物流业碳排放效率评价及动态演化分析:以“丝绸之路经济带”沿线省区为例
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摘要
基于"丝绸之路经济带"沿线9个省市物流业2007～2016年的数据,文章通过BC~2-DEA模型和Malmquist指数,分别从静态和动态的角度对物流业碳排放效率进行计算和评价,同时利用核密度函数对碳排放效率的动态演进趋势进行分析。研究表明物流业碳排放综合效率的差异主要来自于纯技术效率,纯技术效率可以通过增加科研投入、促进低碳节能技术的应用得到改善;物流业全要素碳排放效率受技术效率指数和技术进步变化指数的共同影响,技术效率指数的平均增长率高于技术进步变化指数的平均增长率,可以通过推动技术进步来提高全要素碳排放效率;从整体来看,物流业碳排放效率的地区差异正在逐步缩小,呈现出动态收敛的特征。物流业的低碳化发展需要调整要素投入模式,提高科技创新水平和组织管理水平,同时针对各省市制定差异化的碳减排政策。
Based on the logistics industry data covering 2007-2016 from nine provinces on the Silk Road Economic Belt,this paper measures carbon emission efficiency of logistics industry from the static and dynamic aspects by the method of BC~2-DEA model and Malmquist index. Moreover, the dynamic evolution trend of carbon emission efficiency is analyzed by using Kernel density estimation. The results show that the differences in the comprehensive carbon emission efficiency of logistics industry mainly come from pure technical efficiency. It can be improved by increasing investment of scientific research and promoting the application of low-carbon and energy-saving technologies. The total factor carbon emission efficiency of logistics industry is affected by technical efficiency index and technological progress index. The average growth rate of the technical efficiency index is higher than that of the technological progress index. The total factor carbon emission efficiency can be improved by promoting technological progress. On the whole, regional differences of carbon emission efficiency are narrowing and are characterized by dynamic convergence. The low-carbon development of the logistics industry needs to adjust the factor input model, improve the level of scientific and technological innovation and organizational management. At the same time, it is necessary to make different carbon reduction policies for each province.
Based on the logistics industry data covering 2007-2016 from nine provinces on the Silk Road Economic Belt,this paper measures carbon emission efficiency of logistics industry from the static and dynamic aspects by the method of BC~2-DEA model and Malmquist index. Moreover, the dynamic evolution trend of carbon emission efficiency is analyzed by using Kernel density estimation. The results show that the differences in the comprehensive carbon emission efficiency of logistics industry mainly come from pure technical efficiency. It can be improved by increasing investment of scientific research and promoting the application of low-carbon and energy-saving technologies. The total factor carbon emission efficiency of logistics industry is affected by technical efficiency index and technological progress index. The average growth rate of the technical efficiency index is higher than that of the technological progress index. The total factor carbon emission efficiency can be improved by promoting technological progress. On the whole, regional differences of carbon emission efficiency are narrowing and are characterized by dynamic convergence. The low-carbon development of the logistics industry needs to adjust the factor input model, improve the level of scientific and technological innovation and organizational management. At the same time, it is necessary to make different carbon reduction policies for each province.
引文
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[13]王惠,卞艺杰,王树乔.出口贸易、工业碳排放效率动态演进与空间溢出[J].数量经济技术经济研究,2016(1):3-19.
[14]马越越,王维国.中国物流业碳排放特征及其影响因素分析:基于LMDI分解技术[J].数学的实践与认识,2013,43(10):31-42.
[15]仲云云,仲伟周.中国区域全要素碳排放绩效及影响因素研究[J].商业经济与管理,2012,1(1):85-96.
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[17]范建平,肖慧,樊晓宏.考虑非期望产出的改进EBM-DEA三阶段模型省略:基于中国省际物流业效率的实证分析[J].中国管理科学,2017,25(8):166-174.
[18]王维国,马越越.中国区域物流产业效率:基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012,30(3):66-75.
