Rising socio-economic costs of PM2.5 pollution and medical service mismatching
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Intercity personnel exchange is more effective than policy transplantation at reducing water pollution
Severe spatial disparities exist in water pollution and water governance. A popular solution is that lagging cities transplant policies from cities with successful experiences. However, environmental governance is more than policies. Merely copying policies from elsewhere may not generate intended effects. Here this research argues that intercity personnel exchange can be a more effective policy instrument than policy transplantation. We provide the first nationwide estimates in China of the effect of intercity exchange of city leaders on water pollution reduction. Using large-scale micro-level datasets on city leaders’ curriculum vitae, firm behaviors, patents and policy texts, we show that intercity exchange of city leaders leads to a 4.78–15.26% reduction in firm-level water pollution, which contributes to 39.45–57.98% of the national total water pollution reduction from 2006 to 2013. Exchanged city leaders facilitate the diffusion of governance experience across cities and the formulation of intercity cooperation. They are also more likely to initiate new policies to support industrial upgrading. Our findings highlight the importance and potential of intercity personnel exchange as a policy instrument for water governance in particular and green transition in general.
Water and wastewater infrastructure inequity in unincorporated communities
Uneven access to water and wastewater infrastructure is shaped by local governance. A substantial number of U.S. households lack adequate access and the U.S. is one of the few countries with large populations living outside of city bounds, in unincorporated areas. Few studies address how infrastructure services and local governance are intertwined at a regional scale. We examine the connection between incorporation status and access to centralized infrastructure, using negative binomial regression. A novel dataset informs this analysis, comprised of 31,383 Census block groups located in nine states representing over 25% of the national population. We find evidence that inequities in access are associated with unincorporated status and poverty rates. Sewer coverage rates are significantly lower for unincorporated communities in close proximity to municipal boundaries. Infrastructure equity could be improved by targeting high-poverty unincorporated communities, addressing challenges with noncontiguous service areas, and strengthening regional water planning and participatory governance.
The spatial coupling and its influencing mechanism between rural human-habitat heritage and key rural tourism villages in China
Exploring the influencing factors and its influencing mechanism of the spatial coupling between rural human-habitat heritage (RHH) and key rural tourism villages (RTV) at county scale from the perspective of space can expand the theoretical research on the spatial coupling mechanism between RHH and RTV, and further provide theoretical reference and data support for the coordinated development and high-quality development of RHH and RTV in China. At the same time, previous studies have failed to systematically analyze the influencing factors and influencing mechanisms of the spatial coupling between RHH and RTV at the county scale, which restricted decision makers from formulating coordinated development measures between RHH and RTV at the macro level. In this study, bivariate spatial autocorrelation model and spatial coupling coordination model were used to quantitatively analyze the spatial coupling level between RHH and RTV at the county scale in China. Then, the linear regression (OLS) model, geographically weighted regression (GWR) model, and optimal parameter GeoDetector (OPGD) model were integrated to systematically analyze the linear influencing, spatial heterogeneity effect and interactive effect of natural environment and socioeconomic factors on the spatial coupling level between RHH and RTV in China, and explore the interactive influencing mechanism. The results show that the spatial coupling level of RHH and RTV in China show a significant east-west differentiation. There were 2024, 473, 293, 55 and 6 areas of severe, moderate, mild, basic and moderate coordination between RHH and RTV, respectively. Among them, severe and moderate discoordination areas are mainly distributed in Northeast China, arid and semi-arid areas in Western China, plateau areas in Southwest China, densely populated urban agglomerations and plains agricultural areas in the Middle East China. Mild discoordination areas and basic and moderate coordination areas are mainly located in transition zones in mountainous and plain areas, economically developed mountainous and hilly counties along the southeastern coast, and coastal tourist cities. Economic and population factors are the fundamental factors that affect the spatial coupling between RHH and RTV. Rural tourism facilities and rural public service facilities are important external driving forces for the coupling development of RHH and RTV, and Sociocultural environment factors are the important internal driving forces. Different surface forms, different climate conditions and different ecological environment conditions can form different natural textures and spatial organizations. Suitable climate conditions, sufficient water sources and ecological environment conditions can form more suitable rural settlement construction conditions and production and living conditions, and ultimately affect the protection and activation of rural human settlement heritage and the development and layout of key tourist villages. The spatial coupling relationship between RHH and RTV is the result of the complex interaction between the natural directivity law caused by natural environmental factors and the humanistic directivity law caused by human social and economic activities.
Exploring urban housing disadvantages and economic struggles in Seoul, South Korea
The present study investigates urban poverty in Seoul, South Korea, amid the COVID-19 pandemic, focusing on housing and economic challenges. Employing principal component analysis, clustering algorithms, and visualization techniques, it analyzes archived data to uncover disparities in housing conditions and economic well-being across Seoul. The research reveals significant socio-economic divisions, with over 75% of the city’s areas marked by vulnerability, indicating widespread poverty or the concentration of economically disadvantaged populations. This highlights the pervasive nature of poverty and the precarious situation of the urban poor, who are at risk due to fragile living conditions. The findings advocate for inclusive urban development strategies that prioritize the needs of marginalized groups, suggesting a shift from focusing solely on economic growth to ensuring equitable welfare for all residents.
Machine learning-guided integration of fixed and mobile sensors for high resolution urban PM2.5 mapping
Urban areas exhibit significant gradients in Fine Particulate Matter (PM2.5) concentration variability. Understanding the spatiotemporal distribution and formation mechanisms of PM2.5 is crucial for public health, environmental justice, and air pollution mitigation strategies. Here, we utilized machine learning and integrated air quality sensor monitoring networks consisting of 200 mobile cruising vehicles and 614 fixed micro–stations to reconstruct PM2.5 pollution maps for Jinan’s urban area with a high spatiotemporal resolution of 500 m and 1 h. Our study demonstrated that pollution mapping can effectively capture spatiotemporal variations at the urban microscale. By optimizing the spatial design of monitoring networks, we developed a cost-effective air quality monitoring strategy that reduces expenses by nearly 70% while maintaining high precision. The results of multi-model coupling indicated that secondary inorganic aerosols were the primary driving factors for PM2.5 pollution in Jinan. Our work offers a unique perspective on urban air quality monitoring and pollution attribution.
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