Abstract
Carbon productivity, defined as the gross domestic product (GDP) per unit of CO2 emissions, has been used by provincial governments in China as in indicator for effort and effect in addressing climate-change problems. The aggregate impact of economic growth on carbon productivity is complex and worthy of extensive investigation to design effective environmental and economic policies. Based on a novel combination of the smooth transition regression model and the Markov regime-switching regression model, this paper analyzes time series data on carbon productivity and economic growth from Hubei Province in China. The results show that the influence of economic growth on carbon productivity is highly nonlinear. In general, economic growth has a positive impact on improving carbon productivity. From a longitudinal perspective, this nonlinear positive impact is further divided into three stages, transiting from a high regime to a low regime and then back to a high regime. The high regime stage, in which economic growth has stronger positive influence on enhancing carbon productivity, is expected to last for considerably longer time than the low regime stage. It is more probable for a low regime stage to transit to a high regime. Once the relation of carbon productivity and economic growth enters the high regime status it becomes relatively stable there. If the government aims to achieve higher carbon productivity, it is helpful to encourage stronger economic development. However, simply enhancing carbon productivity is not enough for curbing carbon emissions, especially for fast growing economies.
Highlights
In order to achieve the goal of stopping global warming at 2 ◦ C above pre-industrial levels, anthropogenic greenhouse gas emissions need to be carefully controlled [1]
The high regime, in which economic growth has a stronger positive impact on enhancing carbon productivity, is expected to last for significantly longer time than the low regime. It is more probable for a low regime to transit to a high regime
The results show that the null hypothesis of linear effect cannot be rejected if ln PGDP is the dependent variable in the vector autoregression (VAR) model, but the null hypothesis of linear effect is rejected if ln CP is the dependent variable under the dimension of 3, 4, 5, 6
Summary
In order to achieve the goal of stopping global warming at 2 ◦ C above pre-industrial levels, anthropogenic greenhouse gas emissions need to be carefully controlled [1]. Economic growth, while it improves social welfare, continuously generates huge amounts of greenhouse gas emissions. How to coordinate economic growth and greenhouse gas abatement has become an important problem for governments, especially in developing countries. Since the majority of greenhouse gas emissions is carbon dioxide (CO2 ), in the rest of this article, the focus is on CO2 emissions. To slow down production of CO2 emissions while sustaining economic growth, the Chinese government seeks to increase carbon productivity, which is defined by the gross domestic product (GDP) per unit of CO2 emissions.
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