Exiobase Emission Factors

A reference for the Exiobase multi-regional environmentally-extended input-output (MRIO) model and the emission intensity factors derived from it. Documents the current production state as of May 2026: EXIOBASE 3.8.2 (released 21 October 2021 (Zenodo record 5589597); EXIOBASE has since published 3.9.x (2025), 3.10.x (2026) and 3.11.2 (30 September 2026)), covering 200 products across 49 regions (44 individual countries plus 5 rest-of-world aggregates) in current basic prices (million EUR). GreenCalculus factors use its 2019 tables, the last year with full CO₂ data in 3.8.2, recomputed per gas on AR5 GWP-100. Defines the model’s three-layer MRIO architecture, the 200×49 product–region resolution, the basic-vs-purchaser price problem, the import-substitution trap, and the version-vintage gap. Aligned to the GHG Protocol Scope 3 Standard Chapter 7 spend-based method, recognised by CSRD ESRS E1-6 as the canonical EU-domiciled EEIO companion. Worked example for a European manufacturer with mixed German, French, Chinese, and Indian procurement plus a single direct comparison block running the same spend through USEEIO v1.3 — the two are not interchangeable, and the page documents the magnitude and direction of the discrepancy. Sector × region intensity matrix across 25 high-relevance products and 8 key economies. Audit checklist for ISO 14064-1 reasonable assurance.

This page is the deep treatment of the Exiobase MRIO model and the emission intensity factors derived from it. The Scope 3 Category 1 spend-based methodology covers the full Cat 1 inventory workflow including supplier-specific data preference order, hybrid approaches, and uncertainty quantification; the USEEIO methodology covers the U.S. equivalent model. This page covers Exiobase itself — its multi-regional architecture, why it differs structurally from USEEIO, how to apply it without triggering the three named hazards, and when an EU- or globally-domiciled reporter should reach for Exiobase rather than its U.S. counterpart.

The Regionalisation Problem — Why Geography Matters for Spend-Based Scope 3

What Is Exiobase

Exiobase is the largest publicly-documented multi-regional environmentally-extended input-output (MRIO) model. It couples the input-output tables of 44 individual national economies — plus 5 rest-of-world regional aggregates — with environmental satellite accounts covering greenhouse gases, energy use, land use, water consumption, material extraction, and over 100 other environmental indicators. The model produces per-euro environmental intensity factors for 200 product categories within each of the 49 regions, traced through the full multi-regional supply chain via the Leontief inverse.

The model is developed and maintained by a four-institution consortium: NTNU (the Norwegian University of Science and Technology, the lead institution), 2.-0 LCA Consultants (a Danish LCA software firm), TNO (the Netherlands Organisation for Applied Scientific Research), and Leiden University’s Institute of Environmental Sciences (CML). The model has its roots in the FP6 and FP7 European research framework programmes (the original EXIOPOL project ran 2007–2011) and has been continuously developed under successive grants, with the current v3 series maintained collaboratively and released through the consortium’s GitHub repositories and the Zenodo data archive.

Exiobase is the United States’ USEEIO’s structural counterpart for the global economy. Where USEEIO answers “given a dollar of U.S. procurement spend on commodity X, how many kg CO₂e are embodied in its supply chain treated as if it were U.S.-domestic?”, Exiobase answers “given a euro of procurement spend on product X originating in region Y, how many kg CO₂e are embodied in its multi-regional supply chain traced through all 49 regions?”. The two models share the same analytical engine — both are environmentally-extended input-output models — but Exiobase’s multi-regional structure is the central methodological feature that makes it irreplaceable for non-U.S. and global reporters.

Like USEEIO, Exiobase does not measure emissions — it allocates them. Each region’s environmental satellite accounts are sourced from national inventories (where available) and modelled estimates (for regions and indicators without official inventories). The MRIO supply-chain attribution then propagates those territorial totals through the full network of interregional and intersectoral economic flows. The model’s accuracy ceiling is inherited from the underlying national inventories; what Exiobase adds is the multi-regional propagation that single-region models cannot perform.

