INFLUENCING FACTORS OF SYNERGY DEGREE FOR INDUSTRIAL POLLUTANT AND CARBON REDUCTIONS IN CHINESE CITIES
-
摘要: 当前减污降碳协同控制大多关注国家或省级尺度,且聚焦单一环境介质,缺少城市尺度、多介质污染物与碳排放的协同性研究。分析了2013—2019年中国178个城市工业CO2排放与SO2、NOx、COD、NH3-N 4类不同介质污染物排放的协同度,并探究了影响协同度的社会经济因素。结果表明,工业CO2和各类污染物的年平均减排协同度呈现“N”型变化趋势。约40%的城市已实现工业协同减污降碳,约50%的城市只实现污染物减排,碳污均增排城市约占10%。加强政府政策力度是影响城市工业协同减污降碳的关键因素,且东、中、西部城市工业协同减污降碳的关键影响因素具有异质性。此外,鼓励科技创新和吸引人才回流是促进老工业城市协同减污降碳的关键因素。Abstract: Industrial activities of cities discharge large amounts of CO2 and pollutants. They are important sources of environmental pollution. Existing studies on the synergistic control of pollutants and CO2 emissions mostly focus on the national or provincial scale, as well as in a single environmental medium. It is necessary to investigate the synergistic nature of pollutants and CO2 emissions at the urban scale and across environmental media. This study analyzed the synergy degree between industrial CO2 emissions and the emissions of four pollutants to different environmental media(namely SO2, NOx, COD, and NH3-N) in 178 Chinese cities from 2013 to 2019. Moreover, the socioeconomic factors influencing the synergy degree were revealed.Resultsshowed that the annual mean synergy degrees of industrial CO2 and four pollutants emissions showed an “N”-shaped trend. Nearly 40% of the cities realized the synergistic control of industrial pollutants and CO2 emissions. Around 50% of the cities realized only industrial pollutants reduction. Unfortunately, about 10% of the cities have increased industrial CO2 and pollutant emissions simultaneously. Generally, strengthening government policies is a key factor influencing the industrial synergy degree. However, there is a spatial heterogeneity in the key factors influencing the synergy degree of eastern, central, and western cities. In addition, encouraging innovation and attracting talent are key factors in promoting synergistic control of pollutants and CO2 emissions in old industrial cities.
-
[1] 王韶华,张伟.能源强度的影响因素、地区差异与协同降耗的研究进展[J].环境工程,2018,36(4):176-180. [2] 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. [3] 张瑜,孙倩,薛进军,等.减污降碳的协同效应分析及其路径探究[J].中国人口·资源与环境,2022,32(5):1-13. [4] 王力,冯相昭,马彤,等.典型城市减污降碳协同控制潜力评价研究:以渭南市为例[J].环境科学研究,2022,35(8):2006-2014. [5] 邢有凯,毛显强,冯相昭,等.城市蓝天保卫战行动协同控制局地大气污染物和温室气体效果评估:以唐山市为例[J].中国环境管理,2020,12(4):20-28. [6] LIU J,WOODWARD R T,ZHANG Y.Has carbon emissions trading reduced PM2.5 in China?[J].Environmental Science & Technology,2021,55(10):6631-6643. [7] ZENG A,MAO X,HU T,et al.Regional co-control plan for local air pollutants and CO2 reduction:method and practice[J].Journal of Cleaner Production,2017,140:1226-1235. [8] 刘海艳,于会彬,王志刚.粤港澳大湾区温室气体和大气污染物协同控制现状分析[J].环境工程技术学报,2023,13(2):455-463. [9] LIU L,LIANG Q,SHUAI Y.Common driving forces of provincial-level greenhouse gas and air pollutant emissions in China[J].Environmental Science & Technology,2023,57(14):5806-5820. [10] 黄儒霞,钟秋萌,吴晓慧,等.广东省经济结构转型对协同减污降碳的影响[J].环境科学研究,2022,35(10):2303-2311. [11] 陈小龙,狄乾斌,吴洪宇.中国沿海城市群减污降碳协同增效时空演变及影响因素[J].热带地理,2023(11):2049-2059. [12] YI H,ZHAO L,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. [13] LI J,JIAO L,LI R,et al.How does market-oriented allocation of industrial land affect carbon emissions?evidence from China[J].Journal of Environmental Management,2023,342:118288. [14] MENG L,GRAUS W,WORRELL E,et al.Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (defense meteorological satellite program’s operational linescan system) nighttime light imagery:methodological challenges and a case study for China[J].Energy,2014,71:468-478. [15] 毛显强,曾桉,胡涛,等.技术减排措施协同控制效应评价研究[J].中国人口·资源与环境,2011,21(12):1-7. [16] 毛显强,邢有凯,高玉冰,等.温室气体与大气污染物协同控制效应评估与规划[J].