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Carbon footprint analysis and its underlying drivers in a mega-urban region: Evidence from the Beijing–Tianjin–Hebei urban agglomeration

  • Ke Yang
  • , Rong-Gang Cong
  • , Qi Han
  • , Yifu Ou*
  • *Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

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.
Original languageEnglish
Article number148682
JournalJournal of Cleaner Production
Volume568
Early online date6 Jun 2026
DOIs
Publication statusPublished - 15 Jun 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities
  2. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Carbon footprint
  • Carbon emission
  • EEIO model
  • SDA
  • Urban agglomeration
  • Decarbonization pathway

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