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

Yu Liu,Precious Zikhali,Yingkang Lv,Hongwei Xiao

doi:10.3390/su6096005

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.

Highlights

The challenge of climate change continues to put pressure on countries to shift to a low-carbon economy, which is loosely defined as an economy that produces minimal greenhouse gas (GHG)emissions
The results indicate that the spillover effect of economic development on the carbon intensity is greater than on the scale of carbon emission
Durbin model is applied to test the impacts of the driving factors on the scale and intensity of carbon emissions and to examine spatial correlation of carbon emissions and the spillover effects of the determinants

Summary

Introduction

The challenge of climate change continues to put pressure on countries to shift to a low-carbon economy, which is loosely defined as an economy that produces minimal greenhouse gas (GHG). Some impressive studies have applied spatial panel models to analyze China’s regional or industrial environmental Kuznets curve [1,2,3,4,5,6,7,8,9,10] Most of these studies tend to focus on the impact of economic growth and foreign direct investment (FDI) on carbon emission, while the impacts of other factors, such as population size, urbanization, and energy price, are often ignored. Based on the extended STIRPAT, the papers build a spatial econometric model by taking into account the fact that carbon emissions are heterogeneous and spatially correlated across regions and industries. It measures the impact of the carbon emissions scale and intensity of the adjacent regions on the region’s carbon emissions scale and extent

Spatial Weight Matrix

Model Selection

Spillover Effects of Regional Carbon Emissions

Findings

Conclusions and Policy Recommendations