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交通运输、经济增长和碳排放的动态关系研究——基于1949~2012年数据的实证分析
引用本文:吴继贵,叶阿忠.交通运输、经济增长和碳排放的动态关系研究——基于1949~2012年数据的实证分析[J].交通运输系统工程与信息,2015,15(4):10-17.
作者姓名:吴继贵  叶阿忠
作者单位:福州大学经济与管理学院,福州350108
基金项目:国家自然科学基金(71171057);教育部高等学校博士点基金(20103514110009);教育部人文社科基金 (10YJA790227, 12CJY011).
摘    要:本文采用1949~2012年的数据,应用向量自回归模型(VAR)对中国交通运输、经济增长和碳排放三者间相互影响的动态关系进行探讨.研究表明,经济增长和交通运输对碳排放的贡献存在"拐点效应",即二者在短期内对碳排放的贡献表现出上升趋势明显的现象,然而从长远角度看,这一影响逐渐减弱;碳排放对经济增长的短期刺激效应较为突出,而长期方向上则表现为递减的现象,同时,在样本区间内,经济增长对交通运输业冲击的响应较为微弱,缺乏敏感性;经济增长和碳排放均对交通运输业的发展产生了不同程度的正向刺激作用.

关 键 词:交通运输经济  动态关系  向量自回归  脉冲响应  方差分解  
收稿时间:2015-03-31

Dynamic Linkages among Transportation,Economic Growth and Carbon Emissions in China:Empirical Research on the Data from 1949 to 2012
WU Ji-gui,YE A-zhong.Dynamic Linkages among Transportation,Economic Growth and Carbon Emissions in China:Empirical Research on the Data from 1949 to 2012[J].Transportation Systems Engineering and Information,2015,15(4):10-17.
Authors:WU Ji-gui  YE A-zhong
Institution:School of Economics and Management, Fuzhou University, Fuzhou 350108, China
Abstract:Appling VAR model with the time- series data during 1949 to 2012 period, this paper explores the dynamic relationship between transportation industry, economic growth and carbon emissions. The research results show that: Between the contribution of economic growth and transportation industry to carbon emissions, there is an“inflection point”effect , which indicates that the impacts of both economic growth and transportation industry on carbon emissions strengthen in the short term, while in the long-term scale, the impacts moderate progressively; The impacts of carbon emissions on economic growth are significant in the short term, however, in the long run the impacts decrease progressively, in the sample interval, the response of economic growth is faint under the shock from transportation industry; Both economic growth and carbon emissions can stimulate the development of transportation industry positively in varying degrees.
Keywords:transportation economy  dynamic relation  VAR model  impulse response  variance decomposition  
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