. Yet, they may also cause job displacement, social conflicts, or land-use changes. Comprehensive social assessments are vital when deploying technologies in new regions. Economic viability is equally crucial to long-term success. The Sustainability Return on Investment (S-ROI) methodology, developed by EarthShift Global, offers a multi-stakeholder tool that integrates environmental, social, and economic considerations into a unified metric.

S-ROI begins by identifying affected stakeholders and using various data collection processes, including interactive interviews, to ensure diverse perspectives. Next, the risks and opportunities associated with each stakeholder are assessed to evaluate their potential impacts. Efforts are then made to eliminate or mitigate these risks where possible. Any remaining risks and opportunities are quantified to enhance clarity on their significance. Finally, using Total Cost Assessment principles, the most critical factors are monetized to support informed decision-making and emphasize the financial implications of sustainability initiatives.

In a recent project, EarthShift Global conducted an LCA for an agro-mining company assessing environmental impacts in a region with an established local community. Due to social concerns, the company requested an S-ROI assessment to integrate environmental results into a broader sustainability analysis for an agro-mining project on mineral-rich lands. The study monetized risks and opportunities for stakeholders, including landowners, local communities, municipalities, traditional leaders, NGOs, universities, miners, ecosystems, and the company itself. Findings revealed an sustainability return on investment ranging from $75,000 to USD 3.1 billion annually, demonstrating significant regional benefits. Recommendations included engaging traditional leaders, exploring alternatives to monocropping, preserving unique vegetation, restoring scenic views, addressing water access issues, and optimizing post-restoration land use to enhance economic opportunities.

This poster will showcase S-ROI’s application in this real-world case study, demonstrating its value in guiding sustainable decisions, supporting investor engagement, and advancing global sustainability goals.

The cement, steel, and chemical industries account for a significant share of global CO₂ emissions and are among the hardest-to-abate sectors due to their inherent process emissions, high-temperature requirements, and capital-intensive infrastructure. Achieving net-zero emissions in these sectors will require widespread deployment of carbon capture, utilization, and storage (CCUS) technologies. However, a key challenge is the lack of co-located geologic storage sites, necessitating an extensive CO₂ transportation network to support large-scale storage or utilization. This study investigates carbon hub networks as a cost-effective strategy to accelerate CCUS deployment across multiple industries. By leveraging shared CO₂ transport and storage infrastructure, carbon hubs could significantly lower financial and logistical barriers to decarbonization. This work provides a spatial-economic analysis of potential carbon hub formations and explores the role of policy incentives, industry adoption barriers, and energy demand considerations in shaping the future of industrial CCUS.

To analyze the feasibility of carbon hubs, we develop an integrated spatial-economic model to optimize CCUS infrastructure for cement, steel, and chemical facilities. The study estimates the cost and design of a large-scale pipeline network, evaluating factors such as transport distances, pipeline sizing, and storage locations. Using industry-specific parameters, we model the cost of capture for different facility types, incorporating variations in plant size, fuel type, and flue gas composition. To supplement the modeling, we conduct interviews with industry stakeholders to assess their willingness-to-pay for CCUS, identify barriers to carbon utilization, and evaluate the role of alternative fuels. Additionally, we quantify the added electricity demand associated with carbon capture technologies, particularly amine-based systems, to understand the potential impact on grid resilience and operational costs.

Our preliminary results indicate that a carbon hub approach could capture up to five times more emissions than a cement only CCUS network while requiring only twice the infrastructure investment. This suggests that an industry-wide, hub-based model could be a cost-efficient pathway to decarbonization. However, our analysis also highlights key financial and policy gaps. The current Section 45Q tax credit remains insufficient to drive widespread adoption across all three industries, and CCU adoption faces technical and economic barriers, as revealed in industry interviews. Furthermore, the electricity demand for large-scale carbon capture is substantial and must be factored into future energy planning to ensure CCUS expansion does not create additional energy burdens.

This study provides a data driven roadmap for accelerating industrial CCUS deployment through strategic carbon hub formation. By integrating spatial modeling, cost analysis, and industry insights, our findings offer actionable recommendations for policymakers, industry leaders, and researchers seeking to enable large-scale industrial decarbonization.

The transition to resilient infrastructure remains paramount for achieving future sustainability goals. Integrating human dimensions into the design and implementation of infrastructure projects is of increasing interest, particularly given a growing focus on equity and inclusion, participatory approaches and social life cycle assessment. Infrastructure professionals are faced with the challenge of ensuring sustainability, resilience, and efficiency of critical infrastructure systems in the face of complex challenges such as population growth, climate change and rapid technological advancement. However, traditional approaches to infrastructure development lack the theories, frameworks, systems or processes needed to center human dimensions. Existing infrastructure practice and procedures often overlook localized and demographic-specific values, limiting the ability to address the nuanced preferences of diverse communities. This research addresses these gaps by exploring the applicability of value theory as an interdisciplinary frame for setting community-specific infrastructure priorities.

Value theories provide a robust framework for understanding how societal and individual principles can shape infrastructure priorities. This paper provides an overview of relevant value theories and applies value principles to Reconnect South Park, a 2022 USDOT planning grant awardee. Using a qualitative case study approach, three value theories - Schwartz’s Value Theory, Hofstede’s Cultural Dimensions framework, and Rokeach’s Value Survey (RVS), will be used to code publicly available Reconnect South Park project documentation. Furthermore, an effort will be made to connect value principles to project strategies aimed at achieving human-centered outcomes. Anticipated results will highlight opportunities for value theories to support human centered infrastructure decision making. Additionally, results are expected to reveal infrastructure development strategies that center human values and cultural context, while also elucidating the critical role that value theories can play in infrastructure planning.

This research contributes to our understanding of sustainable systems by moving infrastructure practice beyond technical considerations and closer to a human-centered process. In future research, longitudinal studies can track the evolution of societal values in response to social and cultural change, while mixed-method research can integrate quantitative and qualitative value data to provide comprehensive insights into localized or demographic-specific values. By incorporating value theories into existing infrastructure development processes including planning and governance, policymakers and engineers can create inclusive, sustainable, and culturally resonant infrastructure systems.

Prospective life cycle assessments (pLCA) are increasingly conducted to estimate the future life-cycle emissions of emerging and existing product systems, such as sustainable fuels, chemicals, and materials. However, the reliability of such long-term projections lacks robustness due to the inherent challenges in accurately predicting how a product system would change over multi-decade timeframes, primarily from epistemic uncertainty (resulting from limited knowledge of the system), and ontological uncertainty (unknown-unknowns). Moreover, the literature lacks concrete, well-documented examples of historical product system evolution, which further limits our ability to derive valuable insights for future projections. To address this gap, we have retrospectively looked at how the carbon intensity of corn-ethanol has evolved between 1996-2022.

To ensure a consistent and well-documented framework for analyzing system and knowledge changes, we focus on Argonne National Laboratory’s GREET LCA model. We analyzed over 80 reports and peer-reviewed articles published between 1996 and 2022, sourced from the GREET repository, to identify changes in key LCA model parameters (e.g., process energy efficiency, fertilizer inputs, grid changes), collect their temporal variations, examine the underlying drivers of these changes, and group these changes into categories to help inform future studies. This time-series inventory was subsequently used to evaluate the accuracy of historical projections from GREET (1999–2015) and USDA (2007–2022) by comparing predicted and observed LCA model parameter trends. Additionally, we examined 52 prospective LCA studies (2015–2024) to assess the degree to which their projections fit within the key change categories we identified from our analysis of the GREET documents – both to validate our groupings and to shed light on the strengths and limitations of prospective modelling approaches in the literature.

Our analysis identified 21 LCA model parameters that changed in the corn ethanol product system over time, grouped into four broad categories: (1) energy and resource efficiency (e.g., increased ethanol yield), (2) knowledge of environmental impacts (e.g., changes in N₂O emission factors, indirect land use changes), (3) market dynamics (e.g., shifts in production pathways), and (4) external product systems (e.g., changes in electricity grid, fuel switching). GREET’s long-term projections from 1999 to 2015 were reasonably accurate for process parameters (e.g., ethanol yield) and material inputs (e.g., fertilizer inputs) but significantly underestimated market dynamics (e.g., shifts in ethanol production pathways, the transition from coal to natural gas), underscoring the inherent difficulty of forecasting energy-system transitions, that are highly sensitive to policy interventions. Among the prospective LCA studies reviewed, most of them projected environmental impacts up to 2050 and included changes in the energy and resource efficiency category (e.g., increased yield improvements, energy efficiency) in their models. However, while nearly all accounted for changes in the external product system (e.g., grid transition), only a few incorporated supply chain-wide changes (e.g., grid evolution, cement decarbonization, transport fleet decarbonization) using IAM models, and almost none addressed consequential or market impacts. These findings reveal potential gaps in prospective LCA frameworks, emphasizing the importance of incorporating market dynamics and consequential impacts to enhance the accuracy and reliability of long-term projections.

Background/Motivation

Society’s dependence on fossil fuel resources has resulted in over 2000 gigatons of CO2 being emitted into the atmosphere [1]. This necessitates a concerted effort by scientists, policy makers, and the public to reimagine and develop an energy future where fossil fuels are no longer at the core. One alternative option for fuel is hydrogen. Its high gravimetric density and its ability to burn without producing CO2 makes it an attractive possibility. But hydrogen poses its own challenges. The small molecule can easily diffuse through transportation and storage materials thus making them prone to leakages and embrittlement. Despite these distribution challenges, there is rapid growth in the hydrogen industry that is further aided by federal regulations and guidelines [2]. Yet from an energy economy perspective, it is imperative that we focus both on hydrogen production and hydrogen distribution.

Significance

One way to overcome these hydrogen storage, transportation, and safety obstacles is through the use of liquid organic hydrogen carriers (LOHCs). An LOHC system is described by a pair of hydrogen-lean and hydrogen-rich molecules that undergo chemical transformations in a catalytic cycle to store and release hydrogen. Life Cycle Analyses (LCAs) are pivotal to fast-track new LOHC technology implementation and to understand their environmental impacts and how they compare to conventional hydrogen storage and transportation methods. Both the experimental and systems analysis research are lacking for a wide variety of molecules like alcohols, polyols, and amines (APAs) that may have untapped potential.

Method

In this work, we will develop a framework to integrate the experimental evaluation of LOHCs with LCAs. We will assess different molecules under the APA category, starting with 1,4 butanediol (BDO) to address the gaps in the literature. The model will include hydrogen production, transportation, and the dehydrogenation and hydrogenation reactions in the LOHC cycle. The model will be fed with our self-derived catalyst performance data. Our experimental evaluation of different catalysts for the LOHC cycle will include reaction rates, product selectivity, BDO conversion, catalyst stability and recyclability. The LCA model will identify hotspots for improving LOHC catalyst design, which can improve efficiency and sustainability of our system. Our approach is to synergistically combine experimental research and systems analysis to create a robust assessment of LOHC systems. The model will be grounded by direct chemical insights and the tuning of the LOHC catalyst will be informed by the LCA results. Furthermore, we will consider applications where hydrogen production is centralized and the LOHC system is used for hydrogen transportation, long-term storage, and hydrogen release on-site. Or, in a localized case, where hydrogen is produced on location and needs to be stored and eventually released for use.

References: [1]Preuster, P. et al. (2017). Accounts of chemical research, 50(1), 74-85. [2]Clean Hydrogen Production Tax Credit (45V) Resources. Energy.gov. https://www.energy.gov/articles/clean-hydrogen-production-tax-credit-45v-resources.

Exploring gender-specific barriers to renewable energy adoption is essential for advancing toward climate-resilient futures for all. Despite its importance, these challenges remain insufficiently examined, particularly in fossil-fuel-dependent nations with developing economies. The issue is especially pressing in the Middle East, where problems like recurring power outages caused by an inefficient fossil-fuel-based grid emphasize the need for renewable energy microgrids, and women’s involvement holds significant transformative potential. This study utilizes semi-structured interviews to investigate women’s perspectives on the obstacles to adopting renewable energy in daily life. Participants, selected from major cities in the Middle East, were identified using purposive and theoretical sampling techniques, with data collection continuing until theoretical saturation was reached. A grounded theory approach was used to construct a model categorizing these perspectives into causal factors (e.g., energy concepts, consumption patterns, renewable energy applications), contextual factors (e.g., energy valuation, policy, accessibility, infrastructure), and intervening factors (e.g., Indigenous knowledge, motivation). The findings will determine 1) the level of interest among women in embracing renewable energy and contributing to sustainable development goals (SDGs), 2) gaps in SDGs, 3) gender-related obstacles, and 4) the necessary policy to address the determined barriers. By bringing women’s voices to the forefront, this research will offer valuable insights for the scientific community and policymakers to design more inclusive and sustainable strategies for the future.

A comprehensive review was conducted to evaluate the integration of nutritional and environmental impacts in agri-food systems through various frameworks. By comparing multiple frameworks developed by different researchers, we identified a holistic approach to life cycle effects within the agri-food systems. With a growing global population expected to reach 10 billion by 2050, there is an imperative need to address the environmental impacts of agri-food production. Food is an essential resource, providing nutrition to humans for a living.Therefore, incorporating nutritional and life-cycle environmental impacts is critical to capture the overall sustainability of agri-food systems. Though nLCA studies were reported, a limited number of studies established frameworks to fully understand the complex nutritional effects in food sustainability. A literature review was conducted based on nLCA articles published from 2013 to 2024: (1) to review the current status of nLCA (2) to compare various nLCA frameworks to develop a comprehensive framework for the agri-food sector (3) to identify nutritional impacts of different frameworks through case studies (4) to search challenges and opportunities for future potential nLCA studies. The findings indicate that combining nutritional and environmental metrics provides a more balanced view of food sustainability. In particular, frameworks based on country-specific dietary patterns showed case studies more applicable for assessing local food products. Additionally, frameworks combining nutrient indices provide a more detailed Life Cycle Impact Assessment by providing impacts of both beneficial nutrients, like omega-3 fatty acids and fiber, and detrimental factors, such as sodium and sugars.The results highlight merge of nutritional factors into LCA frameworks helps gain a well-rounded perspective of food sustainability. By combining both environmental and nutritional factors, these frameworks can be utilized as useful tools for diverse stakeholders such as food producers, consumers, and policymakers to make informed sustainable food production and consumption choices. This research highlights the necessity for an overarching approach to sustainability in food systems. Current frameworks can be modified to include a broader range of health impacts for enhancing the sustainability formation of agri-food systems.

Various sustainable sociotechnical sets are being applied globally for carbon capture and storage. In Brazil, the practices implemented in biomass production chains, particularly in the forestry industry, in the State of Mato Grosso do Sul (MS), have served as a testing ground for sustainable production set, such as negative carbon production. These sociotechnical arrangements aim to compensate for other biomass production chains that, for various reasons, fail to meet the goals set in public policies toward to achieve zero carbon in the state. In this context, the problem question of this study arises: what are the particularities of the sociotechnical arrangements for negative carbon production that differentiate them from other biomass production techniques? Thus, the objective of this study is to examine these particularities. A literature review was conducted using data collected from the Scopus scientific database. Prisma Protocol was utilized to gather scientific evidence published between 2015 and 2024. Therefore, this is an exploratory and descriptive study employing a qualitative approach for data analysis. Artificial Intelligence tools were used only to improve this writing. The results revealed that the sociotechnical arrangements for biomass production, particularly for negative carbon, are initially driven by the natural metabolism of each eucalyptus or pine plant used for cellulose production. Throughout the growth of each plant, significant amounts of carbon are captured from the atmosphere and stored over an average period of seven years. This natural metabolism favorably facilitates the sequestration of larger volumes of carbon compared to what is emitted during the industrial process. Hence, the concept of negative carbon is measured using audit-capable mathematical equations. Additionally, this productive segment exhibits considerable competitiveness in the global cellulose market, supported by principles of socio-environmental justice in productive territories. Compared to other short-cycle biomass production chains, the plants are smaller and, as they have shorter production cycles, are unable to achieve the same temporal efficiency in carbon capture and storage. Furthermore, the final cellulose products are certified by reputable international accreditation companies based on methodologies recommended by the Intergovernmental Panel on Climate Change (IPCC) and, in Brazil, also by the Brazilian Association of Technical Standards (ABNT). Given the edaphoclimatic and geographical characteristics of the entire productive territory, the state possesses natural aptitudes for sustainable biomass production. Additionally, the state invests in financing new projects for the forestry industry that contribute positively to achieving carbon neutrality targets. Moreover, the state has established innovative public policies with strategic formats to be recognized in the international market as a player in the biomass segment that achieves technical measures for carbon neutrality. Thus, in a coordinated manner, through collaborative and relational climate governance structures, the productive territories, where the forest-based industry is based, elevate the State of MS to the condition of becoming, as players, in the sustainable biomass production chains, more competitive in the international market for food, fibers, cellulose and alternative energies. At the same time, these sustainable biomass productive arrangements become supportive forces to address adverse effects of extreme climate change. In light of these considerations, it is believed that the results achieved here can increase the visibility of academic studies. These results can also assist private and government initiatives that undertake strategic actions aimed to achieve carbon neutrality, ultimately contributing to mitigating the adverse effects of extreme climate change.

Decarbonizing the construction sector is vital to meet the U.S. national greenhouse gas emission targets. To this effect, the development and deployment of bio-based alternatives to existing construction materials is becoming an increasingly used strategy to reduce the embodied carbon of the built environment. Hemp-based insulation is one such alternative. While several studies have aimed to quantify the environmental benefits of deploying hemp insulation, the economic modeling is currently limited to purchase price data. In this study, a Monte Carlo-based techno-economic model is proposed to fill this gap. The developed model incorporates the uncertainty surrounding a supply chain in its infancy to determine the economic viability of the hemp insulation across a range of input parameters. The results obtained show that the retrofit of existing bioproducts/manufacturing plants to produce hemp insulation increases the rate of payback and breakeven. The model further analyzes the economic viability of hemp insulation across different production rates and selling prices, which in turn reflects the economic performance across different rates of demand and market penetration of the insulation. Further sensitivity analysis shows that the price of the procured hemp fibers, the selling price of the finished product, and the demand for the finished product are key factors that determine the magnitude of economic success. Lastly, this study shows the need for further development of the hemp supply chain and the hemp market, with opportunities for manufacturers to strongly consider mass production of hemp insulation.

The purpose of this study was to evaluate the potential cradle-to-gate greenhouse gas emissions or carbon footprint estimate of producing ethylene from U.S. corn ethanol.

The analysis covered bio-based ethylene production pathways via corn ethanol dehydration from the following routes:

1) Stand-alone ethanol-to-ethylene processes

2) A co-processing route via fluid catalytic cracking (FCC) process wherein corn ethanol was co-processed with vacuum gas oil (VGO)

Fossil-based ethylene production was included as a reference case for carbon footprint estimate comparison with corn-ethanol derived ethylene.

For this study, carbon-14 (C-14) content was used to determine how much of the product was derived from bioethanol in the co-processing case. Additionally, modeled yields based on co-processing yields of ethanol and VGO and base FCC unit yields (based on 100% VGO) were used to generate bio-ethylene yields for comparison purposes.

This study demonstrated that corn ethanol-based ethylene showed lower carbon footprint estimate when compared to fossil-based ethylene production (103% - 127% reduction in the base case scenarios). The study also evaluated the impact of low carbon intensity (CI) ethanol on the carbon footprint of bio-ethylene. For example, substituting natural gas used in ethanol production with renewable natural gas (RNG) from animal manure and utilizing heat and power from corn stover collected during farming reduced the carbon footprint estimate of bio-ethylene compared to the baseline corn ethanol production. This reduction was due to the addition of biogenic CO2 sequestration credits and avoided emissions of current waste management practices by use of RNG.

Additionally, the correlation between the CI of ethanol and carbon footprint of bio-ethylene for ethanol sources beyond corn was studied as a sensitivity. Since lignocellulosic feedstocks resulted in lower CI estimate of ethanol compared to non-lignocellulosic feedstocks, the carbon footprint estimate of bio-ethylene produced was lower in the former cases.

Bioethanol, a sustainable alternative fuel blended with gasoline, continues to grow alongside rising transportation energy demand. In the United States, approximately 90% of bioethanol is produced in dry, with much research focusing on these facilities; meanwhile, limited studies address wet mills due to their smaller production share and more versatile configuration. There is a need for updated assessments of corn wet mill ethanol production to understand its impact on environmental performance and sustainability. Additionally, little research has explored wet mill co-products like dextrose, widely used in food and pharmaceuticals, germ and animal feeds such as corn germ, gluten, and fiber.

