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Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity

Author

Listed:
  • Xing, Peixue
  • Wang, Yanan
  • Ye, Tao
  • Sun, Ying
  • Li, Qiao
  • Li, Xiaoyan
  • Li, Meng
  • Chen, Wei

Abstract

The rational categorization and assessment of carbon emission efficiency (CEE) and its drivers are crucial for coping with the global climate crisis. To address the bias of univariate modeling and challenge of ignoring the heterogeneity of drivers across cities, this study explores differences between carbon emission drivers across different types of cities and regions to reveal the spatial distribution characteristics of urban CEE and heterogeneity of emission reduction potential. We use a non-radial, non-directional relaxation measure-based directional distance function (SBM-DDF) model to assess the CEE of 284 cities over the period from 2006 to 2020. Machine-learning algorithms are applied to identify city characteristics to determine the effects of city- development types and their characteristic drivers. The results of the driver analysis show that energy consumption, gross regional product, spatial area, and population size are the key factors influencing in the heterogeneity of cities' CEE, with an importance ranking of 0.578, 0.507, 0.432, and 0.418, respectively. The results of for the heterogeneity of the cities' heterogeneity further confirm that energy consumption has the greatest impact on energy-dependent cities (EDCs), economic-development cities (ECDCs), and low-carbon potential cities (LPCs), whereas among the Low-carbon growth cities (LCGs), science, technology, and innovation, urban greening, and electricity consumption play an important roles in promoting greening and low- carbon development, which can help to determine the low- carbon development model for each type of city. Finally, energy consumption affects cities in the central region more than in the eastern and western regions. Based on the results of estimating the heterogeneity of urban carbon- emission rates, we propose customized emission- reduction development pathways to guide urban low-carbon development and formulate carbon- reduction policies.

Suggested Citation

  • Xing, Peixue & Wang, Yanan & Ye, Tao & Sun, Ying & Li, Qiao & Li, Xiaoyan & Li, Meng & Chen, Wei, 2024. "Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity," Energy Economics, Elsevier, vol. 129(C).
    • Handle: RePEc:eee:eneeco:v:129:y:2024:i:c:s014098832300720x
    DOI: 10.1016/j.eneco.2023.107222
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