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.

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.

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.

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.

The national U.S. Environmentally Extended Input-Output (USEEIO) models estimate the environmental impacts of economic activities of the US. These models rely on input-output tables from the Bureau of Economic Analysis (BEA). The detailed benchmark tables are updated every five years, with the most recent update being for 2017.

The latest versions of USEEIO are based on the 2017 benchmark input-output tables, while the prior version relies on the 2012 benchmark tables.

This study aims to compare how the transition from 2012 to 2017 benchmark input output tables reflected changes in the structure of the economy, and the resulting influence on environmental impacts across the supply chains. Comparison of final demand across the years is achieved by accounting for inflation between benchmark years. This study provides insights on how changes in the structure of the economy of the US has affected the direct and indirect impact matrix and highlights the importance of updating input output tables for having accurate assessment of the

US economic activities. The next step of the study involves endogenizing the use of capital assets to USEEIO based on updated 2017 benchmark tables by applying the method developed by Miller et al., 2019

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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 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.

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.