Assessing different European Carbon Border Adjustment Mechanism implementations and their impact on trade partners

The EU aims to reduce net greenhouse gas emissions by at least 55% by 2030 relative to 1990, largely via reforms to the EU Emissions Trading System (ETS). While carbon pricing incentivizes low-carbon technologies, it can induce carbon leakage by shifting production to countries with laxer climate policies. The current ETS mitigates this via free allowances to emission-intensive and trade-exposed sectors, but this conflicts with long-term net-zero targets. Under the Fit for 55 package, the EU plans to phase out free allowances and introduce a CBAM that applies the EU carbon price to emissions embodied in imports, with export rebates under discussion. Beyond EU producers and consumers, an EU CBAM will transmit EU carbon pricing along global supply chains, potentially affecting trade partners and provoking retaliatory measures. Existing studies focus on major EU partners and lack high spatial resolution, especially for low- and middle-income countries. This study develops an analytical framework using throughflow-based accounting on detailed global input-output data to assess the coverage of alternative CBAM designs, quantify upstream and downstream pressures on trade partners, and explore inclusive revenue recycling schemes.

Prior work suggests CBAMs can reduce leakage and motivate abatement abroad but may trigger trade retaliation and welfare impacts for partners. Most analyses emphasize major trading partners with limited granularity, leaving the exposure of middle- and low-income countries underexplored. Sub-regional research indicates substantial heterogeneity in distributional effects depending on local conditions. The paper situates itself within literature on carbon pricing, border adjustments, and trade-related emission transfers, highlighting gaps in country-level exposure for lower-income regions and the need for transparent, high-resolution assessments of CBAM coverage and impacts.

The study employs a throughflow-based accounting (TBA) framework on the EORA 26 Multi-Regional Input-Output Table (MRIOT) for 2016 (26 sectors, 189 regions). CO2 emissions data are combined from PRIMAP-hist (broad country coverage with limited sector detail) and PRIMAP-crf (detailed IPCC process categories, Annex I countries). A tailored procedure extrapolates PRIMAP-crf detail to all UNFCCC countries using PRIMAP-hist aggregates and EORA production data, then maps emissions to EORA sectors. Emissions from international transportation, LULUCF, and direct final-use emissions are excluded. The analysis models multiple CBAM implementation options across two dimensions: horizontal coverage (sectoral/product coverage aligned with categories in the EU CBAM and ETS, with conservative, ambitious, and comprehensive variants) and vertical coverage (depth in supply chains: Scope 1 only; Scopes 1 and 2 plus downstream; comprehensive direct and indirect imports; and options with/without export rebates). Scope 1 emissions are estimated by multiplying import values by sector- and country-specific emission intensities. Scope 2 and downstream emissions are computed via MRIOT technical coefficients representing direct inputs to EU imports. Comprehensive coverage uses the TBA method to quantify upstream emissions along all supply chains involving the EU by comparing actual world emissions with a hypothetical economy absent EU-related throughflows, decomposed into intra-EU, imports, exports, and traversing flows. Traversing emissions are excluded under export rebate scenarios. Revenue recycling schemes are evaluated using 2016 World Bank population and GDP data and empirical estimates of historical climate damages (impact of anthropogenic warming on economic output, 1991–2010). Recycling options include: no recycling; proportional to emissions covered; proportional to population-weighted historical climate damages (non-negative only); and targeted by income differences relative to the EU average (per-capita weighting). An illustrative carbon price of 100 €/tCO2 is used to compute revenues and losses. No dynamic trade or technology adjustments are modeled; results reflect a static assessment of coverage and pressures.

