Examining Determinants of CO2 Emissions in 73 Cities in China

Abstract

Issues concerning which factors that influence carbon dioxide emission, and which administrative measures should be imposed to reduce carbon emission in Chinese cities, have been on the agenda in cities' policy-making. Yet little literature has studied this topic from the city level. This paper first measures CO 2 emission of 73 Chinese cities. We find heterogeneity embedded in the cross-city distribution of CO 2 emission per capita and a nonlinear structure in the relationship between carbon emission and GDP per capita. To describe such multimodality and examine the determinants of CO 2 emission in these cities, this article applies a linear mixed effect model covering the quadratic term of GDP per capita to extend the stochastic impact by regression on population, affluence, and technology (STIRPAT) model. The empirical results demonstrate that population size, secondary industry proportion, energy consumption structure, urbanization level and economic level have generally shown a positive influence on CO 2 emissions in Chinese cities. However, the urbanization level is of no significance. The phenomenon of the environmental Kuznets curve varies across Chinese cities, according to which three city groups are formed. Specific policy recommendations are given to each city group in light of their unique influencing modes on carbon emissions.

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

“…However, in the work by Hao et al [20], this variable is criticized out of the influence of population scale, and it is substituted by carbon emission per capita. In a recent work by Zheng et al [1], carbon emission per capita is also used. Following these discussions, we select carbon emission per capita to indicate the I.…”

“…Similarly, Li et al [24] use only the industrialization rate in their study. Referring to Zheng et al [1], we have the both in our STIRPAT model. As to the affluence factor, GDP per capita is commonly applied [22,24,25].…”

“…The other two variables, lnenc and lnind are also ratios. The former is accounted with a conversion [1] and it reflects the use of coal in energy consumption. Considering the role of second industry in carbon emission, lnind is designed as the logarithm percentage of secondary industry.…”

“…In the model, lncarp works as the dependent variable. It indicates the carbon emission per capita accounted by special procedures [1]. we calculate the carbon emission of each city by adding the carbon emission embodied in energy consumption, major industrial productions, urban garbage disposal and substracting the carbon emission sinked by grass land [1,28].…”

“…It links human activities in the form of driving factors to their impacts on environment and indicates intensity of each factor [1,2]. There are many statistical models and the estimation methods to resolve the IPAT or STIRPAT model, including PLS (Partial Least Square) method [3,4], panel data model [5], linear mixed model [1], LMDI (Log-Mean Divisia Index) method [6], scenario analysis [7,8] and structural decomposition analysis [9]. However, how to choose and improve estimation methods of these models when employing the STIRPAT model?…”