中国经济持续增长下的碳减排对策研究——基于LSTR模型的二氧化碳环境库兹涅茨曲线  被引量:3

China's Economic Growth and Countermeasures of Carbon Emission Reduction : Based on LSTR Model of Carbon Dioxide Environmental Kuznets Curve

在线阅读下载全文

作  者:王维国[1] 孟军[1] 

机构地区:[1]东北财经大学数学与数量经济学院,辽宁大连116025

出  处:《统计与信息论坛》2013年第4期31-37,共7页Journal of Statistics and Information

基  金:国家自然科学基金面上项目<基于结构突变和截面相关的省际碳排放面板协整检验方法>(71171035);国家社科基金重大项目<"十二五"时期宏观经济运行动态监测分析研究>(10zd&010);国家社会科学基金重大招标课题<抑制产能过剩与治理重复建设对策研究>(09&ZD026)

摘  要:通过建立中国人均二氧化碳排放和人均GDP关系的LSTR模型,分别以经济增长速度和能源强度为转换变量进行了分析。结果表明,中国人均二氧化碳排放和人均GDP的关系在不同经济环境下有所不同。为实现碳减排和经济增长之双赢,必须在降低能源强度上下功夫。由能源强度的结构向量误差修正模型(SVECM)的脉冲响应分析得知,短期内,能源价格改革和扩大对外开放程度成为"迅速"降低能源强度的关键;长期而言,必须大力发展可再生能源和清洁能源,彻底改变能源消费结构。Based on the establishment of the LSTR model of the relationship between carbon dioxide e- missions per capita and GDP per capita in China, the study analyzes the rate of economic growth and the energy intensity respectively as the threshold variables. It shows that the relationship between carbon di- oxide emissions per capita and GDP per capita in China is different in different economic conditions such as different rates of economic growth and different energy intensities. For the purpose of achieving the win-- win effectiveness on the carbon reduction and economic growth, the main measures should be concentrated on how to reduce energy intensity. According to the analysis on the impulse response of SVECM and vari- ance decomposition of energy intensity, in the short term, the key and prompt approach to reducing the en- ergy intensity in China lies in the reform of energy price and the degree of opening to the outside, in order to realize the decline in energy intensity. In a long run, more vigorous effort should be contributed to the development of renewable energy and clean energy and the improvement of energy consumption structureof our country.

关 键 词:人均二氧化碳排放 人均GDP环境库兹涅茨曲线 LSTR模型 能源强度 

分 类 号:F224.0[经济管理—国民经济]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象