With the serious worldwide problem of carbon emissions, carbon emissions’ control and trade have become necessary policies adopted by the governments, after the establishment of the EU Emissions Trading System (EU ETS), China has also implemented a similar carbon emission trading pilot policy which plays a vital role in environmental regulation and influences enterprise behaviors, so this paper will put more focus on the impact of this policy on the enterprise innovation. In this paper, we construct a theoretical model and use the “Chinese Carbon Emissions Trading Pilot Policy” as a quasi-natural experiment to accurately identify the net causal effect of this environmental policy on enterprise innovation. Moreover, we use the synthetic control and difference-in-differences methods to eliminate the endogeneity to a large extent and conduct the robustness tests, difference-in-differences method, placebo test, and permutation test to respectively confirm these results. The results show that the implementation of carbon emissions trading policy will significantly reduce the enterprise innovation in general, this conclusion is contrary to the EU ETS’s effect, which is confirmed by plenty of previous empirical study. However, this policy has different effects across enterprises of different industries and different types of innovation. It can promote enterprise innovation of environmental industry, but it will inhibit enterprise innovation of non-environmental industry; besides, it can promote the development of green technical innovation and inhibit non-green technical innovation, which is basically identical to the causal effect of EU ETS.

Based on the pattern of difference in Chinese social trust, this study classifies the social trust into trust in family members, trust in acquaintances, and trust in strangers. Then, the correlational relationship between different types of social trust and subjective well-being is examined using the micro survey data in China. It is found that different types of social trust vary greatly in the correlation with subjective well-being. The main findings are as follows: (a) Trust in family members has no significant correlation with subjective well-being; (b) Only “totally trust acquaintances” has a significant positive correlation with subjective well-being; (c) Trust in strangers has a significant positive correlation with subjective well-being—the higher the trust level, the stronger the correlation with subjective well-being will be—and (d) Urban–rural and male–female differences exist in the correlational relationship between trust in strangers and subjective well-being.

China is the largest carbon dioxide emitter in the world, and reducing China’s transportation carbon emissions is of great significance for the world. Using the Chinese provincial data from 2005–2015, this article analyzes the convergence characteristics of per capita transportation carbon emissions in China. It employs the log t regression test method and the club clustering algorithm developed by Phillips and Sul (2007) to separate the provinces and municipalities in China into three convergence clubs with different transportation carbon emission levels and one divergent group. Among them, the divergent group consisted of Beijing and Liaoning; the high carbon emission club consisted of Shanghai and Inner Mongolia; the low carbon emission club consisted of Jiangxi, Henan, Shandong, Hebei, and Sichuan; the medium carbon emission club consisted of the remaining 21 provinces and municipalities. On this basis, this article adopts the Ordered Logit model to explore factors influencing the formation of the convergence clubs. The regression results showed that the per capita transportation carbon emissions in the provinces with a high energy intensity of the transportation sector, a high urbanization level, or a high fixed assets investment intensity of the transportation sector tended to converge into the high carbon emission club.