基于LMDI-SMC中国氮氧化物减排绩效动态演化规律研究
|
摘要
氮氧化物减排作为经济转型发展中的重要环节,需要探索促进区域均衡发展的差异化协调减排政策。文章首先利用对数平均Divisia指数分解模型(LMDI法)将氮氧化物排放分解为排放因子、能源强度、经济结构、能源结构和经济发展5个因素得出2005-2015年中国30个省市氮氧化物排放与经济增长脱钩系数;然后,按照全国氮氧化物排放脱钩系数的平均水平,将其离散化为低、中低、中高和高4种类型构建其马尔可夫转移概率矩阵,考察其排放脱钩是否存在趋同现象;最后,引入空间滞后因素,构造其空间马尔可夫转移概率矩阵,分析在不同周围背景影响下氮氧化物排放脱钩的动态演化规律。结果表明,中国氮氧化物排放与经济增长脱钩系数呈现逐年上升趋势,且存在低脱钩、中低脱钩、中高脱钩及高脱钩4个趋同俱乐部;中国氮氧化物排放脱钩类型转移受周围背景的影响明显,在不同区域背景下同一类型的转移概率有所变化,同一区域背景下不同区域类型的转移概率不尽相同,且周围背景强化了区域氮氧化物排放脱钩的俱乐部趋同现象。通过对氮氧化物排放脱钩系数量化的减排绩效的时空动态变化进行分析可为氮氧化物区域协调减排提供参考建议。
As an important part of economic development, it is essential to explore coordinated and different regional NO_x emission reduction emission policies to promote the balanced development in China. First, the log-mean Divisia index(LMDI) technique was used to decompose the changes in NO_x emission into 5 effects that were NO_x intensity effect, energy structure effect, energy intensity effect, economic structure effect and economic development effect, then 30 provincial decoupling indexes between NO_x emissions and economic grown were implemented in 2005-2015. Next, all the decoupling indexes data were classified into 4 different classes(low, middle-low, middle-high and high) and Markov transition probability matrix was estimated to explore whether the convergence of decoupling indexes between NO_x emissions and economic grown exists during the study period. Finally, spatial Markov matrices were constructed to investigate decoupling indexes transition probability of different regions and their neighbors, and spatial Markov-chain transition matrix was accordingly made in order to visualize the dynamic evolution law. The decomposition result showed that NO_x emission decoupling index in China showed a rising trend year by year, and there were four convergence clubs such as low decoupling, middle low decoupling, middle high decoupling and high decoupling. The improvement of one region's NO_x emission decoupling was influenced by its neighbors: different regional backgrounds made different transition probabilities for the same class; different classes had different transition probabilities with the same regional background. The neighbors strengthened the convergence of decoupling indexes between NO_x emissions and economic grown. Based on NO_x emission reduction performance dynamic evolution, the paper puts forward reasonable references for the regional harmonization of NO_x emission reduction.
As an important part of economic development, it is essential to explore coordinated and different regional NO_x emission reduction emission policies to promote the balanced development in China. First, the log-mean Divisia index(LMDI) technique was used to decompose the changes in NO_x emission into 5 effects that were NO_x intensity effect, energy structure effect, energy intensity effect, economic structure effect and economic development effect, then 30 provincial decoupling indexes between NO_x emissions and economic grown were implemented in 2005-2015. Next, all the decoupling indexes data were classified into 4 different classes(low, middle-low, middle-high and high) and Markov transition probability matrix was estimated to explore whether the convergence of decoupling indexes between NO_x emissions and economic grown exists during the study period. Finally, spatial Markov matrices were constructed to investigate decoupling indexes transition probability of different regions and their neighbors, and spatial Markov-chain transition matrix was accordingly made in order to visualize the dynamic evolution law. The decomposition result showed that NO_x emission decoupling index in China showed a rising trend year by year, and there were four convergence clubs such as low decoupling, middle low decoupling, middle high decoupling and high decoupling. The improvement of one region's NO_x emission decoupling was influenced by its neighbors: different regional backgrounds made different transition probabilities for the same class; different classes had different transition probabilities with the same regional background. The neighbors strengthened the convergence of decoupling indexes between NO_x emissions and economic grown. Based on NO_x emission reduction performance dynamic evolution, the paper puts forward reasonable references for the regional harmonization of NO_x emission reduction.
