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The spatiotemporal pattern evolution and influencing factors of synergistic reduction of pollution and carbon emissions in Chinese counties |
XING Hui, HUO Xiao-qian |
School of Economics and Management, Hebei University of Technology, Tianjin 300401, China |
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Abstract Based on the panel data of 2383 counties in China from 2003 to 2022, the composite system synergy model and super-efficient SBM-DEA model were initially employed to quantify the synergistic reduction of pollution and carbon emissions. Subsequently, the spatiotemporal evolution patterns of synergistic reduction of pollution and carbon emissions in counties were explored by kernel density estimation, spatial autocorrelation analysis, and standard deviation ellipse. Ultimately, the XGBoost algorithm and SHAP value interpretation algorithm were combined to identify the main influencing factors of synergistic reduction of pollution and carbon emissions. The results show that. the level of synergistic reduction of pollution and carbon emissions in Chinese counties has been gradually rising, with a marked acceleration observed after 2020. The synergistic reduction of pollution and carbon emissions exhibits a “high in the east, low in the west” pattern, accompanied by a significant spatial positive correlation. The distribution center of synergistic reduction of pollution and carbon emissions in counties generally migrates to the southeast, reflecting a north-south centripetal trend and an east-west spatial divergence. Energy intensity is the main influencing factor of synergistic reduction of pollution and carbon emissions, and has a negative impact on the synergistic reduction. Technological innovation and industrial structure generally promote the synergistic reduction. The impact of economic development, population density and financial development on the synergistic reduction shows complex nonlinear characteristics. Compared with the eastern region, the impact of energy intensity on the synergistic reduction of pollution and carbon emissions in the central and western regions is more significant.
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Received: 18 July 2024
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Corresponding Authors:
邢会,教授,xinghui@hebut.edu.cn
E-mail: xinghui@hebut.edu.cn
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[1] International Energy Agency. CO2 Emissions in 2023[R]. 2024. [2] Yi M, Guan Y Y, Wu T, et al. Assessing China's synergistic governance of emission reduction between pollutants and CO2[J]. Environmental Impact Assessment Review, 2023,102:107196. [3] 田春秀,李丽平,胡涛,等.气候变化与环保政策的协同效应[J].环境保护, 2009,(12):67-68. Tian C X, Li L P, Hu T, et al. Synergies between climate change and environmental protection policies[J]. Environmental Protection, 2009,(12):67-68. [4] 王敏,杨儒浦,李丽平.城市减污降碳协同度评价指标体系构建及应用研究[J].气候变化研究进展, 2024,20(2):242-252. Wang M, Yang R Y, Li L P. Evaluation method and empirical study on synergistic reduction of pollution and carbon emissions at the urban level[J]. Climate Change Research, 2024,20(2):242-252. [5] 刘华军,郭立祥,乔列成.减污降碳协同效应的量化评估研究--基于边际减排成本视角[J].统计研究, 2023,40(4):19-33. Liu H J, Guo L X, Qiao L C. Quantitative evaluation of co-benefits of air pollution reduction and carbon emission reduction:Based on marginal abatement cost[J]. Statistical Research, 2023,40(4):19-33. [6] Yi H R, Zhao L J, Qian Y, et al. How to achieve synergy between carbon dioxide mitigation and air pollution control?Evidence from China[J]. Sustainable Cities and Society, 2022,78:103609. [7] Guan Y, Xiao Y, Rong B, et al. Assessing the synergy between CO2emission and ambient PM2.5 pollution in Chinese cities:An integrated study based on economic impact and synergy index[J]. Environmental Impact Assessment Review, 2023,99:106989. [8] Chen S Y, Tan Z X, Mu S Y, et al. Synergy level of pollution and carbon reduction in the Yangtze River Economic Belt:Spatial-temporal evolution characteristics and driving factors[J]. Sustainable Cities and Society, 2023,98:104859. [9] Zeng Q H, He L Y. Study on the synergistic effect of air pollution prevention and carbon emission reduction in the context of"dual carbon":Evidence from China's transport sector[J]. Energy Policy, 2023,173:113370. [10] 王少剑,谢紫寒,王泽宏.