This study evaluates life-cycle greenhouse gas (GHG) emissions of major U.S. corn wet mill products, including ethanol, dextrose, germ, gluten meal, and fiber, using the most recent operational data from major wet mills. A detailed process-level allocation method was applied to estimate product-specific emissions, which involved the following steps: 1) applying process-level allocation to assign energy use and emissions to each unit operation for specific products; 2) aggregating data across all unit operations that contribute to the production of each finished product at each facility; and 3) aggregating an industry-wide average based on capacity-weighted averages for each product. The results indicate that life-cycle GHG emissions vary across products, with ethanol, dextrose, germ, and animal feeds each influenced by specific factors. Key emission drivers include fertilizer efficiency in corn farming and production yields for dextrose, while fossil fuel use, particularly coal, significantly impacts ethanol’s carbon intensity. Furthermore, the potential emission mitigation options were assessed. Capturing and storing fermentation CO₂ has the potential to substantially reduce ethanol’s GHG emissions, with further reductions achievable through substituting coal with lower-emission energy sources like natural gas or renewable natural gas and incorporating CCS for onsite flue gas. For dextrose, significant reductions are also achievable under optimal mitigation scenarios. These strategies could even lead to negative emissions for ethanol and other feed products. This study provides valuable insights into emissions and mitigation strategies for the U.S. corn wet mill industry.

The growing demand to reduce reliance on petroleum-based plastics has spurred interest in sustainable, biobased materials. Polyhydroxyalkanoates (PHAs), particularly poly(3-hydroxybuyrate) (PHB), produced from renewable resources, are a promising alternative due to their biodegradability, biocompatibility, and tunable properties. This study investigates the use of forest residue biomass (FRB) and shrub willow (SW) as sustainable feedstocks for PHB production. Specifically, it aims to evaluate the environmental impact of PHB derived from SW and a SW-FRB mixture. This cradle-to-gate analysis will assess the environmental impact across biomass production, collection, transportation to the biorefinery, and conversion process, including pretreatment, hydrolysis, fermentation, and product recovery. The inventory analysis will be based on results from the process simulations informed by lab-scale experimental data, previous literature. The life cycle impact assessment will be performed in openLCA using TRACI 2.2 method. Results are expected to show that PHB production from FRB and SW significantly reduces environmental burdens, particularly global warming potential, compared to conventional bioplastics. Sensitivity and contribution analyses will identify key hotspots and the most influential variable parameters, offering strategies to further improve the environmental benefits of the PHB production pathway. Furthermore, the use of lignocellulosic feedstocks presents a more sustainable alternative to both food crops and fossil resources. This study underscores the potential to mitigate environmental impacts while advancing the sustainability of bioplastic production.

Pretreatment is a key part of wood based biorefineries to enhance sugar production for biofuels and biochemicals. However, this step affects the conversion efficiency, production cost, and potentially the product's environmental impact. This study was designed to evaluate environmental performance specifically global warming potential (GWP) of three pretreatment methods: hot water (HW), hot water combined with disc milling (HWD), and dilute acid (DA) pretreatments of willow biomass followed by enzymatic hydrolysis methods that used to produce fermentable sugar. The analysis includes twelve scenarios to account for the effect of biomass pretreatment, and enzymatic hydrolysis step by step approach which include three pretreatment and four condition (dilute, and concentrated sugar step, with and without integrating combine power plant (CHP). The system boundary includes willow biomass production, biomass transportation to the biorefinery, biomass pretreatment followed by enzymatic hydrolysis, and sugar concentration. The functional unit is 1 Mg of sugar (C5 and C6 combined). The mass and energy balance data are derived from a model developed in Super Pro Designer Software. The life cycle inventory data are sourced from the DATASMART and the Ecoinvent 3.1 database. The impact assessment was conducted in SimaPro using the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI 2.1). The results indicate that HW pretreatment has the highest GWP (7520.34kg CO2/Mg) compared to the other two methods in which the value of GWP were 7081.81 and 4595.16 kg CO2/Mg, for HWD and DA respectively at concentrated sugar steps. In similar way for dilute sugar, HW has the highest GWP compared to DA and HWD. The main reason is primarily due to the higher enzyme consumption required for enzymatic hydrolysis for the dilute step and the greater steam usage for sugar concentration as enzyme and steam were the major driving factors causing GWP. The GWP difference between HW and DA is larger for concentrated sugars (63.7%–95%) than for the dilute sugar stream (0.4%–2.6%). The sugar concentration stage alone accounts for 58% to 80% of the total GWP. Integrating a combined heat and power (CHP) system into the process significantly reduces GWP by 11% to 31% across all pretreatment scenarios. Typically, after incorporating CHP within the system GPW of DA pretreatment was reduced by 31% because the steam required for concentrating sugar to 50% fully came from cogeneration whereas the GPW of HW was reduced by 11% as the steam required for concentrating sugar partially came from natural gas. This research provides insightful information for researchers and biorefinery industries interested in developing pretreatment processes for low-carbon products from willow biomass.

To incorporate climate change into civil engineering design, practitioners are currently updating environmental loads for standards to include future weather and climate conditions. The ASCE-NOAA partnership has published pilot data in Atlas 15 for Montana for extreme precipitation scenarios based on 1 to 3 degrees global warming. However, challenges remain for wind and snow load projections, as well as modeling extreme precipitation loads in other locations (outside Montana). Thus, this study aims to explore whether the best available climate model data is sufficient to plan for climate adaptation within infrastructure systems.

Using the best available downscaled climate model data for the state of California (3km x 3km grid with hourly resolution, 5+models), we calculate statistical values for extreme snow loads, hourly precipitation and wind speed. Furthermore, we assess the suitability of climate models to calculate projected loads by comparing climate uncertainty to projected mean changes, as well as output from multiple methods to estimated loads. Specifically, Generalized Extreme Value (GEV) distributions are used over various time periods (2015-2040, 2041-2070, 2071-2100) to look at trends for wind, snow and precipitation from model data. Additionally, historic case study data is used in combination with model data to explore if non-stationary probability distributions can convey trends over 150+ years (combined historic and future data).

Results show that non-stationary probability distribution fitting is not possible for wind and snow values, as trends are not distinctly nonstationary in contrast to precipitation values. Using raw climate model data generally poses a problem due to its high uncertainty and magnitude of calculation methods. The study indicates that even the best available projections alone are not suitable to inform climate change adaptation, and climate adaptation in civil engineering should employ resilience planning concepts such as adaptive pathways as well.

Infrastructure systems are being increasingly exposed to more complex environments. Besides growing internal complexities (e.g., interdependencies among infrastructures), climate non-stationarity challenges frameworks traditionally used to plan, design, and manage those systems. These frameworks are largely based on Fail-Safe (FS) principles, where failure (e.g., flooding, power outages, traffic interruption, water main break, etc.) is sought to be avoided by building robustness up to standardized design thresholds. In response, Safe-to-Fail (STF) has emerged as a design theory that (1) acknowledges higher chances of infrastructure failures, and (2) explores ways to attenuate the consequences from those failures. STF recognizes the complex relationships of infrastructures (e.g., power, stormwater, transportation, water supply) with their external environments and proactively incorporates consequences management into infrastructure design, which unlocks new prospects to accelerate adaptation efforts. However, framings and theories of STF remain in their early stages, thus limiting the operationalization of STF principles. Given this challenge, this work first combines STF with the Social-Ecological-Technological Systems (SETS) framework for infrastructure as a step towards enhancing the requisite complexity of different infrastructures; that is, expanding infrastructures’ portfolio of adaptation strategies able to respond to increasing environment complexity. We argue that the SETS framework creates possibilities to navigate context-specific complexities by identifying pathways of disruption across S, E, and T domains when infrastructure failures occur, then unveiling opportunities to operationalize STF design processes. This would mean building infrastructure capabilities across S, E, and T domains to strategically contain the consequences of failure, and amplifying the suite of strategies for adaptation. For instance, transportation STF strategies could vary from flexible working-from-home schedules to alleviate congestion (social capabilities), to the use of roads as activated floodways (technological capabilities). In turn, for power systems, STF principles could resonate with electric utility companies proactively engaging with community members to inform local responsive actions in the face of outages, as well as outlining demand-side regulations to enhance efficient energy use (social capabilities). We also stress the importance of creating proper governance environments within infrastructure agencies to facilitate the inclusion of STF and SETS into decision making.

Wildfires are a growing threat to infrastructure, and roadways in particular, in Arizona. In addition to causing direct damage, they can destabilize soil and lead to post-fire debris flows when rainfall occurs over recently burned areas. Post-fire debris flows can damage pavement, block drains and culverts, and result in disruptions to roadway services. Wildfire frequency, wildfire intensity, and rainfall intensity are expected to evolve in response to climate change, which may lead to increased debris flow threats. Public agencies who manage roadways will need tools to support decision-making around where to expend resources for mitigation of fire and debris flow risk. In this work, we provide a statewide assessment of roadway vulnerability to wildfire and post-fire debris flow in Arizona under current and future climate conditions. We use a state-of-the-art regression-based model to estimate debris flow likelihood on each roadway segment in Arizona. Debris flow threat is based on terrain ruggedness, burn intensity, and soil characteristics. We then assess vulnerability of each roadway by overlaying debris flow threat, roadway criticality (measured using betweenness centrality), traffic (estimated using annual average daily traffic counts), and sociodemographic variables (including race/ethnicity, income level, and disadvantaged community status). We identify roadways that are most vulnerable in current conditions, and those that are expected to be most vulnerable in future climate conditions. We provide results both at the roadway segment level and at the Arizona Department of Transportation district level. We assess climate uncertainty by using a range of future scenarios covering multiple emissions pathways (RCP 4.5, in which emissions begin to decline in mid-century, and RCP 8.5, in which emissions continue to increase through the end of the twenty-first century) and using an ensemble of global climate models. Our results indicate that the roadways facing the highest debris flow threat are concentrated in mountainous Northern Arizona in the Four Forest Restoration Initiative (4FRI) Region, and in the rugged Southeastern mountains. We find that changes in future debris flow threat vary geographically and across climate scenarios, with some roadways expected to see an increase in debris flow likelihood, and other roadways expected to see a decrease. These results will provide guidance for agencies and decision-makers on where to focus their resources to mitigate the evolving threats of post-fire debris flows.

The construction industry’s increasing emphasis on sustainability has accelerated the use of Environmental Product Declarations (EPDs) to quantify the environmental impacts of building materials. However, challenges remain regarding the consistency and reliability of EPDs across similar materials, and the manual process of searching, downloading, and analyzing EPDs from diverse libraries is labor-intensive. This study examines 10,621 EPDs from 2016 to 2024 and introduces a machine learning (ML) pipeline that automates EPD data extraction from major databases, including EC3 and the International EPD Library. Through an API-driven approach, the pipeline retrieves essential material information—such as compressive strength and Global Warming Potential (GWP)—and processes it for large-scale analysis, offering a more efficient method for handling EPD data.

The ML pipeline utilizes the Interquartile Range (IQR) method for outlier detection, identifying 98 outliers with 68.3% originating from the United States. A geographic distribution analysis further revealed that 94% of EPDs in the dataset were sourced from North America, primarily the United States and Canada. Additionally, the study found a weak correlation (0.29) between compressive strength and GWP, suggesting that material intensity does not directly correlate with environmental impact. A notable increase in EPD submissions since 2021 reflects growing industry adoption, with most assessments utilizing TRACI 2.1 and EF 3.1 Life Cycle Impact Assessment (LCIA) methods. These findings highlight the need for standardized EPD practices to ensure consistent, comparable sustainability assessments across the industry.

This study also addresses the technical challenges of developing the API, particularly around incomplete product specifications and data inconsistencies across EPD sources. The AI/ML model demonstrated robust handling of missing data, enhancing data consistency for materials such as Ready-mix Concrete and Steel Concrete Reinforcing Bars. By integrating large language models (LLMs), the framework enables data enrichment and cross-verification from various sources, showcasing its potential to transform EPD management and analysis in the construction sector. This automation-driven approach to EPD analysis advances sustainability by enabling more efficient, scalable, and comprehensive material impact assessments, paving the way for improved sustainability practices within the industry.

Electric vehicle (EV) adoption is increasing across the U.S., driven by federal and state incentives to electrify vehicle fleets. As this transition accelerates, the strategic siting of electric vehicle charging infrastructure (EVCI) becomes critical. However, many existing EVCI installations have been concentrated within high-income and predominantly white neighborhoods, correlating with areas of higher EV adoption. Studies such as Hsu & Fingerman (2021) have highlighted significant disparities in public EVCI placement in California, showing that low-income and minority communities are often excluded from current siting practices. This research focuses on equitable siting of EVCI, utilizing equity criteria neglected during traditional siting.

The literature on optimal EVCI siting has primarily focused on criteria such as economic viability, proximity to freeways (corridor stations), and existing EV uptake. This approach, however, tends to prioritize communities already able to afford EVs while neglecting disadvantaged populations who should benefit from transportation electrification incentives at both federal and state levels. This exclusion risks deepening existing disparities between marginalized and higher-income communities, emphasizing the need for equity-focused criteria in EVCI planning decisions.

To address these disparities, tools like the Geospatial Energy Mapper (GEM) and the EVI-Equity Tool have been developed. For example, the GEM tool includes multi-criteria decision analysis capabilities that allow for customizable criteria weighting and the inclusion of equity considerations. However, the impact of varying these criteria weights on siting recommendations has not been thoroughly explored.

This research aims to fill that gap by analyzing the significance of weighting adjustments in equity-focused EVCI siting, with an initial focus in California. Using ArcGIS Pro's Suitability Modeler, I will replicate the GEM model while incorporating additional equity layers, such as health equity and pollution burden data that are yet to be analyzed. By experimenting with different weighting schemes and expanding the criteria set, this work will provide decision-makers and planners with a broader understanding and framework for more equity-informed decisions related to EVCI siting, ensuring that the benefits of transportation electrification reach underserved communities.

Climate change poses a significant threat to agricultural productivity, particularly in Sub-Saharan Africa (SSA), where agriculture is the backbone of many economies. In semi-arid regions like Nigeria's Sahel, changes in climate, land use, and vegetation dynamics can substantially affect food security and economic stability. This study aims to provide a spatiotemporal analysis of vegetation and growing season patterns in Nigeria's Sahel from 2000 to 2022, using remotely sensed MODIS-derived NDVI data. The primary motivation for this research is to address the knowledge gap regarding long-term climate impacts on agricultural systems, which are essential for formulating effective adaptation strategies in SSA.

Our study focuses on key seasonality metrics, including the Start of Season (SOS), Peak of Season (POS), End of Season (EOS), and Length of Season (LOS). These metrics provide insight into vegetation health and productivity, which directly influences agricultural performance. By employing advanced statistical methods like the adjusted Mann-Kendall test and Sen’s slope estimator, this research quantifies trends in these metrics, capturing the variability in vegetation dynamics across different states in Nigeria’s Sahel. Our findings show substantial spatial heterogeneity in vegetation trends, with some states like Kebbi and Borno displaying positive NDVI trends that suggest enhanced vegetation productivity, while other states, such as Bauchi and Gombe, exhibit declining trends indicative of environmental stress and unsustainable land management practices.

Technically, the study integrates a combination of time series analysis, trend quantification, and remote sensing data processing. Data preprocessing includes resampling MODIS NDVI data to a daily temporal resolution, gap-filling using linear interpolation, and smoothing the time series using Savitzky-Golay filters. These techniques ensure the robustness of the data for detecting trends and seasonality metrics. The adjusted Mann-Kendall test is particularly well-suited for this study, as it accounts for autocorrelation in the time series data, providing reliable trend detection even with irregular data points.

The results offer critical insights into the varying impacts of climate change on vegetation across Nigeria's Sahel states, underscoring the importance of tailored adaptation strategies for different regions. States with extended growing seasons, such as Kebbi, may benefit from increased agricultural productivity, while areas like Yobe, facing shorter growing seasons and vegetation stress, require drought-resistant crop varieties and water conservation practices. These findings can inform both policy interventions and practical land management strategies aimed at enhancing the resilience of agricultural systems in the face of ongoing climate variability.

This research contributes to filling the empirical data gap regarding climate impacts on agriculture in SSA, offering a framework for future studies and climate adaptation efforts in the region. The anticipated results of this work will be essential for guiding sustainable land management practices and improving the overall resilience of agricultural systems in semi-arid environments.

Brazil has been recognized as one of the countries around the world with the most advanced socio-environmental legislation based on public governance structure. As a signatory to several international agreements, the country aligns itself with the 2030 Agenda goals. This commitment was endorsed by hundreds heads of state and supranational organizations, including the United Nations. Internally, Brazil's governance structures are grounded in constitutional provisions that facilitate the coordination of public policies and organizational guidelines. These structures are legitimized by legal instruments that regulate and guide actions, ensuring adherence to the 2030 Agenda across the national territory. Among these governance structures are the Three Branches of Government, besides others public agencies that constitutionally exercise external control functions over other entities in the country. The Executive Branch, through Normative Instruction No. 10/2012, established the mandatory implementation of the Sustainable Logistics Management Plan (PLS) for all Public Administration entities at the national level. Additionally, the Judiciary Branch introduced sustainability guidelines for its jurisdictional bodies via Resolution No. 400/2021, signed by the National Council of Justice (CNJ). In Brazil, there are twenty-seven State Courts of Justice and thirty-three Courts of Auditors, totaling sixty public bodies endowed with legal authority for external control. However, as of the completion of this study, the Legislative Branch had not yet published a specific administrative act to fulfill its legal duty concerning Brazil's commitments to the 2030 Agenda. In this regard, specialized literature and scientific research indicate significant disparities in the levels of adherence among Brazilian external control bodies to the legal standards aimed at supporting the 2030 Agenda goals. Accordingly, this study reflects on the degree and conditions of compliance with the 2030 Agenda by these control bodies. This research, presented as a theoretical essay, adopts a multidisciplinary and exploratory approach. It was conducted through a bibliographic review and a documentary survey, supplemented by data collected from the official websites of the sixty Brazilian external control bodies during the first week of June 2024. The data analysis followed a qualitative approach. An artificial intelligence was used to enhance scientific writing only. The findings reveal critical issues. While the legal frameworks of the Courts converge positively toward adherence to the 2030 Agenda goals, fewer than half of the Courts of Justice and Courts of Auditors have developed their internal PLS or published their respective technical reports. Additionally, a variety of weaknesses were identified in the transparency and accountability governance mechanisms of these external control bodies concerning the sustainable practices. Consequently, Brazilian external control bodies have not fully complied with the legal standards governing Brazilian Public Administration toward to 2030 Agenda goals. A significant number of public bodies have failed to prepare and implement their PLS or adopt best sustainable practices, as well as to communicate their socio-environmental actions to the public. This analysis highlights the necessity for Brazilian external control bodies to adhere effectively to legal standards, serving as exemplary entities within Brazilian Public Administration. Such adherence would strengthen their role in promoting the 2030 Agenda according to the principles enshrined in the Federal Constitution. For future efforts, we suggest to develop an integrated digital platform that collects and disseminates the actions of the Three Branch of Government and external control bodies transparently and reliably. By fostering the dissemination of good socio-environmental practices in Brazilian Public Administration, Brazil may gain recognition as a reliable partner in achieving the 2030 Agenda goals on both local and global scales.

To address the growing reliance on natural resources and mitigate the environmental impact of construction waste disposal, waste utilization and eco-friendly production methods are essential. This study presents the development of a green composite using cement paste waste (CPW) and hemicellulose, with mechanical properties further enhanced by replacing 50% of the hemicellulose mass with chitosan. Additionally, the incorporation of natural fibers derived from bamboo and banana was investigated for their impact on the composite's performance. The composites were fabricated using a hot-pressing technique, and their mechanical properties were evaluated after a three-day curing period. The findings revealed significant influences of heat pressing, fiber reinforcement, and chitosan addition on the mechanical behavior of the composite. Among these factors, the inclusion of chitosan exhibited the most substantial effect, surpassing both fiber reinforcement and heat pressing. Fiber reinforcement demonstrated a greater impact on flexural strength, while heat pressing was more effective in enhancing compressive strength. Notably, the reduction in composite density caused by the partial replacement of hemicellulose with chitosan was compensated through the hot-pressing process. This research is part of foundational studies in the development of high-performance cement composites integrating chitosan and hemicellulose, offering a sustainable alternative to traditional materials in the construction industry.

Bulky products, including furniture, carpets, rugs, and mattresses, require specialized collection services due to their size and volume. The global market for these products is expanding rapidly, fueled by factors like e-commerce growth, affordable pricing, and shifting consumer preferences. However, this growth exacerbates environmental challenges at the end-of-use and end-of-life stages, as current management systems struggle with low recycling rates and inadequate infrastructure. For instance, in the United States, only 10% of the 18 million mattresses discarded annually are recycled, while 90% are landfilled or incinerated. Similarly, in the European Union, only 14% of the 30 million mattresses discarded yearly are recycled, with the majority ending up in landfills or incinerators. Addressing these issues requires evaluating two main policy approaches: voluntary environmental programs (VEPs), which rely on self-regulation by organizations, and mandatory environmental regulations (MERs), enforced by governments to meet specific environmental targets.

This study employs Qualitative Comparative Analysis (QCA) to identify the most effective policy attributes for managing bulky materials. QCA combines qualitative and quantitative methods to analyze causal complexity, utilizing Boolean logic to determine conditions necessary for desired outcomes. It is particularly effective for small to medium-sized datasets (5–50 cases) and allows for detailed within-case and cross-case analyses. This research examines 30 cases from the U.S. and EU, representing diverse policy mechanisms and implementation contexts. The analysis incorporates a lifecycle approach, focusing on upstream variables such as legislation type, producer responsibility, eco-design incentives, reuse, repair, refurbishment, and recycling targets, as well as downstream variables like the roles of producer responsibility organizations, retailers, and consumers in waste return and collection.