引文
ANTON J.2001.Decoupling:A Conceptual Qverview[R].Paris:Organisation for Economic Co-operation and Development(OECD).
DE BRUYN S M,OPSCHOOR J B,1997.Developments in the throughput income relationship:Theoretical and empirical observations[J].Ecological Economics,20(3):255-268.
DIAKOULAKI D,MANDARAKA M,2007.Decomposition analysis for assessing the progress in decoupling industrial growth from CO2emissions in the EU manufacturing sector[J].Energy Economics,29(4):636-664.
KANG J D,ZHAO T,REN X S,et al.,2012.Using Decomposition analysis to evaluate the performance of China’s 30 provinces in CO2 emission reductions during eleventh five-year period(2005?2009)[J].Natural Hazards,64(2):999-1013.
刁贝娣,曾克峰,苏攀达,等,2016.中国工业氮氧化物排放的时空分布特征及驱动因素分析[J].资源科学,38(9):1768-1779.DIAO B D,ZENG K F,SU P D,et al.,2016.Temporal-spatial distribution characteristics of provincial industrial NOx emissions and driving factors in China from 2006 to 2013[J].Resources Science,38(9):1768-1779.
林涛,李灵,2013.天津市碳排放与能源强度影响因素研究[J].北京理工大学学报(社会科学版),15(6):33-38.LIN T,LI L,2013.Study on Influencing Factors of carbon emission and energy intensity in Tianjin[J].Journal of Beijing Institute of Technology(Social Sciences Edition),15(6):33-38.
刘建,吴兑,范绍佳,等,2017.前体物与气象因子对珠江三角洲臭氧污染的影响[J].中国环境科学,37(3):813-820.LIU J,WU D,FAN S J,et al.,2017.Impacts of precursors and meteorological factors on ozone pollution in Pearl River Delta[J].China Environmental Science,37(3):813-820.
施亚岚,崔胜辉,许肃,等,2014a.需求视角的中国能源消费氮氧化物排放研究[J].环境科学学报,34(10):2684-2692.SHI Y L,CUI S H,XU S,et al.,2014a.Nitrogen oxide emission in energy consumption in China from a consumption-based perspective[J].Acta Scientiae Circumstantiae,34(10):2684-2692.
施亚岚,崔胜辉,许肃,等,2014b.中国产业能源消费氮氧化物排放影响因素分析[J].环境科学与技术,37(6N):355-362.SHI Y L,CUI S H,XU S,et al.,2014b.Factor decomposition of nitrogen oxide emission of China industrial energy consumption[J].Environmental Science&Technology,37(6N):355-362.
孙才志,童艳丽,刘文新,2017.中国绿色化发展水平测度及动态演化规律[J].经济地理,37(2):15-22.SUN C Z,TONG Y L,LIU W X,2017.Measurement of green development level and its dynamic evolution rule in China[J].Economic Geography,37(2):15-22.
王丽琼,2013.基于探索性空间分析的中国氮氧化物排放强度研究[J].生态环境学报,22(3):494-497.WANG L Q,2013.Study on NOx emission intensity of China based on an exploratory spatial data analysis[J].Ecology and Environmental Sciences,22(3):494-497.
王丽琼,2017.基于LMDI中国省域氮氧化物减排与实现路径研究[J].环境科学学报,37(6):2394-2402.WANG L Q,2017.Investigation of NOx emissions reduction and its strategies over China's 30 provinces based on LMDI[J].Acta Scientiae Circumstantiae,37(6):2394-2402.
徐凯,房艳刚,李悦铮,2016.环渤海地区国内游客旅游消费空间演变[J].经济地理,36(10):207-215.XU K,FANG Y G,LI Y Z,2016.Spatial Evolution of Domestic Tourism Consumption in Areas Surrounding Bohai[J].Economic Geography,36(10):207-215.
张伟丽,2015.中国区域经济增长俱乐部趋同及其演变分析-基于时空加权马尔科夫链的预测[J].经济研究,36(3):108-114.ZHANG W L,2015.Regional Economic Growth Convergence Clubs and Their Evolution Analysis of All Prefecture:Level Cities in China on the Basis of Forecast With Spatial-temporal Weighted Markov Chain s[J].Economic Research Journal,36(3):108-114.
