Cutting Edge
Science
With two of the world’s most prominent Industrial Ecologists, Dr. Tim Smith and Dr. Yi Yang leading the way, TASA’s in-house team of scientists, and their global collaborators, have created products and services to optimize your emissions strategy, powered by the latest scientific advances in supply chain management.
Most Relevant Publications from TASA Scientists
Smith, Yang, September 2025. This article explores and compares three popular open source supply chain emissions factors data sources, TASA-EFX, CEDA and USEEIO.
Pedal to the Lower-carbon Metal: Driving down steel and aluminium supply chain emissions — a collaboration between a global automaker, TASA Analytics, and Climate TRACE June 2025
J Yang, L Duan, S Peng, R Heijungs, X Geng, P Wang, WQ Chen, Y Yang. 2024. Toward More Realistic Estimates of Product Displacement in Life Cycle Assessment, Environmental Science & Technology 58 (37), 16237-16247
Zhang, Y. & Yang, Y. Estimating the carbon footprint of Mexican food consumption based on a high-resolution environmentally extended input-output model. Environ. Sci. Pollut. Res. 31, 27192–27202 (2024).
J Daignault, C Wallace, D Watkins, R Handler, Y Yang, D Heaney. 2023. A household-scale life cycle assessment model for understanding the food-energy-water nexus, Frontiers in Environmental Science 11, 1059301.
R Heijungs, Y Yang, HS Park. 2022. A or I‐A? Unifying the computational structures of process‐ and IO‐based LCA for clarity and consistency, Journal of Industrial Ecology 26 (5), 1824-1836
Yang, Y., Park, Y., Smith, T. M., Kim, T. & Park, H.-S. High-Resolution Environmentally Extended Input–Output Model to Assess the Greenhouse Gas Impact of Electronics in South Korea. Environ. Sci. Technol. 56, 2107–2114 (2022).
J He, Y Yang, Z Liao, A Xu, K Fang. 2022. Linking SDG 7 to assess the renewable energy footprint of nations by 2030, Applied Energy 317
W Wang, WQ Chen, ZW Diao, L Ciacci, L Pourzahedi, MJ Eckelman… Y Yang. 2021. Multidimensional analyses reveal unequal resource, economic, and environmental gains and losses among the global aluminum trade leaders, Environmental Science & Technology 55 (10), 7102-7112
Y Qin, Y Yang, S Cucurachi, S Suh. 2021. Non-linearity in marginal LCA: application of a spatial optimization model, Frontiers in Sustainability 2, 631080
P Wang, M Ryberg, Y Yang, K Feng, S Kara, M Hauschild, WQ Chen. 2021. Efficiency stagnation in global steel production urges joint supply-and demand-side mitigation efforts, Nature communications 12 (1), 2066.
Yang, Y., Pelton, R.E., Kim, T. and Smith, T.M., 2020. Effects of spatial scale on life cycle inventory results. Environmental Science & Technology, 54(3): 1293-1303.
Y Yang, B Liu, P Wang, WQ Chen, TM Smith. 2020. Toward sustainable climate change adaptation, Journal of Industrial Ecology 24 (2), 318-330
Y Yang, R Heijungs. 2019. Moving from completing system boundaries to more realistic modeling of the economy in life cycle assessment, The International Journal of Life Cycle Assessment 24, 211-218
Peng, S., Yang, Y., Li, T., Smith, T.M., Tan, G.Z. and Zhang, H.C., 2019. Environmental Benefits of Engine Remanufacture in China’s Circular Economy Development. Environmental science & technology, 53(19), pp.11294-11301 (d, e).
Y Yang. 2019. A unified framework of life cycle assessment, The International Journal of Life Cycle Assessment 24, 620-626.
Eryilmaz, D., Apland, J., Smith, T.M., 2019. Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing. Energy and Environment Research, 9 (2): p.1-19
Yang, Y., Ingwersen, W.W. and Meyer, D.E., 2018. Exploring the relevance of spatial scale to life cycle inventory results using environmentally-extended input-output models of the United States. Environmental modelling & software, 99, pp.52-57.
