China’s targeted poverty-alleviation policy has eliminated absolute poverty and become the focus of world attention. However, a relative-poverty problem still exists in China, and the large urban–rural income gap is an important issue. Whether the implementation of the targeted poverty-alleviation policy has narrowed the urban–rural income gap, along with its specific effects, requires an accurate analysis, which is particularly critical in order for China to implement a rural-revitalization strategy and further eliminate relative poverty in the future. Given the problems and shortcomings of the existing studies, such as not passing the parallel trend test to overestimate the policy effect, in this study we refer to the previous results, and our analyses divide the 124 counties in Yunnan province into four categories: non-poverty counties and counties with grade-I, grade-II, and grade-III poverty. We selected the panel data of the urban–rural income ratio of each county along with eight influencing factors from 2011 to 2020 for difference-in-difference model (DID) analysis. In this study, we compare the four types of counties level-by-level, and we construct a full-sample spatial DID model. The estimated results, after excluding the impact of COVID-2019, are significant. In addition, we perform robustness and placebo tests and other work on the DID model. All of the results show that the implementation of the targeted poverty-alleviation policy has effectively reduced the urban–rural income ratio in areas experiencing poverty. Finally, we use the intermediary effect analysis method to explore the reasons for the findings: driven by the targeted poverty-alleviation policy, the financial investment in poor areas has substantially increased, further increasing the income level of rural residents in poor areas and thus promoting a notable reduction in the income gap between urban and rural residents in poor areas. We suggest that, although China has achieved comprehensive success in targeted poverty-alleviation, assistance investment still needs to be increased, policies must be adjusted, and income growth must be accelerated to achieve industrial prosperity.

The study of the forest coverage rate (FCR) is related to the ecological environment and sustainable development goals (SDGs) of a region. In light of the lack of an organic integration method of “spatiotemporal evolution, correlation analysis, and change prediction” and the lack of a methodology that integrates methods of “remote sensing (RS) and GIS, multi-phase LUCC, and construction of econometric models” in the research methods at present, this study focus on Yunnan, a typical border province located in China with a relatively fragile “innate” ecological environment, as the research area. Based on the interpretation of land use/land cover (LULC) data retrieved from seven periods RS images (1990, 1995, 2000, 2005, 2010, 2015, and 2020), the spatiotemporal evolution of FCR in 129 counties was analyzed. Complementary research methods, such as the spatial econometric model, geographically weighted regression (GWR), and the geographic detector (GD), are used to reveal the influencing factors of FCR. Finally, this study predicts the FCRs of 129 counties in Yunnan from 2025 to 2050. The FCR in Yunnan presents an increasing trend year by year, increasing from 28.96% in 1990 to 49.05% in 2020. In addition, it exhibits spatial agglomeration characteristics with fewer values in the east and more in the west. The analysis of influencing factors show that the increases in the per capita GDP, land utilization rate, and annual average temperature, and the implementation of the Conversion of Cultivated Land into Forest Project (CCFP) will significantly improve the FCR, while the increases in the population density land reclamation rate, the proportion of construction land area, and the proportion of soil erosion land area will significantly reduce the FCR. Furthermore, the FCR is influenced by multiple factors, and the relative factors observed not only show significant spatial differences, but also present complex and diverse patterns, with the additional characteristics of being interwoven and overlapping. This study contributes to expanding and improving the methods and pathways of exploring the spatiotemporal evolution characteristics of FCR in ecologically fragile areas using RS methods, providing a reference for increasing FCR and improving the ecological environment’s quality in Yunnan Province and other ecologically fragile areas.