Scope 3 emissions—indirect greenhouse gas emissions across a company’s value chain—often constitute the largest share of corporate carbon footprints yet remain inadequately measured due to methodological challenges. Unlike the GHG Protocol’s general guidelines, which leave multi-tier allocation, circular dependencies, and complex ownership to individual practitioners, this paper develops an integrated, algorithmically implementable framework for multi-tier Scope 3 emission calculation. Our three-component methodology comprises: a tier-specific economic allocation model using revenue–transaction ratios, a path-based circular dependency resolution algorithm, and an equity-weighted attribution mechanism for complex ownership structures. Unlike sector-level approaches such as Economic Input–Output Analysis (EIOA) and Multi-Regional Input–Output (MRIO) models, our framework operates at the firm-transaction level, enabling company-specific accountability. We empirically validate this framework using data from Chinese companies across nine industries, tracing carbon flows through supply chains up to five tiers deep. Tier 1 suppliers account for a median of 99.6% (bootstrap 95% CI [98.5%, 99.8%]; mean: 89.4%) of total calculated emissions (mean 37,305.1 tCO2), confirmed by Friedman test (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\chi ^2 = 285.40$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msup> <mi>χ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>285.40</mn> </mrow> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$p = 1.52 \times 10^{-60}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>p</mi> <mo>=</mo> <mn>1.52</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>60</mn> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation>) and pairwise Wilcoxon signed-rank tests (all <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$p_{\textrm{BH}} < 0.001$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>p</mi> <mtext>BH</mtext> </msub> <mo><</mo> <mn>0.001</mn> </mrow> </math></EquationSource> </InlineEquation>). Comparison with company-reported Scope 3 reveals industry-specific discrepancies, with calculated-to-reported ratios ranging from near-zero (Real Estate) to 3.157 (Industrials), indicating significant heterogeneity and potential under-reporting. We identify dominant intra-industry carbon flows within Industrials (2,099,959.4 tCO2) and significant cross-sector transfers from Materials to Financials (91,183.8 tCO2). Network analysis reveals that 32.5% of supply chain nodes participate in circular dependencies, with the largest strongly connected component containing 947 nodes; our resolution algorithm eliminates double counting while preserving mathematical consistency. These findings support focusing carbon reduction efforts on Tier 1 and Tier 2 suppliers and carry direct implications for China’s dual carbon goals through mandatory supply chain carbon disclosure frameworks and industry-specific reduction targets.

A multi-tier methodology for Scope 3 emissions accounting in complex supply chains: mathematical framework and empirical insights from China

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2026年5月29日

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Rui Luo,
Qingge Geng,
Chuan Shi,
Chuangyin Dang

摘要

Scope 3 emissions—indirect greenhouse gas emissions across a company’s value chain—often constitute the largest share of corporate carbon footprints yet remain inadequately measured due to methodological challenges. Unlike the GHG Protocol’s general guidelines, which leave multi-tier allocation, circular dependencies, and complex ownership to individual practitioners, this paper develops an integrated, algorithmically implementable framework for multi-tier Scope 3 emission calculation. Our three-component methodology comprises: a tier-specific economic allocation model using revenue–transaction ratios, a path-based circular dependency resolution algorithm, and an equity-weighted attribution mechanism for complex ownership structures. Unlike sector-level approaches such as Economic Input–Output Analysis (EIOA) and Multi-Regional Input–Output (MRIO) models, our framework operates at the firm-transaction level, enabling company-specific accountability. We empirically validate this framework using data from Chinese companies across nine industries, tracing carbon flows through supply chains up to five tiers deep. Tier 1 suppliers account for a median of 99.6% (bootstrap 95% CI [98.5%, 99.8%]; mean: 89.4%) of total calculated emissions (mean 37,305.1 tCO₂), confirmed by Friedman test ( $\chi ^2 = 285.40$ $χ^{2} = 285.40$ , $p = 1.52 \times 10^{-60}$ $p = 1.52 \times 10^{- 60}$ ) and pairwise Wilcoxon signed-rank tests (all $p_{\textrm{BH}} < 0.001$ $p_{BH} < 0.001$ ). Comparison with company-reported Scope 3 reveals industry-specific discrepancies, with calculated-to-reported ratios ranging from near-zero (Real Estate) to 3.157 (Industrials), indicating significant heterogeneity and potential under-reporting. We identify dominant intra-industry carbon flows within Industrials (2,099,959.4 tCO₂) and significant cross-sector transfers from Materials to Financials (91,183.8 tCO₂). Network analysis reveals that 32.5% of supply chain nodes participate in circular dependencies, with the largest strongly connected component containing 947 nodes; our resolution algorithm eliminates double counting while preserving mathematical consistency. These findings support focusing carbon reduction efforts on Tier 1 and Tier 2 suppliers and carry direct implications for China’s dual carbon goals through mandatory supply chain carbon disclosure frameworks and industry-specific reduction targets.