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.

In response to the call of the Chinese government to support low-carbon development, the issue has come to the view gradually as to whether the behaviors of banks’ green credit will contribute to easing their own credit risk. To reflect the behaviors of green credit of banks in detail, an indicator, named the carbon intensity of loans (CIL), is first proposed in this paper to measure the carbon emissions with association of the loans for commercial banks, on basis of the series of the input–output table. Then, a panel data model is used to explore the relationship between CIL and non-performing loan ratio, which measures the credit risk of banks. Based on the data of China’s commercial banks from 2007 to 2014, an empirical study has been conducted to investigate the impacts of CIL upon the non-performing loan ratio from a microscopic-level perspective. The result indicates that CIL has a positive effect on the non-performing loan ratio of banks. Since CIL is considered a significant indicator for the banks’ green credit, this paper comes to a conclusion that the green credit policy not only contributes to achieving of the emission-reduction targets for the society, but also promotes the development of banks’ credit risk.