中国环境科学,2021,41(7):3390-3398. [17] GAO X,LIU N,HUA Y.Environmental Protection Tax Law on the synergy of pollution reduction and carbon reduction in China:evidence from a panel data of 107 cities[J].Sustainable Production and Consumption,2022,33:425-437. [18] 潘思羽,张美玲.基于BP神经网络的甘肃省二氧化碳排放预测及影响因素研究[J].环境工程,2023,41(7):61-68. [19] 陈阳,逯进,于平.技术创新减少环境污染了吗:来自中国285个城市的经验证据[J].西安交通大学学报(社会科学版),2019,39(1):73-84. [20] 张新生,魏志臻,陈章政,等.基于LASSO-GWO-KELM的工业碳排放预测方法研究[J].环境工程,2023,41(10):141-149. [21] 郭克莎,彭继宗.二三产业结构变动与经济发展质量:上中等收入阶段向高收入阶段演进的国际经验[J].财贸经济,2022,43(8):5-26. [22] 李政,张怡,赵哲.数字经济与工业绿色转型:基于科技创新的中介效应和门槛效应[J].工业技术经济,2023,42(10):3-16. [23] 陈霄,毛霞,曹伟.环境信息公开、外商直接投资与城市空气污染:来自环境空气质量信息实时公开的证据[J].统计研究,2023,40(6):77-90. [24] 刘志华,徐军委,张彩虹.科技创新、产业结构升级与碳排放效率:基于省际面板数据的PVAR分析[J].自然资源学报,2022,37(2):508-520. [25] WANG M,XU M,MA S.The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity[J].Structural Change and Economic Dynamics,2021,59:427-441. [26] MANDERSON E,KNELLER R.Environmental regulations,outward FDI and heterogeneous firms:are countries used as pollution havens?[J].Environmental and Resource Economics,2012,51(3):317-352. [27] WU Y,SHI K,CHEN Z,et al.Developing improved time-series DMSP-OLS-Like data (1992—2019) in China by integrating DMSP-OLS and SNPP-VIIRS[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-14. [28] SHAN Y,GUAN D,ZHENG H,et al.China CO2 emission accounts 1997—2015[J].Scientific Data,2018,5(1):170201. [29] 国家统计局城市社会经济调查司.中国城市统计年鉴2014—2020[M].北京:中国统计出版社,2021. [30] 国家统计局.中国环境统计年鉴2014—2020[M].北京:中国统计出版社,2021. [31] 国家统计局.中国统计年鉴2014—2020[M].北京:中国统计出版社,2021. [32] 苏涛永,张亮亮,单志汶.产业耦合、区域异质性与新零售组织场域[J].商业经济与管理,2020(8):5-21. [33] 李小飞,张明军,王圣杰,等.中国空气污染指数变化特征及影响因素分析[J].环境科学,2012,33(6):1936-1943. [34] 霍伟东,李杰锋,陈若愚.绿色发展与FDI环境效应:从“污染天堂”到“污染光环”的数据实证[J].财经科学,2019(4):106-119. [35] CHEN Y,RAZA K,ALHARTHI M.The nexus between remittances,education,and energy consumption:evidence from developing countries[J].Energy Strategy Reviews,2023,46:101069. [36] CUI Y,WEI Z,XUE Q,et al.Educational attainment and environmental Kuznets curve in China:an aggregate and disaggregate analysis[J].Environmental Science and Pollution Research,2022,29(30):45612-45622. [37] ZHU T,PENG H,ZHANG Y,et al.Does higher education development facilitate carbon emissions reduction in China[J].Applied Economics,2021,53(47):5490-5502. [38] 马倩倩,陈诗一.经济收敛与环境失衡:基于西部大开发战略的研究[J].世界经济,2023,46(8):108-133. [39] SUN H,NI S,ZHAO T,et al.The transfer and driving factors of industrial embodied wastewater in China’s interprovincial trade[J].Journal of Cleaner Production,2021,317:128298. [40] LI J,JIANG Q,CAI K,et al.Uncovering the spatially uneven synergistic effects of China’s enterprise-level industrial water pollutants reduction[J].Resources,Conservation and Recycling,2023,190:106811. [41] LIU C,HONG T,LI H,et al.From club convergence of per capita industrial pollutant emissions to industrial transfer effects:an empirical study across 285 cities in China[J].Energy Policy,2018,121:300-313. [42] FU S,MA Z,NI B,et al.Research on the spatial differences of pollution-intensive industry transfer under the environmental regulation in China[J].Ecological Indicators,2021,129:107921. [43] 王晓林,张华明.外商直接投资碳排放效应研究:基于城镇化门限面板模型[J].预测,2020,39(1):59-65. [44] BU Y,WANG E,QIU Y,et al.Impact assessment of population migration on energy consumption and carbon emissions in China:a spatial econometric investigation[J].Environmental Impact Assessment Review,2022,93:106744. [45] 中国政府网.发展改革委关于印发全国老工业基地调整改造规划(2013—2022年)的通知[EB/OL].https://www.gov.cn/gongbao/content/2013/content_2441018.htm.2013-03-18. [46] 丁晓明,王成新,李梦程,等.中国老工业基地城市收缩的时空演变及影响因素分析[J].世界地理研究,2023(11):94-107.
点击查看大图
计量
- 文章访问数: 161
- HTML全文浏览量: 17
- PDF下载量: 7
- 被引次数: 0