Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China
Establishing a new environmental governance model driven by the dual wheels of pollution control (PC) system and carbon reduction (CR) system is an efficient approach for addressing multiple ecological challenges. However, given the broad connotations of pollution control and carbon reduction (PCCR)...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25000329 |
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| author | Qinggang Meng Xiaolan Chen Hui Wang Wanfang Shen Peixin Duan Xinyue Liu |
| author_facet | Qinggang Meng Xiaolan Chen Hui Wang Wanfang Shen Peixin Duan Xinyue Liu |
| author_sort | Qinggang Meng |
| collection | DOAJ |
| description | Establishing a new environmental governance model driven by the dual wheels of pollution control (PC) system and carbon reduction (CR) system is an efficient approach for addressing multiple ecological challenges. However, given the broad connotations of pollution control and carbon reduction (PCCR), studies that comprehensively quantify their synergistic effects are scarce. This paper develops a coupling coordination indicator system for PCCR grounded in the concepts of “source emission reduction” and “end-of-pipe treatment”. Subsequently, a modified coupling coordination degree (CCD) model is used to calculate the CCD of PCCR in China from 2011 to 2021. The distribution patterns, transition probabilities, and driving mechanisms of the CCD of PCCR are explored using kernel density estimation, Markov chains, and geographically and temporally weighted regression (GTWR). The findings indicate that a preliminary model for coordinated development in PCCR has been established. However, regional disparities in PCCR, a persistent and dynamically evolving feature, manifested a spatial pattern of “higher in the east, lower in the west”. The kernel density distribution curves of the CCD of PCCR in the four major regions showed varying distances of rightward shifts. The absolute differences in the CCD of PCCR in the eastern, western, and northeastern regions narrowed, whereas those in the central region widened. Additionally, a pronounced trend of multipolar differentiation in the CCD of PCCR was evident in the eastern region. Nationally, and particularly in the central region, a pattern of bipolar differentiation gradually formed, while in the western and northeastern regions, the distribution curves transitioned from bimodal or multimodal to unimodal. The development trends of the CCD of PCCR across Chinese provinces were relatively stable, exhibiting a “rich-get-richer” club convergence phenomenon. Overall, the drivers displayed zonal or patch-like spatial differentiation characteristics. Green innovation, digital economy, energy intensity, and human capital drivers showed a clear east–west zonal distribution. This paper provides methodological support for quantitatively evaluating the synergistic effects of PCCR, and its findings offer a reference for regions to develop differentiated PCCR synergy governance strategies. |
| format | Article |
| id | doaj-art-1702f6587a964c62a4a096e23aef7165 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-1702f6587a964c62a4a096e23aef71652025-01-31T05:10:52ZengElsevierEcological Indicators1470-160X2025-01-01170113103Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in ChinaQinggang Meng0Xiaolan Chen1Hui Wang2Wanfang Shen3Peixin Duan4Xinyue Liu5School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Finance, Shandong Technology and Business University, Yantai 264005, China; Corresponding author.Shandong Key Laboratory of Blockchain Finance, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Public Administration and Policy, Shandong University of Finance and Economics, Jinan 250014, ChinaChinese Academy of Finance and Development, Central University of Finance and Economics, Beijing 100081, ChinaEstablishing a new environmental governance model driven by the dual wheels of pollution control (PC) system and carbon reduction (CR) system is an efficient approach for addressing multiple ecological challenges. However, given the broad connotations of pollution control and carbon reduction (PCCR), studies that comprehensively quantify their synergistic effects are scarce. This paper develops a coupling coordination indicator system for PCCR grounded in the concepts of “source emission reduction” and “end-of-pipe treatment”. Subsequently, a modified coupling coordination degree (CCD) model is used to calculate the CCD of PCCR in China from 2011 to 2021. The distribution patterns, transition probabilities, and driving mechanisms of the CCD of PCCR are explored using kernel density estimation, Markov chains, and geographically and temporally weighted regression (GTWR). The findings indicate that a preliminary model for coordinated development in PCCR has been established. However, regional disparities in PCCR, a persistent and dynamically evolving feature, manifested a spatial pattern of “higher in the east, lower in the west”. The kernel density distribution curves of the CCD of PCCR in the four major regions showed varying distances of rightward shifts. The absolute differences in the CCD of PCCR in the eastern, western, and northeastern regions narrowed, whereas those in the central region widened. Additionally, a pronounced trend of multipolar differentiation in the CCD of PCCR was evident in the eastern region. Nationally, and particularly in the central region, a pattern of bipolar differentiation gradually formed, while in the western and northeastern regions, the distribution curves transitioned from bimodal or multimodal to unimodal. The development trends of the CCD of PCCR across Chinese provinces were relatively stable, exhibiting a “rich-get-richer” club convergence phenomenon. Overall, the drivers displayed zonal or patch-like spatial differentiation characteristics. Green innovation, digital economy, energy intensity, and human capital drivers showed a clear east–west zonal distribution. This paper provides methodological support for quantitatively evaluating the synergistic effects of PCCR, and its findings offer a reference for regions to develop differentiated PCCR synergy governance strategies.http://www.sciencedirect.com/science/article/pii/S1470160X25000329Pollution controlCarbon reductionSynergistic effectsSpatiotemporal evolutionDriving mechanisms |
| spellingShingle | Qinggang Meng Xiaolan Chen Hui Wang Wanfang Shen Peixin Duan Xinyue Liu Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China Ecological Indicators Pollution control Carbon reduction Synergistic effects Spatiotemporal evolution Driving mechanisms |
| title | Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China |
| title_full | Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China |
| title_fullStr | Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China |
| title_full_unstemmed | Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China |
| title_short | Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China |
| title_sort | spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in china |
| topic | Pollution control Carbon reduction Synergistic effects Spatiotemporal evolution Driving mechanisms |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25000329 |
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