Carbon Emissions in China: A Spatial Econometric Analysis at the Regional Level

Abstract

An extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, incorporating factors that drive carbon emissions, is built from the regional perspective. A spatial Durbin model is applied to investigate the factors, including population, urbanization level, economic development, energy intensity, industrial structure, energy consumption structure, energy price, and openness, that impact both the scale and intensity of carbon emissions. After performing the model, we find that the revealed negative and significant impact of spatial-lagged variables suggests that the carbon emissions among regions are highly correlated. Therefore, the empirical results suggest that the provinces are doing an exemplary job of lowering carbon emissions. The driving factors, with the exception of energy prices, significantly impact carbon emissions both directly and indirectly. We, thus, argue that spatial correlation, endogeneity and externality should be taken into account in formulating polices that seek to reduce carbon emissions in China. Carbon emissions will not be met by controlling economic development, but by energy consumption and low-carbon path.

Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model

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Changes in CO2 Emissions Induced by Agricultural Inputs in China over 1991–2014

“…Rapid economic development is usually accompanied by highly intensive fossil energy combustion and increased use of industrial processes, which are the most important anthropogenic sources of the greenhouse gases [13], especially in developing countries. China is the largest developing and agricultural country in the world.…”

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