Y Yang, R Heijungs, M Brandão. 2017. Hybrid life cycle assessment (LCA) does not necessarily yield more accurate results than process-based LCA, Journal of Cleaner Production 150, 237-242.
Y Yang, R Heijungs, M Brandão. 2017. Hybrid life cycle assessment (LCA) does not necessarily yield more accurate results than process-based LCA, Journal of Cleaner Production 150, 237-242.
Li, M., Smith, T.M., Yang, Y. and Wilson, E.J., 2017. Marginal Emission Factors Considering Renewables: A Case Study of the US Midcontinent Independent System Operator (MISO) System. Environmental Science & Technology, 51(19): 11215-11223
Smith, T.M., Goodkind, A.L., Kim, T., Pelton, R.E., Suh, K. and Schmitt, J., 2017. Subnational mobility and consumption-based environmental accounting of US corn in animal protein and ethanol supply chains. Proceedings of the National Academy of Sciences, 114(38), pp.E7891-E7899
Yang, Y., Ingwersen, W. W., Hawkins, T. R., Srocka, M. & Meyer, D. E. USEEIO: A new and transparent United States environmentally-extended input-output model. J. Clean. Prod. 158, 308–318 (2017).
Pelton, R.E.O., Li, M., Smith, T.M., Lyon, T. 2016. Optimizing Eco-Efficiency Across the Procurement Portfolio, Environmental Science & Technology, 50 (11): 5908-5918
Pelton, R.E.O., Smith, T.M. 2015. Hotspot Scenario Assessment: Comparative Streamlined LCA Approaches for Green Supply Chain and Procurement Decision Making, Journal of Industrial Ecology 19(3) 427-440
Methods
TASA-EFX
Learn more about the science behind TASA-EFX emissions factors including our National Models and rLCA Models.
HYBRID-PATH
Learn more about the science behind TASA’s custom built Hybrid Path Emissions Management Tool.
READ ME FILES
Access the Read Me Documentation that details the data behind our EEIO tables and rLCA models.
EXPLORE ADDITIONAL RESOURCES
Case Studies
Strategic Sourcing of Hard-to-Abate Commodities
PROBLEM
A Fortune 50 company sought to explore procurement scenarios by comparing unique sources of primary steel and aluminum and measuring the embedded downstream emissions from these commodities in their supply chain.
SOLUTION
TASA deployed Hybrid-Path, combining TASA-EFX country-specific EEIO data, hybridized with Climate Trace process LCA data (satellite-enabled, facility-level capacity data developed by Climate Trace and GEI).
TASA produced Hybrid-Path emissions factors for steel and aluminum purchases across the client’s supply base, providing deep, multi-tier, and locationally-specific carbon insights.
OUTCOME
TASA created a parameterized tool that allows the client to compare the emissions from different sources of Steel and Aluminum and procure these metals strategically. This allows for a lower carbon footprint and informs corporate reporting and future compliance with increased regulation under the EU’s Corporate Sustainability Reporting Directive and Carbon Border Adjustment Mechanism.
Engaging Suppliers to Achieve Net Zero
PROBLEM
A Fortune 15 client sought to engage with its suppliers, align ambition, and secure emissions reductions to meet its Net Zero goals.
SOLUTION
TASA built and currently implements a data-driven supplier engagement program that:
- Utilizes the Hybrid-Path approach to measure supplier-specific emissions footprints for Scope 3, Cat 1 & 2, purchased goods, services, and capital purchases,
- Supports ongoing supplier engagement toward modeled emissions reduction commitments contributing to a 2030 supply chain wide Net Zero goal,
- Reports real-time results through external-facing supplier scorecards and internal-facing managerial dashboards to support decision-making
OUTCOME
The program has helped identify tens of millions of dollars of emissions exposure risk in the client’s supply chains, and to date has worked with key suppliers to document >40% emissions reduction commitments, program wide.
