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Poverty eradication and environmental protection as the two global goals of sustainable development. China’s poverty alleviation policy attempts to achieve green development in poverty-stricken areas by eliminating poverty while also promoting environmental protection. Since the Poverty-stricken counties on the Qinghai-Tibet Plateau also have the dual attributes of ecological degradation and ecological fragility, it is of great significance to study the impact of poverty alleviation policy on their environment. In this research, taking poverty alleviation policy as the entry point, based on panel data and Remote Sensing Ecological Index for poverty-stricken counties on the Qinghai-Tibet Plateau from 2011 to 2019, and using the difference-in-differences (DID) method to verify the impact of policy on environmental quality. The main findings of the study were: 1) The poverty alleviation policy has a significant improvement effect on the ecological environment quality of counties in the Qinghai-Tibet Plateau region, and this conclusion still holds in a series of robustness tests using methods including the changing sample size method and the variable replacement method. Moreover, the policy effect has a certain time lag and its effect persists in the long term; 2) It is mainly due to the increased level of government public expenditure and the easing of government financial pressure that has contributed to the improvement of environmental quality in poverty-stricken areas; 3) Policy heterogeneity suggests that industrial poverty eradication policies are more conducive to promoting synergistic economic and environmental development in poverty-stricken areas.

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Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of “one large and two small” distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for “epidemic spatial risk classification and prevention and control level selection” to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.