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Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces

Author

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  • Hongtao Jiang

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Jian Yin

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China
    School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Xiangfang District, Harbin 150050, China)

  • Yuanhong Qiu

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Bin Zhang

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Yi Ding

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

  • Ruici Xia

    (Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
    College of Big Data Application and Economics, Guizhou University of Finance and Economics, Guiyang 550025, China)

Abstract

In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world.

Suggested Citation

  • Hongtao Jiang & Jian Yin & Yuanhong Qiu & Bin Zhang & Yi Ding & Ruici Xia, 2022. "Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces," Land, MDPI, vol. 11(8), pages 1-22, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:8:p:1129-:d:869666
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    2. Hanxin Wang & Weiqian Liu & Yi Liang, 2023. "Measurement of CO 2 Emissions Efficiency and Analysis of Influencing Factors of the Logistics Industry in Nine Coastal Provinces of China," Sustainability, MDPI, vol. 15(19), pages 1-21, October.
    3. Weijia Cui & Xueqin Lin & Dai Wang & Ying Mi, 2022. "Urban Industrial Carbon Efficiency Measurement and Influencing Factors Analysis in China," Land, MDPI, vol. 12(1), pages 1-21, December.
    4. Yuting Jian & Yongchun Yang & Jing Xu, 2023. "The Impact and Mechanism of the Increased Integration of Urban Agglomerations on the Eco-Efficiency of Cities in the Region—Taking the Chengdu–Chongqing Urban Agglomeration in China as an Example," Land, MDPI, vol. 12(3), pages 1-21, March.
    5. Jiang Zhu & Xiang Li & Huiming Huang & Xiangdong Yin & Jiangchun Yao & Tao Liu & Jiexuan Wu & Zhangcheng Chen, 2023. "Spatiotemporal Evolution of Carbon Emissions According to Major Function-Oriented Zones: A Case Study of Guangdong Province, China," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    6. Chuansong Zhao & Ran Geng & Jianxu Liu & Liuying Peng & Woraphon Yamaka, 2023. "Spatiotemporal Evolution and Driving Factors of Land Development: Evidence from Shandong Province, China," Sustainability, MDPI, vol. 15(20), pages 1-21, October.

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