基于LSDV估计法的中国主要品种能源消费影响碳排放强度效应分析
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摘要
基于LSDV估计法,采用固定影响效应模型回归分析了2005—2017年年鉴数据中的主要品种能源消费对碳排放强度的影响效应.对传统STIRPAT模型进行了改进.通过分析发现,样本期间我国各主要品种能源消费对碳排放强度的影响差异较大,具有正向作用的能源消费因素包括:煤炭消费、焦炭消费、汽油消费、柴油消费和天然气消费;具有负向作用的因素包括原油消费、燃料油消费和煤油消费.此外,从全国角度来看,近年来的人口规模变化对于降低碳排放强度具有积极作用,而人均财富和煤炭消费仍然是拉高碳排放强度的主要原因.
The data in China stats yearbooks(2015-2017) was examined to explore the impact of wasunplion of main types of energy on carbon emission intessity. The Influence of main types of energy consumption on carbon emission intensity between 2005-2017 China statistical yearbooks were evaluated with the fixed effect model based on LSDV estimation and Hausman Test. The results showed that consumptions of different types of energy had different effects on carbon emissions intensity from 2004 to 2016 in China. Five types energy consumption,coal,coke,gasoline,diesel oil,and natural gas,played positive role to the increase of carbon emission intensity. Other variables,types energr suchas crude oil consumption,fuel oil consumption,and kerosene consumption played opposite role in carbon emission. Moreover,change of population had the most significantly favorable influence on decrease of carbon emission intensity. Unfortunately,per capita GDP and coal consumption contributed to increase the carbon emission intensity in China during the studied period.
The data in China stats yearbooks(2015-2017) was examined to explore the impact of wasunplion of main types of energy on carbon emission intessity. The Influence of main types of energy consumption on carbon emission intensity between 2005-2017 China statistical yearbooks were evaluated with the fixed effect model based on LSDV estimation and Hausman Test. The results showed that consumptions of different types of energy had different effects on carbon emissions intensity from 2004 to 2016 in China. Five types energy consumption,coal,coke,gasoline,diesel oil,and natural gas,played positive role to the increase of carbon emission intensity. Other variables,types energr suchas crude oil consumption,fuel oil consumption,and kerosene consumption played opposite role in carbon emission. Moreover,change of population had the most significantly favorable influence on decrease of carbon emission intensity. Unfortunately,per capita GDP and coal consumption contributed to increase the carbon emission intensity in China during the studied period.
引文
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[4]颜艳梅,王铮,吴乐英,等.中国碳排放强度影响因素对区域差异的作用分析[J].环境科学学报,2016,36(9):3436-3444.
[5]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,2017,33(18):141-143.
[6]张珍花,方勇.我国区域碳排放强度影响因素及动态关系探究[J].统计与决策,2012,33(16):90-93.
[7]周维维.京津冀能源消费碳排放影响因素分析及情景预测研究[D].北京:华北电力大学经济与管理学院,2016.
[8]张翠菊.中国碳排放强度影响因素-收敛性及溢出性研究[D].重庆:重庆大学经济与工商管理学院,2016.
[9]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.
[10]魏景赋,徐政,田文举.经济增长、能源消耗、金融发展对碳排放的影响:基于金砖四国的实证研究[J].重庆工商大学学报(社会科学版),2018,35(1):42-49.
[11]宋健,赵怡芳.京津冀能源消费碳排放分解比较研究[J].重庆理工大学学报(自然科学),2018,32(2):141-148.
[12]EHRLICH P, HOLDREN J. Impact of population growth[J]. Sci,1971,171(3977):1213-1217.
[13]ROSA E,DIERZ T. Climate change and society:speculation,construction and scientific investigation[J]. Int Sociol,1998,13(4):421-455.
[14]DAMODAR N. Gujarati. Basic Econometrics(Fourth Edition)[M]. Boston:McGraw-Hill,2003:642.
[15]中华人民共和国国家统计局.中国统计年鉴2005—2017[M].北京:中国统计出版社,2005-2017.
[16]中华人民共和国国家统计局.中国能源统计年鉴2005—2017[M].北京:中国统计出版社,2005-2017.
[17]涂华,刘翠杰.标准煤二氧化碳排放的计算[J].煤质技术,2014,29(2):57-60.