IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v129y2024ics014098832300720x.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S014098832300720X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2023.107222?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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).
    4. 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).
    5. 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.
    6. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Emission abatement cost in China with consideration of technological heterogeneity," Applied Energy, Elsevier, vol. 290(C).
    7. 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).
    8. 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).
    9. 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.
    10. 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).
    11. Yu, Yantuan & Zhang, Ning, 2021. "Low-carbon city pilot and carbon emission efficiency: Quasi-experimental evidence from China," Energy Economics, Elsevier, vol. 96(C).
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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).
    17. 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).
    18. Kao, Chiang & Liu, Shiang-Tai, 2020. "A slacks-based measure model for calculating cross efficiency in data envelopment analysis," Omega, Elsevier, vol. 95(C).
    19. 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.
    20. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tianqun Xu & Ping Gao & Qian Yu & Debin Fang, 2017. "An Improved Eco-Efficiency Analysis Framework Based on Slacks-Based Measure Method," Sustainability, MDPI, vol. 9(6), pages 1-21, June.
    2. Gómez-Calvet, Roberto & Conesa, David & Gómez-Calvet, Ana Rosa & Tortosa-Ausina, Emili, 2014. "Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures?," Applied Energy, Elsevier, vol. 132(C), pages 137-154.
    3. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    4. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    5. Zhang, Bin & Lu, Danting & He, Yan & Chiu, Yung-ho, 2018. "The efficiencies of resource-saving and environment: A case study based on Chinese cities," Energy, Elsevier, vol. 150(C), pages 493-507.
    6. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Hsu, Shih-Hsun & Managi, Shunsuke, 2015. "The enhanced Russell-based directional distance measure with undesirable outputs: Numerical example considering CO2 emissions," Omega, Elsevier, vol. 53(C), pages 30-40.
    7. Gang Tian & Jian Shi & Licheng Sun & Xingle Long & Benhai Guo, 2017. "Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 585-607, November.
    8. Xiaoqing Wang & Qiuming Wu & Salman Majeed & Donghao Sun, 2018. "Fujian’s Industrial Eco-Efficiency: Evaluation Based on SBM and the Empirical Analysis of lnfluencing Factors," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    9. 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.
    10. Long, Xingle & Sun, Mei & Cheng, Faxin & Zhang, Jijian, 2017. "Convergence analysis of eco-efficiency of China’s cement manufacturers through unit root test of panel data," Energy, Elsevier, vol. 134(C), pages 709-717.
    11. Arabi, Behrouz & Munisamy, Susila & Emrouznejad, Ali, 2015. "A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs," Omega, Elsevier, vol. 51(C), pages 29-37.
    12. D’Inverno, Giovanna & Carosi, Laura & Romano, Giulia & Guerrini, Andrea, 2018. "Water pollution in wastewater treatment plants: An efficiency analysis with undesirable output," European Journal of Operational Research, Elsevier, vol. 269(1), pages 24-34.
    13. Zhuang Miao & Tomas Baležentis & Zhihua Tian & Shuai Shao & Yong Geng & Rui Wu, 2019. "Environmental Performance and Regulation Effect of China’s Atmospheric Pollutant Emissions: Evidence from “Three Regions and Ten Urban Agglomerations”," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(1), pages 211-242, September.
    14. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    15. Ane Elixabete Ripoll-Zarraga & Sebastián Lozano, 2020. "A centralised DEA approach to resource reallocation in Spanish airports," Annals of Operations Research, Springer, vol. 288(2), pages 701-732, May.
    16. Juan Aparicio & Magdalena Kapelko & Bernhard Mahlberg & Jose L. Sainz-Pardo, 2017. "Measuring input-specific productivity change based on the principle of least action," Journal of Productivity Analysis, Springer, vol. 47(1), pages 17-31, February.
    17. Chunhua Xin & Xiufeng Lai, 2022. "Does the Environmental Information Disclosure Promote the High-Quality Development of China’s Resource-Based Cities?," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    18. R. Robert Russell & William Schworm, 2018. "Technological inefficiency indexes: a binary taxonomy and a generic theorem," Journal of Productivity Analysis, Springer, vol. 49(1), pages 17-23, February.
    19. Bo Li & Jing Liu & Qian Liu & Muhammad Mohiuddin, 2022. "The Effects of Broadband Infrastructure on Carbon Emission Efficiency of Resource-Based Cities in China: A Quasi-Natural Experiment from the “Broadband China” Pilot Policy," IJERPH, MDPI, vol. 19(11), pages 1-27, May.
    20. Chuanxin Xia & Yu Zhao & Qingxia Zhao & Shuo Wang & Ning Zhang, 2022. "Exact Eco-Efficiency Measurement in the Yellow River Basin: A New Non-Parametric Approach," Sustainability, MDPI, vol. 14(20), pages 1-18, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:129:y:2024:i:c:s014098832300720x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.