Carbon footprints and embodied CO2 transfers among provinces in China

Zhaohua Wang*, Yuantao Yang, Bo Wang

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    107 Citations (Scopus)

    Abstract

    China is the top CO2 emitter with enormous regional variation in economic development, resource endowment, and consumption patterns, which leads to great variations in regional CO2 emissions and embodied CO2 transfer among provinces. Analyzing the mechanisms responsible for carbon footprint (CF) and embodied CO2 transfer among provinces via trade could help policy makers allocate emission responsibilities and reduction targets. Based on a multi-regional input–output model, this paper investigates the CFs and embodied CO2 transfer in/among 30 provinces of China in 2007 and 2010, and analyzes emissions from different perspectives. Results indicate that China's CF grew from 5230 Mt in 2007 to 6922 Mt in 2010, with an annual growth rate of 9.8%. The provinces with the large CFs and per capita CF were mainly those locate in eastern, developed regions along the coast and several highly populated ones. CO2 emissions due to capital formation contributes the most to the CFs and the increment in this period, and this was followed by urban consumption. Significant embodied CO2 transfer were observed to drift from the developing and energy-abundant provinces like Hebei and Inner Mongolia, to the developed coastal provinces. Total embodied CO2 transfer account for 43.9% of the national CF in 2007, and decreased to 41.0% in 2010. In order to control CO2 emissions and make reduction allocations for provinces, interprovincial embodied CO2 transfer and practical capacities need to be taken into account.

    Original languageEnglish
    Pages (from-to)1068-1078
    Number of pages11
    JournalRenewable and Sustainable Energy Reviews
    Volume82
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Carbon footprint
    • Embodied CO transfer
    • Multi-regional input–output
    • Provincial level
    • Regional variation

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