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

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  • 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.

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  • 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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    as
    1. Du, Minzhe & Wu, Fenger & Ye, Danfeng & Zhao, Yating & Liao, Liping, 2023. "Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 124(C).
    2. Caiming Wang & Jian Li, 2020. "The Evaluation and Promotion Path of Green Innovation Performance in Chinese Pollution-Intensive Industry," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    3. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Emission abatement cost in China with consideration of technological heterogeneity," Applied Energy, Elsevier, vol. 290(C).
    4. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Can environmental regulation solve pollution problems? Theoretical model and empirical research based on the skill premium," Energy Economics, Elsevier, vol. 94(C).
    5. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    6. Huijun Li & Jianhua Zhang & Edward Osei & Mark Yu, 2018. "Sustainable Development of China’s Industrial Economy: An Empirical Study of the Period 2001–2011," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    7. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    8. Meangbua, Onicha & Dhakal, Shobhakar & Kuwornu, John K.M., 2019. "Factors influencing energy requirements and CO2 emissions of households in Thailand: A panel data analysis," Energy Policy, Elsevier, vol. 129(C), pages 521-531.
    9. Wang, Yanan & Yin, Shiwen & Fang, Xiaoli & Chen, Wei, 2022. "Interaction of economic agglomeration, energy conservation and emission reduction: Evidence from three major urban agglomerations in China," Energy, Elsevier, vol. 241(C).
    10. Lin, Jinyao & Lu, Siyan & He, Xiaoyu & Wang, Fang, 2021. "Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression," Energy, Elsevier, vol. 236(C).
    11. Ge, Tao & Ding, Ziqi & Lu, Xiaoya & Yang, Keling, 2023. "Spillover effect of energy intensity targets on renewable energy consumption in China: A spatial econometric approach," Renewable Energy, Elsevier, vol. 217(C).
    12. Picazo-Tadeo, Andres J. & Reig-Martinez, Ernest & Hernandez-Sancho, Francesc, 2005. "Directional distance functions and environmental regulation," Resource and Energy Economics, Elsevier, vol. 27(2), pages 131-142, June.
    13. Li, Rongrong & Han, Xinyu & Wang, Qiang, 2023. "Do technical differences lead to a widening gap in China's regional carbon emissions efficiency? Evidence from a combination of LMDI and PDA approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    14. Wang, Huiping & Liu, Peiling, 2023. "Spatial correlation network of renewable energy consumption and its influencing factors: Evidence from 31 Chinese provinces," Renewable Energy, Elsevier, vol. 217(C).
    15. Huiping Wang & Qi Ge, 2023. "Spatial association network of economic resilience and its influencing factors: evidence from 31 Chinese provinces," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    16. Yu, Yantuan & Zhang, Ning, 2021. "Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 96(C).
    17. Song, Yang & Liu, Dayu & Wang, Qiaoru, 2021. "Identifying characteristic changes in club convergence of China's urban pollution emission: A spatial-temporal feature analysis," Energy Economics, Elsevier, vol. 98(C).
    18. Wu, Rongxin & Tan, Zhizhou & Lin, Boqiang, 2023. "Does carbon emission trading scheme really improve the CO2 emission efficiency? Evidence from China's iron and steel industry," Energy, Elsevier, vol. 277(C).
    19. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    20. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Yawei Du & Hongjiang Liu & Tiantian Du & Junyue Liu & Ling Yin & Yang Yang, 2024. "Dynamic Simulation of Carbon Emission Peak in City-Scale Building Sector: A Life-Cycle Approach Based on LEAP-SD Model," Energies, MDPI, vol. 17(21), pages 1-24, October.
    2. Du, Ruijin & Zhang, Nidan & Zhang, Mengxi & Kong, Ziyang & Jia, Qiang & Dong, Gaogao & Tian, Lixin & Ahsan, Muhammad, 2024. "Identifying the optimal node group of carbon emission efficiency correlation network in China based on pinning control theory," Applied Energy, Elsevier, vol. 368(C).

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