Abstract
From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.
Highlights
The problem of carbon emissions and its influencing factors has become an important subject in relation to climate problems; China’s carbon emissions problems are in a very complex and uncertain environment
According to the mean carbon emission Moran scatter plots of 2000 and 2015 (Figures 1 and 2), it can be seen that the mean concentration of carbon emissions in province is mostly concentrated in the first and third quadrants; the region of low carbon emissions is surrounded by other regions of low carbon emissions; and the region of high carbon emissions is surrounded by other regions of high carbon emissions, which proves that the carbon emissions in these provinces have the characteristics of agglomeration
The significance level indicates the need for provincial carbon emissions for the local spatial correlation index analysis (LISA)
Summary
The problem of carbon emissions and its influencing factors has become an important subject in relation to climate problems; China’s carbon emissions problems are in a very complex and uncertain environment. Libo Wu and other scholars used the logarithmic mean Divisia index decomposition method to study the relationship between the per capita carbon dioxide emissions of different regions in China, as well as the influencing factors affecting energy consumption [3,4,5]. Existing research methods on carbon emissions and their influencing factors mainly use the decomposition method to decompose China’s carbon emissions into population, energy consumption intensity, per capita GDP, and other factors. Kaneko, and other scholars decomposed the influencing factors affecting carbon emissions by means of the logarithmic mean decomposition and studied the impact of factors such as per capita carbon dioxide emissions, energy consumption structure, energy efficiency, and energy intensity in China. The results show that economic activity and energy intensity decline are the two most important factors affecting carbon dioxide emissions in the chemical industry. Song used the two-stage logarithmic mean decomposition method to analyze the factors affecting China’s carbon emissions from 1990 to 2005 and reached the same conclusion [15]
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