Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces
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...
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MDPI AG
2022-07-01
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| Online Access: | https://www.mdpi.com/2073-445X/11/8/1129 |
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| author | Hongtao Jiang Jian Yin Yuanhong Qiu Bin Zhang Yi Ding Ruici Xia |
| author_facet | Hongtao Jiang Jian Yin Yuanhong Qiu Bin Zhang Yi Ding Ruici Xia |
| author_sort | Hongtao Jiang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:06:40Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T13:06:40Z |
| publishDate | 2022-07-01 |
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| series | Land |
| spelling | doaj.art-67916679e1234f45910fad0faa5abbb12023-11-30T21:46:51ZengMDPI AGLand2073-445X2022-07-01118112910.3390/land11081129Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving ForcesHongtao Jiang0Jian Yin1Yuanhong Qiu2Bin Zhang3Yi Ding4Ruici Xia5Western Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaWestern Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaWestern Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaWestern Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaWestern Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaWestern Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, ChinaIn 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.https://www.mdpi.com/2073-445X/11/8/1129industrial carbon emission efficiencysuper-efficient SBM modelexploratory spatiotemporal data analysisspace–time transitionthe geographical detectorsgeographically weighted regression model |
| spellingShingle | Hongtao Jiang Jian Yin Yuanhong Qiu Bin Zhang Yi Ding Ruici Xia Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces Land industrial carbon emission efficiency super-efficient SBM model exploratory spatiotemporal data analysis space–time transition the geographical detectors geographically weighted regression model |
| title | Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces |
| title_full | Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces |
| title_fullStr | Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces |
| title_full_unstemmed | Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces |
| title_short | Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces |
| title_sort | industrial carbon emission efficiency of cities in the pearl river basin spatiotemporal dynamics and driving forces |
| topic | industrial carbon emission efficiency super-efficient SBM model exploratory spatiotemporal data analysis space–time transition the geographical detectors geographically weighted regression model |
| url | https://www.mdpi.com/2073-445X/11/8/1129 |
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