In cities’ policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities’ emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs) to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.

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 CO₂ emission of 73 Chinese cities. We find heterogeneity embedded in the cross-city distribution of CO₂ 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 CO₂ 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 CO₂ 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.

Ascertaining the influencing factors of carbon dioxide emissions in Chinese cities is an important issue for policy-makers. This paper investigates the effect of several determinants on carbon emissions per capita in Chinese cities. Non-normally distributed and heterogeneous features of carbon emissions per capita in Chinese cities are considerably important. The empirical results demonstrate that GDP per capita has an increasingly positive impact on carbon emissions per capita due to the growth in household consumption. Urbanization has a slightly decreasing positive effect on carbon emissions per capita with a quantile increase resulting from continuous highway construction. Industrialization has a decreasing positive effect with carbon emission per capita quantile increases because of increasing energy efficiency and lower costs related to carbon reductions. The population has a decreasing negative effect on carbon emissions because of people’s increasing demand for environmental safety. The distributions of emissions per capita conditional on the 10th and 90th quantiles of independent variables also vary considerably. Specific policy implications are provided based on these results.