Low-carbon city pilots (LCCP) is a key policy for realizing emission peak and carbon neutrality in China, using China’s samples from 280 towns from 2006 to 2016. The article utilizes PSM-DID, mediated effects, and moderating effects approach for validating a CO₂ reduction effect, mechanisms, and synergistic elements of LCCP. The regression outcomes suggest that (1) LCCP significantly decreases CO₂ emissions levels and average annual carbon emissions in LCCP fall by 2.6 percent. (2) LCCP focus on reducing carbon emissions by increasing R&D investment, the efficiency of energy, and decreasing the high CO₂ emissions industry. Among them, the reduction of the high carbon emission industry is mainly FDI, while the reduction of local industry is not obvious. (3) LCCP’s carbon reduction effects suggest a reversed U-shape relationship with city size. Digitalization and marketization of LCCP are crucial to the carbon reduction effect. Carbon reduction and pollution reduction have a strong synergistic effect.

Environmental quality assessment is an important way to promote the improvement of urban environmental quality. Environmental performance is usually used to evaluate the improvement of environmental quality, and residents’ satisfaction with environmental quality is also an important method to evaluate environmental improvement. At present, in many cities in China, the results of the two evaluation methods vary greatly. Residents’ environmental satisfaction is not high in some cities with good environmental performance; however, in cities with poor environmental performance, residents’ environmental satisfaction is higher. Here, based on the environmental subjective assessment of more than 4,600 independent samples from 56 cities in 2014, this paper constructed an index between subjective and objective scores for each sample and its city, separating the total samples into two groups. In order to analyze the differences between groups, firstly, the important factors driving the differences were extracted by random forest. Secondly, the key individual characteristics were identified by the model based on conditional inference tree. Finally, the regional heterogeneity was analyzed by nonmetric multidimensional scaling. The results show that population density is the main factor that affects the difference between subjective and objective evaluations. Furthermore, in those cities with low population density, investment increasing in transportation infrastructure helps to improve urban air quality, which can bring about more perceptual environmental optimization to people. As individuals, education is the key factor for residents when it comes to environment evaluation, but it is not a simple linear relationship. In terms of regional heterogeneity, the consistency of important factors among regions is not obvious, and the situation that “neighboring” cities share the same factors is not significant.