碳排放强度与经济增长的交互机制--基于GVAR模型的分析  

The Interaction Mechanism between Carbon Emission Intensity and Economic Growth:Analysis based on Global Vector Auto-Regressive(GVAR)Model

在线阅读下载全文

作  者:毕玉江 石金海 桑瑞聪 BI Yujiang;SHI Jinhai;SANG Ruicong(School of International Economics and Trade,Shanghai Lixin University of Accounting and Finance,Shanghai 201209,China)

机构地区:[1]上海立信会计金融学院国际经贸学院,上海201209

出  处:《技术经济与管理研究》2025年第4期15-21,共7页Journal of Technical Economics & Management

基  金:国家社会科学基金项目“数字技术驱动出口产品创新的机制与路径研究”(23BJY056)。

摘  要:全球化背景下一国或地区碳排放强度变化与经济增长率之间不仅有直接的相互影响关系,还存在着通过其他国家和地区的间接传导机制。研究发现,当前世界主要国家还存在较为明显的低碳经济特征。当碳排放强度发生负冲击之后,中国和美国经济增长率的下降幅度较为明显;中国和美国碳排放强度下降对其他国家经济增长产生了显著的负面影响。研究表明,推动低碳经济发展不仅需要各国从生产技术、能源利用等方面持续发力,还需要加强碳排放控制的国际合作,降低由于碳减排非同步性产生的额外成本。This paper investigates the mutual influence relationships between carbon emission intensity changes and economic growth rate for main countries or region under the background of globalization by using the Global Vector Auto-Regressive(GVAR)model.The results indicate that major countries in the world still have obvious low-carbon economic characteristics.When the economic growth rate experiences negative shocks,in order to cope with the impact of negative economic shocks,the carbon emission intensity of most countries shows an upward trend.The decrease in carbon emission intensity in major countries and regions will also have a significant adverse impact on the economic growth rate of other countries.These results show that promoting the development of low-carbon economy not only requires sustained efforts from various countries in production technology,energy utilization,and other aspects,but also requires strengthening international cooperation in carbon emission control to reduce additional costs caused by non-synchronous carbon emission reduction.

关 键 词:碳排放强度 经济增长 国际传导 全球向量自回归模型 

分 类 号:F113[经济管理—国际贸易]

 

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