Absolute poverty has historically been solved in China, and the focus on poor areas has shifted to addressing relative poverty. To realize the organic combination of the rural revitalization strategy and relative poverty governance, multi-dimensional relative poverty identification and governance path research at the village scale in an alpine-gorge region is required. For this study, the Nujiang Lisu Autonomous Prefecture’s research area in a typical alpine-gorge was chosen. This paper constructed an evaluation index system for the rural regional system based on location conditions, ecological environment, productive resources, economic base, and public service, based on the theory of multi-dimensional regional poverty and the human–land relationship. The level of poverty, types of poverty, and spatial distribution characteristics of 255 administrative villages were systematically analyzed, and poverty governance paths were proposed. The results show that: (1) There were 215 multi-dimensional relative poverty villages in Nujiang Prefecture, accounting for 84.31% of the total. The relatively poor villages with poverty grades I and II, which are classified as mild poverty, account for 77.21% of all poor villages; this demonstrated that the relatively poor villages in Nujiang Prefecture had a high potential for poverty alleviation. (2) There are 19 different types of constraints in poor villages. Grades III and IV poor villages were mostly found in high-altitude areas. The economic foundation was very weak, the infrastructure was imperfect, the land use type was relatively single, and traffic conditions were relatively backward. (3) The priority model accounted for 16.67% of relative poverty governance, the steady improvement accounted for 28.79%, and key support accounted for 54.54%. Relative poverty governance paths for various counties have been proposed, including rural revitalization priority demonstration, ecological environment governance, eco-tourism, modern agriculture + mountain agroforestry, and improved people’s livelihood and well-being. The findings provided scientific support and direction for future research on the mode and course of relative poverty governance in poor villages in the alpine-gorge area, as well as the rural revitalization strategy’s implementation.

The ecological barrier is a complex ecosystem that couples the human–nature relationship, and the ecologically critical area is an irreplaceable area with a special value in the ecosystem. Therefore, protecting the ecologically critical area is vital for maintaining and improving regional ecological security. Limited research has been conducted on the evolution of ecologically critical areas, and none of the studies have considered the spatiotemporal heterogeneity of the driving factors for different evolution modes and types. Therefore, this research adopts the ecologically critical index, landscape expansion index, and the random forest model to analyze the pattern, driving factors, and its spatial-temporal heterogeneity to the evolution modes and specific types of ecologically critical areas in the Sichuan–Yunnan ecological barrier area in the last 15 years. The results showed that: (1) the ecologically critical areas in the Sichuan–Yunnan ecological barrier have changed dramatically, with the area reduction being 61.06%. Additionally, the spatial distribution characteristics of the ecologically critical area from north to south include planar, point, and linear forms. (2) The evolution trend of the ecologically critical area is ‘degradation–expansion–degradation’. Spread is the predominant type of expansion mode, whereas atrophy is the predominant type of degradation mode, indicating that the evolution mainly occurs at the edge of the original ecologically critical areas. (3) In general, precipitation, area of forest, area of cropland, and GDP have contributed significantly to the evolution of ecologically critical areas. However, the same driving factor has different effects on the expansion and degradation of these areas. Expansion is driven by multiple factors at the same time but is mainly related to human activities and land use change, whereas for degradation, climate and policy are the main driving factors. The present research aimed to quantitatively identify the evolution modes and specific types of ecologically critical areas and explore the spatiotemporal heterogeneity of driving factors. The results can help decision-makers in formulating ecological protection policies according to local conditions and in maintaining and enhancing the regional ecological functions, thereby promoting the sustainable development of society-economy-ecology.

The process of eliminating absolute poverty is inevitable for China’s social and economic transformation. However, there are currently few studies on the relationship between land use transformation (LUT) and rural income under different stages of poverty governance. This study, therefore, uses spatial autocorrelation analysis and a multiscale geographic weighted regression (MGWR) model to explore the mechanisms of LUT on rural income and its spatiotemporal heterogeneity in Yunnan Province during the comprehensive poverty alleviation (CPA) period and the targeted poverty alleviation (TPA) period at the county scale. The results demonstrate that: (1) the numbers of both low-income and high-income counties continued to decrease, while the number of middle-high-income counties increased, and rural income demonstrated a positive spatial correlation. (2) Most of the variables in the dominant recessive increased in the CPA and decreased in the TPA period. As for recessive morphology, the ecological function variables decreased first and then increased. (3) The driving force of dominant morphology is strong and sustained, and the driving force of recessive morphology is gradually enhanced. The results are vital for consolidating the results of poverty eradication and bridging rural revitalization. They may also provide useful references for sustainable land use and effective poverty alleviation in other developing countries.