Conference Agenda

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

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

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

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

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

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

Background and Significance

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

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

Methods

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

Outcomes

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

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

References:

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

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

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

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

Background and Significance

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

Methods

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

Results

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

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

References

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

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

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

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

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

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

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

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

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

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

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

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

Disclaimer:

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