长江经济带物流业碳排放测算及其驱动要素研究
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摘要
应用碳足迹方法测算了长江经济带11省市物流业2006—2016年的碳排放量,并利用灰色关联法剖析了不同省市碳排放的主要驱动要素。结果表明:长江经济带物流业碳排放总量整体呈波动上升趋势,与经济发展水平相符,江西、贵州、重庆、安徽、云南5省市的碳排放量较低,四川、湖南、浙江、湖北4省的碳排放量居中,江苏、上海2省市的碳排放量较高,中下游地区的碳排放总量与增速大于上游地区;主要驱动要素的影响力总体为:碳排放效率>城镇化水平>能源结构>人口规模>物流业固定资产投资>物流规模,但6大因素在不同省市物流业碳排放量中的贡献不一致;物流业碳排放与经济发展多数年份处于弱脱钩状态。建议完善物流碳减排政策制度、建立长江经济带物流信息平台、调整能源结构等低碳路径,达到生态优先和绿色发展的目标。
In this paper, the carbon emissions of logistics industry are calculated in 11 provinces within the Yangtze River Economic Zone from 2006 to 2016 using the carbon footprint method, and the main driving factors of carbon emissions are analyzed in different provinces using the grey relational analysis method. The results find that the total carbon emissions of logistics industry in 11 provinces generally show an increasing trend with fluctuation pattern, and are highly related to the economic development levels. The total carbon emissions of logistics industry are the lowest in Jiangxi, Guizhou, Chongqing,Anhui and Yunnan provinces, followed by Sichuan, Hunan, Zhejiang and Hubei provinces, and the highest in Jiangsu and Shanghai provinces. Both the total amount and growth rate of carbon emissions are higher in the middle and lower region than in the upper region of the Yangtze River. The influencing intensity of the main driving factors is shown as carbon emission efficiency>urbanization level>energy structure>population size>logistics fixed assets investment>logistics scale, but the contributions of the six driving factors to carbon emission of logistics industry are different among the 11 provinces. Further,the carbon emissions of logistics industry are weakly decoupled from economic development in most years from 2006 to 2016.Our results indicate that we shall improve the policy system of logistics emission reduction, establish the logistics information platform of the Yangtze River Economic Zone, and adjust the energy structure and other low-carbon path to achieve the goal of ecological priority and green development.
In this paper, the carbon emissions of logistics industry are calculated in 11 provinces within the Yangtze River Economic Zone from 2006 to 2016 using the carbon footprint method, and the main driving factors of carbon emissions are analyzed in different provinces using the grey relational analysis method. The results find that the total carbon emissions of logistics industry in 11 provinces generally show an increasing trend with fluctuation pattern, and are highly related to the economic development levels. The total carbon emissions of logistics industry are the lowest in Jiangxi, Guizhou, Chongqing,Anhui and Yunnan provinces, followed by Sichuan, Hunan, Zhejiang and Hubei provinces, and the highest in Jiangsu and Shanghai provinces. Both the total amount and growth rate of carbon emissions are higher in the middle and lower region than in the upper region of the Yangtze River. The influencing intensity of the main driving factors is shown as carbon emission efficiency>urbanization level>energy structure>population size>logistics fixed assets investment>logistics scale, but the contributions of the six driving factors to carbon emission of logistics industry are different among the 11 provinces. Further,the carbon emissions of logistics industry are weakly decoupled from economic development in most years from 2006 to 2016.Our results indicate that we shall improve the policy system of logistics emission reduction, establish the logistics information platform of the Yangtze River Economic Zone, and adjust the energy structure and other low-carbon path to achieve the goal of ecological priority and green development.
引文
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[15]Talbi B.CO2 emissions reduction in road transport sector in Tunisia[J].Renewable&Sustainable Energy Reviews,2017,69:232-238.
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[20]张立国.中国物流业二氧化碳排放变化驱动因素分析[J].中国流通经济,2016(12):29-39.
[21]李建豹,黄贤金.基于空间面板模型的碳排放影响因素分析--以长江经济带为例[J].长江流域资源与环境,2015(10):1665-1671.
[22]宁亚东,章博雅,丁涛.长江经济带碳排放脱钩状态及其驱动因素研究[J].大连理工大学学报,2017(5):459-466.
[23]中华人民共和国国家统计局.中国统计年鉴2017[M].北京:中国统计出版社,2017.
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[26]钱明霞.区域制造业及其影响因素灰色关联分析--以常州市为例[J].城市问题,2008(1):23-28.
[27]彭佳雯,黄贤金,钟太洋,等.中国经济增长与能源碳排放的脱钩研究[J].资源科学,2011(4):626-633.
[28]中华人民共和国国家统计局.中国第三产业统计年鉴2017[M].北京:中国统计出版社,2017.
[29]中国物流与采购联合会.中国物流年鉴2017[M].北京:中国财富出版社,2017.