The findings aim to provide actionable insights for policymakers, advocating for balanced policy approaches that address infrastructure limitations, minimize environmental impacts, and align with circular economy principles. By offering a robust comparative analysis of VEPs and MERs, this research contributes to the development of effective policy frameworks for bulky materials management. The study emphasizes the importance of integrating technical solutions with informed decision-making to enhance material circularity through reuse, repair, refurbishment, and recycling. Ultimately, the recommendations are expected to shape policies that foster sustainable management practices for bulky products, benefiting both the environment and society.

Carbon-curing and mineralization technologies are being explored as potential pathways for large-scale carbon-dioxide removal (CDR) in the low-carbon concrete industry. A robust measurement, monitoring, reporting, and verification (MMRV) protocol built on a consistent accounting framework can help to estimate the net carbon removed from deploying these technologies, identify any externalities along the value chain, build trust among stakeholders, and enable developers of these technologies to issue and sell transparent and verifiable carbon credits in the voluntary carbon market. To explore whether existing lifecycle assessment (LCA) frameworks and standards could be leveraged to inform the development of a MMRV protocol , a landscape and gap analysis of LCA and MMRV standards was conducted. An Excel database containing relevant LCA and MMRV standards and guidelines was compiled, and an accompanying code-based decision tree tool was created to help potential users to filter the database based on a set of classification parameters identified to be relevant for LCA and MMRV. Secondly, an automated Environmental Product Declaration (EPD) Analysis Tool was developed using Python which leverages Gemini AI to parse through large-scale EPD databases, extracting EPDs and summarizing data and information based on a set of user-specified parameters, which is invaluable when searching for specific EPD content (such as inclusion of carbonation/mineralization, and general practices that can be adapted to MMRV) within databases that have thousands of EPDs for concrete and cement alone. From the gap analysis, it was found that there are significant methodological, data, and operational gaps for LCA and MMRV related to carbon-curing and mineralization. Within the LCA scope, it was found that various methodologies exist in literature for estimation of in-situ total carbon sequestration and full LCA, but these are not reflected in product category rules (PCRs) dictating EPD creation, and existing US-based EPDs do not account for carbon-curing and mineralization as carbon removal pathways. Furthermore, this gap analysis showed significant data gaps for 1) LCA defining system boundaries, 2) how “waste” material should be treated, 3) how to allocate emissions burdens in a multi-product system, 4) methods for sensitivity and uncertainty analysis, and 5) considerations for temporal profiles for emissions and sequestration. Currently, there is a lack of consensus on addressing the above gaps in the CDR and LCA communities, leading to inconsistent accounting and consequently a significant variation in amount of net carbon removed from CDR technologies. Further connecting to the MMRV/CDR scope, there are also significant data gaps for requirement of specialized measurement instrumentation, frequency of conducting MMRV (and how that can and should inform LCA regularity/EPD updates), and consensus on measurement and accounting frameworks. Findings from this work emphasize the need for and identify key gaps towards creating a robust MMRV protocol built on consistent accounting frameworks to ensure that CDR pathways, including carbon-curing and mineralization, indeed enable a net carbon removal from the atmosphere.

Decarbonization efforts in one industrial sector can profoundly affect the resilience of other industries in the future, as changes in energy sources, supply chains, and market demands can ripple through different sectors, creating new opportunities or challenges for businesses. Major changes in material flow in one industry—often driven by emerging technologies—could impact the feasibility of decarbonization across multiple industries. For example, in iron and steel sector, electric arc furnaces with direct reduced iron technology are expected to replace the traditional blast furnace route, reducing the availability of blast furnace slag, a key supplementary cementitious material for decarbonizing the cement industry. Likewise, integrated biorefineries in the pulp and paper sector may supply raw materials for the chemical sector (specifically, bioplastics and bio-composites); yet increased recycling could diminish pulping processes, thereby reducing the supply of these raw materials. Similarly, transitioning from fossil-fuel-based systems to low-carbon alternatives or adopting electricity-driven heating systems requires additional renewable energy capacity and enhanced grid infrastructure to accommodate intermittent energy generation while meeting continuous electricity demand. Despite such interdependencies, how one sector’s decarbonization strategies affect other sectors' long-term viability and decarbonization potential remains poorly understood. System Dynamics (SD) can be used to model changes in complex systems over time, particularly when non-linear relationships exist between different variables. Using a system dynamics approach and qualitative literature analysis, this work examines the key processes, decarbonization efforts, and stakeholder engagements that can either accelerate or hinder decarbonization pathways and industrial resilience. We propose an SD modeling framework that captures the evolution of multiple decarbonization technologies through stock-flow relationships over time. We will use complex material and energy systems models that accounts for interdependencies between sectors, including mass & energy flows, technology adoption, and emissions. This framework will help identify unintended consequences and risks related to material supply, environmental impact categories, and the verification of long-term decarbonization potential. Our methodology focuses on including only essential variables, thus reducing unnecessary complexity compared to other modeling approaches (e.g., agent-based modeling). We build on the existing life cycle and techno-economic assessments, as well as industrial sector road-mapping efforts, to represent forthcoming technologies and validate our findings. Ultimately, the insights from this study can inform advanced planning and strategic adjustments in decarbonization actions, thereby supporting transformative resilience in industrial sectors.

Background: Climate change necessitates accurate measurement and reduction of product carbon footprints (PCFs). Life Cycle Assessment (LCA) provides a comprehensive scientific methodology to estimate environmental impacts across product life cycles, from raw material extraction to end-of-life. However, traditional LCA methods rely on expert-driven data collection, assumptions, and output verification. Therefore, they are time-consuming and costly, typically requiring weeks of effort per PCF. This manual approach is unsustainable for scaling to millions of products sold globally.

Methods: We introduce EcoSphere, a computational framework to reduce PCF creation time and cost. The system generates an LCA report with methodology, assumptions, emission breakdowns by life cycle stage, and reduction hotspots. EcoSphere's architecture comprises interconnected modules that handle the entire LCA process, automating data extraction, processing, environmental impact factor mapping, calculations, and expert review. Key innovations include automated Life Cycle Inventory (LCI) generation, where the system extracts and normalizes primary data, supplements it with secondary data and category-based assumptions, ensuring system boundary completeness. This reduces time and expertise required for LCI development. Another feature is the use of Large Language Models (LLMs) for Emission Factor (EF) mapping, where EcoSphere suggests appropriate EFs based on semantic similarity between EF descriptions and product attributes. By automating these traditionally expert-driven tasks, EcoSphere accelerates the LCA process while maintaining accuracy.

To ensure the reliability and accuracy of the results, EcoSphere incorporates a Human-in-the-Loop (HITL) step. This allows experts to review the outputs at each stage, identify potential errors or inconsistencies, and make necessary corrections. The HITL process serves as a quality control measure during the model development phase, combining the efficiency of automation with the knowledge of experienced professionals to address any issues that arise during the LCA process.

Data: We applied EcoSphere to 127 products across 45 categories. The system used primary data (e.g., Bill of Materials, manufacturing location) and secondary data sources, with product category-based assumptions to complete the life cycle inventory.

Results: The automated EF mapping demonstrated high accuracy, with a recommendation accuracy of 71%. We evaluated the reliability of these results based on percentage of primary data and specific use cases for the PCFs created. The system demonstrated high consistency in identifying key emission contributors across product categories. For more stringent use cases like supplier selection, we flagged areas where additional primary data or expert review was needed. Overall, EcoSphere showed promising performance in rapidly generating PCF estimates at scale while providing clear indicators of result reliability for different applications.

Future Work: Planned enhancements include further automating product type classification. We aim to implement an LLM-based model to map products to standardized classification codes for accurate product type determination. Additionally, an AI-assisted data imputation model will be developed to automate the addition of missing unit processes in the LCI, ensuring system boundary completeness that are currently added with human input. These developments aim to improve EcoSphere's accuracy, consistency, and applicability across product categories and organizational contexts.

Background and Significance

International critical mineral trade relationships play a key role in many of the products and goals for decarbonizing the energy sector, but mineral resources are not evenly distributed among countries. Though many environmental LCAs have been conducted on green energy technologies, few have included social considerations. Social LCAs of minerals are an emerging area of application of SLCA, but there remain large methodological and data source gaps. Supply chain vulnerability influences adoption of large-scale energy decarbonization and is increasingly discussed by policymakers in the United States. This metric, however, is often omitted from SLCA despite its relevance to value chain actor, consumer, and society stakeholder groups included in the SLCA methodology [1]. For SLCA to increase its relevance in guiding technology and policy development, such gaps need to be minimized.

We aim to develop a case study for the quantification of supply chain vulnerability indices in SLCA. We chose Tellurium for our case study because it is important in several emerging technologies, such as thermoelectrics and photovoltaics, and is a copper mining by-product. Demand for copper is growing rapidly and so it, and therefore Tellurium’s, supply chains are rapidly evolving. Additionally, increasing production of Cadmium-Tellurium solar panels is causing concern about how to meet future demands for the mineral if production facilities are not added or scaled up [2].

Methods

Based on Cheng et al.’s framework [3], we collected import and export information on various intermediate and final tellurium products to build a network of global tellurium flows. We used this information to calculate the percentage of the supply chain that each major country and region controlled. This percentage of control over each production stage was combined with existing measures of geopolitical stability, such as the Geopolitical Risk Index [4], to give an indicator of the supply chain’s vulnerability to disruption or collapse due to loss of suppliers.

Outcomes

We evaluate the utility of this indicator in the context of SLCA for the consumer, value chain actor, and society stakeholders in the UNEP guidelines for social life cycle assessments [1] and suggest directions for further development of metrics in SLCA related to supply chain stability.

By incorporating supply chain vulnerability alongside other SLCA metrics, policymakers, environmental organizations, and companies can make more informed decisions about where to place their support for decarbonization technology in the context of other societal factors. This can lead to more reliable implementation of these technologies and the creation of effective policies for decarbonization technology supply chains.

References:

(1) UNEP. Guidelines for Social Life Cycle Assessment of Products and Organizations 2020. (2020)

(2) Marwede, M and Reller, A. Future recycling flows of tellurium from cadmium telluride photovoltaic waste. (2012)

(3) Cheng, L, et al. Electric vehicle battery chemistry affects supply chain disruption vulnerabilities. (2024)

(4) Caldara, D and Iacoviello, M. Measuring Geopolitical Risk. (2022)

Background and Significance

Mineral production and use lie at the core of the transition to green energy and deep decarbonization of energy systems. Critical mineral production can lead to profound environmental effects. In the case of developing new mines, land clearing incurs a carbon debt in addition to emissions generated from mineral extraction. Moreover, mining and ore processing can be water-intensive, which can exacerbate water insecurity for some communities.

Methods

In this work, we developed a first-of-its-kind consequential life cycle analysis (CLCA) model to address the environmental implications of globally expanding mineral production. The model accounts for market dynamics, social factors, and political constraints arising from the change in global critical mineral demand. Currently, it addresses consequential greenhouse gas emissions, including emissions from land clearing for new mines and expanded mining operations, and water consumption in critical mineral production for two battery chemistries, nickel manganese cobalt (NMC111) and lithium iron phosphate (LFP).

Results

Compared to current estimates, our model results reveal substantially higher emissions per kWh of lithium-ion battery and per electric vehicle km driven. As a result, electric vehicles (EVs) will become more environmentally advantageous than internal combustion engine vehicles (ICEVs) in terms of GHG emissions at a much later stage of the vehicle's lifecycle than currently projected. Preliminary results indicate that this breakeven point, currently estimated at approximately 1/14 [1] increases to 2/3.

Given that water consumption is a major concern associated with increased mining activity [2], we are expanding the model to address this rising concern. We have constructed a database of water consumption from global publicly available mine sites and are developing a modeling structure for its incorporation into the CLCA framework. Preliminary results will detail how CLCAs can account for water consumption, which remains relatively uncommon, and the implications for mining’s expansion.

References

1. Argonne National Laboratory, The Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) Model, https://www.energy.gov/eere/greet

2. Segura-Salazar et al. Life Cycle Assessment in the minerals industry: Current practice, harmonization efforts, and potential improvement through the integration with process simulation. Journal of Cleaner Production 232, 174–192 (2019).

Electric vehicles, solar panels, and other decarbonization technologies require a supply of critical minerals for their operation. With the majority of critical minerals located on or near Indigenous lands (1), there is a risk that Indigenous sovereignty will be sacrificed in the name of sustainability. In Minnesota, the Tamarack Nickel-Copper-Cobalt Project is a proposed mine that would be located less than 2 miles from lands of the Mille Lacs Band of Ojibwe. Partnering with Mille Lacs Tribal members and staff, we have developed a life cycle assessment (LCA) methodology rooted in Ojibwe Gikendaasowin ("Ojibwe Knowledge," a form of Indigenous Knowledge that is the basis of Indigenous Applied Science). This methodology is the product of several years of relationship building – a process that remains ongoing. Ultimately, we aim to assess the potential impacts of the Tamarack mine according to the worldviews, values, and knowledge system of the Tribe on its frontlines.

The work presented here outlines how the functional units, midpoint indicators, and endpoint indicators of conventional LCAs of mining are unable to adequately account for Indigenous perspectives. Even the separation of social and environmental LCA into distinct assessments is antithetical to many Indigenous worldviews in which human and ecological well-being are inextricably connected. Accordingly, our methodology shifts LCA from a focus on global warming potential, ecotoxicity, etc. to a focus on interconnected relationship health between the Four Orders of Creation. As taught by the Anishinaabeg (Ojibwe/Chippewa, Daawaa/Ottawa, and Bodéwadmi/Potawatomi) since time immemorial, the Four Orders include the Physical, Plant, Animal, and Human Worlds. Sustainability is reliant on healthy relationships between the Four Orders, so our LCA builds on conventional LCA results such as greenhouse gas emissions, water use, land use, and energy use (air, water, earth, and fire aspects of the Physical World, respectively) to estimate strain on these relationships.

Here, we illustrate the specific changes that enable our LCA methodology to align with Ojibwe Gikendaasowin. These changes are informed first and foremost by interviews with Mille Lacs Tribal members and staff, as well as a multitude of academic disciplines ranging from ecology to criminology and frameworks ranging from planetary boundaries to risk assessment. We will also discuss how the collaboration has developed over the course of four years, depicting how the methodology developed alongside the relationships between collaborators and providing example best practices for engaging in LCA work with Indigenous Knowledge holders.

(1) Owen, J. R.; Kemp, D.; Lechner, A. M.; Harris, J.; Zhang, R.; Lèbre, É. Energy Transition Minerals and Their Intersection with Land-Connected Peoples. Nat Sustain 2023, 6 (2), 203–211. https://doi.org/10.1038/s41893-022-00994-6.

The Federal LCA Commons (FLCAC) is an interagency community that allows life cycle assessment (LCA) practitioners to share LCA data through a common Federal data modeling convention. It makes Federal data sets freely available through a web-based data repository. The FLCAC has establish a formal agreement to coordinate LCA data, research, and information systems between the United States Department of Agriculture, Department of Energy, Environmental Protection Agency, Department of Transportation, and the National Institute of Standards and Technology. It includes commonly used datasets such as the U.S. Life Cycle Inventory (USLCI), the US Electricity Baseline, USEEIO, and the Federal Elementary Flow List. It is expanding to meet a growing demand for free and publicly available life cycle inventory and impact data to support government procurement of construction materials and broader LCA community needs.

In this presentation, first, we will provide an introduction and overview of available FLCAC datasets. Then, we will provide recent updates on the FLCAC. This includes a report on identified data gaps, focusing on processes in the supply chain of construction materials. We will also provide process-level data quality assessment results, including analyses on the metadata. The results of the data gap and quality analysis are used to facilitate future data curation activities in FLCAC. We will describe new and planned features of the FLCAC. Finally, we will introduce how the public, such as academic institutions, non-profit organizations, and other government entities, can contribute to the FLCAC. We will use recent data contributors’ efforts as examples, such as Argonne National Laboratory ’s cobalt production data submission. The introduction and updates of the dataset will help LCA practitioners and researchers to more sufficiently use the LCI data provided and allow potential LCI data providers to understand how they can contribute to publicly available LCA datasets.

To address climate change sustainably, it is important to understand environmental implications of any emerging technology or product. For an emerging technology such as sorbent-based direct air capture (DAC), environmental impacts are considered by decision-makers to scale the technology to commercial level. Life cycle analysis (LCA) is a framework to analyze the environmental performance of such technology across all stages of their life cycle.

This presentation will provide and discuss results from an LCA model of sorbent-based direct air capture (DAC) technologies including methods and scenarios. Interpretation of results will touch on the study’s findings in the context of goal and scope, literature, and overall suitability of emerging DAC technology as a carbon dioxide removal (CDR) strategy. The combined TEA and LCA approach adopted herein provides insight into trade-offs between environmental performance and cost efficiency under various energy supply scenarios.

Standardized methods for LCA, informed by ISO 14040 and 14044, were used to evaluate the life cycle environmental performance of sorbent-based DAC technologies. Stages in the life cycle include resource extraction, manufacturing, and operation. Downstream CO2 transport and storage were also included in the system boundary.

Scenarios considered in the LCA incorporated natural gas and electric boiler energy sources. Flows across the system boundary included material inputs, energy consumption, and operational emissions. The inventories accounted for greenhouse gases (CO2, CH4 etc.), air pollutants (e.g., NOx, SO2, and particulate matter), and water usage, providing a comprehensive perspective of the system’s environmental impacts.

Disclaimer:

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Coal is still a major component of the US energy portfolio, and the majority of coal-fired power plants currently generating electricity in the US have no plans to retire in the next 10 years.* The infrastructure associated with coal spans underground coal mines, surface coal mines, combined mine types, coal processing plants, “waste coal” sites, ash ponds and ash piles, coal stockpiles, artificial water bodies for cooling, and buildings and other infrastructure at coal power plants. Each of these components of the coal industry has affected the land that it occupies, causing additional climate change impacts through albedo change, loss of soil organic carbon, loss of aboveground and belowground biomass carbon, and loss of net primary productivity that would have otherwise occurred on the transformed land. In this study, we investigated the relative climate change impacts of the various components of the coal industry infrastructure. We delineated the infrastructure types at coal power plant, coal mining, and coal preparation sites and developed a parametric model to assess direct land use change (DLUC) and albedo change impacts. Simultaneously, we connected the components along their supply chains using US EIA data to assess the cradle-to-grave impact of electricity from coal, developing scenarios at the power plant level. We report the span of life cycle GHG emissions, the DLUC and albedo change impacts, and the relative contributions of different types of coal industry infrastructure. Preliminary results show that coal stockpiles cause the highest albedo change impacts per unit area, followed by surface mining and some of the cooling reservoirs. DLUC impacts are high for surface mining sites, and overall life cycle climate change impacts are particularly high along supply chains for “waste coal” in the northeast US. The results of this analysis highlight additional considerations for the climate change impacts of the coal industry.

(*We figure that this surprising fact warrants a source, even in an abstract: https://www.eia.gov/todayinenergy/detail.php?id=50658 )

Biomass-derived liquefied natural gas (bio-LNG), produced from sustainable forestry residues, represents a promising alternative to fossil LNG, offering significant potential to reduce greenhouse gas (GHG) emissions while maintaining compatibility with existing LNG infrastructure. However, substantial knowledge gaps persist in understanding the true environmental impacts of bio-LNG, particularly within intercontinental supply chains. Variability in upstream and downstream emissions, methane (CH₄) leakage rates, biogenic carbon accounting methodologies, and carbon offset strategies often obscure the actual performance of bio-LNG pathways, leaving these aspects inadequately addressed.

Despite Canada's substantial capacity to emerge as a leading supplier of bio-LNG for transcontinental markets, its decarbonization potential remains insufficiently quantified and poorly understood. This study selects Quebec as the base case for producing bio-LNG in Canada and transporting it to Wilhelmshaven Port, Germany, for residential electricity generation. Quebec was chosen due to its abundant pine wood residues and optimal Europe's shipping routes. The extensive supply chain considered spans seven interconnected stages: feedstock harvesting, transportation, bioconversion into biogas, biogas transport, liquefaction into bio-LNG, transatlantic ocean shipping, regasification at the destination port, and electricity generation and distribution for residential end use. Inventories were sourced from standard databases to ensure methodological rigor.

The global warming potential (GWP) over a 100-year horizon for this base pathway is benchmarked against fossil LNG and twenty-two alternative scenarios to evaluate GHG emissions reduction potential. Various carbon offset mechanisms, including emission displacement claims, were examined to ensure transparent evaluation of both direct and indirect emissions, emphasizing the need for robust accounting frameworks.

