中部六省能源消费碳排放时空演变特征及影响因素
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摘要
采用2005—2016年中部六省能源消费碳排放面板数据,对其能源消费碳排放时空演变特征及其收敛性进行分析,在此基础上运用LMDI分解方法,探索产出规模、能源强度、产业结构、人口规模及能源结构等因素对中部六省能源消费碳排放的影响,并分析各影响因素的空间特征。结果表明:(1)各省能源消费碳排放均存在收敛,且收敛于各自的稳态水平,各省之间的差异短时间内不会消失;(2)除能源结构变化对中部六省碳排放表现为负效应之外,其余五个因素对其碳排放均表现为正效应,各效应按累积贡献率绝对值大小依次是:产出规模效应、能源强度效应、产业结构效应、人口规模效应和能源结构效应;(3)各因素对中部六省能源碳排放影响均存在明显的空间差异,其中产出规模扩大对湖南能源碳排放拉动作用最大,能源强度下降对湖南能源碳排放的抑制作用最大,产业结构变化仅对山西能源碳排放起到抑制作用,人口规模变化仅对安徽省能源碳排放起到抑制作用,能源结构变动对安徽、河南以及湖北能源碳排放表现为正效应。
Using the panel data of energy consumption carbon emissions from the sixcentral provinces in 2005-2016, the spatial and temporal evolution characteristics and convergence of energy consumption carbon emissions are analyzed.On this basis, the LMDI decomposition method is used to explore the impact of factors such as output scale, energy intensity,industrial structure, population size and energy structure on carbon emissions of energy consumption in the six central provinces, and analyze the spatial characteristics of each influencing factor.The results show that: 1) Carbon emissions of energy consumption in all provinces are convergent and converge to their respective steady-state levels, and the differences between provinces will not disappear in a short time; 2) In addition to the negative effects of energy structure changes on carbon emissions in the six central provinces, the other five factors have positive effects on their carbon emissions. The absolute value of each effect in terms of cumulative contribution rate is: output scale effect, energy intensity effect, industrial structure effect, population size effect and energy structure effect; 3) There are obvious spatial differences in the impact of various factors on energy carbon emissions in the six central provinces. The expansion of output scale has the largest pulling impact on energy carbon emissions in Hunan Province. The decline in energy intensity has a greatest inhibitory effect on energy carbon emissions in Hunan Province. Structural changes only inhibits energy carbon emissions in Shanxi Province. Population size changes only inhibits energy carbon emissions in Anhui Province.Energy structure changes have positive effects on energy carbon emissions in Anhui, Henan and Hubei provinces.
Using the panel data of energy consumption carbon emissions from the sixcentral provinces in 2005-2016, the spatial and temporal evolution characteristics and convergence of energy consumption carbon emissions are analyzed.On this basis, the LMDI decomposition method is used to explore the impact of factors such as output scale, energy intensity,industrial structure, population size and energy structure on carbon emissions of energy consumption in the six central provinces, and analyze the spatial characteristics of each influencing factor.The results show that: 1) Carbon emissions of energy consumption in all provinces are convergent and converge to their respective steady-state levels, and the differences between provinces will not disappear in a short time; 2) In addition to the negative effects of energy structure changes on carbon emissions in the six central provinces, the other five factors have positive effects on their carbon emissions. The absolute value of each effect in terms of cumulative contribution rate is: output scale effect, energy intensity effect, industrial structure effect, population size effect and energy structure effect; 3) There are obvious spatial differences in the impact of various factors on energy carbon emissions in the six central provinces. The expansion of output scale has the largest pulling impact on energy carbon emissions in Hunan Province. The decline in energy intensity has a greatest inhibitory effect on energy carbon emissions in Hunan Province. Structural changes only inhibits energy carbon emissions in Shanxi Province. Population size changes only inhibits energy carbon emissions in Anhui Province.Energy structure changes have positive effects on energy carbon emissions in Anhui, Henan and Hubei provinces.