Exiobase is documented in three foundational peer-reviewed publications: Tukker et al. 2013 (the original EXIOPOL synthesis in Economic Systems Research), Stadler et al. 2018 (the v3 model paper in the Journal of Industrial Ecology, which remains the canonical citation), and Wood et al. 2015 (the structural decomposition methodology). The data archive is on Zenodo, the codebase and supply tooling are on GitHub at github.com/IndEcol (Industrial Ecology Programme), and the model is widely used by EU statistical bodies including Eurostat and the European Environment Agency for footprint indicators.

Model Architecture — Three Layers

Exiobase’s architecture mirrors the conceptual three-layer pattern of any EEIO model but extends each layer with explicit multi-regional structure. Understanding what each layer contributes is the prerequisite for diagnosing any methodology question.

Product Classification — CPA and NACE Harmonisation

Exiobase uses a harmonised classification derived from the European Statistical Classification of Products by Activity (CPA) and the European industrial classification system NACE (Nomenclature statistique des Activités économiques dans la Communauté Européenne). Both are EU statistical standards aligned to the UN’s Central Product Classification (CPC) and International Standard Industrial Classification (ISIC), giving Exiobase a globally-recognised product taxonomy at 200 categories. The 200 categories were chosen to balance two pressures: granularity sufficient for sectoral analysis, and harmonisation with national supply-use tables across 44 countries with varying levels of detail. Reporters with procurement systems coded in NACE Rev. 2 can map directly; reporters coded in NAICS, UN CPC, or ISIC require a crosswalk (Eurostat publishes the canonical NACE↔NAICS and NACE↔ISIC correspondences).

Basic Price vs Purchaser Price — The Default and the Adjustment

Exiobase’s native denominator is the basic price — the price at which a producer transfers a good or service to the next purchaser, excluding trade and transport margins, taxes on products, and subsidies on products. Procurement invoices, however, are recorded in purchaser price — the price actually paid by the buyer, inclusive of margins and applicable taxes. Applying a basic-price-denominator factor to purchaser-price spend without adjustment overstates emissions by the magnitude of the margins ratio (typically 15–35% depending on product category and distribution channel).

Exiobase publishes an explicit margins extension that allows construction of purchaser-price-equivalent factors. The published 3.8.2 release includes both formulations: the basic-price factors as the default scientific reference, and the purchaser-price-converted factors as the practitioner-grade variants. Reporters must declare which formulation they are using — applying basic-price factors to purchaser-price invoices without conversion is the single most common Exiobase-specific calculation error (see hazard 9.1 below).

Where B is the environmental satellite matrix (kg of each GHG per euro of industry output, region-specific), L is the multi-regional Leontief inverse (9,800 × 9,800 for the 200 products × 49 regions structure), y is the final-demand vector isolating one product-region combination, and GWP_AR5 is the AR5 GWP-100 characterization vector. The equation is structurally identical to USEEIO’s; what differs is the dimensionality of L and B (9,800 vs 1,016 for USEEIO at the NAICS-6 level) and the underlying interpretation: each cell of Exiobase’s L matrix represents an interregional or intersectoral flow that USEEIO’s domestic-only L cannot represent.

The 44-Country and 5-Rest-of-World Region Structure

Two structural observations. First, the 200-product × 49-region matrix produces 9,800 distinct emission factor cells for each impact category — Exiobase’s full GHG factor table is approximately 9,800 rows long, before considering the basic-vs-purchaser-price duplication. Reporters working with the dataset directly should plan for this scale. Second, the RoW regions are not interchangeable substitutes for missing countries: applying the WA factor for Singaporean procurement, for instance, is using an aggregate dominated by the Vietnamese and Thai economies — appropriate as a conservative fallback but not as a precise representation. Where a reporter has material spend in a country covered by a RoW aggregate, the cleanest approach is to apply the RoW factor and flag the limitation, with a note that more precise factors would require either supplier-specific data or a region-aware MRIO with finer resolution (GLORIA at 164 countries, EORA at 190 countries).

Exiobase Version Timeline — What Reporters Need to Know

The Exiobase model has evolved through five identifiable release generations since the consortium’s first publication in 2011. Reporters with multi-year Cat 1 trend baselines must understand which version transitions trigger factor-value drift. Table 7.1 is the standards-document log.

