空气污染物的时空演化及社会经济驱动因素研究——以长江三角洲地区为例
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摘要
本文以2006—2015年长江三角洲城市群为研究对象,分析该地区不同部门因能源消费而产生的典型污染物排放量,然后利用LMDI模型,对空气污染进行社会经济驱动因素分析。结果表明:该地区CO_2、SO_2、PM_(2.5)与PM_(10)等空气污染物排放量均呈现先快速增长后缓慢减少的趋势,排放的峰值多出现在了2013年,而NO_x则一直保持增长的趋势。其中,电力与工业部门是空气污染物的主要排放源,但对排放量贡献呈减少趋势,生活部门与交通部门污染物排放量则逐步增长,尤其是对PM_(2.5)与PM_(10)排放量的贡献不可忽视。人口与经济增长对污染物排放量起到了正向拉动作用,经济因素的驱动作用最为明显,其效应值呈现先小幅增加后大幅下降的趋势,能源效率与能源结构有抑制作用,其对污染物排放的效应值仅次于经济因素,而能源结构变化的效应很小。
The Yangtze River Delta urban agglomeration was taken as the research object to analyze the emissions of typical pollutants caused by energy consumption of difference sectors during 2006 to 2015. And then the socio-economic drivers of air pollution emissions were analyzed based on Logarithmic Mean Divisia Index(LMDI). The results showed that the emissions of CO_2, SO_2, PM_(10) and PM_(2.5) in the Yangtze River Delta showed a rapid growth and then slowly decreased, and the peak of emissions appeared in 2013, NO_x kept growing. In terms of sectors, the electricity and industrial sectors were the main sources of air pollutants in the Yangtze River Delta, and the contribution to air pollution emissions was decreasing. The pollutant emissions from the civil and transportation sectors were gradually increasing, especially the contribution to emissions of PM_(2.5) and PM_(10) could not be ignored. The population and economy played a positive role in the growth of energy consumption and air pollution emissions. The driving force of economic factors was the most obvious, and its effect value showed a slight increase and then a sharp decline. The energy efficiency and energy structure had an inhibitory effect on the growth of energy consumption and air pollution emissions in the Yangtze River Delta. The effect value and the economic factor showed the same trend, and the effects of energy structure were very small.
The Yangtze River Delta urban agglomeration was taken as the research object to analyze the emissions of typical pollutants caused by energy consumption of difference sectors during 2006 to 2015. And then the socio-economic drivers of air pollution emissions were analyzed based on Logarithmic Mean Divisia Index(LMDI). The results showed that the emissions of CO_2, SO_2, PM_(10) and PM_(2.5) in the Yangtze River Delta showed a rapid growth and then slowly decreased, and the peak of emissions appeared in 2013, NO_x kept growing. In terms of sectors, the electricity and industrial sectors were the main sources of air pollutants in the Yangtze River Delta, and the contribution to air pollution emissions was decreasing. The pollutant emissions from the civil and transportation sectors were gradually increasing, especially the contribution to emissions of PM_(2.5) and PM_(10) could not be ignored. The population and economy played a positive role in the growth of energy consumption and air pollution emissions. The driving force of economic factors was the most obvious, and its effect value showed a slight increase and then a sharp decline. The energy efficiency and energy structure had an inhibitory effect on the growth of energy consumption and air pollution emissions in the Yangtze River Delta. The effect value and the economic factor showed the same trend, and the effects of energy structure were very small.
引文
[1]王慧文,潘秀丹,林刚.沈阳市空气二氧化硫污染对心血管疾病死亡率的影响[J].环境与健康杂志, 2002, 19(1):50-52.
[2] BEATTY T K M, SHIMSHACK J P. Air pollution and children’s respiratory health:a cohort analysis[J]. Journal of environmental economics and management, 2014, 67(1):39-57.
[3]王跃思,姚利,刘子锐,等.京津冀大气霾污染及控制策略思考[J].中国科学院院刊, 2013, 28(3):353-363.
[4]张智胜,陶俊,谢绍东,等.成都城区PM2.5季节污染特征及来源解析[J].环境科学学报, 2013, 33(11):2947-2952.
[5]顾为东.中国雾霾特殊形成机理研究[J].宏观经济研究, 2014(6):3-7,123-123.
[6] TAI A P K, MICKLEY L J, JACOB D J. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States:implications for the sensitivity of PM2.5 to climate change[J].Atmospheric environment, 2010, 44(32):3976-3984.
[7]佘倩楠,徐茜,周陶冶,等.长三角地区2015年大气重污染特征及其影响因素[J].环境科学学报, 2018, 38(8):3185-3196.
