In recent years, environmental degradation and the COVID-19 pandemic have seriously affected economic development and social stability. Addressing the impact of major public health events on residents’ willingness to pay for environmental protection (WTPEP) and analyzing the drivers are necessary for improving human well-being and environmental sustainability. We designed a questionnaire to analyze the change in residents’ WTPEP before and during COVID-19 and an established ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), geographically weighted regression (GWR), and multiscale GWR to explore driver factors and scale effects of WTPEP based on the theory of environment Kuznets curve (EKC). The results show that (1) WTPEP is 0–20,000 yuan before COVID-19 and 0–50,000 yuan during COVID-19. Residents’ WTPEP improved during COVID-19, which indicates that residents’ demand for an ecological environment is increasing; (2) The shapes and inflection points of the relationships between income and WTPEP are spatially heterogeneous before and during COVID-19, but the northern WTPEP is larger than southern, which indicates that there is a spatial imbalance in WTPEP; (3) Environmental degradation, health, environmental quality, and education are WTPEP’s significant macro-drivers, whereas income, age, and gender are significant micro-drivers. Those factors can help policymakers better understand which factors are more suitable for macro or micro environmental policy-making and what targeted measures could be taken to solve the contradiction between the growing ecological environment demand of residents and the spatial imbalance of WTPEP in the future.

Worldwide most agroecosystems effort to increase production and yields and leads to damages of a series of non-provisioning ecosystem services (ESs). To fill in the knowledge gaps pertaining to the understanding of complex relationship between agricultural harvests and other ESs, therefore this study aims to estimate the existence of Environmental Kuznets Curve (EKC) for agricultural ESs by incorporating the spatial factors. Based on the test of the spatial autocorrelation of agricultural ESs, the estimation results of spatial model are compared with general regression to explain the spatial effect of agricultural ESs. The results show that (1) contrary to expectation, the curve of the nonlinear relationship between agricultural ESs and annual household income is an inverted U-shape, and not an upright U-shape; (2) compared to non-spatial model, the turning point of the inverted U-shaped curve for agricultural ESs under the direct effect would happen earlier and happen later under the indirect effect; (3) years of formal education, vegetation coverage of field margin and cultivated land area have significantly impact on local agricultural ESs, and local perennial crops has significantly impact on agricultural ESs of neighboring villages. Results of this study have a promising application prospect to promote sustainable development of agriculture.

Objectives: As the digital wave reshapes the future of cities, smart city construction (SCC) is emerging as a key driver for improving public health and bridging social gaps through its inclusive potential. This research aims to investigate the impact of SCC on the health of middle-aged and older disabilities individuals, focusing on its dynamic trends, underlying mechanisms, unequal selection effects, and overall welfare. Methods: Based on data from the China Health and Retirement Longitudinal Study, this paper estimates the impact of SCC on the health of middle-aged and older disabilities using the double machine learning method with the random forest algorithm, and examines the dynamic trend of the health effects using event study method. Results: The results indicate that SCC significantly enhances the health of middle-aged and older disabilities, without resulting in a concomitant increase in healthcare expenditure. In the long term, the positive health effect increases year by year. Mechanism analysis reveals that SCC promotes health improvements through two key channels: enhanced accessibility of living infrastructure, including access to running water and internet, and increased household annual income. Heterogeneity analysis reveals that there are unequal selection effects in impact, the health-enhancing effects of SCC on middle-aged and older disabilities are more pronounced among non-retired individuals and those with a more extensive household composition. Finally, welfare analysis shows that SCC reduced their healthcare expenditures and improved labor market performance, with a conservatively estimated welfare benefit of US$3448.65. Conclusion: By unraveling the underlying mechanisms and inequality selection effects, this study provides a comprehensive analysis of the potential for digital inclusion to inform scientific understanding and guide policy aimed at promoting health equity and improving the well-being of disadvantaged populations.