Two points for practitioners. First, the reference year is as important as the model version — EXIOBASE publishes a time series, and in 3.8.2 the years after 2019 (CO₂) and 2017 (other gases) are now-casts rather than data, so cite the table year as well as the release. The reference year tracks the underlying economic and environmental data; the model version tracks the analytical framework. Second, most commercial Scope 3 platforms that integrate Exiobase update annually as new reference years become available within the current model generation; reporters using a platform should confirm which Exiobase reference year their tool currently applies. Mid-cycle version updates can cause unexplained year-on-year drift that is purely a vintage effect.

Method Selection — When to Use Exiobase

The GHG Protocol Scope 3 Standard Chapter 7 names spend-based EEIO calculation as the screening-grade method for Category 1 and Category 2 purchases. Exiobase is the canonical EEIO model for non-U.S. supply chains; the method-selection flowchart below applies on a per-spend-line basis.

Two structural points. First, real-world Cat 1 inventories typically blend Exiobase with at least one other method — supplier-specific PCFs for major vendors, ICE Database or DEFRA factors for material commodity purchases with defensible physical factors, Exiobase as the long-tail coverage layer for everything else. The mix is the rule, not the exception. Second, the choice between Exiobase and USEEIO is geographic, not preferential — neither model is “better” in isolation; each is calibrated to a different economic geography. Reporters with multinational operations commonly apply both: Exiobase to EU and non-U.S. spend, USEEIO to U.S.-domestic spend. The methodology statement names the mix explicitly.

Limitations and Known Hazards — Read Before Reaching for Factors

Three hazards account for the great majority of audit findings on Exiobase-based Cat 1 disclosures. Each is named for citation and has a structural detection signature.

The Calculation Method, Step by Step

The full method for computing a Cat 1 line using Exiobase factors has five steps. Each step has a discrete audit deliverable.

Step 1 — Identify procurement spend and assign CPA / NACE product code

Pull procurement spend from the accounting system, categorised by the supplier’s primary NACE code where available. Map procurement category descriptions to the closest of Exiobase’s 200 CPA product categories. The Eurostat CPA↔NACE↔NAICS crosswalks are the canonical mapping references.

Step 2 — Assign the producing region

For each spend line, identify the region where the product is actually produced — not necessarily the supplier’s invoicing region. Where the producing region is uncertain, use the consumption-perspective factor for the invoicing region (which incorporates a region-typical import mix). This is the hazard 9.2 prevention step.

Step 3 — Select factor formulation: basic-price or purchaser-price

Match the factor denominator to the invoice basis. Procurement invoice spend is purchaser-price by accounting convention; use the purchaser-price-converted factor. Apply basic-price factors only where the procurement data has been pre-adjusted to basic-price equivalence (rare in practice). This is the hazard 9.1 prevention step.

Step 4 — Apply the emission intensity factor

Multiply spend (in EUR, in the factor’s reference currency year) by the appropriate product-region factor. The unit is kg CO₂e per euro; the result is kg CO₂e. For non-EUR procurement, apply the relevant year’s average FX rate to convert to EUR first.

Step 5 — Aggregate and classify by Scope 3 category

Sum across all spend lines. Classify each as Cat 1 (operating spend) or Cat 2 (capital goods). The factors themselves don’t differ between Cat 1 and Cat 2; what differs is the inventory line the result is reported under.

Worked Example — European Manufacturer with Mixed Procurement

One audit-record example at this page’s review date (2026-05-16) for a fictional European manufacturer with FY2024 procurement spread across German, French, Chinese, and Indian supply chains. Fixed values throughout — these are audit-record snapshots and must reconcile to stated inputs regardless of future factor releases.

Direct Comparison — Same Spend Run Through USEEIO

The instrument-equivalence question for spend-based Scope 3 is not whether USEEIO and Exiobase produce numbers — both do — but how those numbers differ structurally when applied to the same procurement spend. The block below runs the FY2024 inventory from §11 through USEEIO v1.3 (the most recent EPA-published practitioner release) with FX conversion to USD, mapping CPA codes to nearest-equivalent NAICS-6 codes per the Eurostat crosswalk. The two totals are not equivalent — and the gap is the regionalisation correction that Exiobase makes explicit.