• Coverage under current and extended CBAM scopes (2016 baseline; no trade adjustments): • Initial CBAM scope (direct Scope 1 emissions for iron & steel, aluminum, cement, fertilizers, electricity, hydrogen): 83 MtCO2. • Extending to all EU ETS sectors (Scope 1, ambitious coverage): 179 MtCO2. • Including Scope 2 electricity and downstream products: 328 MtCO2 (conservative coverage) or 490 MtCO2 (ambitious coverage). • Extending Scope 1 beyond current ETS sectors: 504 MtCO2 (comprehensive coverage, excluding downstream) and 932 MtCO2 (including Scope 2 and downstream). - Comprehensive CBAM (CCBAM) impacts: • A CCBAM covering all sectors and full upstream supply chains (direct and indirect imports, without export rebates) would price 1,558 MtCO2 embodied in EU imports. • Even with conservative product coverage, moving from Scope 1-only to comprehensive upstream coverage increases priced emissions roughly ninefold, from 83 to 716 MtCO2. • Indirect electricity-use emissions surge from 14 to 511 MtCO2 under comprehensive depth coverage; direct electricity imports account for only ~3% of electricity-related emissions embodied in EU imports. - Combined domestic carbon pricing and CCBAM: • A comprehensive domestic carbon price plus CCBAM would cover 3,871 MtCO2 total industrial emissions linked to EU throughflows (~60% domestic, ~40% via imports). • At 100 €/tCO2, annual revenue is estimated at €293 billion with export rebates and €387 billion without, equivalent to ~€660–€870 per EU citizen per year. - Distributional exposure (relative upstream pressure): • Many middle- and low-income countries—particularly in North and Sub-Saharan Africa with high export dependence on the EU and low diversification—face disproportionately high shares of domestic emissions covered by an EU CCBAM. - Revenue recycling scenarios (per capita net gains/losses at 100 €/tCO2): • No recycling: fossil fuel exporters (e.g., Russia, Algeria, Saudi Arabia) incur large per-capita losses. • Recycling proportional to emissions covered favors fossil fuel exporters, potentially easing implementation but risking neutralizing price incentives unless targeted to decarbonization. • Recycling by historical climate damages or by income shortfalls relative to EU average benefits Africa, Southeast Asia, and South America, aligning with loss-and-damage objectives but leaving major EU partners (e.g., US, China, fuel exporters) as net losers. - Robustness to conservative implementations: • Even with narrower sectoral or vertical coverage and with export rebates, low- and middle-income, EU-dependent exporters remain relatively more exposed, though absolute pressures are smaller.

The study addresses how different EU CBAM designs vary in coverage and how a comprehensive design would transmit EU carbon pricing upstream to trade partners and downstream along supply chains. Results show that the currently envisioned CBAM covers a modest share of emissions relative to total embodied imports, limiting competitiveness leveling. A comprehensive approach would substantially increase coverage and better align with EU abatement targets while generating significant fiscal resources. However, it concentrates relative burdens on middle- and low-income countries closely tied to EU markets, raising equity and cooperation concerns. Revenue recycling can mitigate adverse impacts and improve international acceptability, but different principles (proportional to covered emissions vs. damages vs. income) imply trade-offs between reducing retaliation risks from major partners and supporting vulnerable countries. Policy significance lies in designing CBAM scope and recycling mechanisms to balance competitiveness, environmental effectiveness, and global equity, acknowledging administrative feasibility and governance constraints.

The paper develops a high-resolution, throughflow-based framework to quantify emission coverage under alternative EU CBAM implementations and to assess international distributional effects. It finds that: (1) the current CBAM proposal covers a relatively small fraction of emissions embodied in EU imports; (2) a comprehensive CBAM would markedly expand coverage and strengthen alignment with EU climate goals but impose disproportionate upstream pressure on low- and middle-income, EU-dependent exporters; and (3) thoughtfully designed international revenue recycling could enhance inclusivity and global cooperation. Future research should integrate dynamic responses (technology shifts, trade reallocation) while retaining spatial detail, and explore practicable governance for combined internal and international recycling schemes, product-level emissions tracking, and administrative cost-benefit analyses of broader CBAM scopes.

• Static analysis: No modeling of dynamic adjustments in production, trade, or consumption (e.g., technology adoption, energy mix shifts), likely overestimating coverage and revenues relative to an adaptive world. - Data quality and resolution: MRIO and emissions data for low- and middle-income countries can be limited in accuracy and sectoral granularity; 2016 base year may not reflect current/future structures. - Process-to-product mapping: Emissions are allocated by process categories while CBAM applies at the product level, introducing mismatch and uncertainty. - Exclusions: International transport, LULUCF, and direct final-use emissions excluded; hydrogen trade not represented for 2016, though future relevance may rise. - Administrative feasibility: Comprehensive product- and scope-level CBAM would require precise, up-to-date product-level emissions data and entail high monitoring and trade friction costs; practical implementations would likely cover fewer emissions and yield lower revenues. - Potential bias in exposure: Faster decarbonization by high-income partners relative to lower-income countries could maintain or increase relative exposure of the latter, but exact future distributions are uncertain.