[30]钟昌宝,钱康.长江经济带省域物流效率及空间差异研究--基于改进的空间权重矩阵[J].北京交通大学学报(社会科学版),2016(5):120-127.
[2]冯宗宪,陈志伟.区域能源碳排放与经济增长的脱钩趋势分析[J].华东经济管理,2015(1):50-54.
[3]Duan H B,Hu M W,Zhang Y K,et al.Quantification of carbon emissions of the transport service sector in China by using streamlined life cycle assessment[J].Journal of Cleaner Production,2015,95:109-116.
[4]Focas C.The unsustainability of exurban development in London and New York:Calculating transport CO2 emissions[J].Journal of Environmental Planning and Management,2017,60(5):901-919.
[5]Yoon Y,Yang M,Kim J.An analysis of CO2 emissions from international transport and the driving forces of emissions change[J].Sustainability,2018,10(5):1677.
[6]刘丙泉,程凯,马占新.城镇化对物流业碳排放变动影响研究[J].中国人口·资源与环境,2016(3):54-60.
[7]孙强,沈玉志.逆向物流碳足迹动态变化及影响因素分析[J].生态经济,2016(6):52-56.
[8]尹鹏,段佩利,陈才.中国交通运输碳排放格局及其与经济增长的关系研究[J].干旱区资源与环境,2016(5):7-12.
[9]王丽萍,刘明浩.基于投入产出法的中国物流业碳排放测算及影响因素研究[J].资源科学,2018(1):195-206.
[10]周叶,郭玲俊,唐恩斌.基于EKC的省域物流行业碳排放特征--中国四省域的实证[J].北京交通大学学报(社会科学版),2018(1):85-93.
[11]黄慧,李康.基于碳足迹测定的湖北省服务业低碳水平及影响因素研究--长江经济带主要省市的区域比较[J].湖北社会科学,2017(6):69-74.
[12]黄国华,刘传江,李兴平.长江经济带工业碳排放与驱动因素分析[J].江西社会科学,2016(8):54-62.
[13]Xu B,Lin B Q.Factors affecting carbon dioxide(CO2)emissions in China’s transport sector:A dynamic nonparametric additive regression model[J].Journal of Cleaner Production,2015,101:311-322.
[14]Lin B,Benjamin N I.Influencing factors on carbon emissions in China transport industry.A new evidence from quantile regression analysis[J].Journal of Cleaner Production,2017,150:175-187.
[15]Talbi B.CO2 emissions reduction in road transport sector in Tunisia[J].Renewable&Sustainable Energy Reviews,2017,69:232-238.
[16]Melo P C.Driving down road transport CO2 emissions in Scotland[J].International Journal of Sustainable Transportation,2016,10(10):906-916.
[17]Chandran V G R,Tang C F.The impacts of transport energy consumption,foreign direct investment and income on CO2emissions in ASEAN-5 economies[J].Renewable&Sustainable Energy Reviews,2013,24:445-453.
[18]Andreoni V,Galmarini S.European CO2 emission trends:Adecomposition analysis for water and aviation transport sectors[J].Energy,2012,45(1):595-602.
[19]姚冠新,赵子琪,胡百灵.中国农村物流碳排放与地区经济增长的脱钩趋势研究[J].华东经济管理,2017(11):51-56.
[20]张立国.中国物流业二氧化碳排放变化驱动因素分析[J].中国流通经济,2016(12):29-39.
[21]李建豹,黄贤金.基于空间面板模型的碳排放影响因素分析--以长江经济带为例[J].长江流域资源与环境,2015(10):1665-1671.
[22]宁亚东,章博雅,丁涛.长江经济带碳排放脱钩状态及其驱动因素研究[J].大连理工大学学报,2017(5):459-466.
[23]中华人民共和国国家统计局.中国统计年鉴2017[M].北京:中国统计出版社,2017.
[24]IPCC.2006年IPCC国家温室气体清单指南[R].日本:日本全球环境战略研究所,2006.
[25]国家发展改革委能源研究所,清华大学,中科院大气所,等.省级温室气体清单编制指南(试行)[EB/OL].(2011-05).https://wenku.baidu.com/view/c28d051b52d380eb62946df5.html.
[26]钱明霞.区域制造业及其影响因素灰色关联分析--以常州市为例[J].城市问题,2008(1):23-28.
[27]彭佳雯,黄贤金,钟太洋,等.中国经济增长与能源碳排放的脱钩研究[J].资源科学,2011(4):626-633.
[28]中华人民共和国国家统计局.中国第三产业统计年鉴2017[M].北京:中国统计出版社,2017.
[29]中国物流与采购联合会.中国物流年鉴2017[M].北京:中国财富出版社,2017.
[30]钟昌宝,钱康.长江经济带省域物流效率及空间差异研究--基于改进的空间权重矩阵[J].北京交通大学学报(社会科学版),2016(5):120-127.