Practitioner takeaways: for any EU-domiciled reporter and for any global reporter with material non-U.S. content in Cat 1, Exiobase is the correct first-line tool. USEEIO retains its place for U.S.-domestic supply chains; the two are complementary, not competing. The methodology statement should declare which model handles which portion of the inventory. See the USEEIO methodology for the U.S. domestic complement.

Exiobase Emission Intensity Factors — Sector × Region Reference

Table 13.1 is a cross-jurisdictional intensity reference for 23 EXIOBASE products across 8 key economies, calculated by GreenCalculus from the EXIOBASE 3.8.2 2019 product-by-product tables: the total (direct plus supply-chain) GHG multiplier per euro at basic prices, rebuilt from the per-gas accounts on AR5 GWP-100. Where a row combines several EXIOBASE products (electricity, petroleum products, trade), the values are output-weighted. For a product-region combination not shown, consult the full dataset on the consortium’s Zenodo archive (linked below the table).

Need a specific product-region combination not shown above? Download the full Exiobase v3.8.2 dataset on Zenodo — 200 products × 49 regions × multiple environmental indicators across the time series from 1995 (now-cast after the last data years). The Zenodo archive is the authoritative source. Companion documentation: exiobase.eu.

Reading the heatmap. Three patterns repay study. First, the regional spread is largest for energy-intensive sectors: electricity ranges from 0.45 in France (nuclear-dominant grid) to 5.20 in India (coal-dominant grid) — an 11.6× spread. Chemicals, basic metals, and cement show 2–3× spreads. Services show 1.5–2× spreads, the smallest range. Second, China and India consistently sit at the top of every product factor because their underlying grids and industrial supply chains are more emissions-intensive than European or Japanese equivalents — this is the structural reason that reporters with material Asian procurement see large Cat 1 totals even on modest spend. Third, France sits at the bottom of nearly every electricity-coupled factor because of its nuclear-dominant grid; this is the most extreme example in the OECD of a single national grid mix driving the entire economy’s Scope 3 emissions profile downward.

Highest-Intensity Exiobase Products — Visual

Figure 14.1 ranks the 15 highest values in Table 13.1 (2019 tables). The chart is dominated by electricity in coal-heavy economies and basic materials production in those same economies — a structural pattern that explains why supply-chain decarbonisation tracks closely with electricity-grid decarbonisation.

Exiobase vs Other MRIO Models — Comparison

Five published MRIO databases are available to reporters; each is calibrated to a different geographic footprint and resolution. Table 15.1 is the canonical cross-database reference.

The selection rule is geographic-and-purpose-driven. Exiobase is the right model when EU detail matters — its 28 EU member states at individual resolution plus the UK is unmatched in any other MRIO. GLORIA wins when country resolution outside the EU matters more than EU detail — its 164 individual countries cover emerging markets that Exiobase aggregates into RoW. EORA wins where country count matters more than sector count. GTAP is the right tool for trade-policy modelling and CGE work, not for everyday Scope 3 inventories. Most large reporters with diversified operations use Exiobase as the default and supplement with GLORIA or supplier-specific data where Exiobase’s RoW aggregates create material uncertainty.

Standards Alignment

Six standards govern the methodological status of Exiobase-derived Cat 1 disclosures.

What the Calculator Handles vs What You Decide

The Scope 3 Cat 1 Spend-Based Calculator automates the mechanical steps. This methodology page covers the upstream decisions the calculator cannot make for you.

Audit Checklist — Exiobase-Based Reasonable-Assurance Pre-Flight

Eight items, Exiobase-specific. Run before submitting any Cat 1 / Cat 2 disclosure for reasonable-assurance review under ISO 14064-1 or equivalent.

Methodology Metadata — for GHG Inventory Documentation

Copy verbatim into your GHG inventory methodology statement for ISO 14064-1 transparency compliance. Adjust the version, reference year, region assignment, and FX basis lines to match your inventory choices.

Frequently Asked Questions

Calculator

Apply Exiobase v3.8.2 factors with product-region lookup, basic-vs-purchaser price selection, FX conversion, RoW aggregate handling, hybrid supplier-specific override integration, and audit-grade output: Scope 3 Cat 1 Spend-Based Calculator. The companion USEEIO methodology covers the U.S. domestic supply-chain complement. In the meantime, the methodology above can be applied in any spreadsheet; the worked example shows the full calculation chain.