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.

Green development is necessary for building a high-quality modern economic system. The contribution of the article mainly includes the following three parts: First is the study on the urban land green use efficiency (ULGUE) in 30 provinces of China from 2008 to 2018 by adopting the epsilon-based measure (EBM) model with undesirable outputs to yield a more accurate and reasonable assessment result. In addition, the spatial agglomeration characteristics were analysed according to the spatial autocorrelation analysis. Thirdly, the spatial Durbin model was applied to analyse the driving factors of the WRGUE, which considers the spatial effects. The findings are as follows: (1) The regional differences in ULGUE were very significant, with the number decreasing from the coastal region to inland. (2) ULGUE showed a significantly positive spatial autocorrelation, and the spatial homogeneity was more significant than the spatial heterogeneity for ULGUE. (3) Economic development level, technical progress level, and urban population density have a significant impact on ULGUE, while the higher the proportion of the secondary industry in GDP, the lower the level of ULGUE. The research results may be a useful reference point for policymakers.

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

The contributions of this research are making the theoretical analysis of the impact mechanism of urbanisation on carbon emissions efficiency (CEE), and then empirically analysing the effects of urbanisation on CEE in Western China from 2010 to 2019 by applying the super-efficiency epsilon-based measure model with undesirable outputs and the Tobit model. Major findings are: (1) The mean CEE of the 11 western provinces show a trend of declining and then increasing over those 10 years, demonstrating U-shaped change characteristics. (2) The CEE of Guangxi has the most significant decrease, while the CEE of Chongqing showed only a small fluctuation; the CEE of Guizhou has the characteristic that first it rises, then descends, then rises again; the CEEs of Inner Mongolia and Yunnan have been in the production frontier surface from 2010 to 2015, and presents the trend of declining, then increasing after 2015. The CEEs of the other six western provinces present a trend of declining, then increasing. (3) Chongqing, Sichuan, Inner Mongolia, and Yunnan have a high level of CEE, followed by Shaanxi, Xinjiang, Guangxi, and Guizhou, with an intermediate level of CEE, and Gansu, Ningxia, and Qinghai, with the lowest level of CEE. (4) The effects of urbanisation on CEE in Western China present a U-shaped relationship, meaning that the process of influence was first inhibition, then stimulation. At present, the process of urbanisation shows negative impacts on the CEE, while the tipping point has not yet arrived. The western provinces of China should follow the path of high-quality urbanisation to quickly shift the impact of urbanisation on CEE to the right side of the U-shaped curve.