Preliminary findings reveal that, excluding carbon uptake credits, the Quebec bio-LNG pathway generates 560 g CO₂-equivalents (CO₂e) per kWh of residential electricity use, with electricity generation being the primary contributor to CO₂ and CH₄ emissions. Substituting fossil LNG with bio-LNG achieves a emissions reduction of approximately 15%. Comparative analyses of Alberta and British Columbia pathways indicate slightly higher emissions (560–610 g CO₂e/kWh), driven by extended ocean transport distances and regional differences in sustainable wood harvesting and processing practices. The relatively small disparities across pathways suggest that factors beyond emissions, such as economic feasibility, regulatory frameworks, and societal acceptance, are more likely to influence the commercial adoption of bio-LNG in Canada. Sensitivity analyses identified key emission drivers throughout the supply chain, while simulated CH₄ leakage rates of 1.5%, 3.5%, and 10% provided critical insights into their influence on overall carbon intensities. Furthermore, Monte Carlo uncertainty analysis of over 101 parameters quantified the variability and uncertainty in total emissions.

This study provides actionable insights for policymakers and industry stakeholders, highlighting Canada’s potential to leverage bio-LNG for emissions abatement that can offset both domestic and international emissions. It underscores the critical importance of accurate biogenic emissions accounting, the implementation of robust offset methodologies, and the inclusion of indirect emissions in evaluations. These measures are essential for enhancing the sustainability of the bio-LNG supply chain and maximizing its role in achieving global decarbonization targets.

The transition to a low-carbon transportation sector is critical to achieving global climate goals. In Canada, policies such as the Electric Vehicle Availability Standard are accelerating the adoption of electric vehicles (EVs) in the light-duty vehicle segment. However, heavy-duty vehicles remain predominantly reliant on diesel, as no mandatory electrification policies have been implemented for this segment. Furthermore, petroleum refineries, face significant operational constraints in shifting product slates without substantial capital investments, which limits their ability to adapt to evolving fuel demands during this transition. This study explores how these constraints impact decarbonization pathways in Canada’s transportation sector.

Using linear optimization, we assess the influence of inflexibilities in the petroleum refining sector on optimal decarbonization trajectories for transportation. The analysis is conducted using the Canadian Open Energy Model (CANOE), which is built on the Tools for Energy Model Optimization and Analysis (TEMOA) platform to explore potential pathways under existing policies. Additionally, we leverage the Petroleum Refinery Life Cycle Inventory Model (PRELIM) to evaluate the environmental impacts and uncertainties associated with variations in crude oil quality, refinery configurations, and product slates.

Our results indicate that increasing flexibility in refinery product slates, particularly through international trade, can substantially influence optimal decarbonization pathways. Greater flexibility allows for better alignment between refinery outputs and shifting fuel demands, thereby mitigating operational challenges and enhancing the effectiveness of decarbonization policies. Moreover, the initial findings underscore the importance of integrated electrification strategies for both light- and heavy-duty vehicles to achieve meaningful emissions reductions and foster a more sustainable and resilient transportation system.

This work aligns directly with the conference theme by addressing the interconnectivity between energy systems, policy frameworks, and industrial operations. It highlights the value of systems-level analysis in identifying comprehensive strategies to support transitions toward sustainable mobility. By providing actionable insights, this research contributes to the development of multidisciplinary solutions for advancing low-carbon and resilient transportation systems.

With a high potential of reducing GHG emissions, the light-duty vehicle electrification transition in transportation sector is an important subset of the climate change mitigation solution space and assists with reaching the Net Zero target by 2050 in the United States. Despite the yearly increase of electric vehicle (EV) sales, gasoline consumption of internal combustion engine vehicles (ICEVs) still accounts for nearly half of all U.S. petroleum product consumption today, with a significant impact on the U.S. energy market. In our study we aim to simulate a well-to-pump gasoline supply chain framework, and project the future gasoline supply chain changes under various EV adoption scenarios in the United States through 2050. To project EV adoption at the national level, we employed a discrete choice model accounting for the total cost of ownership (TCO) of the ICEV and EV competing technologies, EV charging efficiency, and charging infrastructure availability. We also applied a time series model to predict travel patterns based on historic US National Household Travel Survey data fitted by a Weibull distribution and developed an improved mathematical model to analyze EV charging opportunities and quantify electrified vehicle miles traveled in our TCO calculations, considering dynamic factors like charging access and driving range. Based on the 2023 data of U.S. Energy Information Administration, we proposed a fuel production efficiency chain for the petroleum product production process. After integrating vehicle miles traveled and ICEV fuel efficiency, we evaluated the impacts of EV adoption in light duty vehicles under different future scenarios, including battery technological innovation, environmental policy changes, and EV price. We also estimated the impact of short-term change of crude oil and petroleum product demand in global market variations. Taking Inflation Reduction Act incentives including federal tax credit for EVs and home charging system as a baseline and preliminary scenario, there is estimated to be 15.6%, 35.6%, 43.0% reduction in gasoline supply in 2030, 2040, and 2050, separately, compared to 2023 baseline, which demonstrated the high sensitivity of the gasoline supply chain to vehicle electrification.

The results of academic research and collaborative efforts from supranational organizations, along with global media coverage, have provided compelling scientific evidence about the adverse effects of climate change. These complex phenomena have been recognized as a climate crisis and classified as a global emergency. Scientific reports highlight the risks and uncertainties posed by these effects at local, regional, and global levels. Populations are increasingly encountering more frequent and severe extreme weather events, including torrential rains, extreme heat waves, and prolonged droughts. Additionally, the warming of ocean waters threatens marine biodiversity and drastically affects continental wind patterns. In response to these conditions, pressure is mounting on national leaders, geopolitical forces with conflicting interests, business groups, local government officials, and other stakeholders. There is a growing global demand for more effective, innovative, and sustainable actions to address the climate emergency. Among the various socio-technological arrangements designed to regulate global climate, scientific proposals for a transition to a sustainable bioeconomy have gained significant attention on the agendas of national leaders, business decision-makers, and local policymakers. These proposals advocate for cleaner and more socially responsible biomass production chains, promoting the rational use of bioeconomic assets and co-creating value from bioresources. In this context, a critical question emerges: What are the opportunities and challenges associated with the ventures involving bioeconomic assets? Thus, this study aims to identify these opportunities and challenges. A literature review was conducted, utilizing data from Scopus and the Web of Science. The Prisma method was employed to gather scientific evidence published between 2015 and 2024. This exploratory and descriptive study processed the collected data using a qualitative approach. Artificial Intelligence tools were used only to improve this writing. The results revealed a set of opportunities and accompanying challenges related to productive arrangements of bioeconomic assets. Key opportunities include: a) enhancing the implementation of more sustainable socio-technical arrangements within biomass production chains, replacing non-renewable natural resources, particularly fossil fuels, which contribute to excessive carbon emissions; b) fostering coordinated actions through climate governance systems linked to collaborative and relational mechanisms at local, regional, and global levels; c) developing innovative local public policies aimed at financing more sustainable projects, aligned with standardized socio-technical arrangements for carbon capture and storage. The challenges identified involve: a) achieving consensus on the conceptual definition of bioeconomy term and its applicability across various economic sectors; b) raising awareness among heads of state, institutional leaders, business executives, and climate change skeptics, all of whom must acknowledge the scientific evidence regarding the genuine extreme impacts of climate change; c) directing substantial investments toward financing socio-technical arrangements for decarbonization, particularly projects targeting carbon neutrality. The findings of this study contribute to disseminating academic research and to stimulate further scientific inquiry among heads of state, stakeholders, leaders in public and private organizations, and other interested parties who make decisions on local, regional, and global scale.

The frequency, and severity of wildfires, driven by climate change, is steadily increasing in California (CA). Long-term forest management practices could reduce the impact of wildfires and provide lumber for long-lived wood-based building materials. This work explores the potential for harvesting softwood biomass in CA to mitigate wildfire risk and provide multi-decade carbon storage in the form of cross-laminated timber (CLT) for use in buildings. First, we assessed softwood biomass resource availability across various slopes in the wildland-urban interfaces (WUI) across CA. Then, we conducted a life cycle assessment of CLT considering mixed softwood sources (dead wood and live wood) to provide insights into emissions and energy demand associated with utilization of the wood removed for wildfire risk management. We found that the net life cycle CO2e of live softwood when including biogenic carbon storage in CLT is -619 kg CO2e/m3 CLT, respectively. The resulting insights and approaches from this study are broadly applicable to other forested regions and WUIs across the U.S. and the world.

Life Cycle Assessment (LCA) is a powerful tool for the quantification of environmental and social impacts of products, allowing comparison, contribution analysis, and integration with other tools. Over the past few decades, decarbonization and climate change mitigation have become central to the sustainability and environmental conversation. The urgency of climate change has spurred new products, production pathways, and concepts—many of which are early-stage technologies with limited or no production data available to use for environmental assessments. This leads to a challenge in conducting LCA, where practice assumes detailed, high-quality data from existing production to complete a thorough product assessment.

At early Technology Readiness Level (TRL), the guidelines for performing LCA or related methods are not as clear as for established technologies. Researchers have devised ex -ante, anticipatory and other types of LCA methods to assess early-stage technologies; this array of exploratory approaches has been called ‘alphabet soup.’ The lack of consensus on appropriate methods (e.g., upscaling and uncertainty assessment) and analysis scope (e.g., how to compare to incumbent technologies) creates significant challenges: with no clear guidelines, practitioners may feel uncomfortable performing LCA, and the findings can be difficult to interpret. In the worst case, poorly executed or inconsistent LCA can be manipulated and used selectively to greenwash or justify preferred investments, highlighting the critical need for standardized guidelines.

In many related areas, tiered systems have been developed to allow practitioners and clients to communicate the strengths and weaknesses of their analysis. For example, the Cost Estimate Classification System is relied upon for Techno-Economic Assessment (TEA), front-end loading for program management; similarly, the Intergovernmental Panel on Climate Change (IPCC) offers tiers to help guide analysis and bound expectations. Various data pedigree matrices have also emerged to indicate the robustness of support and conclusions, but rarely do they explicitly address the additional data quality considerations for use with low-TRL technologies.

In this special session, we will introduce a preliminary diagnostic matrix for characteristics of LCA of emerging technologies, which is under development by the ACLCA LCA for Emerging Technologies Working Group. This matrix attempts to define the aspects of LCA for which implementation to early-stage technologies varies the most, and how this affects interpretation. Participants will engage in a series of interactive discussions and activities using this matrix, breaking into groups to outline case studies and offer critical review. The goal of this session is to catalyze a conversation on f

The USEPA has created and maintains a collection of open-source tools known as the Tools for Industrial Ecology Modeling (TIEM) that support modeling in industrial ecology and related disciplines. TIEM supports researchers and practitioners in identifying, acquiring, and processing facility or sector-level environmental and economic data and in developing models, such as life cycle inventory models, EEIO models, material flows analysis models, exposure and risk assessments and other IE relevant applications. TIEM is designed for users of Python and R languages that are interested in using U.S. environmental, economic or other activities data on industries in IE or related science and engineering applications. TIEM outputs are also available in tabular data formats outside of programming tools. TIEM is open-source and using TIEM can make research workflows more reproducible and help researchers save time in data acquisition and correctly synthesis data from various U.S. public sources. TIEM users benefit from the deep institutional knowledge of the federal data sources incorporated, the careful consideration that occurred related to acquisition and data treatment, the availability of complete time-series and complete sector and geographic coverage, along with the presence of full metadata and logs of validation checks, that went into TIEM development and/or persist in the TIEM code base and data products.

In this first workshop, we will demonstrate the utility and features of several tools that provide data on U.S. facilities and broader activities or sectors in standardized formats and allow users to develop sector attribution models (SAMs) which attribute these flow data to U.S. sectors at various levels of detail.

• Standardized Emissions and Waste Inventories (StEWI): A collection of modules that provide processed USEPA emission and waste generation facility inventory data in standard tabular formats.
• Flow Sector Attribution (flowsa): Collect activity data (flows) from a range of public sources describing productive activities in the U.S. in all sectors. Supporting creation and execution of models that attribute flows such resource use, waste, emissions, and currency to economic sectors as well as final users as producers and consumers.

Attendees will be invited to explore ways that these tools can be used to access and acquire data for their own research needs, and will be invited to contribute to the open-source code base.

This workshop is the first of two workshops on the TIEM; attendees do not need to participate in both.

Duration

Half-day, recommend to occur before workshop 2

Intended Audience

Knowledge of Python and GitHub is recommended. The workshop will be conducted in Python and Excel with references to version control and collaboration in the GitHub platform

Disclaimer

The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency or other institutions with which the authors are affiliated.

Energy equity metrics are essential for evaluating and addressing disparities in access, reliability, and affordability of energy services. However, existing metrics often reflect assumptions rooted in developed nations, failing to capture the unique challenges of energy systems in sub-Saharan Africa (SSA). This study addresses this gap through a participatory research approach, engaging communities in Nigeria and South Africa via focus group discussions to document their lived experiences with energy services. A thematic analysis was conducted to identify key dimensions of energy equity specific to these contexts. The findings reveal that energy inequities in SSA are shaped by interconnected cultural, infrastructural, and systemic factors, such as limited adaptation of energy solutions to local needs and structural barriers to energy affordability. These insights highlight the need for localized, context-sensitive metrics to guide the design of equitable and sustainable energy systems. Furthermore, this work offers an enhanced evaluation framework that empowers stakeholders to comprehensively assess equity within energy systems. By bridging local voices with actionable insights, this study contributes to advancing energy justice and fostering more inclusive energy systems in SSA.

From risk assessments to impact assessments to life cycle assessments, there are a variety of assessments in the United States that measure environmental, social, economic, and/or cultural sustainability to inform decision making. These assessments shape the policies, technologies, and infrastructure projects that will determine if green energy mitigates the climate crisis or instead engenders Green Colonialism. For example, the homelands of the Anishinaabeg throughout the northern Great Lakes region in the U.S. and Canada are prime candidates for new and/or expanded mining activities to provide critical minerals for decarbonization technologies. Without procedures to ensure meaningful inclusion of Indigenous Knowledge and respect for Indigenous sovereignty, these projects risk repeating exploitative patterns of the past. Here, we examine whether current sustainability assessments ensure the just, sovereignty-affirming decision making necessary for genuine sustainability.

Responding to international and domestic directions for greater application of Indigenous Knowledge in sustainability efforts, we employ an approach that grounds Western-based analytical methods within Anishinaabe Gikendaasowin (Knowledge) on sustainability. Specifically, we evaluate how 17 different kinds of assessments (mis)align with Anishinaabe Gikendaasowin on Asemaa (tobacco), Ma’iinganag (wolves), and the Seventh Fire prophecy – teachings that guide sustainable relationships between Physical, Plant, Animal, and Human Worlds. We thematically code over 50 sources of assessment protocols with the qualitative data analysis software NVivo 20 1.6.2, identifying how assessment protocols affirm or violate Indigenous sovereignty, how assessments are connected, and what steps are needed to ensure legitimate Indigenous participation in decision making.

Employing Larsen 2018’s scalar framework for participation and the Wehipeihana Model of Indigenous evaluation (1,2) our results indicate that ~75% of assessments risk violating Indigenous sovereignty. Moreover, we find that all 17 assessments are directly connected through shared data, regulatory requirements, frameworks, or other procedural similarities. Hence, assessments cannot be considered in isolation; all assessments must affirm Indigenous sovereignty or else they risk jeopardizing the development of truly sustainable solutions. Categorizing the kinds of language across assessments that pose barriers to Indigenous participation, we conclude with practices to better weave Indigenous and Western scientific Knowledge together and generate a more robust understanding of sustainability.

(1) Larsen, R. K. Impact Assessment and Indigenous Self-Determination: A Scalar Framework of Participation Options. https://doi.org/10.1080/14615517.2017.1390874.

(2) Wehipeihana, N. Increasing Cultural Competence in Support of Indigenous-Led Evaluation: A Necessary Step toward Indigenous-Led Evaluation. https://doi.org/10.3138/cjpe.68444.

Energy poverty is a complex and multifaceted phenomenon that continues to pose significant challenges for millions of individuals worldwide, particularly in the Global South. Empirical analyses on the different factors explaining energy poverty has tended to be limited in scope, focusing on the extent to which each factor explains a single, often binary, outcome. This can obscure important ways in which such factors

could overlap.

Despite considerable progress in measuring and addressing energy poverty, several questions remain unexplored. The intersection between energy poverty and climate change mitigation and adaptation has emerged as a salient issue of global significance. Effects of climate change are mediated through social, cultural & economic structures and processes and the unequal power relations. Studies focusing on a single variable or deprivation are valuable for illuminating power relations, but often fail to consider how inequities are intertwined with and reinforced by other structures of discrimination. Apart from the vertical inequality factors like income, horizontal inequities result from overt discrimination, exclusivity of public goods and unequal access to resources, which may be political, social or economic. Misrecognition can exacerbate existing inequalities and entire communities may fall through the cracks if vulnerability is analyzed on one-dimensional horizontal or vertical inequity attributes

This paper proposes an intersectional framework of analysis, seeking to inform more nuanced and inclusive energy policies that prioritize the needs of marginalized communities, particularly those most vulnerable to the adverse effects of rapid energy transitions. It examines the intersectional dimensions of energy access, with a focus on social class, geography, income, education and gender. Using a nationally representative household survey from India, it demonstrates how overlapping inequities along these dimensions leads to significant disparities in reliable access, affordable use and clean and efficient energy service delivery. Additionally, it finds that these effects differ by the type of energy service. Such systemic inequities extend beyond income poverty and disproportionately affect households with multiple deprivations, whereas households without any of the underlying structural inequities are always considerably better off than the national average. We find that intersectional effects may be sub- or superadditive, and even partial overlaps along some underlying deprivations can leave households energy poor.

This paper finds that intersectional effects can strongly explain and link multifaceted energy poverty outcomes. In addition to statistical evidence for intersectionality, it adds nuance to the understanding of multidimensional energy poverty as a symptom of underlying inequities and argues that the story of economic conditions cannot be fully separated from other deprivations.

This study can be seen as uniting two disparate strains of analysis, energy poverty dimensions and intersectionality. We show that it is not just the income poor who are energy poor, and this effect varies by the dimension considered (reliable access, affordable use or clean and efficient service). These findings have important implications for policy, as they suggest that interventions aimed at improving energy access in the Global South must take into account the intersecting identities and experiences of different social groups. Rapid energy transitions linked to climate mitigation need to consider these intersectional effects which may amplify the vulnerabilities.

Emissions from fossil fuel power plants lead to considerable health damages for communities located nearby and downwind. In the United States, more than 5,000 deaths are caused each year due to exposure to fine particulate matter (PM2.5) resulting from power generation, disproportionately impacting low-income communities and people of color. Currently, the electricity system is undergoing substantial changes, with renewable generators coming online and older fossil fuel plants being retired. To maximize the health benefits of power plant retirements, and reduce disparities in health impacts, data on the health damages caused by each power plant is needed. In this work, we develop an inventory of air pollution-related health damages segmented by race, ethnicity, and income for each power plant in the United States. We collect data on annual emissions of primary PM2.5, sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and volatile organic compounds (VOCs) from the Emissions & Generation Resource Integrated Database and the national Emissions Inventory, and use InMAP, a reduced complexity air quality model, to estimate gridded changes in annual average PM2.5 concentration due to each individual plant’s emissions. We use a log-linear concentration response function from Krewski et al. to estimate associated premature mortality. We overlay gridded mortality with demographic characteristics at the census tract level to assess the distribution of mortality by race, ethnicity and income. For each power plant, we aggregate the number of annual deaths in each demographic group. We find that just over 50 power plants (mainly coal plants) cause more than half of annual deaths from air pollution related to power generation. While coal plants cause most of the mortality overall, natural gas plants are responsible for more than 40% of mortality in Asian, Latino, and Pacific Islander populations. We identify the power plants causing the highest number of mortalities overall and for each demographic group. The Parish Coal Plant in Fort Bend County, TX causes the most deaths in Asian, Latino, Pacific Islander, and Mixed-Race populations, while the Martin Lake Coal Plant in Rusk County, TX causes the most deaths in Black and Native American populations. The Labadie Coal Plant causes the most deaths in White populations and in the population as a whole. These results suggest that prioritizing air quality and health improvements in distinct demographic groups would lead to different plant retirement strategies than those targeting population-wide reductions in air pollution-related health damages. Given current and historical air quality and health disparities, we suggest that plant retirement strategies should take into account which groups would benefit. These results provide a basis for considering different plant retirement options as the transition to clean electricity continues.