引文
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[21]张利国,鲍丙飞,董亮.鄱阳湖生态经济区粮食单产时空格局演变及驱动因素探究[J].经济地理,2018,38(2):154-161.
[22] Kaya Yoichi. Impact of Carbon Dioxide Emission on GNP Growth:Interpretation of Proposed Scenarios[R]. Paris:Pre‐sentation to the Energy and Industry Subgroup,Response Strategies Working Group,IPCC,1989.
[2]马晓君,董碧滢,于渊博,等.东北三省能源消费碳排放测度及影响因素[J].中国环境科学,2018,38(8):3 170-3 179.
[3]黄蕊,王铮,刘慧雅,等.中国中部六省的碳排放趋势研究[J].经济地理,2012,32(7):12-17.
[4]朱勤,彭希哲,陆志明,等.中国能源消费碳排放变化的因素分解及实证分析[J].资源科学,2009,31(12):2 072-2 079.
[5]许士春,习蓉,何正霞.中国能源消耗碳排放的影响因素分析及政策启示[J].资源科学,2012,34(1):2-12.
[6]范丹.中国能源消费碳排放变化的驱动因素研究——基于LMDI-PDA分解法[J].中国环境科学,2013,33(9):1 705-1 713.
[7]张馨.中国能源消费碳排放的时空差异及驱动因素研究[J].干旱区地理,2018,41(5):1 115-1 122.
[8]邓光耀,任苏灵.中国能源消费碳排放的动态演进及驱动因素分析[J].统计与决策,2017(18):141-143.
[9]王钦池.家庭规模对中国能源消费和碳排放的影响研究[J].资源科学,2015,37(2):299-307.
[10]程叶青,王哲野,叶信岳,等.中国能源消费碳排放强度及其影响因素的空间计量(英文)[J]. Journal of Geographical Sci‐ences,2014,24(4):631-650.
[11]揣小伟,黄贤金,王婉晶,等.中国能源消费碳排放的空间计量分析(英文)[J]. Journal of Geographical Sciences,2012,22(4):630-642.
[12]孙艳伟,李加林,李伟芳,等.海岛城市碳排放测度及其影响因素分析——以浙江省舟山市为例[J].地理研究,2018,37(5):1 023-1 033.
[13]余明成,徐占军,余健.山西省CO2排放影响因素研究及情景分析[J].环境科学研究,2018,31(8):1 357-1 365.
[14]李欢,杨珊,陈建宏,等.湖南省能源消费碳排放驱动因素及趋势预测实证分析[J].环境工程,2018,36(2):152-157.
[15]何永贵,于江浩.河北省碳排放及其影响因素变化趋势研究[J].环境科学与技术,2018,41(1):184-191.
[16]汪菲,王长建.新疆能源消费碳排放的多变量驱动因素分析——基于扩展的STIRPAT模型[J].干旱区地理,2017,40(2):441-452.
[17]王长建,张虹鸥,叶玉瑶,等.广东省能源消费碳排放的多变量驱动因素——基于扩展的STIRPAT模型[J].科技管理研究,2017,37(3):210-214.
[18]吴彼爱,高建华,徐冲.基于产业结构和能源结构的河南省碳排放分解分析[J].经济地理,2010,30(11):1 902-1 907.
[19]杨武,王贲,项定先,等.武汉市能源消费碳排放因素分解与低碳发展研究[J].中国人口·资源与环境,2018,28(S1):13-16.
[20]黄勤,何晴.长江经济带碳排放驱动因素及其空间特征——基于LMDI模型[J].财经科学,2017(5):80-92.
[21]张利国,鲍丙飞,董亮.鄱阳湖生态经济区粮食单产时空格局演变及驱动因素探究[J].经济地理,2018,38(2):154-161.
[22] Kaya Yoichi. Impact of Carbon Dioxide Emission on GNP Growth:Interpretation of Proposed Scenarios[R]. Paris:Pre‐sentation to the Energy and Industry Subgroup,Response Strategies Working Group,IPCC,1989.