[8]张人文,范绍佳.珠江三角洲风场对空气质量的影响[J].中山大学学报(自然科学版), 2011, 50(6):130-134.
[9] ZAKEY A S, SOLMON F, GIORGI F. Implementation and testing of a desert dust module in a regional climate model[J]. Atmospheric chemistry and physics, 2006, 6(12):4687-4704.
[10] ADAMS J W, RODRIGUEZ D, COX R A. The uptake of SO2 on Saharan dust:a flow tube study[J]. Atmospheric chemistry and physicsdiscussions, 2005, 5(3):2643-2676.
[11] SARRAT C, LEMONSU A, MASSON V, et al. Impact of urban heat island on regional atmospheric pollution[J]. Atmospheric environment, 2006, 40(10):1743-1758.
[12] WANG M Y, CAO C X, LI G S, et al. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China[J].Atmospheric environment, 2015, 102:112-121.
[13]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报, 2016, 71(8):1357-1371.
[14] TONG Z M, CHEN Y J, MALKAWI A, et al. Energy saving potential of natural ventilation in China:the impact of ambient air pollution[J].Applied energy, 2016, 179:660-668.
[15]燕丽,杜小申.典型城市空气污染特征对比分析[J].中国环境管理,2018, 10(6):92-98.
[16]刘亦文,文晓茜,胡宗义.中国污染物排放的地区差异及收敛性研究[J].数量经济技术经济研究, 2016, 33(4):78-94.
[17]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济, 2014(4):19-31.
[18]魏巍贤,马喜立.能源结构调整与雾霾治理的最优政策选择[J].中国人口·资源与环境, 2015, 25(7):6-14.
[19]王锋,吴丽华,杨超.中国经济发展中碳排放增长的驱动因素研究[J].经济研究, 2010(2):123-136.
[20]刘满芝,杨继贤,马丁,等.基于LMDI模型的中国主要大气污染物的空间差异及其影响因素分析[J].资源科学, 2015, 37(2):333-341.
[21]文扬,马中,吴语晗,等.京津冀及周边地区工业大气污染排放因素分解——基于LMDI模型分析[J].中国环境科学, 2018, 38(12):4730-4736.
[22] ZHANG Y X, WANG H K, LIANG S, et al. A dual strategy for controlling energy consumption and air pollution in China’s metropolis of Beijing[J]. Energy, 2015, 81:294-303.
[23]陈菡,於世为.中国生活能源消费的典型污染物排放及驱动因素研究[J].中国人口·资源与环境, 2017, 27(12):40-51.
[24] GUAND B, SU X, ZHANG Q, et al. The socioeconomic drivers of China’s primary PM2.5 emissions[J]. Environmental research letters,2014, 9(2):024010.
[25]王立平,陈俊.中国雾霾污染的社会经济影响因素——基于空间面板数据EBA模型实证研究[J].环境科学学报, 2016, 36(10):3833-3839.
[26]李惠娟,周德群,魏永杰.中国主要城市空气污染的影响因素——基于污染源解析的研究[J].生态经济, 2018, 34(4):197-203.
[27]邵帅,李欣,曹建华,等.中国雾霾污染治理的经济政策选择——基于空间溢出效应的视角[J].经济研究, 2016, 51(9):73-88.
[28]韩楠,于维洋.基于LMDI的我国工业废气排放量驱动因素研究[J].生态经济, 2014, 30(3):135-138.
[29]宋杰鲲.基于LMDI的山东省能源消费碳排放因素分解[J].资源科学,2012, 34(1):35-41.
[30] LI L, LEI Y L, WU S M, et al. Evaluation of future energy consumption on PM2.5 emissions and public health economic loss in Beijing[J].Journal of cleaner production, 2018, 187:1115-1128.
[31]江小珂,唐孝炎.北京市大气污染控制对策研究[R].北京:北京市环保局, 2002.
[32]张强, KLIMONT Z, STREETS D G,等.中国人为源颗粒物排放模型及2001年排放清单估算[J].自然科学进展, 2006, 16(2):223-231.
[2] BEATTY T K M, SHIMSHACK J P. Air pollution and children’s respiratory health:a cohort analysis[J]. Journal of environmental economics and management, 2014, 67(1):39-57.
[3]王跃思,姚利,刘子锐,等.京津冀大气霾污染及控制策略思考[J].中国科学院院刊, 2013, 28(3):353-363.
[4]张智胜,陶俊,谢绍东,等.成都城区PM2.5季节污染特征及来源解析[J].环境科学学报, 2013, 33(11):2947-2952.
[5]顾为东.中国雾霾特殊形成机理研究[J].宏观经济研究, 2014(6):3-7,123-123.