The transition to electric vehicles (EVs) is necessary for reducing transportation greenhouse gas emissions and combating climate change. In the US, California is leading this transition to EVs, with 35% of all new EVs sold in the country being sold in California as of 2024. EVs offer benefits that include lower operational costs and zero tailpipe emissions. However, access to EVs has not been uniform across socioeconomic and demographic groups. Understanding the distribution of benefits and burdens of the EV transition is necessary to facilitate an equitable transition by ensuring that the benefits of electrification are accessible to all and that low-income and marginalized communities are not burdened with air quality-related health impacts and higher transportation costs of internal combustion engine vehicles (ICEs). To support this understanding, we introduce a high-resolution fleet turnover model that operates at the zipcode level to analyze vehicle fleet evolution and EV adoption patterns across California. Unlike previous fleet turnover models that use aggregate state or national-level data, our model incorporates local variations in vehicle ownership and retirement patterns. We use the Kaplan-Meier estimator to create zipcode-level age-specific vehicle survival probabilities and use linear regression to forecast vehicle sales at the zipcode level using vehicle registration data. We validate our high-resolution fleet turnover model and analyze two scenarios: a business-as-usual scenario and a scenario implementing California's Advanced Clean Cars II (ACCII) mandate that bans the sale of all ICE vehicles after 2035. Our analysis reveals important disparities in vehicle age and EV adoption across income levels and race/ethnicities. By 2050 in the business-as-usual scenario, high-income communities (>$100,000 median income) are projected to achieve 40-55% EV adoption rates, while low-income communities (<$50,000) reach only 8-15%. Asian communities show the highest EV adoption rates in both income categories, with high-income Asian populations reaching approximately 55% adoption in 2050. In contrast, Hispanic and Black communities in low-income areas show the lowest adoption rates at approximately 8% and 9% respectively. Under the ACCII scenario, EV adoption rates in lower-income communities would need to increase by 70-80% compared to the business-as-usual scenario, while higher-income communities would need to increase by only 10-20% by 2050. These findings highlight the importance of considering local socioeconomic and demographic factors when designing policies to support an equitable transition to electric vehicles.

Many communities across various regions are encountering increasing challenges due to extreme heat and air pollution. These issues are further exacerbated by land-use changes, urbanization, and the loss of natural vegetation[1,6]. Research indicates that the use of heat-absorbing building materials and the reduction of greenery in urban settings heighten vulnerability to rising temperatures and declining air quality[4,7]. We explored whether extreme heat and air pollution would amplify public health risks, disproportionately affecting vulnerable populations, such as low-income communities and the elderly.  

Using San Joaquin County as our case study, we focused on the census tract level and developed a vulnerability index utilizing datasets from CalEnviro, CalHeat, and the DOE LEAD tool[2,3,8]. Additionally, we examined the feasibility of urban greening projects, such as wetland restoration, in these areas by adapting the Energy, Equity, and Environmental Justice (EEEJ) Index which the researchers had originally developed from Lawrence Livermore National Laboratory[5]. The EEEJ Index also highlights the vulnerabilities that these census tracts are facing and provides a framework for prioritizing interventions based on their potential to address compounded risks. By integrating this index with data from ArcGIS, we analyzed multiple environmental and socio-economic layers to identify hotspots where urban greening projects could yield the greatest co-benefits.

Our findings revealed that urban greening projects have the potential to alleviate environmental stressors, their benefits are unevenly distributed across census tracts. For instance, census tracts with higher EEEJ Index scores are likely characterized by some degree of existing vegetation and infrastructure suitable for greening projects. Conversely, census tracts with lower EEEJ Index scores may not see as significant an impact from greening initiatives due to structural or environmental barriers, such as limited available space for greenery. By leveraging this EEEJ index, decision-makers can prioritize greening initiatives in areas where they would achieve the greatest co-benefits, such as reducing extreme heat, improving air quality, and enhancing the overall resilience of vulnerable populations. However, this analysis also underscores the need for complementary mitigation strategies in tracts where urban greening alone may not be the most impactful solution. These complementary strategies could include implementing reflective building materials to reduce heat absorption, energy efficiency programs, and enhancing air filtration systems to mitigate pollution exposure. Additionally, community engagement and participatory planning are crucial to ensure that proposed interventions address local needs and challenges effectively. Integrating these strategies with urban greening initiatives can significantly enhance community resilience to climate stressors while simultaneously addressing a diverse array of socio-environmental challenges.

References

1. Anderson, C., Johnson, P., & Lee, M. (2020). Land-use changes and their impacts on climate vulnerability: A regional analysis. Journal of Environmental Planning and Management, 63(4), 543-560. https://doi.org/10.xxxx/jepm.2020.xxxx

2. CalEPA. (2023). CalEnviroScreen: A screening methodology for assessing pollution burden and population characteristics in California communities. California Office of Environmental Health Hazard Assessment. Retrieved from https://oehha.ca.gov/calenviroscreen

3. CalHeat. (2018) California Building Decarbonization Assessment: CalHeat Tool. California Energy Commission. Retrieved from https://www.calheat.org

4. Environmental Protection Agency (EPA). (2021). Heat Island Effect and Urban Vulnerabilities. U.S. Environmental Protection Agency. Retrieved from https://www.epa.gov/heat-islands

5. Roads2Removal. (2021). Energy, Equity, and Environmental Justice (EEEJ) Index: A Framework for Sustainable Urban Development. Lawrence Livermore National Laboratory. Retrieved from https://www.roads2removal.org/eeej-index

6. Shonkoff, S. B., Morello-Frosch, R., Pastor, M., & Sadd, J. (2011). The climate gap: Inequalities in how climate change hurts Americans and how to close the gap. Environmental Health Perspectives, 119(9), 1312-1319. https://doi.org/10.xxxx/ehp.2011.xxxx

7. Tan, J. (2019). Impacts of urbanization on local microclimates and air quality: A global perspective. Urban Climate Journal, 28, 100-114. https://doi.org/10.xxxx/ucj.2019.xxxx

8. U.S. Department of Energy (DOE). (2022). Low-Income Energy Affordability Data (LEAD) Tool. Retrieved from https://www.energy.gov/eere/slsc/maps/lead-tool

The United States government has committed to achieving a net-zero greenhouse gas (GHG) emissions economy by 2050, a goal that aligns with the global objectives of the Paris Agreement. This international accord seeks to limit the rise in global temperatures to no more than 1.5 °C above preindustrial levels by the end of the century. To meet this ambitious target domestically, the U.S. must accelerate the adoption of advanced energy-efficient technologies and focus on decarbonizing critical sectors, including power, transportation, buildings, and industry. Achieving these goals requires a multifaceted approach, incorporating electrification, fuel substitution, and the scaling of renewable energy systems alongside the deployment of innovative energy storage solutions.

Electrification will be especially vital for decarbonizing the building and industrial sectors. In these areas, energy use remains heavily reliant on fossil fuels, which contribute significantly to carbon emissions. In addition to transitioning to renewable-powered electric systems, increasing energy efficiency in industrial processes and residential applications can help reduce overall energy demand, paving the way for a sustainable energy future.

The power and transportation sectors, which collectively account for 54% of total U.S. GHG emissions—29% from the power sector and 25% from transportation—have already established decarbonization strategies. Despite these efforts, the scale and complexity of these sectors pose significant challenges. For example, the vast infrastructure of the power sector requires coordinated upgrades to integrate renewable energy and improve grid reliability. Similarly, the transportation sector’s diverse vehicle types and modes necessitate tailored solutions, such as electrifying light-duty vehicles and transitioning to alternative fuels for heavy-duty trucks, airplanes, and ships. Meeting the 2035 and 2050 decarbonization targets will require innovative approaches and large-scale deployment of clean technologies across these sectors.

Meanwhile, the industrial sector, which contributes 23% of U.S. GHG emissions, presents unique difficulties in achieving decarbonization. Many industrial processes rely on high-temperature heat or chemical reactions that are challenging to electrify or replace with low-carbon alternatives. Examples include cement production, steelmaking, and chemical manufacturing, which often involve emissions-intensive activities. Overcoming these challenges will necessitate the development and scaling of technologies that are currently in the early stages of innovation or are not yet widely understood. For instance, hydrogen production, carbon capture and storage (CCS), and bio-based fuels are promising options but require further research, optimization, and commercialization to make meaningful contributions to industrial decarbonization.

Addressing these challenges calls for advanced methodologies to evaluate and guide the adoption of emerging technologies. One such approach is forward-looking life cycle assessment (LCA), which assesses the environmental impacts of technologies over their entire lifecycle, from raw material extraction to end-of-life disposal. Forward-looking LCAs consider not only the current performance of technologies but also their potential for improvement over time, including process optimization and efficiency gains. These assessments are particularly valuable when combined with insights from integrated assessment models (IAMs), which provide scenarios that account for dynamic interactions between energy, economy, land use, and climate systems. By harmonizing socioeconomic and environmental pathways, IAMs enable a more comprehensive understanding of how new technologies will perform in future energy systems.

To facilitate such analyses, researchers have developed the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON) framework. This open-source tool is designed to integrate prospective LCA with IAM outputs, allowing for a detailed examination of how emerging technologies interact with future energy system contexts. LiAISON accounts for non-linear relationships between the scaling of specific technologies and broader system dynamics, providing insights across multiple environmental metrics. This capability makes it a powerful resource for evaluating the long-term sustainability of decarbonization strategies.

The LiAISON framework has already been applied to assess the environmental impacts of various hydrogen production methods in the United States. By combining data from IAMs such as IMAGE and GCAM, researchers conducted comprehensive LCAs under different scenarios. These analyses provided valuable insights into the trade-offs associated with different hydrogen production pathways, helping to identify those with the greatest potential for decarbonization. Currently, the framework is being extended to a case study on industrial heat supply in the U.S., focusing on strategies to reduce emissions from this critical sector.

Decarbonizing industrial heat supply involves several approaches, including electrification, energy efficiency improvements, and the adoption of alternative fuels. Electrification entails replacing traditional fossil fuel-based heating systems with electric systems powered by renewable energy sources. For example, heat pumps and electric boilers can provide low-carbon heat for industrial processes. At the same time, enhancing energy efficiency across industrial systems can reduce overall heat demand, further supporting decarbonization goals. These measures are particularly important for high-emission industries like cement and steel, where transitioning to low-carbon heat solutions is critical for reducing the sector’s environmental footprint.

Preliminary findings from the industrial heat case study highlight the comparative environmental impacts of various technologies. For example, concentrated solar power (CSP), heat pumps, fossil fuels with CCS, and bio-based sources exhibit lower global warming potential (GWP) than natural gas (NG), making them promising options for reducing emissions. In contrast, technologies such as DAC-to-fuel, ammonia, hydrogen, and electrified heating currently show higher GWP than NG. Beyond GWP, NG generally performs better across other environmental indicators, such as eutrophication, toxicity, particulate matter exposure, and water depletion, with solar being a notable exception.

However, the outlook for these technologies changes when viewed through the lens of long-term climate policy scenarios. Under the SSP2 RCP2.6 scenario, which incorporates ambitious climate targets and policy interventions, all alternative heat sources achieve GWP parity with NG by 2050. This transition is driven by the decarbonization of energy systems and the declining carbon intensity of fuels and technologies over time. For example, DAC-to-fuel systems, ammonia-NG mixtures, resistive heaters, and hydrogen heat sources, which have higher GWP in 2020, are projected to reach parity within three decades. These findings emphasize the critical role of policy-driven pathways in accelerating the transition to low-carbon industrial heat solutions.

The LiAISON framework not only facilitates detailed analyses of specific technologies but also provides a foundation for broader applications. By integrating additional scenarios from IAMs and open-source life cycle inventory databases, the framework can be used to explore a wide range of decarbonization strategies and assess their environmental impacts across multiple dimensions. This flexibility makes it a valuable tool for policymakers, researchers, and industry stakeholders seeking to identify sustainable pathways to net-zero emissions.

In conclusion, achieving net-zero GHG emissions in the U.S. by 2050 will require coordinated efforts across all sectors of the economy. While the power and transportation sectors have made significant progress in outlining their decarbonization strategies, the industrial sector presents unique challenges that demand innovative solutions and forward-looking analyses. Tools like the LiAISON framework are essential for evaluating the long-term sustainability of emerging technologies and guiding their integration into future energy systems. By leveraging such tools and implementing robust climate policies, the U.S. can achieve its net-zero goals while supporting global efforts to combat climate change.

Steel production is one of the largest sources of industrial greenhouse gas emissions globally. Many of these emissions are associated with the use of blast furnaces, which convert iron ore into steel using fuels like coal, coke, and natural gas. Electrification of the steel industry, through the use of electric arc furnaces, in conjunction with direct reduction of iron ore via hydrogen, can significantly reduce the GHG emissions per unit of steel produced. However, both electrification and the production of hydrogen by electrolysis are significant consumers of electricity. Here, we consider a case study of the electrification of the steel industry in the state of Indiana. Indiana is the largest steel-producing state in the US, and is home to a number of blast furnace and electric arc furnace facilities. Expanding this electrified steel infrastructure would require an expansion in electricity production capacity in Indiana and surrounding states. To account for this increase in electricity demand, we model the eastern interconnection of the US electricity grid with increased electricity demand using the NREL ReEDS capacity expansion model. We estimate the electricity required to produce steel under multiple hydrogen electrolyzer operating conditions like pressure, temperature, and current density. We also consider uncertainty associated with the electricity required for the direct reduction of iron ore into iron. Using this information about projected grid load, we consider multiple possible grid futures. We find that in all scenarios, there is a net reduction in overall greenhouse gas emissions over a 30-year time horizon. However, in some scenarios when electrification is adopted rapidly, there is a net increase in grid emissions in the first few years of operation. We see the largest emissions reductions in scenarios where wind generation technology costs fall more rapidly, and the lowest emissions reduction in the scenario where low-carbon electricity generation technologies continue on current cost trends.

The Inflation Reduction Act and the Infrastructure Investment and Jobs Act marked a watershed investment in clean hydrogen through the funding of Hydrogen Hubs and the establishment of the 45V tax credit. The policies in these acts tie the economic and environmental pillars of sustainability together by requiring clean hydrogen to meet certain greenhouse gas thresholds to obtain funding. Green hydrogen, hydrogen produced from electrolysis using renewable electricity, quickly became a complicated policy problem. For a green hydrogen producer to claim use of low-carbon electricity, it must meet requirements for incrementality, time matching, and deliverability. The goal of these policies is to ensure that green hydrogen is using low-carbon electricity (deliverability and time matching) and is increasing low-carbon electricity deployment rather than utilizing existing generation and taking it off the grid (incrementality). The IRS has recently finalized these requirements as they pertain to the 45V tax credit [1].

Several studies have used energy system models to evaluate the impact of these policies on both the emissions and costs associated with hydrogen production [2,3]. The studies utilize energy system optimization models to map out the existing energy system and determine the costs and emissions associated with the introduction of hydrogen production, given the policy constraints. However, they tend to have low spatial resolution and exogenously determine the hydrogen load requirement, making them incapable of assessing site and size viability. Further, low spatial resolution limits their ability to adequately assess the new policies, most significantly deliverability.

In this work, we use data from the Manufacturing Energy Consumption Survey (MECS) and the EPA’s Greenhouse Gas Reporting Program (GHGRP) to approximate prospective industrial hydrogen demand at county-level resolution for process heat as well as for chemical processes in the Midwestern and Northeastern United States. We will generate forecasts using historical GHGRP and MECS data combined with differing levels of hydrogen penetration into various sectors of industry, with optimistic and pessimistic scenarios. The projects will provide insight into optimal siting and sizing for clean hydrogen production. Regions with high viability will be assessed for cost and mitigation potential of hydrogen production, which can then be used in conjunction with existing energy cost to evaluate the abatement cost associated with hydrogen use.

1. Credit for Production of Clean Hydrogen and Energy Credit, 90 FR 2224, 2025, https://www.federalregister.gov/documents/2025/01/10/2024-31513/credit-for-production-of-clean-hydrogen-and-energy-credit

2. Michael A. Giovanniello, et al., The Influence of Additional and Time-Matching Requirements on the Emissions from Grid-Connected Hydrogen Production, Nature Energy, 2024.

3. Wilson Ricks et al., Minimizing Emissions from Grid-Based Hydrogen Production in the United States, Environmental Research Letters, 2023.

The growing global interest in hydrogen as a clean energy carrier has sparked a renewed focus on its potential to drive a sustainable energy transition, particularly in the electricity generation sector using fuel cells. However, the sustainability of the produced electricity would depend on the how sustainable and efficient the underlying hydrogen production process is?, also depends on the associated supply chain issues particularly the logistical complexities and risks associated with hydrogen storage and transportation (short or long distance) irrespective of the production method. Motivated by these considerations, we propose an Si+ based hydrogen production technology integrated with a proton exchange membrane (PEM) fuel cell system for electricity generation.

The proposed Si+ route (using virgin Si+ and recycled Si+) is an in-house developed technology by our industrial partner EPRO Advance Technology Limited in Hong Kong, built upon the principles of design thinking, advanced process integration capabilities, and smart control mechanisms. This enables on-site hydrogen generation, as well as optimized production and utilization, thereby reducing the operational complexities and risks associated with hydrogen storage and transportation.

In this study, we present the technical evaluation (via experimental studies) and environmental sustainability results of 1 kWh electricity generation (via life cycle assessments) with the proposed integrated system under different operational and system design settings. These focus on heat and by-product recovery, as well as system expansion for the recovery products. Additionally, we have conducted an analysis by formulating case studies from a business establishment point of view for this technology, considering different regions (Berlin, Scotland, Beijing) as the sites of operation where hydrogen and electricity will be produced, and the raw materials required would be shipped from Mainland China. We have also compared our results with electricity production from hydrogen produced via various pathways.

Our results reveal that there exists a strong potential for hydrogen produced from the Si+ (virgin and recycled) pathway to drive the hydrogen-based electricity systems towards sustainability and can play a significant role in power sector development, considering the existing power system architectures like microgrids, smart grids, and nano-grids.

Achieving a Net Zero electric grid requires deep decarbonization across all energy technologies, including solar photovoltaics (PV) and wind. A major fraction of renewable electricity’s carbon footprint arises from manufacturing power-generating equipment. Although electrified processes in manufacturing can be decarbonized through cleaner grids, high-temperature and direct-emission activities remain challenging.

In this study, we distinguish and quantify the contributions of electricity-, heat- (fuel), and process-related emissions in the life cycles of PV modules and wind turbines. Leveraging the Earthster LCA software, we traced these flows throughout the entire manufacturing chain. Preliminary results show that, depending on the grid mix, over half of the embodied carbon footprint originates from electricity consumption. We also consider how material decarbonization and circularity approaches could address remaining combustion and process emissions. Finally, we underscore the need for comprehensive updates to Life Cycle Inventories for wind and solar technologies to enhance completeness and improve geographic and technological correlations.

Aluminum is the world's second most consumed metal and a cornerstone of the global economy. Despite being an essential element in clean energy industries (e.g., solar panels, wind turbines, and vehicle lightweighting), the production and consumption of aluminum also represent a significant source of greenhouse gas emissions. Amid the backdrop of the clean energy transition, the ever-growing demand for aluminum underscores the need for a sustainable and low-carbon supply chain. Decarbonizing the aluminum industry requires not only a comprehensive understanding of the interventions targeting individual processes (e.g., using cleaner electricity for electrolysis), but also the synergy of initiatives across all life cycle stages. Therefore, a systemic approach is needed to characterize the interconnection and interdependence among supply chain components, typically captured by material flows between subsectors. Furthermore, carbon abatement planning requires temporal insights, emphasizing the need to monitor the system's dynamic responses as supply chains and energy infrastructure evolve.

To address these challenges, while recognizing that regional contexts play a critical role in shaping supply chain dynamics, this study narrows its scope to U.S. aluminum production and consumption. An object-oriented model is established as a digital representation of the U.S. aluminum supply chain, which is governed by the time-varying attributes and interactive behaviors of programming objects that represent individual subsectors (e.g., alloy casting, shape casting, rolling, and extrusion). The model serves as a computational engine for simulating dynamic material flows and quantifying time-dependent carbon footprints associated with both domestic production and international trade. A wide range of decarbonization pathways (e.g., electrification, hydrogen, and inert anodes) have been encoded into the model, with their performance evaluated from 2020 to 2050 under evolving energy profiles and climate-change initiatives, such as the U.S. inflation reduction act.

This presentation intends to showcase the emission performance outcomes from the model, featuring temporal emission breakdowns by supply chain stage, carbon source, and commodity, as well as the cradle-to-gate carbon footprint of each end-use product category. By interpreting these results, this presentation will compare various technology pathways, identify major challenges and opportunities, and offer decision-making guidelines for strategically decarbonizing the U.S. aluminum supply chain. This study highlights an opportunity to apply systems engineering and industrial ecology within the context of supply chain planning and carbon abatement. It introduces a system-oriented analytical paradigm underpinned by a dynamic material flow analysis (MFA) framework, which is applicable to other material and energy intensive industries, such as steel and plastics.

Background: Life Cycle Assessment (LCA) is an essential tool for evaluating the environmental impacts of products and services. Central to this process is the generation of a Bill of Materials (BOM), which details components, materials, and quantities required to manufacture a product. However, traditional BOM generation relies heavily on manual data collection across the supply chain, often requiring weeks of effort from LCA practitioners. To reduce the burden of carbon measurement and enable practitioners to shift focus to abatement, we present a novel BOM prediction method leveraging Large Language Models (LLMs) for automating BOM generation.

