中国居民直接生活能源消费碳排放区域差异及影响因素分析
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摘要
基于2003—2015年中国30个省份的居民直接生活能耗碳排放数据,利用空间自相关分析其空间格局变化,并利用地理加权回归模型分析了居民消费所产生的二氧化碳排放影响因素。结果表明,中国省域的碳排放空间依赖作用较为显著;居民消费水平、居民直接生活能耗碳排放强度、劳动年龄人口比重对居民人均直接生活能耗碳排放量的增加具有促进作用;家庭规模对人均碳排放的影响呈现较强的负向抑制作用。中国经济发展的区域不平衡在碳排放方面凸显为居民直接生活能源消费碳排放的差异,并在集聚格局上表现出多种类型。
The given paper is to devote itself to a study of the regional differences and the influential factors of the carbon emissions from the direct-living energy consumption of the Chinese residents. As is known,carbon emissions from the direct-living energy consumption of the residents can serve as a dispensable part for creating an energy policy of the country. And,for the said research,we have been tracing the spatial pattern changes of the carbon emission situation of the direct-living energy consumption in the 30 provinces of the country for the 13 years from~2003 to 2015 based on the reference provided by IPCC via the spatial analysis method. And,then,we have also analyzed the influential factors by using the geographically weighted regression( GWR) model. The results of our analysis indicate that:( 1)The spatial dependence of the direct-living energy carbon emissions of the residents in the above-mentioned provinces during the period under study is more significant by the global autocorrelation. The overall Moran's I value in 2003-2015 reveals an upward trend of fluctuation;( 2) The results of the local autocorrelation LISA agglomeration analysis prove that the high agglomerated areas of the high-high( HH-type) consumption are mainly concentrated in Beijing,Tianjin,and Hebei; whereas the lowvalue accumulation areas of low-low( LL-type) are mainly clustered in the local areas of central Anhui,Hubei and some other agriculturally stressed provincial places. The contrasted agglomeration areas of high and low( HL-type) energy consumption areas can be typically found in Guangdong,but the low-high( LHtype) agglomerated areas of the province also tend to be scarce in the whole country.( 3) The results of GWR indicate that the consumption levels,the emission intensity of the direct-living energy carbon of the residents,and the ratio of the working-age population can also have positive effects on the increase of percapita direct-living energy carbon emissions,which can be found of a strong negative effect on the per capita carbon consumption.Therefore,the above mentioned statistical analysis results can clearly indicate the regional imbalance of the country's economic development,which also highlights the differences or the gaps in the carbon emissions from the direct domestic energy consumption,whereas the circular economy and sustainable development may account for the regional difference in the economic development. The carbon emissions, when directly acting on the residents' energy consumption,can make the agglomeration turn out in various patterns. Just for such a purpose,the current study prefers to clarify and work out the influential factors of the carbon emissions from the daily living consumption,and,in turn,we just want to propose some feasible carbon emission reduction measures and suggestions,so as to promote the development of the green and low-carbon emission energy resources of the country.
The given paper is to devote itself to a study of the regional differences and the influential factors of the carbon emissions from the direct-living energy consumption of the Chinese residents. As is known,carbon emissions from the direct-living energy consumption of the residents can serve as a dispensable part for creating an energy policy of the country. And,for the said research,we have been tracing the spatial pattern changes of the carbon emission situation of the direct-living energy consumption in the 30 provinces of the country for the 13 years from~2003 to 2015 based on the reference provided by IPCC via the spatial analysis method. And,then,we have also analyzed the influential factors by using the geographically weighted regression( GWR) model. The results of our analysis indicate that:( 1)The spatial dependence of the direct-living energy carbon emissions of the residents in the above-mentioned provinces during the period under study is more significant by the global autocorrelation. The overall Moran's I value in 2003-2015 reveals an upward trend of fluctuation;( 2) The results of the local autocorrelation LISA agglomeration analysis prove that the high agglomerated areas of the high-high( HH-type) consumption are mainly concentrated in Beijing,Tianjin,and Hebei; whereas the lowvalue accumulation areas of low-low( LL-type) are mainly clustered in the local areas of central Anhui,Hubei and some other agriculturally stressed provincial places. The contrasted agglomeration areas of high and low( HL-type) energy consumption areas can be typically found in Guangdong,but the low-high( LHtype) agglomerated areas of the province also tend to be scarce in the whole country.( 3) The results of GWR indicate that the consumption levels,the emission intensity of the direct-living energy carbon of the residents,and the ratio of the working-age population can also have positive effects on the increase of percapita direct-living energy carbon emissions,which can be found of a strong negative effect on the per capita carbon consumption.Therefore,the above mentioned statistical analysis results can clearly indicate the regional imbalance of the country's economic development,which also highlights the differences or the gaps in the carbon emissions from the direct domestic energy consumption,whereas the circular economy and sustainable development may account for the regional difference in the economic development. The carbon emissions, when directly acting on the residents' energy consumption,can make the agglomeration turn out in various patterns. Just for such a purpose,the current study prefers to clarify and work out the influential factors of the carbon emissions from the daily living consumption,and,in turn,we just want to propose some feasible carbon emission reduction measures and suggestions,so as to promote the development of the green and low-carbon emission energy resources of the country.
