The construction and development of China’s forest cities revolves around the goal of building an environmentally friendly society and achieving harmonious development between human beings and nature, which is essentially the implementation of the concept of green development and helps promote the sustainable development of sustainable cities. Based on the panel data of 263 prefecture-level cities in China from 2001 to 2020, the National Forest City Policy (NFCP) is used as a quasi-natural experiment to assess the effect of NFCP on haze pollution management and their heterogeneity using a time-varying DID (difference-in-differences) model, and a mediating effect model is used to analyze the mechanism of the effect of NFCP on haze pollution. The research found that (1) the NFCP can reduce urban haze pollution, and this finding remained robust after placebo tests and the replacement of explanatory variables; (2) the NFCP is more conducive to reducing haze pollution in the Pearl River Delta urban agglomeration, Type II large cities, cities in the eastern region, and cities east of the Hu Huanyong line; (3) the NFCP will improve urban green space coverage, raise residents’ awareness of environmental protection, and promote the development of tertiary industries, thereby promoting urban haze reduction.

Understanding the determination factors of the spatial distribution of forest cover is crucial for global forest governance. This study contributed a nuanced case, focusing on the determination factors for the spatial distribution of forest cover in Fujian Province, China, in 2020. In order to achieve this, a high-resolution GIS-based data set was used, and spatial auto-correlation and geographic detector approaches were adopted. Three findings are presented in the results. First, the spatial distribution of forest cover is affected by natural conditions. In regions with more precipitation, higher altitude, or cooler temperatures, forest cover is higher. The relationship between the spatial distribution of forest cover and slope is an inverted-U shape. Second, socioeconomic factors have a greater explanatory capacity. In particular, regions with dense populations or roads have less forest cover. Third, there is an inverted-U-shaped relationship between the spatial distribution of forest cover and GDP per capita. With the growth of GDP per capita, forest cover is first positive, but subsequently negative. The results indicate that natural factors could shape the spatial distribution of forest cover, while socioeconomic factors could play a more significant role in the spatial distribution of forest cover.

Understanding the livelihood strategy choices and influencing factors of farmers in the area of GGP is conducive to improving the livelihood level of farmers in the area and promoting the quality and efficiency of the project. For this study, we combine the characteristics of farmers in the area of GGP, constructs a livelihood evaluation index system for farmers, and uses a binary logistic model to explore the influencing factors of farmers’ livelihood strategy choices. The research found: (1) The livelihood capital of farmers is ranked from high to low as part-time agricultural type (0.183), non-agricultural type (0.174), and pure agricultural type (0.126). (2) The stability level of farmers’ livelihoods is ranked from high to low as part-time agricultural type (0.607), non-agricultural type (0.519), and pure agricultural type (0.409). (3) The impact of livelihood capital on the livelihood strategy choices of different types of farmers varies significantly. The quality of economic forest site and the health status of the head of household is positively affecting the part-time agricultural type, negatively affecting pure agricultural type; Productive assets are positively affecting pure agricultural type, negatively acting on non-agricultural type, while labor is opposite. It is recommended that the government needs to pay attention to changes in farmers livelihoods after the expiration of the subsidy for GGP, and develop differentiated livelihood level improvement policies based on the characteristics of farmers’ livelihood capital.