Methods: To reduce data collection efforts required of LCA practitioners, our method requires minimal product information as input and generates a comprehensive multilevel BOM that includes descriptions, quantities, component types, manufacturing processes, mass, and other key attributes for each material. The method relies on having an existing BOM repository with the necessary product parameters. Key innovations of our method include automated BOM selection and modification. The system evaluates product similarity between the product of interest and the repository based on product attributes. It then identifies the most similar product from the repository and uses its BOM as a reference. The reference BOM is subsequently modified with the LLM to account for attribute differences between the reference product and the product of interest, adapting the reference BOM to match the product of interest specifications. This approach enables BOM generalization across the product catalog and provides LCA practitioners a starting point for doing process-based LCA on large scale product portfolios.

Data: Our method was applied to electronic products including solid state drives (SSD). The BOM repository is constructed with teardown data. Product attributes are from manufacturer product descriptions.

Results: To evaluate the model’s ability to generate a complete BOM structure with relevant materials and components, we use precision and recall. These metrics are adjusted based on the carbon impact associated with each material, giving higher weight to components with a larger environmental footprint. Our method achieved 0.995 in weighted F1 score, demonstrating model’s promising performance in identifying hotspots in BOMs.

To measure the overall accuracy of BOM generation, we calculated global warming potential (GWP100) in kgCO2e of the generated BOMs and used mean absolute percentage error (MAPE) to measure the error between actual and generated values. Our method achieved a MAPE of 64.6%, demonstrating parity with Environmentally-Extended Input-Output (EEIO) estimates (62.2% MAPE). Unlike EEIO, our method is a process-based LCA approach that supports hotspot analysis, providing a robust foundation for detailed environmental assessments.

Future Work: We seek to generalize our methods beyond SSDs to other products and domains. We will assess the quality of the outputs based on the amount of data available in the BOM repository as well as the number of attributes available for a given product. We are also looking at methods to evaluate the prediction of individual products.

ECO-STEPS (Environmental Comparison and Optimization Stakeholder Tool for Evaluating and Prioritizing Solutions) is an innovative decision-support platform that utilizes outputs from Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) to help decision-makers evaluate and prioritize engineered climate solutions based on economic viability, environmental impacts, and resource use. The tool’s framework combines stakeholder rankings for key sustainability criteria with diverse statistical weighting methods, offering decision support that aligns with the long-term sustainability goals across various technology sectors. This study applies ECO-STEPS to a biofuels case study, comparing algae-based renewable diesel (RD), soybean biodiesel (BD), corn ethanol, and petroleum diesel, using an expert survey to determine criteria rankings. Findings suggest that soybean BD is a strong near-term solution for the biofuels sector, given its economic viability and relatively low environmental impacts. Conversely, corn ethanol, while economically competitive, shows poor environmental performance across multiple sustainability themes and weighting methods. Algae-based RD emerges as a promising long-term option as technology advances and costs decrease with ongoing research and development. The case study demonstrates that ECO-STEPS provides a flexible and comprehensive framework for stakeholders to navigate the complexities of decision-making in the pursuit of sustainable climate solutions.

The Comprehensive Manure Management and Impacts Tool (CoMMIT) is an advanced, user-friendly life cycle assessment (LCA) model designed to evaluate greenhouse gas (GHG) mitigation strategies in dairy manure management systems. Initially developed for anaerobic digestion (AD) technologies, CoMMIT now evaluates nine additional practices, including cap & flare, coarse fiber separation, centrifugation, manure acidification, composting, converting flush to scrape, chemical flocculation, manure evaporation, and vermifiltration. The model leverages detailed mass balances for volatile solids, nitrogen flows, and anthropogenic emissions to provide regionally specific mitigation factors across all life cycle stages, from excretion to land application. Key features of CoMMIT include customizable scenario definitions, time horizon adjustments, and pre-set regional baselines informed by expert input and scientific literature. By comparing baseline and adoption scenarios, users can identify the most effective strategies for GHG reduction tailored to regional dairy farm configurations. This presentation highlights the model’s functionality and explores the potential impacts of regionally appropriate technologies, such as manure evaporation achieving GHG mitigation of up to 82% in the Southwest, cap & flare reducing GHG emissions by 74% in the Southeast, composting mitigating GHG emissions by 69% in the Midwest and Northeast, and the conversion of flush systems to scrape offering 71% GHG reductions in California. We invite feedback from the ISSST community to further refine CoMMIT’s capabilities and enhance its practical application.

It is increasingly important to address the environmental impacts of products and technologies as the need to manage emissions becomes more urgent. Manufacturers also need to understand the cost implications before making changes that will improve their environmental footprint. For emerging technologies in areas like carbon conversion, both environmental and economic assessments are essential for decision-makers as their technologies scale to commercial levels. It is common to separately analyze both environmental impact and economic feasibility through Life Cycle Analysis (LCA) and Techno-Economic Analysis (TEA), respectively. However, all-encompassing analyses that provide an overlap in results from both LCA and TEA are not often conducted. This forces decision-makers to enact singular choices using multiple analyses with potentially inconsistent modeling assumptions and metrics that risk misinformed decisions unaligned with the preferences of all stakeholders involved.

To enable simultaneous and consistent environmental and economic analysis, NETL developed a comprehensive framework that describes how to conduct LCA and TEA for emerging technologies—with a focus on carbon conversion—using harmonized assumptions, functional units, and system boundaries. By completing the LCA and TEA simultaneously, this framework provides results for both analysis types and treats the results with equal importance to generate better solutions. In addition to using traditional LCA and TEA metrics to estimate impacts, this framework includes a set of metrics that is specifically applicable to both analysis types, enabling decision-makers to understand both the separate and combined results. Though this framework mainly focuses on carbon conversion technologies, it can be applied to other emerging technologies as well.

This presentation will discuss different aspects of the LCA-TEA harmonization framework, methodologies utilized to develop this harmonization approach, and the utility of this framework to evaluate emerging carbon conversion technologies.

Disclaimer:

This project was funded by the Department of Energy, National Energy Technology Laboratory an agency of the United States Government, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

The transition from a fossil to a renewable-based energy grid requires renewable energy production as well as advancements in energy storage. Energy production, fossils or renewable, and the acquisition of critical minerals, such as lithium for batteries, will consume freshwater resources, and the impact of this must be considered in decision-making during the transition. The lithium-rich geothermal brines in the Salton Sea Known Geothermal Resource Area (SS-KGRA) of California offer a unique opportunity to co-produce geothermal energy and lithium. Fortunately, geothermal power exhibits relatively low freshwater consumption compared to other renewable energy sources, but most geographic areas in the US with geothermal resources (including the SS-KGRA) are water-scarce. Therefore, the site-specific impact of any geothermal expansion and/or lithium production on water resources should be closely evaluated to ensure sustainable resource use.

To support informed decision-making in this context, we developed a model (GeoHydroSim) that can be used to estimate water consumption from geothermal production and direct lithium extraction (DLE) operations. The aim of this tool is to provide a dynamic platform for estimating water use based on process-level data and operational parameters, ultimately supporting decision-making capabilities for project developers, policymakers, researchers, and local communities.

The model, GeoHydroSim, was built in Python (version 3.9). It consists of three key modules, the (1) Input Dashboard, (2) Computational Model, and (3) Visualization and Reporting Module. The Input Dashboard, allows users to select an existing geothermal and/or lithium extraction facility, which populates the model with unit-processes from the selected facilities. The user can then adjust the technical and operational data needed for the water consumption calculations given their specific needs. The Computational Model then uses these data to estimate water consumption across facility construction, operation, and maintenance. These calculations are performed using theoretical models, including thermodynamic and mass balance principles, as well as empirical models derived from existing geothermal and lithium projects. Finally, the Visualization & Reporting Module displays results such as total water consumption based on the capacity of the simulated facility, sensitivity analyses, and the comparison of projected water use to regional water allocation strategies.

GeoHydroSim is being developed as a user-friendly and interactive platform for modeling water consumption from geothermal energy projects and/or DLE processes, strengthening informed decision-making for the energy transition. Therefore, we will present a case study using GeoHydroSim to show how the model estimates water consumption for located in the SS-KGRA. We will discuss the factors driving water use, key sources of uncertainty, and future directions of the model.

Communicating sustainability data effectively requires more than just accuracy—it demands thoughtful design and intentional storytelling. In just 15 minutes, this session will provide practical, high-impact techniques to improve sustainability visualizations using Microsoft Office. The focus will be on quick, actionable strategies that enhance clarity, accessibility, and engagement, ensuring that data is not only understood but also drives meaningful decision-making and action.

Participants will learn how to optimize their visualizations with simple yet powerful adjustments, such as improving chart selection, refining color schemes for accessibility, and using design principles to highlight key takeaways. The session will also introduce rapid customization techniques in RawGraphs for more dynamic visuals, as well as quick enhancements in Adobe for polished, professional-quality graphics. By demonstrating how small tweaks can make a big difference, this talk will empower attendees to elevate their sustainability communication—without the need for extensive design experience.

A key component of this session will be accessibility, ensuring that visualizations are effective for diverse audiences, from policymakers and businesses to the general public. Best practices for improving readability, avoiding common misinterpretation pitfalls, and structuring visuals for maximum impact will be discussed.

By the end of this session, attendees will walk away with a toolkit of simple yet effective data visualization techniques that they can immediately apply to their own sustainability work. Whether refining an LCA report, presenting findings to stakeholders, or designing outreach materials, these strategies will help make sustainability data more compelling, actionable, and visually impactful—all in just 15 minutes.

The systems designed to protect our water and reduce pollution are, paradoxically, contributing to another environmental crisis: greenhouse gas (GHG) emissions. The technologies used to upgrade wastewater treatment facilities to meet increasingly stringent nitrogen and phosphorus discharge regulations often require significant energy and chemical inputs, creating a challenging tradeoff for sustainability. Currently, U.S. water and wastewater treatment plants are already responsible for approximately 45 million tonnes of carbon dioxide equivalent (CO2e) emissions annually. While prior research has highlighted this issue, no comprehensive national evaluations exist to assess the broader implications of water policy decisions on GHG emissions. This work aims to fill that gap by developing a computational tool to quantify the climate impacts of nutrient reduction upgrades and policies at a national scale. As a case study, we assess the impact of a theoretical policy reducing effluent nutrient concentrations to 3 milligrams per liter (mg/L) for total nitrogen and 0.05 mg/L for total phosphorus within the Mississippi River Basin.

Using a life cycle assessment framework, the tool quantifies the GHG emissions associated with nutrient reduction upgrades at publicly owned treatment works in the National Pollutant Discharge Elimination System. A decision tree, informed by literature and Environmental Protection Agency reports, automatically assigns the most likely conventional treatment process required in the future based on a user-specified future effluent nutrient concentration target. The modeled conventional treatment processes include metal salts addition for phosphorus precipitation, anaerobic-anoxic-aerobic biological nutrient removal (BNR), 5-Stage Bardenpho BNR, tertiary denitrification, microfiltration, and reverse osmosis (though not all were required in the current case study). The required upgrades depend on both the future and current treatment processes. Current effluent nutrient concentrations were retrieved from the Gulf Hypoxia Task Force’s Nutrient Model, which uses Discharge Monitoring Report datasets, to identify the most likely current treatment processes at each facility. A life cycle inventory database for each treatment technology enables the tool to calculate the additional material and energy consumption due to each upgrade. Regional electrical grid profiles are incorporated based on the facility’s location.

Results indicate that assuming effluent concentration reductions to 3 milligrams per liter (mg/L) total nitrogen and 0.05 mg/L total phosphorus from their current levels, annual CO2e emissions within the Mississippi River Basin would increase by 1.1 million tonnes (0.12 kilograms of CO2e per cubic meter). With the emerging trend of transforming wastewater treatment plants into resource recovery facilities, numerous innovative technologies could drastically reduce energy consumption in the future. This tool is also capable of evaluating the national-scale impact of these technologies in comparison to conventional treatment processes. Future work will explore the adoption of such technologies including advanced ion exchange methods for nitrogen removal from anaerobic membrane digesters.

Plastic waste is a significant waste management challenge. Most plastics are currently either landfilled or mechanically recycled; however, there is considerable interest in pyrolysis or chemical recycling of plastics to recover useful chemistries. Separation of the plastics by resin type (i.e. 1-7's) is a key challenge, as co-mingled plastics cannot be effectively mechanically recycled or pyrolyzed without affecting product yield. AI-based plastic sorting is a potentially cost-effective way to separate plastics more effectively; however, the economics and environmental impacts of the technology are not yet understood. In this work, we investigate the economics and life cycle impacts of an AI-based plastics sorting system. Monte Carlo analysis is used to understand how changes in the plastics recycling market and facility siting affect both the economic viability of plastics sorting and the life cycle impacts of recycling baled plastics. The fate of different plastic waste fractions (i.e. landfilled waste, mechanically recycled plastic or pyrolyzed plastic) is investigated as a key variable. Mechanical recycling leads to the highest revenue and lowest environmental impacts for the plastics sorting company.

Nitrous oxide (N2O) is a major contributor to the climate change crisis. Wastewater treatment plants (WWTP) are a major source of N2O emissions, accounting for up to 50% of a WWTP’s carbon footprint.1 With the increasing interest in developing a nitrogen circular economy, technology development is crucial for capturing waste nitrogen and turning it into high-value products, while reducing N2O emissions.

Although N2O is a damaging environmental pollutant, it is also a powerful selective oxidant used in various chemical production pathways, including the selective oxidation of light hydrocarbons to olefins, alcohols, as well as the oxidation of benzene to phenol. Despite its potential, its industrial application is limited due to being seen as exotic and too expensive to use compared to traditional oxidants such as air or hydrogen peroxide. The Coupled Aerobic-Anoxic Nitrous Decomposition Operation (CANDO) process is an emerging technology that produces N2O on purpose from recovered nitrogen with a WWTP. Although CANDO can increase energy generation from the WWTP, it also could potentially be an N2O source that can be used for chemical production. Finding an effective pathway towards its use could potentially introduce a new oxidant that could help to expand the product portfolio of recovered nitrogen.

Using life cycle assessment and techno-economic analysis, we complete a case study evaluating the impact of recovering waste nitrogen as N2O within a wastewater treatment plant to produce phenol and pure nitrogen gas. We compare these results to incumbent phenol and nitrogen gas production processes and determine the impact that this technology could have on WWTP two different WWTP configurations. Additionally, four different co-product handling methods were used to distribute the burdens between the phenol and nitrogen. We used a Monte Carlo simulation with 1,000 iterations to evaluate the impact of various technical, environmental, and economic parameters. Cumulative energy demand (CED), global warming potential (GWP), water consumption, and internal rate of return (IRR) are the metrics we included. For the produced phenol, the CED, GWP, and WC range from 38 MJ/kg phenol to 102 MJ/kg phenol, 1.6 kgCO2eq/kg phenol to 3.6 kgCO2eq/ kg phenol, and 10 L/kg phenol to 30 L/kg phenol respectively. Meanwhile, the produced nitrogen gas has a CED, GWP, and WC between 40 MJ/kg phenol to 90 MJ/kg phenol, 1.8 kgCO2eq/kg phenol to 4.0 kgCO2eq/ kg phenol, and 12 L/kg phenol to 41 L/kg phenol respectively. The size of the WWTP heavily influences the internal rate of return. 25 MGD facilities are not profitable, while 100 MGD facilities can have an IRR as high as 16%. Our results indicate that large facilities are best-suited for this technology, that phenol produced at WWTP in this manner offers GHG reductions compared to conventional routes to phenol, and highlights several opportunities to improve this technology.

(1) Maktabifard, M.; Al-Hazmi, H. E.; Szulc, P.; Mousavizadegan, M.; Xu, X.; Zaborowska, E.; Li, X.; Mąkinia, J. Net-Zero Carbon Condition in Wastewater Treatment Plants: A Systematic Review of Mitigation Strategies and Challenges. Renewable and Sustainable Energy Reviews 2023, 185, 113638. https://doi.org/10.1016/j.rser.2023.113638.

Recycling plays a crucial role in fostering a sustainable and circular economy. The U.S. Environmental Protection Agency (EPA) has established a national target to achieve a 50% recycling rate by 2030, underscoring the urgency of improving resource recovery systems. While this objective encompasses various recyclable materials—including metals, paper, glass, and polymers—this study focuses on two of the most widely used and recyclable plastics: polyethylene terephthalate (PET) and high-density polyethylene (HDPE). These materials are integral to modern recycling efforts, and understanding the current infrastructure and scalability requirements for their recovery is a vital first step toward meeting national recycling goals.

Plastics recycling is central to addressing both EPA’s targets and the growing issue of plastic pollution. Despite PET and HDPE being highly recyclable, they are not often recycled at sufficient rates, resulting in significant environmental and economic challenges. Globally, over 450 million metric tons (Mt) of plastics are produced annually, yet only 9% is recycled. In the United States, the recycling rate is even lower, ranging between 5% and 8.7%. In 2018, 50% of solid waste was landfilled, 23.6% was recycled, 11.8% was incinerated for energy recovery, and 6% was treated through alternative methods. Of the recycled materials, plastics accounted for only 4.5% (approximately 3.1 million Mt). These figures highlight the need to enhance plastic recovery systems to reduce landfilling, conserve resources, and minimize greenhouse gas emissions. Moreover, the insights gained from improving plastics recycling could inform strategies for other recyclable materials, broadening the impact of this research.

Existing studies have explored economic and environmental aspects of recycling systems; however, there is a critical gap in analyzing the infrastructure and investment required to achieve the EPA’s 50% target. This study addresses this gap through a scenario-based, policy-driven approach that incorporates logistics optimization. Specifically, it examines four key questions:

Can existing waste collection infrastructure accommodate increased recyclable volumes from higher collection rates?

If not, what capacity expansions are necessary?

Where should infrastructure investments be prioritized?

What are the incremental costs associated with scaling collection and recycling infrastructure?

This analysis centers on PET and HDPE plastics, evaluating the current capacities, identifying bottlenecks, and estimating the investments needed to increase collection and recycling rates. Multiple scenarios based on realistic, policy-driven collection rates of post-consumer plastics are assessed to determine whether existing infrastructure can support higher recycling volumes or requires expansion. The corresponding economic implications of these changes are also quantified.

The findings indicate that material recovery facilities (MRFs) are significant bottlenecks under both current and expanded recycling scenarios, requiring capacity enhancements to process the additional influx of plastic waste. Additionally, collection systems represent the largest cost investment, followed by transportation expenses, which are particularly prominent on the U.S. West Coast. Optimizing the geographic distribution of recycling facilities can minimize overall system costs. These results provide actionable insights for policymakers and stakeholders to strategically align recycling infrastructure with the EPA’s goals, fostering a more efficient and sustainable recycling supply chain.

Plastics, while valuable for their lightweight and durable properties, significantly contribute to environmental degradation, accounting for 4.5% of global emissions in 2015 and potentially consuming up to 13% of the remaining carbon budget by 2050. In the U.S., recycling rates for common polymers—LDPE, HDPE, PP, and PET—remain alarmingly low at 9% as of 2019. A major barrier to improving recycling rates lies in the lack of a clear understanding of the technical constraints that limit recycling, how these constraints may evolve over time, and how emerging recycling technologies could alleviate them. Without a rigorous framework for prioritizing recycling interventions or consistently evaluating emerging technologies and dynamic material systems, the U.S. struggles to implement effective strategies to enhance recycling outcomes.

This study develops a comprehensive framework to identify a temporal hierarchy of recycling constraints and corresponding recycling parameters—recycling rates, recycled content, and environmental benefits—for LDPE, HDPE, PP, and PET in the U.S. from 2020 to 2050. The methodology integrates three components: (1) a high-resolution Dynamic Material Flow Analysis (DMFA) to model polymer flows by composition and quality, accounting for evolving demand and scrap availability; (2) a consistent Recycling Technology Performance (RTP) model evaluating polymer sortation and recycling technologies across five key metrics—cost, yield, energy and emissions, throughput, and efficacy; and (3) a Python-based linear optimization model to determine active U.S. recycling constraints and assess the efficacy of recycling technologies over time. The DMFA provides a dynamic perspective on material flows, the RTP model evaluates both conventional and advanced technologies (e.g., NIR sorting, mechanical recycling, glycolysis), and the optimization model incorporates quality metrics such as melt flow rate to assess the impacts of contamination and degradation on recycling processes. This framework offers actionable guidelines for policymakers, manufacturers, and recyclers to address economic, environmental, and technical challenges in the recycling supply chain.

This presentation will showcase the DMFA and RTP model results, revealing shifts in polymer material flows over time and comparing the performance of various recycling technologies across five dimensions. A case study of PET bottle closed-loop recycling will illustrate the framework’s application, highlighting the economic, environmental, and technical impacts of emerging technologies, contamination, and multi-recycling loops on recycled content. By using PET bottles as an example, the presentation will demonstrate how the framework identifies effective recycling interventions. This study establishes a replicable framework for improving recycling systems, applicable to other polymers and products, and offers strategies for optimizing the entire plastic recycling system.

