How do varying socio-economic driving forces affect China's carbon emissions? New evidence from a multiscale geographically weighted regression model.

Abstract

The increase in carbon emissions has had great negative impacts on the healthy developments of the human environment and economic society. However, it is unclear how specific socio-economic factors are driving carbon emissions. Based on the multiscale geographically weighted regression (MGWR) model, this paper analyzes the impact mechanism of China's carbon emission data during 2010-2017. The results show that (1) during the study period, China's carbon emissions have obvious positive correlations in the spatial distribution, and the spatial autocorrelation of carbon emissions on the time scale has a further strengthening trend. (2) Compared with the results of the geographically weighted regression (GWR) model, the MGWR model is more robust, and the results are more realistic and reliable. The impacts of energy intensity, proportion of green coverage in built-up areas, and industrial structure on provincial carbon emissions are close to the global scale, and their spatial heterogeneity is weak. Other factors have spatially heterogeneous impacts on carbon emissions with different scale effects. (3) Except for proportion of green coverage in built-up areas, the industrial structure and trade openness have insignificant impacts on carbon emissions, but other variables have significant impacts. The total population, urbanization rate, energy intensity, and energy structure have positive impacts on carbon emissions, while the GDP per capita and foreign direct investment have negative impacts on it. This study shows that the main socio-economic factors have different degrees of impacts on carbon emissions with different scale, and we can refer to it to formulate more scientific measures to reduce carbon emissions.