基于GRA的河南省交通运输碳排放影响因素研究
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摘要
针对河南省交通运输碳排放影响因素的研究,提出一种基于灰色关联分析法(GRA)和Kaya恒等式的定量分析方法。首先根据河南省的交通运输综合能源消耗量计算得到交通运输碳排放量,然后应用变形后的Kaya恒等式将影响交通运输业碳排放的因素分解为人口效应、经济效应、能源强度效应、能源结构效应和碳排放因子效应,最后应用GRA对各影响因素进行关联分析。实证结果表明:经济效应、能源强度效应以及能源结构效应对碳排放量的影响较大;人口效应和碳排放因子效应对碳排放量的影响较小。
In order to study the influencing factors of transportation carbon emissions in Henan province, a quantitative analysis method based on grey relational analysis(GRA) and Kaya identities was proposed. Firstly, the carbon emissions of transportation were calculated according to the comprehensive energy consumption of transportation in Henan province. Then the factors affecting carbon emissions of transportation industry were decomposed into population factor, economic factor, energy intensity factor, energy structure factor and carbon emission factor. Finally, the correlation of these factors was calculated by correlation analysis. The empirical results show that economic factor, energy intensity factor and energy structure factor have a greater influence on carbon emissions, while population factor and carbon emission factor have a smaller influence on carbon emissions.
In order to study the influencing factors of transportation carbon emissions in Henan province, a quantitative analysis method based on grey relational analysis(GRA) and Kaya identities was proposed. Firstly, the carbon emissions of transportation were calculated according to the comprehensive energy consumption of transportation in Henan province. Then the factors affecting carbon emissions of transportation industry were decomposed into population factor, economic factor, energy intensity factor, energy structure factor and carbon emission factor. Finally, the correlation of these factors was calculated by correlation analysis. The empirical results show that economic factor, energy intensity factor and energy structure factor have a greater influence on carbon emissions, while population factor and carbon emission factor have a smaller influence on carbon emissions.
引文
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[2]袁长伟,张倩,芮晓丽,等.中国交通运输碳排放时空演变及差异分析[J].环境科学学报,2016,36(12):4555-4562.
[3]W.W.Wang,M.Zhang,M.Zhou.Using LMDI method to analyze transport sector CO2emissions in China[J].Energy,2011(36):5909-5915.
[4]Lee Schipper,et al.Transport and Carbon Emissions in the United States:The Long View[J].Energies,2011(4):563-581.
[5]喻洁,达亚彬,欧阳斌.基于LMDI分解方法的中国交通运输行业碳排放变化分析[J].中国公路学报,2015(10):112-119.
[6]欧阳斌,凤振华,李忠奎,等.交通运输能耗与碳排放测算评价方法及应用——以江苏省为例[J].软科学,2015(1):139-144.
[7]Kaya Y.Impact of carbon dioxide emission on GNP growth:interpretation of proposed scenarios[R].Paris:IPCC Energy and Industry Subgroup,1989.
[8]戴小文,何艳秋,钟秋波.基于扩展的Kaya恒等式的中国农业碳排放驱动因素分析[J].中国科学院大学学报,2015,32(6)751-759.
[9]姚丽敏.陕西省交通运输碳排放影响因素与减排路径研究[D].西安:长安大学(硕士学位论文),2016.
[10]刘思峰,蔡华,杨英杰,等.灰色关联分析模型研究进展[J].系统工程理论与实践,2013,33(8):2041-2046.
[11]于丽静,柴晓杰,姜永强.基于熵权-灰色关联分析的区域物流创新能力评价[J].物流科技,2018,41(2):10-15.
[12]汪青松,程莉.企业技术创新与企业经济绩效的灰色关联度分析[J].中国科技论坛,2009(12):64-68.