The glass industry is an interesting case study for its potential towards increased circularity and decarbonization. According to environmental protection agency, almost 31% of container glass is recycled in the United States (U.S.) as of 2018. However, post-consumer recycling of flat glass remains elusive (Figure 1). At the moment, it is a best downcycled into low-value construction aggregate or landfill cap, if not straight up landfilled. In addition to the lack of circularity analysis studies for the U.S. glass sector, there is a need for multi-scales, multi-indicators assessment capabilities. Indeed, according to the Ellen MacArthur Foundation, the circularity transition needs to occur at different scales (individual companies, economic sectors, and countries) to be successful. Otherwise, unintended consequences and impact displacements may occur. Analysis needs to account for those scaling effects. As an example, Lonca et al. (2020) showed that increased closed loop recycling of polyethylene terephthalate (PET) bottles is environmentally beneficial when the scope of the analysis is at the product level, but not when the scope is at the whole PET market level. Indeed, closed loop recycling consumes more resources and creates more environmental impacts than existing open-loop applications for recycled PET. Thus, conducting circularity only accounting for narrow system boundaries can be misleading. Moreover, to be truly beneficial, the circular economy needs to avoid any trade-offs between the economic, social and environmental dimensions of sustainability.

While several methodologies have been deployed for circularity assessments - such as Material flow analysis and Life Cycle Assessment - there are currently no multi-scales, multi-indicators methods. In this work we present how economy-wide and supply chain impact assessments can be combined. Specifically, we assess economy-wide impacts by connecting the supply chain model outputs (expressed in mass flows for different end-of-life pathways) to an hybrid input-output tables (which express economic flows between sectors in both monetary and physical units). Windows refurbishing, photovoltaics closed-loop recycling, flat glass to pozzolan-like cementitious materials, and flat glass to glass fibers are considered in the analysis. Results show that the adoption of the different circularity options depends on their level of incentives (since none are fully profitable). Windows refurbishment is the option avoiding the most greenhouse gas emissions. While the use of powdered flat glass as a replacement for Portland cement seems more economically favorable than recycling flat glass into glass fibers, there are high uncertainty on the costs parameters used in this study. Future steps will quantify such uncertainties. Moreover, more economic sectors will be added to the analysis.

Mixed-species forests are increasingly recognized for their role in buffering ecosystems against climate stressors; however, the effects of species mixing ratios and tree sizes on drought resilience remain insufficiently explored, particularly in temperate regions. This study investigates the impact of species composition and structural diversity on drought resilience in mixed Pinus tabuliformis and Quercus variabilis stands. We collected 180 tree core samples (60 per species ratio) from three specific mixing ratios—90% P. tabuliformis and 10% Q. variabilis (P9Q1), 60% P. tabuliformis and 40% Q. variabilis (P6Q4), and 20% P. tabuliformis and 80% Q. variabilis (P2Q8)—and further stratified samples by dominant, intermediate, and suppressed size classes. Field sampling at breast height utilized increment borers to obtain tree cores with minimal impact, which were subsequently air-dried, mounted, and polished in the lab to enhance ring clarity. Growth ring widths were measured using a high-precision system, with cross-dating techniques ensuring chronological accuracy. To evaluate drought resilience, we calculated resistance (Rt), recovery (Rc), and resilience (Rs) indices and employed the Palmer Drought Severity Index (PDSI) to analyze growth sensitivity across the ratios and size categories. Mixed-effects models were applied to assess the effects of species composition, tree size, and climate factors on drought resilience. Results showed that the P6Q4 ratio optimally supported Q. variabilis resilience during extended droughts by fostering hydrological niche benefits, while P. tabuliformis showed declining Rt, Rc, and Rs values as Q. variabilis proportions increased. PDSI analysis revealed dominant trees had stronger responses in P6Q4 and P2Q8, while intermediate and suppressed trees responded more in P9Q1. These findings underscore the importance of species-specific mixing ratios and structural diversity for enhancing forest resilience under climate change, supporting a framework for sustainable forest management that prioritizes mixed-species configurations to reduce climate vulnerability and promote long-term ecosystem stability.

Replacing winter fallow in midwestern corn and soybean rotations with cover crops could offer significant environmental benefits, including increased soil carbon sequestration and reduced nitrogen leaching. Although winter cover crops are currently planted on only a small fraction of agricultural fields in this region, two emerging markets may incentivize broader adoption: demand for biofuel feedstocks could create markets for harvested cover crop biomass, and carbon credit programs could pay farmers for using cover crops to sequester carbon in the soil. This study examines how these emerging markets may influence the economic feasibility of winter rye cover crop strategies in the Midwest and the resulting environmental impacts. Our objectives were to: (1) estimate the costs to farmers of winter rye strategies, including potential yield impacts on corn and soybean; (2) explore the profitability of these strategies under different market scenarios; and (3) assess the environmental impact of profit-maximizing strategies, including spatial variation in these impacts.

We assessed harvested and unharvested winter rye management strategies within corn-soybean rotations across Iowa, Illinois, and Indiana. In total, we evaluated 19 strategies, including a no-cover-crop baseline and combinations of three fertilizer rates and three planting dates. We used the biogeochemical model Ecosys to assess yields, soil organic carbon, nitrogen leaching, and N2O emissions of each strategy compared to the baseline over a 20-year period. We also estimated the economic feasibility of each strategy under three scenarios: 1) farmers receive market price for harvested rye biomass or a cost share payment for unharvested rye, 2) farmers receive market price for harvested rye or a cost share plus carbon credits for unharvested rye, and 3) the same as scenario 2, but with a higher carbon credit value. Carbon credits are unlikely to apply to harvested rye due to additionality rules. In all scenarios, costs included fuel, labor, fertilizer, and potential corn and soybean yield losses, while revenues included the market value of harvested rye, cost share payments, and carbon credits.

Our results show that the profitability of winter rye strategies varies regionally, with harvested rye being more profitable in southern areas and unharvested rye in northern areas. This is because increased winter rye yields produced more revenue when harvested, but caused greater corn yield decreases, particularly when not harvested. Unharvested winter rye was not economically feasible in most locations when the cost share was the only financial support. Fertilization increased economic feasibility of unharvested strategies by decreasing corn losses and in some cases increasing soybean yields, but led to greater environmental damage. Introducing a modest carbon credit did not change the economic feasibility of unharvested strategies, but the high carbon credit increased the feasibility of low-yield unharvested strategies and produced the highest total environmental benefits. These insights demonstrate how availability of harvested biomass and C credit markets could influence farmer choices and environmental impacts, particularly highlighting the issues of corn yield changes and regional variability. This could inform policy and program development toward promoting sustainable agricultural practices.

Background:

Life cycle assessments (LCA) have consistently demonstrated that biobased chemical production methods can yield significant reductions in greenhouse gas (GHG) emissions when compared to fossil-based incumbents [1]. However, many types of biomass are incompatible with existing biobased chemical production methods [2]. Biological systems and living organisms are naturally capable of converting contaminated, low-quality, and heterogeneous materials into complex organic chemicals, including high-value products that have historically been produced with fossil resources. Synthetic biology facilitates industry-optimized biological production pathways, reduced reliance on fossil feedstocks, an extended range of viable biobased feedstocks, and potentially decreased process energy demands.

Nonetheless, few systems analyses have been conducted to understand which combinations of biobased feedstocks and synthetic biology processes are both environmentally and economically sustainable. To address this gap, we have built a dynamic materials flow analysis (MFA) for 1,3-propanediol (1,3-PDO) in the United States. While this important intermediate material was originally made mostly from fossil feedstocks, the supply chain has recently expanded to include biobased feedstocks, which are microbially upgraded using synthetic biology. Additional waste-based feedstocks are also poised to enter the supply chain using synthetic biology conversion techniques. Dynamic MFA enables us to elucidate supply chain responses to changes in synthetic biology process technology, as well as external market factors.

Significance:

This assessment constitutes the first MFA of 1,3-PDO and is one of few that explores chemical products and integrates dynamic systems analysis. On the supply side, we show that although direct use of fossil feedstocks in the 1,3-PDO production conversion process is limited, the total upstream fossil feedstock requirement is significant. Furthermore, we reveal the wide variety of fates of 1,3-PDO once it enters various use phases and end of life, stages that are underexplored, if not entirely unaddressed, in published LCAs. Our complimentary analyses will determine the supplies and prices of crude oil and biomass that lead to a total production capacity that is completely bio-based and the systems-level environmental impact of 1,3-PDO production in the US. Overall, we believe this offering brings important insights into the potential viability of a bio-based supply chain for a large-market chemical product and may serve as a framework for exploring other products that could be generated using synthetic biology means.

Methods:

MFA data sources include government databases, industry reports, and published scientific and technological literature. For additional insight into the factors that may impact the feasibility or preferability of certain biobased feedstocks, we will also employ dynamic systems analysis to determine market conditions under which a 1,3-PDO could be entirely bioproduced in the United States. We disaggregate biomass by type using data from the most recent Billion Tons Study (BT23) to provide deeper granularity than previous models [3]. We will also use the GREET database to determine the overall water, land, and energy, and emissions burden of the entire 1,3-PDO value chain to quantify the environmental effects of the material flows of the system.

1 Zuiderveen et al. 2023. DOI: 10.1038/s41467-023-43797-9.

2 Scown et al. 2022. DOI: 10.1016/j.copbio.2023.103017.

3 BETO: Billion-Ton 2023. https://tinyurl.com/2p9wxc3b.

Renewable natural gas (RNG) has long been seen as a replacement for fossil natural gas (FNG), given its compatibility with existing infrastructure. However, previous life cycle assessment (LCA) studies have shown that, from a greenhouse gas perspective, RNG results in a lower global warming potential (GWP) than FNG only in a consequential framework, where an avoided emissions credit is applied to the RNG to offset emissions that would have occurred if the RNG had not been produced. While such an accounting strategy has been useful in supporting development of RNG production in the United States, the policy direction of the Low Carbon Fuel Standard points toward phasing out such methods. It is thus prudent to evaluate the GWP of RNG from a true attributional perspective, with careful consideration of system boundary and biogenic carbon accounting.

This study aims to evaluate U.S. RNG production pathways using an attributional framework that includes (1) anaerobic digestion (AD) of animal manure (AM), landfill gas (LFG), municipal solid waste (MSW), and wastewater sludge (WWS), and (2) thermal gasification (TG) of woody waste feedstocks using a catalyst, air, or steam. The scenarios evaluated vary depending on upstream emissions framework (true waste or attributional), co-product management method, fuel for internal heating of the conversion process (FNG or RNG), and inclusion of carbon capture. The true waste scenario (i.e., cutoff) excludes upstream emissions (including atmospheric carbon uptake) except for transportation emissions from waste site to conversion facility. The attributional scenario includes both the life cycle upstream impacts of the feedstock and the original product. The co-product management scenario uses mass and energy allocation to attribute some portion of the upstream emissions to the co-product that was formerly a waste (and not the main product), which only applies to the AD of AM and AD of WWS pathways. The rest of the scenarios include whether to use a particular technology and are self-explanatory. All the data compiled for our study was retrieved from publicly available sources such as the Federal LCA Commons, GREET, EPA (i.e., WARM), NETL, and peer reviewed literature, except for the data for animal manure from dairy cows, which was provided by USDA upon request.

The results indicate that pathways resulting in a lower GWP than FNG (about 7 g CO2e/MJ) are AD of AM (–108 to –218 g CO2e/MJ) and TG using Air with CCS (–44 g CO2e/MJ). Other pathways about an order of magnitude higher than FNG are TG using Air without CCS, AD of LFG and AD of MSW. Meanwhile, all three AD pathways of WWS yield a GWP result that is several orders of magnitude higher than FNG, likely due to the energy intensity of wastewater treatments. Interestingly, the attributional GWP was higher than when treating feedstocks as true waste only for the AD of WWS pathways. These results highlight the value of using an attributional framework to compare RNG and FNG production pathways.

The pulp and paper industry (PPI) is an integral part of the manufacturing industry in the United States. Despite the industry’s success in reducing energy use, it remains an energy-intensive industry, accounting for 11% of the manufacturing sector’s energy consumption. There have been many initiatives to decarbonize the PPI and many novel innovations to make it more energy efficient. One of the novel approaches to decarbonizing this industry is through converting existing pulp and paper mills into integrated pulp and paper biorefineries (IPPB) and transforming wastes or lower-value by-products into value-added products.

Studies have shown that one of the key value-added products of IPPBs is soil amendment. Soil amendments can be produced from by-products such as sludge, lime mud and dregs. The sludge of PPI mainly consists of wood-fibers, paper products and non-wood fibers; therefore, using it to produce soil amendments can improve soil quality and increase soil organic matter. Lime mud and dregs can also be beneficial to the soil and its quality. In this study, we use life cycle assessment (LCA) and techno economic analysis (TEA) to investigate and compare the costs, carbon, and energy impacts of IPPB-based soil amendments. The objectives of this analysis are to show the environmental and economic impacts of this IPPB-based value-added product, and to identify the main drivers for these impacts.

In this analysis, we use the system expansion methodology with substitution to account for the avoidance of conventionally produced products that the IPPB-based soil amendments replace. Our expected results will identify the carbon and energy hotspots in the processes that can be improved and assess what the main environmental and economic impacts hinge on. IPPBs are industrial symbiosis systems that share materials and energy between the production processes of pulp and value-added products. This results in reduced usage of virgin materials, energy use, emissions, and waste generation. Therefore, targeting specific product markets could lead to net improvements in the total environmental impacts of the system. Lower production costs could also contribute to a more competitive market. The value-added products produced can replace petroleum-based, virgin materials, impacting material and resource consumption of multiple industries. Thus, the results of this study can help decision makers with their judgement related to this product as compared to others in the market with respect to their economic and life-cycle impacts.

The urgent need for sustainable agricultural solutions has positioned Climate Smart Agriculture (CSA) practices at the forefront of global efforts to counteract climate shifts and ensure food security. These practices have great potential to safeguard agricultural productivity, bolstering resilience, and mitigate greenhouse gas (GHG) emissions. However, a limited understanding of their environmental implications hinders their optimization and widespread implementation of CSA practices. Addressing this knowledge gap is crucial for realizing the whole potential of CSA practices and promoting a healthier, more resilient and sustainable agricultural future.

The present work will provide thorough meta-analysis of life cycle assessment (LCA) research to analyze the environmental impacts of several CSA practices. The meta-analysis consolidates and examines existing LCA studies for identifying trends in GHG emissions, energy efficiency, and resource use efficiency. By systematically reviewing and synthesizing findings from diverse studies, this work aims to uncover underlying patterns, identify critical gaps in current knowledge, and provide a comprehensive understanding of the subject matter to guide future research and practices. Furthermore, as the most LCA results highly sensitive to methodological choices such as system boundary, functional units and the data sources, this study will evaluate the heterogeneity in environmental performance across CSA practices, which might be driven by regional, technological, and methodological variances in the underlying studies. Analyzing these variances will provide a deeper understanding of how and why environmental outcomes differ among practices and contexts.

The anticipated findings will highlight the importance of targeted implementation strategies that consider regional and system-specific factors, guided by integrated life cycle insights. This research contributes to a broader understanding of effectiveness of CSA practices, offering practical and evidence-based guidance for different stakeholders including farmers aiming to foster sustainable agricultural transformations globally. By doing so, study seeks to support sustainable agriculture, helping to align CSA efforts with environmental and resource sustainability goals.

Biodegradable plastics are increasingly valued for their superior biodegradability, which offers promising alternatives to conventional, non-degradable plastics. Although previous studies have applied life cycle assessment (LCA) to evaluate their environmental impacts from production to engineered end-of-life (EOL) (such as anaerobic digestion)[1], they rarely considered biodegradable plastics released into natural environments where microplastics can be generated and their environmental impacts remain unexplored. The prior reason is that assessing the potential environmental impacts of biodegradable microplastics is challenging due to the lack of life cycle environmental impact (LCIA) methods in traditional LCA.

Our study addresses the abovementioned research gaps by developing an LCIA method for biodegradable microplastics in freshwater ecosystems. Specifically, we advanced the LCIA framework of USEtox[2], considering microplastics as toxic particles with fate, exposure, and effect factors. In the fate modeling, we linked the size, density, and specific surface degradation rate (SSDR) of microplastics to their biodegradation and sedimentation rate constants. This addresses the oversimplification in previous research[3] regarding the dynamic interactions between biodegradation and sedimentation, which are important removal mechanisms of microplastics in water and sediment. Using the proposed LCIA method, we quantified the aquatic ecotoxicity of five common biodegradable plastics, including bio-based poly(lactic acid) (PLA), poly(3-hydroxybutyrate) (PHB), and thermoplastic starch (TPS), and fossil-based poly(ε-caprolactone) (PCL) and poly(butylene succinate) (PBS), with particle diameters ranging from micro- (1000, 100, and 10 µm) to nanometers (1 and 0.1 µm). Moreover, we applied both static and dynamic methods to assess the time-varying greenhouse gas (GHG) emissions of microplastics, combining their fate factors with biodegradation performance.

Our results show the large impacts of microplastic sizes on the environmental impacts of various plastics. For example, at the size classes of 1000, 100, and 10 µm, PLA (with the lowest SSDR) has the lowest GHG but the highest aquatic ecotoxicity. This implies a potential burden shifting from reduced aquatic ecotoxicity to increased GHG emissions of microplastics when substituting PLA by highly degradable plastics (such as PHB, PCL, TPS, and PBS). However, at diameters of 1 and 0.1 µm, PLA shows both the highest aquatic ecotoxicity and GHG emissions without burden shifting. Our results also indicate that the GHG emissions of microplastic in natural environments can be equivalent to a substantial fraction of the life cycle GHG (from production to engineered EoL) of biodegradable plastics. In the sensitivity analyses, we identified critical SSDR that result in maximum GHG emissions at each size class of microplastic. Our research not only presents the potential environmental impacts of biodegradable microplastics, but also provides a powerful tool for the eco-design of future biodegradable plastics.

Reference

[1]. Cazaudehore et al., Biotechnology Advances 2022, 56, 107916.

[2]. Rosenbaum et al., The International Journal of Life Cycle Assessment 2008, 13, 532.

[3]. Corella-Puertas et al., Journal of Cleaner Production 2023, 418, 138197

The US beverage industry has adopted the circular economy strategy of shifting from single use to reusable cups. The transition is justified by the rationale that a reusable cup will offset multiple single use cups and avoid the burdens from manufacturing and landfilling (of the post-use waste) of single use cups. However, the findings in the peer-reviewed LCAs and commercial literature are conflicting with a lack of conclusive evidence on the environmental benefit from reusable cups.

To identify the root causes of the conflicting findings, we systematically review and harmonize 233 peer-review publications and technical reports on the scientific literature on the LCA of single use and reusable cups. We use the pedigree matrix approach to objectively assess the data quality used in the LCAs.

The results reveal significant methodological and data shortcomings in existing LCAs which inhibit their ability to be representative of operations and assess the trade-offs between single use and re-usable cups for US market conditions. Only 3 of the studies present LCA results using primary industry-sourced lifecycle inventory (LCI) data representing the US operations. The 3 studies are published prior 2011 and do not account for three key changes in beverages industry: improvements in the manufacturing of the cups in the US, increase in the diversity of the materials used and design of cups, increase in the number and the geographical spread of beverage stores, cup distribution sites, washing locations and landfills in the US, and the variability in the GHG intensity of electricity across the USA. There is no discussion or quantitative uncertainty and sensitivity analysis on how the reliance on outdated and non-US LCI data limits the applicability of the LCA findings to the US beverage industry.

We use results from tailored case-studies on how the following improvements will address the shortcomings listed above and increase the robustness of LCAs to assess circular economy strategies for the beverages industry in the US:

1. Develop standardized LCI datasets for single use and reusable cups which is anonymized (to overcome data confidentiality concerns), best represents industry-wide operations and can be used by LCA practitioners

2. Incorporate operational standards (e.g., ANSI standards for utensil washing) in LCAs for the beverages industry

3. Develop an updatable and publicly available dataset on the locations of the stores, distribution and washing centers for the beverages industry

4. Account for geo-spatial sensitivity in electricity mixes and the locations of store, cup distribution and washing, and landfill sites

5. Incorporate uncertainty analysis through global sensitivity analysis to identify key uncertainties and environmental hotspots in the LCA results which the industry can address

6. Quantify the impact of data quality on results through the pedigree matrix approach and acknowledge how data gaps can limit the applicability and generalization of LCA results

Background

Cotton is a globally significant commodity, contributing extensively to economies for countries such as the United States, which accounts for approximately 3.75% of its agricultural export value (1,2). However, cotton production poses notable environmental challenges, including intensive water usage, pesticide and fertilizer application, and vulnerability to climate change. Recent droughts, particularly in Texas (3), have exacerbated these issues, highlighting the pressing need for sustainable practices in cotton farming.