DE BRUYN S M,OPSCHOOR J B,1997.Developments in the throughput income relationship:Theoretical and empirical observations[J].Ecological Economics,20(3):255-268.
DIAKOULAKI D,MANDARAKA M,2007.Decomposition analysis for assessing the progress in decoupling industrial growth from CO2emissions in the EU manufacturing sector[J].Energy Economics,29(4):636-664.
KANG J D,ZHAO T,REN X S,et al.,2012.Using Decomposition analysis to evaluate the performance of China’s 30 provinces in CO2 emission reductions during eleventh five-year period(2005?2009)[J].Natural Hazards,64(2):999-1013.
刁贝娣,曾克峰,苏攀达,等,2016.中国工业氮氧化物排放的时空分布特征及驱动因素分析[J].资源科学,38(9):1768-1779.DIAO B D,ZENG K F,SU P D,et al.,2016.Temporal-spatial distribution characteristics of provincial industrial NOx emissions and driving factors in China from 2006 to 2013[J].Resources Science,38(9):1768-1779.
林涛,李灵,2013.天津市碳排放与能源强度影响因素研究[J].北京理工大学学报(社会科学版),15(6):33-38.LIN T,LI L,2013.Study on Influencing Factors of carbon emission and energy intensity in Tianjin[J].Journal of Beijing Institute of Technology(Social Sciences Edition),15(6):33-38.
刘建,吴兑,范绍佳,等,2017.前体物与气象因子对珠江三角洲臭氧污染的影响[J].中国环境科学,37(3):813-820.LIU J,WU D,FAN S J,et al.,2017.Impacts of precursors and meteorological factors on ozone pollution in Pearl River Delta[J].China Environmental Science,37(3):813-820.
施亚岚,崔胜辉,许肃,等,2014a.需求视角的中国能源消费氮氧化物排放研究[J].环境科学学报,34(10):2684-2692.SHI Y L,CUI S H,XU S,et al.,2014a.Nitrogen oxide emission in energy consumption in China from a consumption-based perspective[J].Acta Scientiae Circumstantiae,34(10):2684-2692.
施亚岚,崔胜辉,许肃,等,2014b.中国产业能源消费氮氧化物排放影响因素分析[J].环境科学与技术,37(6N):355-362.SHI Y L,CUI S H,XU S,et al.,2014b.Factor decomposition of nitrogen oxide emission of China industrial energy consumption[J].Environmental Science&Technology,37(6N):355-362.
孙才志,童艳丽,刘文新,2017.中国绿色化发展水平测度及动态演化规律[J].经济地理,37(2):15-22.SUN C Z,TONG Y L,LIU W X,2017.Measurement of green development level and its dynamic evolution rule in China[J].Economic Geography,37(2):15-22.
王丽琼,2013.基于探索性空间分析的中国氮氧化物排放强度研究[J].生态环境学报,22(3):494-497.WANG L Q,2013.Study on NOx emission intensity of China based on an exploratory spatial data analysis[J].Ecology and Environmental Sciences,22(3):494-497.
王丽琼,2017.基于LMDI中国省域氮氧化物减排与实现路径研究[J].环境科学学报,37(6):2394-2402.WANG L Q,2017.Investigation of NOx emissions reduction and its strategies over China's 30 provinces based on LMDI[J].Acta Scientiae Circumstantiae,37(6):2394-2402.
徐凯,房艳刚,李悦铮,2016.环渤海地区国内游客旅游消费空间演变[J].经济地理,36(10):207-215.XU K,FANG Y G,LI Y Z,2016.Spatial Evolution of Domestic Tourism Consumption in Areas Surrounding Bohai[J].Economic Geography,36(10):207-215.
张伟丽,2015.中国区域经济增长俱乐部趋同及其演变分析-基于时空加权马尔科夫链的预测[J].经济研究,36(3):108-114.ZHANG W L,2015.Regional Economic Growth Convergence Clubs and Their Evolution Analysis of All Prefecture:Level Cities in China on the Basis of Forecast With Spatial-temporal Weighted Markov Chain s[J].Economic Research Journal,36(3):108-114.