[19]张雪青.“一带一路”区域物流协同发展分析[J].统计与决策,2016(8):108-110.
[2]Zaim O,Taskin F.Environmental efficiency in carbon dioxide emissions in the OECD:a non-parametric approach[J].Journal of Environmental Management,2000,58(2):95-107.
[3]Zhou P,Ang BW,Han JY.Total factor carbon emission performance:a Malmquist index analysis[J].Energy Economics,2010,32(1):194-201.
[4]Wang Sufeng,Chu Chu,Chen Guozhong,et al.Efficiency and reduction cost of carbon emissions in China:a non-radial directional distance function method[J].Journal of Cleaner Production,2016,113:624-634.
[5]Hu Xiancun,Si Tongguang,Liu Chunlu.Total factor carbon emission performance measurement and development[J].Journal of Cleaner Production,2017,142:2804-2815.
[6]Cheng Zhonghua,Li Lianshui,Liu Jun,et al.Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution[J].Renewable and Sustainable Energy Reviews,2018,94:330-339.
[7]雷玉桃,杨娟.基于SFA方法的碳排放效率区域差异化与协调机制研究[J].经济理论与经济管理,2014(7):13-22.Lei Yutao,Yang Juan.A study of regional difference of carbon emission efficiency based on stochastic frontier analysis[J].Economic Theory and Business Management,2014(7):13-22.
[8]袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J].资源科学,2017,39(4):687-697.Yuan Changwei,Zhang Shuai,Jiao Ping,et al.Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China[J].Resources Science,2017,39(4):687-697.
[9]陈文新,潘宇.低碳约束下物流产业全要素生产率的空间分异及时空演化[J].工业技术经济,2016,35(11):42-52.Chen Wenxin,Pan Yu.Logistics industry total factor productivity spatial differentiation and space-time evolution at low carbon constraints[J].Journal of Industrial Technological Economics,2016,35(11):42-52.
[10]刘丹丹,赵颂扬旸,郭耀.全要素视角下中国西部地区能源效率及影响因素[J].中国环境科学,2015,35(6):1911-1920.Liu Dandan,Zhang Songyangyang,Guo Yao.Energy efficiency and influencing factors in western China from the perspective of total factors[J].China Environmental Science,2015,35(6):1911-1920.
[11]岳立,杨帆.“丝绸之路经济带”框架下中国与中亚五国能源效率评价:基于CCR-BCC和Malmquist指数分析方法的DEA-Tobit模型[J].统计与信息论坛,2016,31(6):37-43.
[12]陈黎明,黄伟.基于随机前沿的我国省域碳排放效率研究[J].统计与决策,2013(9):136-139.Chen Liming,Huang Wei.Research on carbon emission efficiency in China's provinces based on stochastic frontier[J].Statistics&Decision,2013(9):136-139.
[13]王惠,卞艺杰,王树乔.出口贸易、工业碳排放效率动态演进与空间溢出[J].数量经济技术经济研究,2016(1):3-19.
[14]马越越,王维国.中国物流业碳排放特征及其影响因素分析:基于LMDI分解技术[J].数学的实践与认识,2013,43(10):31-42.
[15]仲云云,仲伟周.中国区域全要素碳排放绩效及影响因素研究[J].商业经济与管理,2012,1(1):85-96.
[16]Seiford L M,Zhu J.Modeling undesirable factors in efficiency evaluation[J].European Journal of Operational Research,2002,142(1):16-20.
[17]范建平,肖慧,樊晓宏.考虑非期望产出的改进EBM-DEA三阶段模型省略:基于中国省际物流业效率的实证分析[J].中国管理科学,2017,25(8):166-174.
[18]王维国,马越越.中国区域物流产业效率:基于三阶段DEA模型的Malmquist-luenberger指数方法[J].系统工程,2012,30(3):66-75.
[19]张雪青.“一带一路”区域物流协同发展分析[J].统计与决策,2016(8):108-110.