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

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

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

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

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

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

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

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

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

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

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

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

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

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

Disclaimer:

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

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

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

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

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

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

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

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

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