中国县域碳排放的时空演变及影响因素[J].地理学报, 2021,76(12):3103-3118. Wang S J, Xie Z H, Wang Z H. The spatiotemporal pattern evolution and influencing factors of CO2 emissions at the county level of China[J]. Acta Geographica Sinica, 2021,76(12):3103-3118. [11] 毛显强,曾桉,邢有凯,等.从理念到行动:温室气体与局地污染物减排的协同效益与协同控制研究综述[J].气候变化研究进展, 2021,17(3):255-267. Mao X Q, Zeng A, Xing Y K, et al. From concept to action:A review of research on co-benefits and co-control of greenhouse gases and local air pollutants reductions[J]. Climate Change Research, 2021, 17(3):255-267. [12] Zhu J P, Wu S H, Xu J B. Synergy between pollution control and carbon reduction:China's evidence[J]. Energy Economics, 2023,119:106541. [13] Xu M, Qin Z F, Zhang S H. Carbon dioxide mitigation co-effect analysis of clean air policies:Lessons and perspectives in China's Beijing-Tianjin-Hebei region[J]. Environmental Research Letters, 2021,16(1):015006. [14] 刘华军,张一辰.减污降碳协同效应的生成逻辑、内涵阐释与实现方略[J].当代经济科学, 2024,46(3):32-44. Liu H J, Zhang Y C. Synergistic effect of pollution reduction and carbon emission reduction:Generating logic, connotation explanation and realization strategy[J]. Modern Economic Science, 2024,46(3):32-44. [15] 姜华,高健,阳平坚.推动减污降碳协同增效建设人与自然和谐共生的美丽中国[J].环境保护, 2021,49(16):15-17. Jiang H, Gao J, Yang P J. Promote co-control of air pollutants and GHGs to build a beautiful China with harmonious coexistence between human and nature[J]. Environmental Protection, 2021, 49(16):15-17. [16] 郑逸璇,宋晓晖,周佳,等.减污降碳协同增效的关键路径与政策研究[J].中国环境管理, 2021,13(5):45-51. Zeng Y X, Song X H, Zhou J, et al. Synergetic control of environmental pollution and carbon emissions:Pathway and policy[J]. Chinese Journal of Environmental Management, 2021,13(5):45-51. [17] 戴静怡,曹媛,陈操操.城市减污降碳协同增效内涵、潜力与路径[J].中国环境管理, 2023,15(2):30-37. Dai J Y, Cao Y, Chen C C. Synergistic connotations, potential and paths of urban pollution and carbon emissions reduction[J]. Chinese Journal of Environmental Management, 2023,15(2):30-37. [18] He N C, Zeng S B, Jin G. Achieving synergy between carbon mitigation and pollution reduction:Does green finance matter?[J]. Journal of Environmental Management, 2023,342:118356. [19] 张雪纯,曹霞,宋林壕.碳排放交易制度的减污降碳效应研究--基于合成控制法的实证分析[J].自然资源学报, 2024,39(3):712-730. Zhang X C, Cao X, Song L H. The effect of pollution control and carbon reduction of the carbon emission trading system:An empirical analysis based on the Synthetic Control Method[J]. Journal of Natural Resources, 2024,39(3):712-730. [20] 王敏,李丽平.城市减污降碳协同增效:内涵特征、实践困囿与创新建议[J].环境保护, 2024,52(7):13-16. Wang M, Li L P. Synergies of urban pollution and carbon reduction:Connotative characteristics, oractical difficulties and innovative suggestions[J]. Environmental Protection, 2024,52(7):13-16. [21] 狄乾斌,陈小龙,侯智文."双碳"目标下中国三大城市群减污降碳协同治理区域差异及关键路径识别[J].资源科学, 2022,44(6):1155-1167. [22] Jia W L, Li L, Lei Y L, et al. Synergistic effect of CO2 and PM2.5 emissions from coal consumption and the impacts on health effects[J]. Journal of Environmental Management, 2023,325:116535. [23] Gao Z Q, Zhou X H. A review of the CAMx, CMAQ, WRF-Chem and NAQPMS models:Application, evaluation and uncertainty factors[J]. Environmental Pollution, 2024,343. [24] 段林丰,李振亮,蒲茜,等.基于综合减污降碳策略的成渝地区中长期空气质量改善模拟[J].中国环境科学, 2024,44(3):1756-1768. Duan L F, Li Z H, Pu X, et al. Simulation of medium and long-term air quality improvement in the Cheng-Yu district based on comprehensive pollution reduction and carbon reduction strategies[J]. China Environmental Science, 2024,44(3):1756-1768. [25] Tone K. A slacks-based measure of efficiency in data envelopment analysis[J]. European Journal of Operational Research, 2001,130(3):498-509. [26] 郭沛,王光远.数字经济的减污降碳协同作用及机制--基于地级市数据的实证检验[J].