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.