With the challenge to reach targets of carbon emission reduction at the regional level, it is necessary to analyze the regional differences and influencing factors on China’s carbon emission efficiency. Based on statistics from 2005 to 2015, carbon emission efficiency and the differences in 30 provinces of China were rated by the Modified Undesirable Epsilon-based measure (EBM) Data Envelopment Analysis (DEA) Model. Additionally, we further analyzed the influencing factors of carbon emission efficiency’s differences in the Tobit model. We found that the overall carbon emission efficiency was relatively low in China. The level of carbon emission efficiency is the highest in the East region, followed by the Central and West regions. As for the influencing factors, industrial structure, external development, and science and technology level had a significant positive relationship with carbon emission efficiency, whereas government intervention and energy intensity demonstrated a negative correlation with carbon emission efficiency. The contributions of this paper include two aspects. First, we used the Modified Undesirable EBM DEA Model, which is more accurate than traditional methods. Secondly, based on the data’s unit root testing and cointegration, the paper verified the influencing factors of carbon emission efficiency by the Tobit model, which avoids the spurious regression. Based on the results, we also provide several policy implications for policymakers to improve carbon emission efficiency in different regions.

The overabundance of carbon emissions is widely considered as a serious world problem. This paper focuses on analyzing the influence of economic factors on carbon emissions. Based on the traditional STIRPAT model, in terms of the “pollution haven hypothesis” and “pollution halo hypothesis”, this paper employs the dynamic panel data model to explore the impact of economic elements such as economic growth, population, foreign direct investment and others on carbon emissions. Based on our research, China’s urban carbon emissions do not follow the inverted U-shaped hypothesis of the traditional EKC curve theory and presents an inverted N-type. Moreover, current foreign direct investment increases the carbon emissions of Chinese cities due to the “implicit trade carbon”. However, during the lagging period of one phase, it significantly reduced urban carbon emissions. In addition, the lag of one period of carbon emissions statistically led to carbon emissions at the current stage. According to the empirical analysis results, this paper proposes some reasonable improvements for carbon dioxide emission reduction, which have certain reference values for other developing countries facing similar carbon emission reduction challenges.

Regionally coordinated green development has been widely documented in China. However, most previous studies have investigated it from the perspective of linearity, while the spatial correlation of green development is nonlinear. Based on 48 cities in Bohai Rim, this study used a social network analysis to measure the spatial network, with an emphasis on the internal structure of regional green development, and analyzed the driving factors of regionally coordinated green development from the perspective of nonlinearity. We found that large cities have formed a “siphon effect” and that the polarization of eco-efficiency has become increasingly serious. There are limited connections, some of which are redundant, in the spatial network of green development, while the stability of the network is strong. Additionally, reducing the differences in environmental regulation approaches among cities can have a positive impact on the spatial correlation and spillover effect of green development, thereby promoting regionally coordinated green development among cities in the Bohai Rim.