摘要
为促进省域低碳经济发展,以碳排放量为研究对象,采用5个解释变量建立面板数据模型。研究发现:经济增长和产业结构是影响东中部碳排放量的2个最重要变量,影响系数东部依次为0.602,0.544,中部依次为1.441,0.407,其次是人均收入水平、人口增长和能源价格;在西部,人均收入水平和产业结构是影响其碳排放量的2个最重要变量,影响系数分别为0.967,0.788,其次是人口增长、能源价格和经济增长状况;能源价格影响度从东到西依次提高,特别是人均收入对西部碳排放量影响度远远超过东部;因地制宜,对于东、中部地区政府要重视优化经济增长方式和调整其产业结构,对于西部更应重视调整人均收入政策及加强能源价格控制。面板单位根检验我国东中西部各变量均存在一阶单整,面板协整检验省域碳排放量与人们收入水平、经济增长水平、产业结构、能源价格和人口增长之间存在长期稳定内生经济关系。
The article which is focus on carbon emission uses five explanatory variables to establish panel data models for development of a low-carbon economy of Chinese provincial. The results show as follows： the most important factors which affect carbon emission of Eastern China and Central China are economic growth and industrial structure, the influencing coefficients of Eastern China are 0. 602 , 0. 544 respectively, and they are 1. 441,0.407 gy pricing and economic growth; The influencing coefficients of energy pricing is increasing gradually from east to west, especially the influencing coefficients which the level of per capita income has effect on carbon emission of Western China is far more greater than Eastern China; Suiting measures to local conditions, government should fo- cus on optimizing the mode of economic growth and adjusting industrial restructure in Eastern China and Central China, but emphasizing on control of per capita income level and energy pricing in Western China. By using of unit root tests of panel data, all variables are exist first order cointegration in Eastern China, Central China and Western China. Through the cointegration tests of panel data, there are exist endogenous economic relationships among carbon emission of Chinese provincial and the level of per capita income, economic growth, industrial structure, energy pricing and population growth. At last, relevant policy recommendations are put forward.
出处
《地域研究与开发》
CSSCI
北大核心
2011年第1期19-24,共6页
Areal Research and Development
基金
广西壮族自治区科技厅软科学项目(桂科软0897003)
2009桂林市第四批科学研究与技术开发项目(16)
关键词
低碳经济
碳排放量
面板数据模型
面板单位根检验
面板协整检验
a low-carbon economy
carbon emission
panel data models
unit root tests of panel data
the cointegration tests of panel data