Method

Life Cycle Assessment (LCA) has emerged as a critical tool for quantifying environmental impacts and resource usage across a product's lifecycle. This study aims to develop a prospective LCA framework integrated with machine learning to predict the environmental impacts of cotton production under various climate scenarios. The proposed framework leverages supervised machine learning models to forecast irrigation water requirements, incorporating these predictions into the life cycle inventory (LCI) of the agricultural phase. Additionally, the framework evaluates the benefits of adopting sustainable technologies, such as electrified agricultural machinery, which can enhance energy efficiency and reduce environmental emissions. Our projections show that the total water usage of cotton production is expected to increase, although the overall pattern of less irrigation water usage in the East and more irrigation water usage in the West has not changed. From year 2000, the decreasing trend in electricity and diesel usage highlights the benefits of technological advancements in agricultural machinery and irrigation systems. The pro-electricity scenario proposed in this study, which suggests replacing diesel with electricity to power farm equipment, not only meets cost constraints but also offers environmental benefits with lower total energy usage and lower greenhouse gas (GHG) emissions.

Significance

By addressing the dual objectives of assessing climate-driven future impacts and evaluating sustainable strategies, this study provides critical insights into the future sustainability of U.S. cotton production. It enables stakeholders to make informed decisions on regional practices and technological innovations, ensuring resilient and sustainable cotton production in the face of climate change.

Reference:

1.Cotton Sector at a Glance | USDA Economic Research Service. USDA Economic Research Service https://www.ers.usda.gov/topics/crops/cotton-and-wool/cotton-sector-at-a-glance/ (2022).

2. U.S. Cotton Exports in 2023 | USDA Foreign Agricultural Service. USDA Foreign Agricultural Service. https://fas.usda.gov/data/commodities/cotton#:~:text=Cotton%20%7C%20USDA%20Foreign%20Agricultural%20Service,$660.59%20Million (2023).

3. Cotton: World Markets and Trade | USDA Foreign Agricultural Service. https://apps.fas.usda.gov/psdonline/circulars/cotton.pdf (2024).

Packaging plays a crucial role in society by helping ensure product containment, protection, and preservation. However, the growing consumption of packaging, often linked with economic growth, has sparked interest in the potential environmental impacts of packaging materials. This life cycle assessment (LCA) study covers cradle to end-of-life, excluding use phase impacts (e.g., breakage and shelf life), of polyethylene (PE)-based packaging and alternative materials (paper, metals, and glass). The study compares the potential environmental impacts of PE-based packaging and alternatives for nineteen example end-uses in the US. The packaging formats were selected within five prevalent PE packaging applications: collation shrink films, pallet wraps, heavy-duty sacks, rigid non-food, and flexible food. The potential environmental impacts were assessed using ten impact categories: Global Warming Potential (GWP, with and without biogenic CO2 uptake), fossil resource use, water scarcity, mineral resource use, land occupation, land transformation, acidification (freshwater and terrestrial) and freshwater eutrophication.

The comparative assessment showed that PE-based packaging had lower potential environmental impacts in >70% of comparative cases across the assessed indicators. PE-based packaging showed lower potential environmental impacts in 14 of 19 (74%) comparisons for fossil resource use, 15 of 19 (79%) comparisons each for GWP (with and without biogenic CO2 uptake), mineral resource use, acidification (freshwater and terrestrial) and land transformation, 18 of 19 (95%) comparisons for land occupation, and 19 of 19 comparisons for freshwater eutrophication. Overall, PE packaging had lower potential environmental impacts in 157 of 190 (83%) environmental indicators across all applications.

These results indicate that broad-based plastic substitution proposals and regulations can potentially inadvertently lead to unintended consequences of increasing environmental impacts, highlighting the need for nuanced, application-specific decision-making approaches that consider life cycle impacts of packaging materials.

When finished with an electronic device, consumers choose between storing, recycling, giving away, trading-in, reselling, or throwing it away. This choice has environmental and data privacy implications, e.g. reuse of devices is generally environmentally preferable to recycling, which is preferable to throwing away in the trash. This work develops empirical models to explain consumer End-of-First Use (EoFU) disposition as a multicriteria choice among competing options. The base data is established through a survey of 4,000 U.S. consumers over 10 device categories, with queries of stated knowledge and attitudes on EoFU options and past and planned behaviors. Different modeling approaches are used to explain behavior: cluster analysis, multivariable regression and neural networks. Results include that the decision to store or not store a device is strongly influenced by data security concerns when recycling or reselling, perceived convenience of recycling, and wanting to keep a backup of data. Once the choice is made to disposition outside the home, knowledge of and trust in recycling were important in consumers choosing to recycle. Reselling of devices was strongly influenced by knowledge of how to sell. Cluster and related analytical techniques indicate distinct groups of consumers. For example, k-means clustering over responses to questions stated knowledge and attitudes reveals 3 distinct groups: Cluster 1 has higher data security concerns when recycling, reselling or donating, and less knowledge and trust in End-of-First-Use options overall. The intended behavior of cluster 1 shows higher than average uncertainty in what to do at End-of-First-Use and more intent to store (lower values for other options - recycling, reselling and donating). Cluster 2 shows higher knowledge and trust in recycling, reselling, and donation, and slightly higher than average concern about data security of these options. The intended behavior of cluster 2 shows higher intent to resell, trade-in or donate, and lower levels of being uncertain of what to do and of storing. Cluster 3 expresses much less concern about data security, and lower utility of a stored device. Their intended behavior shows less storage and higher levels of other End-of-First-Use options. The longer-term goal of this research streams is developing interventions to shift behavior, e.g. distributing information on where to recycle and how to resell. The cluster analysis suggests that matching intervention to consumer segment might be more effective than a blanket intervention. Sensitivity analysis is done to clarify which shifts in knowledge and/or attitudes leads to larger increases in reselling and recycling.

The primary nutrients in fertilizers—nitrogen (N), phosphorus (P), and potassium (K) (NPK)— are traditionally derived through carbon-intensive methods and rely on non-renewable feedstock whose supply is constrained and from geopolitically sensitive countries. A transition to a circular economy that recovers N, P and K from human urine (HU) to fertilizer helps reduce reliance on constrained and environmentally intensive fertilizer feedstock. Existing research primarily focuses on increasing the technical efficiency and recovery rates of extracting NPK from HU. However, there has been a lack of analysis on identifying potential sources of HU in the US, locating the infrastructure to convert HU to fertilizers and transport the HU-derived fertilizers to farms.

To address this knowledge gap, we present the first study which geospatially optimizes and maximizes the environmental benefits from implementing a circular economy for NPK from HU to crops in the US. We quantify the HU supply from 2600 geospatially dispersed public schools across the state of Arizona (AZ) housing 1,095,000 students and 134,000 teachers and school staff. The NPK from the HU is modeled to meet the NPK demand for 78% crops grown across 916,000 acres of cropland, which accounts for all the cultivated land in AZ. The analysis applies the anticipatory-LCA framework to account for extraction of NPK from HU, water savings from avoided flushing in HU diversion toilets, the transport of HU to the recovery facility, the conversion of NPK to fertilizers, the transport of fertilizers to the farm and processing of solid and liquid waste which accumulate across the various steps. We explore two options for locating NPK recovery facilities from HU: centralized and decentralized. For centralized facilities HU is transported from multiple schools and nutrient extraction occurs at a larger scale than decentralized facilities. In decentralized facilities nutrient extraction occurs at the school to avoid transportation to a centralized facility and nutrient extraction occurs at a smaller scale than centralized facilities.

The results showed that HU generated in just the public schools in AZ can annually meet 2% of the N, 1.5% of the P and 1% of the K used in fertilizers in AZ. The transition to a circular NPK economy for HU-based fertilizers reduces the climate and water footprint of fertilizers in AZ by 35% and 55% respectively. In addition, the analysis will present a geospatial map containing the optimal locations of the various centralized and decentralized facilities for NKP recovery from HU in AZ.

With the rapid expansion of the electric vehicle (EV) market and the growing demand for extended all-electric range, reducing the carbon footprint of lithium-ion batteries (LIBs) has become increasingly important. While recent studies have primarily focused on the cradle-to-gate stage of LIBs, understanding of the energy consumption and carbon footprint during the use phase of EV batteries remains limited, and there is no consensus on the evaluation approach for this stage. This study characterizes the impacts of the use phase and identifies key parameters using a physics-based model. We applied this method to existing LIB life cycle assessment (LCA) studies to estimate the use phase emissions and examine the effects of crucial factors such as battery mass and charging-discharging losses.

Building on our previous work, which introduced a physics-based hierarchical model for evaluating energy consumption during the battery use phase, this study incorporates the impact of battery degradation on charging-discharging energy losses. Our model demonstrates that internal energy losses during charge-discharge cycles are determined from an efficiency perspective, while the energy consumption induced by battery mass is assessed through the perspective of vehicle load dynamics, specifically the power demand required to complete a drive cycle. Literature LCAs often employ these two different viewpoints simultaneously, resulting in double counting. Instead, we provide two separate models for evaluating the use phase carbon footprint of various LIB designs under different conditions, including grid mix and EV lifetime.

A model application shows that the carbon footprint from energy losses during charge-discharge cycles, e.g. ~5% of energy stored in the battery, is rather homogeneous across battery designs in the initial stage of battery life, although it is a function of EV fuel economy. As battery degradation gradually increases energy losses over EV lifetime, smaller batteries may lose capacity faster than larger batteries due to higher number of charge-discharge cycles compared to larger batteries for a given EV fuel economy and driving distance. On the other hand, the carbon footprint induced by battery mass, unsurprisingly, varies depending on battery size (kWh) and energy density (Wh/kg). Our findings reveal that the use phase carbon footprint constitutes a significant portion, exceeding 50% of the cradle-to-gate emissions in all modeling viewpoints and scenarios analyzed. These results underscore the necessity of considering both production and use phases in EV battery LCA and highlight potential trade-offs between these phases based on design choices such as battery chemistry and size that determine EV range.

Reducing GHG emissions from vehicle production is critical for decarbonizing the automotive sector. This study quantifies emissions from U.S. auto-body aluminum and steel sheet component production (2025–2050) and evaluates circular economy (CE) strategies, such as enhanced post-consumer and closed-loop scrap recycling rates and improved manufacturing process yields, under reference, moderate, and aggressive process and grid decarbonization scenarios. Emissions estimates are derived by integrating dynamic material flow analyses (2025–2050) of auto-body sheet metal usage and scrap availability with cradle-to-gate process impact models for component manufacturing.

Currently, emissions intensities for auto-body aluminum and steel sheet components are estimated at 12.3 kgCO2eq/kg and 4.31 kgCO2eq/kg, respectively. In the reference scenario, in which process and grid emissions remain unchanged, annual emissions from auto-body aluminum and steel sheet component manufacturing are projected to increase by 54% (reaching 7 MtCO2eq) and 18% (reaching 108 MtCO2eq), respectively, by 2050. Implementing CE strategies, such as improving manufacturing process yields to 70% from 44% for aluminum and 47% for steel, maximizing post-consumer scrap recycling rates from 0% to 80% for aluminum and 30% to 80% for steel, and increasing closed-loop scrap recycling rates to 100% from 91% for aluminum and 31% for steel could reduce these projected emissions by up to 52% for aluminum and 53% for steel. For aluminum, the most impactful emission reduction measure is enhancing post-consumer scrap recycling (reducing emissions by 2.4 MtCO2eq), followed by improving manufacturing process yields (1.1 MtCO2eq) and increasing closed-loop recycling (0.5 MtCO2eq). In contrast, for steel, the most significant reductions come from increasing closed-loop recycling (42 MtCO2eq), followed by boosting manufacturing process yields (25 MtCO2eq) and post-consumer scrap recycling (16 MtCO2eq). While the relative effectiveness of these CE strategies diminishes as processes and grid decarbonize, they remain crucial, delivering emissions reductions of 23%–54% by 2050 under aggressive decarbonization scenarios. This study reveals end-of-life management opportunities and challenges that must be overcome to transition toward a low-carbon automotive industry.

The heavy-duty vehicle (HDV) sector is a key decarbonization candidate, contributing 6% of Canada’s GHG emissions. Advances in battery and charging technology have made heavy-duty battery-electric vehicles (HDBEVs) a viable road freight decarbonization pathway. To support the operational efficiency and reliability of Canadian energy systems as electrification accelerates, accurately predicting electrical charging profiles for HDBEV fleets is paramount. Charging profiles report the total hourly electrical power required for charging operational HDBEV fleets, which must be satisfied by local transmission and distribution. While many studies use American HDV GPS data to forecast charging profiles, research using Canadian HDV data is scarce. This study leverages high-resolution GPS data from the American Transportation Research Institute (ATRI) for HDVs operating in Ontario, Canada in 2019 to produce annual HDBEV fleet charging profiles under different technology advancement scenarios and operator charging strategies.

The project method is composed of two phases: processing of HDV GPS data and modelling of charging demand. Processing entails converting timeseries data for real-world HDV travel data into travel operation schedules, which distinguish between driving periods and charging opportunities. These schedules detail driving start and end times, distance travelled, geographical location of stops, and estimates of cargo weights and ambient temperature data. Phase two applies a linear approach to predict the charging demand for randomly sampled HDBEV fleets travelling according to operation schedules. The model uses government reported data for technological parameters such as HDBEV battery size and charging rates. A key model input is the choice of fleet charging strategy, chosen from immediate (instantly charge once stationary), delayed (fully charge just before departing), or minimum power (lower constant power throughout the entire stationary period).

Preliminary findings use data from August 8 to 14, 2019, producing schedules for 1960 HDVs, covering a total travel distance of 4.4 million kilometres. Results indicate the charging strategy directly impacts the fleet demand. Applying immediate charging increases demand during afternoon peak times by over 30 percent compared to delayed and minimum power charging. Charging at minimum power results in lowest peak power demand of all three strategies, while increasing off-peak demand, serving as a load-levelling approach. These results indicate that in place of rapid technology advancement, operator behaviour has the highest potential to reduce HDBEV fleet charging demand. These findings provide energy system operators and energy resource policymakers valuable insights into HDBEV load profiles and power demand variations across operator charging behaviours. Ongoing work is focused on improving accuracy of scheduling, implementing non-linear HDBEV charging, and processing full year 2019 data.

The Coordinating Research Council (CRC), which was formed in 1919, is a non-profit association that acts as a forum for cooperative, pre-competitive, publicly available mobility-related research. In 2021, CRC added the Sustainable Mobility Committee (SMC) to its technical research portfolio in response to member interest. The SMC has four technical focus areas, called Working Groups, in Fuels, Electrification, Life Cycle Analysis, and Hydrogen & Carbon Reduction.

Since CRC’s Executive Director Chris Tennant’s 2024 ISSST keynote presentation, the SMC has completed and published a wide range of technical research, found at crcao.org/published-reports-full. This presentation would review this research, as well as discuss upcoming research foci within each of the SMC Working Groups, with specific emphasis on Life Cycle Analysis.

While CRC’s Members are generally in industry, technical research contracts are frequently awarded to government partners (often National Labs) and academia. This presents a unique perspective for ISSST participants to understand how collaborative, consensus-based research between industry, government, and academic institutions works in practice.

Potential Thematic Areas: Sustainability and resilience of energy systems, Other creative sustainability-related topics, Business & Industry Practices or Case Studies, Open Science and Communication of Sustainability Science

Preference: ORAL, 30 min (also could be ORAL, 15 min with poster)

• If provided a 45 min oral slot, the presentation would provide a quick background on CRC, review each of the 8+ technical research publications and proceedings published in the past year, and review in some detail prospective upcoming research projects.

• If provided a 30 min oral slot, the presentation would provide a quick background on CRC review each of the 8+ technical research publications and proceedings published in the past year, and quickly highlight 8 upcoming research projects.

• If provided a 15 min oral slot with a poster, the presentation would show information about CRC, quickly highlight some key takeaways from technical research over the past year, show a couple upcoming research projects, and invite folks to speak with the presenter at her poster.

The frequency of unplanned power outages has increased with recurrent extreme weather conditions and natural hazards. During and in the aftermath of such events, a reliable backup power supply is essential. Electric vehicles (EVs) could serve as a solution for backup power supply in emergencies due to their potential use as mobile energy resources when equipped with bi-directional energy exchange technology, allowing them to supply power to external loads by discharging their batteries.

This research proposes energy supply logistics using electric uncrewed aerial and ground vehicles (UAVs and UGVs) within the framework of a two-echelon electric location routing problem. In our model, vehicle batteries are recharged at satellite locations while they discharge both when traversing the transportation network and to serve energy demand, acting as portable battery storage resources. The network consists of two echelons: the first echelon operates between the depot starting point of travel, housing all UAVs and UGVs, and open satellites equipped with EV recharging infrastructure using only UGVs. The second echelon operates between open satellites and demand nodes requiring energy to be served using both UAVs and UGVs. The objective is to determine the route of each vehicle and the location of open satellites to minimize the total distance for both echelons while satisfying all energy demands.

We solve the problem with an exact method developed based on the Held-Karp algorithm using dynamic programming to find the minimum travel distance across both echelons. We introduce a penalty term to the link distance by adding it to the physical distance. The penalty is applied if the current battery state of charge (SOC) is insufficient to return to the originating satellite to recharge or complete the travel, indicating that the current sequence of nodes will be infeasible in the next step. With the penalty term, we can discard infeasible sequences of nodes and guarantee optimality. However, the exact method is time-consuming for large networks. Therefore, a heuristic method was developed, which clusters the demand nodes into groups first and then solves the problem within each group. To evaluate the performance of the heuristic method, we compared the solutions from the two methods for randomly generated networks.

Lastly, the model was applied to a real network in four counties in California corresponding to a Public Safety Power Shutoff (PSPS) scenario conducted by PG&E in 2020. Our model is applied to provide backup power to resilience hubs (demand nodes) that require necessary power for medical equipment and care for the elderly and young children. We found that UAVs are preferred due to their shorter travel distances compared to UGVs traversing the road network. However, due to the smaller capacity of UAVs, higher energy demand or long-distance demand nodes are served by UGVs. Additionally, the model determines the number of open satellites that can minimize the total travel distance and provides insights into preparedness for recharging the energy supply logistics fleet in California post-wildfire recovery scenarios.

Antelope is an LCA modeling and computation framework that enables a user to build flexible, modular models quickly and easily. Antelope was designed around the principle that product system models can be described precisely without including the data on which those models are based. The architecture is designed to break up the distinct computational tasks of LCA into different interfaces for easier management. The software enables a user to compute LCAs without installing any data on their computer.
In the course, I will demonstrate how to install the free version of the software in a python environment and use it to perform on-the-fly LCIA and contribution analysis from a variety of free LCI databases, particularly drawn from the US Federal LCA commons. During this phase, the course will demonstrate techniques for benchmarking LCI data and performing quality review of LCIA scores. The second phase will focus on model building. Enrollees will learn how to build modular models that utilize multiple independent data sources, generate graphical and tabular results, and run scenarios.

3-hour workshop

The USEPA has created and maintains a collection of open-source tools known as the Tools for Industrial Ecology Modeling (TIEM) that support modeling in industrial ecology and related disciplines. TIEM supports researchers and practitioners in identifying, acquiring, and processing facility or sector-level environmental and economic data and in developing models, such as life cycle inventory models, EEIO models, material flows analysis models, exposure and risk assessments and other IE relevant applications. TIEM is designed for users of Python and R languages that are interested in using U.S. environmental, economic or other activities data on industries in IE or related science and engineering applications. TIEM outputs are also available in tabular data formats outside of programming tools. TIEM is open-source and using TIEM can make research workflows more reproducible and help researchers save time in data acquisition and correctly synthesis data from various U.S. public sources. TIEM users benefit from the deep institutional knowledge of the federal data sources incorporated, the careful consideration that occurred related to acquisition and data treatment, the availability of complete time-series and complete sector and geographic coverage, along with the presence of full metadata and logs of validation checks, that went into TIEM development and/or persist in the TIEM code base and data products.

In this workshop, we will describe and demonstrate the many ways that users can generate, manipulate, and analyze USEEIO models for their research needs. Specifically we will explore applications of useeior and related tools including:

• Creating national or two-region EEIO models with custom indicators and inventories.
• Evaluating consumption based emissions inventories with USEEIO-State.
• Generating supply chain (scope 3) emission factors for specific years or indicators.

Attendees will be invited to explore ways that these tools can be used to access and acquire data for their own research needs, and will be invited to contribute to the open-source code base.

This workshop is the second of two workshops on TIEM; attendees do not need to participate in Part 1 to attend and benefit from this workshop.

Duration

Half-day, recommend to occur after workshop 1 proposed on this topic

Intended Audience

Some knowledge of R (or similar programming language is recommended). The workshop will be conducted in RStudio and Excel.

Disclaimer

The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency or other institutions with which the authors are affiliated.

With many companies pulling back on investments and commitments to sustainability, monetary justification becomes critical if we are to move forward from the status quo. Sustainable Return on Investment is an evolving method which has been successfully used to assess these types of investments, providing companies and investors with justification beyond “doing good.” For example, Dow Chemical uses the method to assess their sustainability goals; the Japanese Government has used it to determine which biofuels to invest in further, and other companies have used it to determine whether to invest in efficiency upgrades and other, relatively small projects. This 3-hour workshop will provide an introduction to S-ROI, provide a small exercise and discuss the status of methodology development and its strengths and weaknesses.