TY - JOUR
T1 - Carbon footprint analysis and its underlying drivers in a mega-urban region
T2 - Evidence from the Beijing–Tianjin–Hebei urban agglomeration
AU - Yang, Ke
AU - Cong, Rong Gang
AU - Han, Qi
AU - Ou, Yifu
N1 - Publisher Copyright:
© 2026 The Authors.
PY - 2026/6/15
Y1 - 2026/6/15
N2 - Achieving global climate targets under a shrinking carbon budget requires understanding not only where urban emissions occur, but how they are transmitted across intercity supply chains. Existing studies often quantify urban carbon footprints or decompose emission drivers separately, yet lack an integrated diagnostic framework capable of comparing structural emission dependence across cities within metropolitan regions. This study analyzes embodied carbon flows among 13 cities in China's Beijing–Tianjin–Hebei (BTH) urban agglomeration for 2012 and 2017 using environmentally extended input–output (EEIO) accounting combined with structural decomposition analysis (SDA). To translate complex supply-chain information into policy-interpretable diagnostics, we introduce two indicators: Coverage Rate (CR), measuring the breadth of sectoral amplification, and Resource-Chain Coverage (RCC), measuring dependence on upstream resource-intensive production chains. Results reveal a stable upstream–downstream production structure in which emission-intensive material production is spatially separated from final demand. Construction and service activities act primarily as demand-inducing sectors, generating large upstream emissions despite low direct emission intensity. Structural decomposition shows that efficiency improvements reduce emission intensity, but expanding consumption and investment scale offset these gains through supply-chain amplification along a materials–energy–construction corridor. Consequently, emission growth is driven less by local technological inefficiency than by intercity economic specialization and demand-induced production. The proposed CR–RCC framework links carbon accounting to coordinated mitigation planning by identifying structurally dependent cities and sectors rather than isolated emitters. Because it relies on benchmark input–output data rather than long time series, the approach is transferable to other urban agglomerations where supply-chain-driven emissions complicate city-level climate governance.
AB - Achieving global climate targets under a shrinking carbon budget requires understanding not only where urban emissions occur, but how they are transmitted across intercity supply chains. Existing studies often quantify urban carbon footprints or decompose emission drivers separately, yet lack an integrated diagnostic framework capable of comparing structural emission dependence across cities within metropolitan regions. This study analyzes embodied carbon flows among 13 cities in China's Beijing–Tianjin–Hebei (BTH) urban agglomeration for 2012 and 2017 using environmentally extended input–output (EEIO) accounting combined with structural decomposition analysis (SDA). To translate complex supply-chain information into policy-interpretable diagnostics, we introduce two indicators: Coverage Rate (CR), measuring the breadth of sectoral amplification, and Resource-Chain Coverage (RCC), measuring dependence on upstream resource-intensive production chains. Results reveal a stable upstream–downstream production structure in which emission-intensive material production is spatially separated from final demand. Construction and service activities act primarily as demand-inducing sectors, generating large upstream emissions despite low direct emission intensity. Structural decomposition shows that efficiency improvements reduce emission intensity, but expanding consumption and investment scale offset these gains through supply-chain amplification along a materials–energy–construction corridor. Consequently, emission growth is driven less by local technological inefficiency than by intercity economic specialization and demand-induced production. The proposed CR–RCC framework links carbon accounting to coordinated mitigation planning by identifying structurally dependent cities and sectors rather than isolated emitters. Because it relies on benchmark input–output data rather than long time series, the approach is transferable to other urban agglomerations where supply-chain-driven emissions complicate city-level climate governance.
KW - Carbon emission
KW - Carbon footprint
KW - Decarbonization pathway
KW - EEIO model
KW - SDA
KW - Urban agglomeration
UR - https://www.scopus.com/pages/publications/105041119913
U2 - 10.1016/j.jclepro.2026.148682
DO - 10.1016/j.jclepro.2026.148682
M3 - Article
AN - SCOPUS:105041119913
SN - 0959-6526
VL - 568
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 148682
ER -