TASA-EFX Methodology

One approach to LCI development uses national economic accounts. This “top-down” inventory method leverages input-output (IO) data of industry production and final-use transactions within an economy. Specifically, IO data is the representation of a nation’s economic activity that closely aligns to the statistics collected by business—e.g., sales and cost of sales by industry. IO data tables can further be supplemented with environmental extensions, which relate the economic transactions to GHG emissions and resource use, to create a cradle-to-gate life cycle inventory (LCI) for commodities produced by industry sectors within an economy. ​

TASA develops multi-tier emissions profiles based on national EEIO models and leverages these data for custom hybrid-path emissions modeling and analytics. TASA scientists review and reconcile hundreds of data sources to produce the TASA-EFX global emissions factor database. These includes high-resolution national input-output tables, regional input-output tables (e.g., OECD and EU), GHG inventories (e.g. EPA, ONS, UNFCCC, etc.), global economic data (e.g., U.N. Comtrade, country- and sector-level producer price data), as well as a myriad of academic papers. Only the best available data and most robust assumptions are used, reducing aggregation error and continuously improving data quality over time.

A hierarchical approach is used to adopt and develop country-level environmentally extended input-output (EEIO) models, tailored to varying levels of data availability and quality. First, from countries where refined input-output (IO) tables are well represented, TASA constructs and manages 12 National EEIO models, representing approximately 70% of global gross domestic product.

Second, TASA harmonizes differences across country reporting and classification systems and expands the matrix structure of more aggregated countries’ IO data by disaggregating select sectors (e.g., electronics, automotive, steel, etc.) using well-established matrix augmentation and IO-based hybrid protocols.

Third, where national IO tables don’t exist or are highly aggregated or poorly represented, TASA applies regional, technological, economy-wide, proxy structures, with modifications to key economic sector inputs (e.g., electricity and heavy primary industries where possible). These country data are identified as “extended” models, for transparency.

Structural Path Analysis (SPA) is used to estimate the round-by-round input–output relationships within a supply chain, tracing the contributions of each tier to the final demand output of a given sector. In other words, the inverse Leontief power series provides an estimate of the upstream production-consumption requirements ultimately contributing to the emissions of goods and services at final use. These supply chain paths are further organized to reflect the GHG protocol nomenclature of scope 1, scope 2 and scope 3 emissions.​

The resulting structured emissions data are standardized across TASA-EFX data products:​

Rapid IO-LCA (rLCA) Industry Sector Models: For rLCA Models, more than 250,000 emissions exchanges are provided within the multi-tier supply chain structures of each of the 35,000 sector-level, global production systems modeled.

National Models: For National Models, aggregated emissions contributions by tier and direct-purchased hotspot emissions exchanges are provided, generating more than 60,000 emissions exchanges within each of the 148 countries/regions. ​

Our approach addresses three critical challenges in supply chain carbon management by: 

1. Allowing for unique multi-regionality through hybrid-path constructions, as opposed to relying on average country-level trade flows,
2. Providing improved commodity granularity,
3. Creating visibility into supply chain input pathways for attribution across actors

TASA-EXF emission factors are available in units of MgCO2e/$million (Mega grams of carbon dioxide equivalency per million dollars spent) as well as in local currencies. Sector definitions follow the industry and commodity classification of the US Bureau of Economic Analysis (BEA) and are mapped to the North American Industry Classification System (NAICS). This classification is “crosswalked” with other national classification systems employed by other country governments in developing their primary IO models. A crosswalk tool is provided with all models to easily identify corresponding sectors and commodities.

Due to time and resource constraints, governments typically update economic input-output (IO) models only once every five years or more. To address this lag, TASA follows established best practices to adjust emissions factors from the IO model’s reference year to the most recent available year. This is done using country- and sector-specific producer price indices—both reported and calculated—for each country modeled. As a result, users can confidently apply TASA’s emissions factors to the economic year most relevant to their analysis.

TASA provides emissions factors at producer prices, the amount received by the producer prior to any trade margin adjustments. Some emissions factor databases apply coarse and generic assumptions to calculate a “purchaser price,” a price after adjustments for wholesale/retail trade margins and some transportation and logistics costs. Given the tremendous variation across purchasing agreements in the marketplace,
TASA has chosen not to provide emissions factors at a calculated purchaser price. Like other life cycle assessment methodological guidance, we recommend a partitioning approach and the development of additional analytical modules to account for transportation and retail/wholesale functions appropriately, when purchasing agreements include retail or wholesale margins. ​

TASA’s emissions factors are appropriate for manufacturer-direct purchases, without alteration, and are most comparable to process-based life cycle assessments for products at producer gate. Users may also wish to convert TASA emissions factors to ones more reflective of a consumer purchaser’s price. Contact our scientists if you have any questions. We are happy to help you. 

TASA is committed to rigorous peer-review of its methods. TASA’s methods and a number of our EEIO models have been published in academic and technical literature, to both highlight the consistency across models and the differences between regional production environments.

TASA’s chief science officer, Dr. Yi Yang, led the publication of the original technical manuscript describing the U.S. EEIO model (Yang et al., 2017), and, subsequently, Dr. Tim Smith’s (TASA CEO) former student and postdoctoral advisee, Dr. Mo Li, co-authored its most recently published revision (Ingwersen et al. 2022). TASA’s South Korea model (Yang et al., 2022) and Mexico model (Zhang & Yang, 2024) have also been made available following peer review, and a comparison of TASA’s US, China, Korea and Mexico is also published (Yang et al. 2025).​ Please consult these publications for further methodological detail.