资源科学 ›› 2017, Vol. 39 ›› Issue (4): 687-697.doi: 10.18402/resci.2017.04.10

• • 上一篇    下一篇

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

袁长伟1(), 张帅1, 焦萍1, 武大勇2   

  1. 1. 长安大学经济与管理学院,西安 710064
    2. 德克萨斯理工大学土木与环境工程系,德克萨斯卢伯克 TX79409
  • 收稿日期:2016-09-18 修回日期:2016-11-29 出版日期:2017-04-30 发布日期:2017-04-25
  • 作者简介:

    作者简介:袁长伟,男,湖南邵阳人,博士,教授,主要研究方向为交通-能源-环境复杂作用机制。E-mail:yuanchangwei@126.com

  • 基金资助:
    国家自然科学基金项目(51278057);霍英东青年教育基金(151075);中央高校基本科研费项目创新团队项目(310823160103)

Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China

Changwei YUAN1(), Shuai ZHANG1, Ping JIAO1, Dayong WU2   

  1. 1. School of Economics and Management,Chang'an University,Xi'an 710064,China
    2. Department of Civil and Environmental Engineering,Texas Tech University,Lubbock, Texas TX79409,USA
  • Received:2016-09-18 Revised:2016-11-29 Online:2017-04-30 Published:2017-04-25

摘要:

正确、客观地测算交通运输全要素碳排放效率有利于推动技术进步与制定差异化碳减排政策。基于2004-2013年交通运输碳排放数据,采用考虑非期望产出超效率SBM模型测度中国省域交通运输全要素碳排放效率,探讨中国省域及东、中、西部交通运输碳排放效率空间分布及趋势变动,分析中国交通运输全要素碳排放效率的空间聚集特性及其主要影响因素。研究结果显示:①中国交通运输全要素碳排放效率变动趋势随发展阶段符合典型的环境库兹涅茨曲线,呈现2005-2009年下降,2009-2013年效率缓慢上升的趋势;②交通运输全要素碳排放效率呈现东部、中部和西部依次递减的空间规律,且相邻省份之间存在明显的空间正相关关系;③中国交通运输全要素碳排放效率在空间上存在明显的聚集状态,其中河北、山东、江苏、天津等东部沿海省份形成“H-H”聚集区;广东、江西、湖南、湖北等中南华南地区和东北地区普遍形成“L-L”聚集区;④通过空间计量模型,发现影响交通运输碳排放效率的主要因素为人口规模、收入水平、交通运输强度、要素禀赋、交通运输结构和节能技术水平,其中节能技术水平与碳排放效率呈现正相关关系,其它与碳排放效率呈现负相关关系。

关键词: 全要素碳排放效率, 交通运输碳减排, 超效率SBM模型, 空间相关分析, 空间计量模型

Abstract:

Measuring the correct and objective total factor efficiency of provincial transportation carbon emissions in China promotes technological progress and helps to develop differentiated carbon emission reduction policies. Using data of transportation carbon emissions from 2004 to 2013,we measure provincial total factor transportation carbon emissions efficiency by the model of superefficient-SBM-undesirable output. On this basis,we research the spatial difference and change trend changing of transportation carbon emission efficiency in china and three regions, and analyze space clustering characteristics and influencing factors of efficiency. The results show:①The tendency of total factor efficiency on transportation carbon emissions fits the trendency of typical Environment Kuznets Curve,during 2005-2009 efficiency quickly being declined,and then rising slowly from 2009-2013. ②Average efficiency in the eastern regions is significantly higher than be in the central regions and western regions. The carbon emission efficiency has the relation of spatial autocorrelation between close provinces. ③Obvious spatial aggregation features on China's transportation total factor efficiency of carbon is existing. Among these,the eastern regions exhibit a H-H assembly of carbon efficiency,including Hebei,Shandong,Jiangsu and Tianjin. The central southern,southern and northeast regions exhibit L-L assemblies of efficiency,Guangdong,Jiangxi,Hunan and Hubei included. ④According to the spatial econometric model,the main factors influencing transportation carbon emissions efficiency are population size,income level,transport intensity,elements endowment,transportation structure and the level of energy-saving technology. Among these,the level of energy-saving technology has positive impact on efficiency,and others have negative impact on efficiency.

Key words: total factor carbon emissions efficiency, transport carbon reduction, supper efficient-SBM model, spatial autocorrelation analysis, spatial econometrics model