Integration of Automated LCA at the Component Level
PROBLEM
A network of supply chain actors and a semiconductor industry consortium building process-based life cycle inventory data wanted to accurately baseline industrywide emissions and explore reduction scenarios.
SOLUTION
Hybrid-Path emissions factors were defined at the component level using a combination of data from different sources:
- Detailed LCA inventories were used to estimate emissions associated with material and energy flows of wafer fabrication,
- TASA-EFX National Models (EEIO) were used to estimate emissions generated in design, R&D, and upstream capital and services,
- Company-provided emissions data were incorporated to estimate device packaging and assembly activities.
OUTCOME
Each company in the consortium was able to baseline the emissions of each characterized component and attribute investments in renewable energy and abatement technologies to emissions reduction targets. Consortium members were able to estimate the impact of projected interventions and devise strategic sourcing strategies to mitigate the embedded emissions in their value chains.
Case Study Spotlight
Supplier Engagement at Microsoft Cloud
What do you do once you have set a public-facing “moonshot” target to reduce supply chain-wide emissions by more than 50% by 2030?
You assemble a best-in-class team of scientists and practitioners to help you design and implement an award-winning program. One that engages your most carbon-intensive suppliers, builds trust and common ambition, and monitors progress and assurance over the short and long-term through scorecards and dashboards.
Recognized early on by the Sustainable Purchasing Leadership Council for advancing best practices in supplier engagement, TASA partnered with Microsoft’s hardware systems infrastructure team to identify key supply chain partners, build data exchange capacity, and apply the Hybrid-Path methodology. The result was a detailed structural representation of emissions associated with procured components, services, and suppliers—enabling Microsoft to baseline and track supplier-specific commitments and performance across its supply chain.
Contact us to learn how TASA might help you scale your scope 3 strategies.
Meet Our Founding Scientists
Timothy M. Smith
Dr. Timothy M. Smith is CEO and managing partner of TASA Analytics, and professor of sustainable systems management and international business at the University of Minnesota.
Dr. Smith founded TASA in 2020. Since then, its primary practice has grown around scope 3 GHG emissions management, with advisory services and global data sets adopted by some of the world’s largest corporations and most innovative start-ups and NGOs. Throughout his career, Dr. Smith has significantly contributed to the understanding and application of sustainability performance measurement and decision-making within the contexts of supply chain management, product/process design, marketing, and public policy. In collaboration with partners, Smith’s work has attracted more than $50 million in research support from governmental agencies, nongovernmental organizations, and corporations. Dr. Smith’s research has appeared in top-tier scholarly journals and has been highlighted in popular media outlets. Smith has also served as an advisor to the US Environmental Protection Agency, US General Services Administration, American National Standards Institute, National Research Council, Center for Green Market Activation, and Sustainable Purchasing Leadership Council, among others.
Yi Yang
Dr. Yi Yang is the Chief Science Officer and partner at TASA Analytics, and professor of environmental and ecological engineering at Chongqing University, China.
Dr Yi Yang is a world-renowned scholar of sustainable systems development and climate change mitigation and adaptation. Dr Yang received his Master’s degree from the University of Minnesota and his PhD degree from the University of California, Santa Barbara. Dr Yang has over 15 years of research experience in environmental life cycle assessment (LCA), environmentally-extended input-output modeling (EEIO), and hybrid-LCA approaches. Among others, he co-developed one of the most widely used EEIO models, USEEIO (or US-EEIO), which is part of the Sustainable Materials Management (SMM) program run by the USEPA. USEEIO has been used by leading corporations like Amazon, GM, and Merck, government agencies like the US Department of Energy, national research labs like the NREL, scholars in leading universities around the world including Yale, Princeton, MIT, and Northwestern, and is used as the core dataset by many emissions management software providers.