引文
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[11] CHEN Zhijian(陈志建),WANG Zheng(王铮). Provincial differences of pressure drivers for carbon abatement by local governments in China based on stirpat model[J]. Resource Science(资源科学),2012,34(4):718-724.
[12] YUAN Changwei(袁长伟),RUI Xiaoli(芮晓丽),WU Dayong(武大勇),et al. Pressure index of China's provincial traffic carbon emission reduction based on geoweighted regression model[J]. Chinese Journal of Highway(中国公路学报),2016,29(6):262-270.
[13] LIU Lina(刘莉娜),QU Jiansheng(曲建升),HUANG Yusheng(黄雨生),et al. Analysis of regional differences and influencing factors of domestic carbon emission in China[J]. Journal of Natural Resources(自然资源学报),2016,31(8):1364-1377.
[14] FORTHERINGHAM A S,CHANRLTON M,BRUNSDON C. The geographically of parameter space:an investigation of spatial nonstationarity[J]. International Journal of Geographical Information Systems,1996,10(5):605-627.
[2] WEBER C,PERRELS A. Modelling lifestyle effects on energy demand and related emissions[J]. Energy Policy,2000,28(8):549-566.
[3] KOK R,BENDERS R M J,MOLL H C. Measuring the environmental load of household consumption using some methods based on input-output energy analysis:a comparison of methods and a discussion of results[J]. Energy Policy,2006,34(17):2744-2761.
[4] REINDERS A H M E,VRINGER K,BLOCK K. The direct and indirect energy requirement of households in the european union[J]. Energy Policy,2003,31(2):139-153.
[5] ZHANG Yan(张艳),QIN Yaochen(秦耀辰). Research progress on the factors affecting carbon emission of household direct energy consumption[J]. Economic Geography(经济地理),2011,31(2):284-288,293.
[6] FENG Ling(冯玲),LIN Tao(吝涛),ZHAO Qianjun(赵千钧). Analysis on dynamic characteristics of energy consumption and carbon emission of urban residents[J].Population Resources and Environment in China(中国人口·资源与环境),2011,21(5):93-100.
[7] WAN Wenyu(万文玉),ZHAO Xueyan(赵雪雁),WANG Weijun(王伟军),et al. Spatial and temporal patterns of energy and carbon emissions from urban residents in China and their influencing factors[J]. Journal of Environmental Science(环境科学学报),2016,36(9):3445-3455.
[8] LI Yanmei,ZHAO Rui,LIU Tianshu,et al. Does urbanization lead to more direct and indirect household carbon dioxide emissions? Evidence from China during 1996-2012[J]. Journal of Cleaner Production, 2015, 102:103-104.
[9] HU Yuna(胡宇娜),MEI Lin(梅林). spatial differentiation and dynamic mechanism analysis of regional travel agency efficiency in China based on GWR mode l[J].Geography Science(地理科学),2018,38(1):107-113.
[10] WANG Yanan(王雅楠),ZHAO Tao(赵涛). Research on spatial difference of carbon emission in China based on GWR Model[J]. Population Resources and Environment in China(中国人口·资源与环境),2016,26(2):27-34.
[11] CHEN Zhijian(陈志建),WANG Zheng(王铮). Provincial differences of pressure drivers for carbon abatement by local governments in China based on stirpat model[J]. Resource Science(资源科学),2012,34(4):718-724.
[12] YUAN Changwei(袁长伟),RUI Xiaoli(芮晓丽),WU Dayong(武大勇),et al. Pressure index of China's provincial traffic carbon emission reduction based on geoweighted regression model[J]. Chinese Journal of Highway(中国公路学报),2016,29(6):262-270.
[13] LIU Lina(刘莉娜),QU Jiansheng(曲建升),HUANG Yusheng(黄雨生),et al. Analysis of regional differences and influencing factors of domestic carbon emission in China[J]. Journal of Natural Resources(自然资源学报),2016,31(8):1364-1377.
[14] FORTHERINGHAM A S,CHANRLTON M,BRUNSDON C. The geographically of parameter space:an investigation of spatial nonstationarity[J]. International Journal of Geographical Information Systems,1996,10(5):605-627.
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