资源科学, 2023,45(11):2117-2129. Guo P, Wang G Y. The synergistic effect of digital economy on pollution and carbon reduction and the influence mechanism:An empirical test based on prefecture-level city data[J]. Resources Science, 2023,45(11):2117-2129. [27] Yang X H, Yang X Y, Zhu J G, et al. Synergic emissions reduction effect of China's"Air Pollution Prevention and Control Action Plan":Benefits and efficiency[J]. Science of the Total Environment, 2022, 847:157564. [28] Chen T, Guestrin C. Xgboost:A scalable tree boosting system[C]//Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016:785-794. [29] Zhang J Y, Ma X L, Zhang J L, et al. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model[J]. Journal of Environmental Management, 2023,332:117357. [30] 陈小亮,程硕,陈衎,等.基于机器学习方法的一线城市房价影响因素研究[J].南开学报(哲学社会科学版), 2023,(6):146-163. Chen X L, Cheng S, Chen K, et al. Research on the factors affecting housing prices in first-tier cities based on machine learning methods[J]. Journal of Nankai University (Philosophy,Literature and Social), 2023,(6):146-163. [31] Lundberg S M, Lee S I. A unified approach to interpreting model predictions[J]. Advances in Neural Information Processing Systems, 2017,30:4765-4774. [32] Chen J, Gao M, Cheng S, et al. County-level CO2 emissions and sequestration in China during 1997~2017[J]. Scientific data, 2020, 7(1):391. [33] 李云燕,张硕.中国城市碳排放强度时空演变与影响因素的时空异质性[J].中国环境科学, 2023,43(6):3244-3254. Li Y Y, Zhang S. Spatio-temporal evolution of urban carbon emission intensity and spatiotemporal heterogeneity of influencing factors in China[J]. China Environmental Science, 2023,43(6):3244-3254. [34] 李云燕,杜文鑫.京津冀城市群减污降碳时空特征及影响因素异质性分析[J].环境工程技术学报, 2023,13(6):2006-2015. Li Y Y, Du W X. Spatial and temporal characteristics and the heterogeneity of influencing factors of the synergism of pollution and carbon emissions reduction in Beijing-Tianjin-Hebei urban agglomeration[J]. Journal of Environmental Engineering Technology, 2023,13(6):2006-2015. [35] 许嘉俊,杨晓军,李睿.城市居民生活碳排放及影响因素的时空异质性[J].中国环境科学, 2024,44(3):1732-1742. Xu J J, Yang X J, Li R. The spatial and temporal heterogeneity of carbon emission and its driving forces in urban households[J]. China Environmental Science, 2024,44(3):1732-1742. [36] Xue W B, Lei Y, Liu X, et al. Synergistic assessment of air pollution and carbon emissions from the economic perspective in China[J]. Science of the Total Environment, 2023,858:159736. [37] 熊华文.减污降碳协同增效的能源转型路径研究[J].环境保护, 2022,50(Z1):35-40. Xiong H W. The Study on Energy Transformation Path of Achieving Synergizing the Reduction of Pollution and Carbon Emissions[J]. Environmental Protection, 2022,50(Z1):35-40. [38] 胡萌,伍雅思,常娇娇.降碳减污协同效应:区域差异与协调路径[J].环境经济研究, 2023,8(4):191-208. Hu M, Wu Y S, Chang J J. Synergistic effects of carbon emissions and pollution reduction:Regional differences and coordination paths[J]. Journal of Environmental Economics, 2023,8(4):191-208. [39] Fan X X, Zhou Y L, Xie Q. Assessment on the synergistic effect of pollution and carbon reductions in low-carbon city pilot policy:Based on effectiveness and efficiency perspectives[J]. Environment Development and Sustainability, 2024:1-25. [40] Yang H W, Liu X R, Liu Y L, et al. Revolutionizing biochar synthesis for enhanced heavy metal adsorption:Harnessing machine learning and Bayesian optimization[J]. Journal of Environmental Chemical Engineering, 2023,11(5):110593. [41] 霍晓谦,张爱国.数字经济对碳排放强度的影响机制及空间效应[J].环境科学与技术, 2022,45(12):182-193. Huo X Q, Zhang A G. Mechanisms and spatial effects of the digital economy on carbon emissions intensity[J]. Environmental Science& Technology, 2022,45(12):182-193. [42] Yu J Z, Hu W Z. The impact of digital infrastructure construction on carbon emission efficiency:Considering the role of central cities[J]. Journal of Cleaner Production, 2024,448:141687. [43] Zhao C, Wang B. How does new-type urbanization affect air pollution?Empirical evidence based on spatial spillover effect and spatial Durbin model[J]. Environment International, 2022,165:107304. [44] Wu T, Kung C C. Carbon emissions, technology upgradation and financing risk of the green supply chain competition[J]. Technological Forecasting and Social Change, 2020,152:119884. |
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