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Maximizing the effectiveness of carbon emissions abatement in China across carbon communities

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  • Huang, Li
  • Kelly, Scott
  • Shi, Xunpeng
  • Lv, Kangjuan
  • Lu, Xuan
  • Giurco, Damien

Abstract

The invisible and complex transfer of embodied carbon emissions makes the traditional production or consumption approach insufficient to inform emissions abatement actions because carbon communities have emerged during the transmission procedure of embodied carbon emissions. The carbon community—a group of sectors with more intensive embodied carbon emissions trades within the group than outside—provides the missing critical information about carbon abatement beyond the commonly used production and consumption approaches. This research aims to detect the carbon communities and examine the effect of community structure on sectors' direct carbon emissions. Unlike the industrial agglomeration in traditional economics and management studies, where the border is predefined in a geographical or administrative region, the hybrid input-output analysis and network analysis method detects the carbon communities data-driven, focusing on the embodied carbon emissions trades. Moreover, the hierarchical linear model examines the effect of community structure on sectors' direct carbon emissions to inform climate change policy-making and planning. The findings suggest around 19 carbon communities existing in China, which can advise local governments on their external cooperation strategies for a synergy. In addition, the regression results indicate that the increasing size and density of carbon communities can help mitigate sectors' direct carbon emissions.

Suggested Citation

  • Huang, Li & Kelly, Scott & Shi, Xunpeng & Lv, Kangjuan & Lu, Xuan & Giurco, Damien, 2022. "Maximizing the effectiveness of carbon emissions abatement in China across carbon communities," Energy Economics, Elsevier, vol. 106(C).
  • Handle: RePEc:eee:eneeco:v:106:y:2022:i:c:s0140988321006368
    DOI: 10.1016/j.eneco.2021.105801
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    Cited by:

    1. Ding, Song & Zhang, Huahan, 2023. "Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model," Energy Economics, Elsevier, vol. 121(C).

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