[6] TAI A P K, MICKLEY L J, JACOB D J. Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States:implications for the sensitivity of PM2.5 to climate change[J].Atmospheric environment, 2010, 44(32):3976-3984.
[7]佘倩楠,徐茜,周陶冶,等.长三角地区2015年大气重污染特征及其影响因素[J].环境科学学报, 2018, 38(8):3185-3196.
[8]张人文,范绍佳.珠江三角洲风场对空气质量的影响[J].中山大学学报(自然科学版), 2011, 50(6):130-134.
[9] ZAKEY A S, SOLMON F, GIORGI F. Implementation and testing of a desert dust module in a regional climate model[J]. Atmospheric chemistry and physics, 2006, 6(12):4687-4704.
[10] ADAMS J W, RODRIGUEZ D, COX R A. The uptake of SO2 on Saharan dust:a flow tube study[J]. Atmospheric chemistry and physicsdiscussions, 2005, 5(3):2643-2676.
[11] SARRAT C, LEMONSU A, MASSON V, et al. Impact of urban heat island on regional atmospheric pollution[J]. Atmospheric environment, 2006, 40(10):1743-1758.
[12] WANG M Y, CAO C X, LI G S, et al. Analysis of a severe prolonged regional haze episode in the Yangtze River Delta, China[J].Atmospheric environment, 2015, 102:112-121.
[13]蔺雪芹,王岱.中国城市空气质量时空演化特征及社会经济驱动力[J].地理学报, 2016, 71(8):1357-1371.
[14] TONG Z M, CHEN Y J, MALKAWI A, et al. Energy saving potential of natural ventilation in China:the impact of ambient air pollution[J].Applied energy, 2016, 179:660-668.
[15]燕丽,杜小申.典型城市空气污染特征对比分析[J].中国环境管理,2018, 10(6):92-98.
[16]刘亦文,文晓茜,胡宗义.中国污染物排放的地区差异及收敛性研究[J].数量经济技术经济研究, 2016, 33(4):78-94.
[17]马丽梅,张晓.中国雾霾污染的空间效应及经济、能源结构影响[J].中国工业经济, 2014(4):19-31.
[18]魏巍贤,马喜立.能源结构调整与雾霾治理的最优政策选择[J].中国人口·资源与环境, 2015, 25(7):6-14.
[19]王锋,吴丽华,杨超.中国经济发展中碳排放增长的驱动因素研究[J].经济研究, 2010(2):123-136.
[20]刘满芝,杨继贤,马丁,等.基于LMDI模型的中国主要大气污染物的空间差异及其影响因素分析[J].资源科学, 2015, 37(2):333-341.
[21]文扬,马中,吴语晗,等.京津冀及周边地区工业大气污染排放因素分解——基于LMDI模型分析[J].中国环境科学, 2018, 38(12):4730-4736.
[22] ZHANG Y X, WANG H K, LIANG S, et al. A dual strategy for controlling energy consumption and air pollution in China’s metropolis of Beijing[J]. Energy, 2015, 81:294-303.
[23]陈菡,於世为.中国生活能源消费的典型污染物排放及驱动因素研究[J].中国人口·资源与环境, 2017, 27(12):40-51.
[24] GUAND B, SU X, ZHANG Q, et al. The socioeconomic drivers of China’s primary PM2.5 emissions[J]. Environmental research letters,2014, 9(2):024010.
[25]王立平,陈俊.中国雾霾污染的社会经济影响因素——基于空间面板数据EBA模型实证研究[J].环境科学学报, 2016, 36(10):3833-3839.
[26]李惠娟,周德群,魏永杰.中国主要城市空气污染的影响因素——基于污染源解析的研究[J].生态经济, 2018, 34(4):197-203.
[27]邵帅,李欣,曹建华,等.中国雾霾污染治理的经济政策选择——基于空间溢出效应的视角[J].经济研究, 2016, 51(9):73-88.
[28]韩楠,于维洋.基于LMDI的我国工业废气排放量驱动因素研究[J].生态经济, 2014, 30(3):135-138.
[29]宋杰鲲.基于LMDI的山东省能源消费碳排放因素分解[J].资源科学,2012, 34(1):35-41.
[30] LI L, LEI Y L, WU S M, et al. Evaluation of future energy consumption on PM2.5 emissions and public health economic loss in Beijing[J].Journal of cleaner production, 2018, 187:1115-1128.
[31]江小珂,唐孝炎.北京市大气污染控制对策研究[R].北京:北京市环保局, 2002.
[32]张强, KLIMONT Z, STREETS D G,等.中国人为源颗粒物排放模型及2001年排放清单估算[J].自然科学进展, 2006, 16(2):223-231.
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