Decarbonizing global shipping is crucial to achieving international climate goals. However, efforts in this field may be inhibited by geopolitical risks that profoundly affect global shipping. Here, we use natural language processing on an over 500-million-word corpus to quantify the shipping decarbonization attitudes of 52 countries. Using a causal inference approach, we provide evidence that geopolitical risks exert a substantial and statistically significant negative impact on national decarbonization willingness. This dampening effect threatens to delay the International Maritime Organization’s timeline for zero-carbon shipping. Causal evidence also reveals that countries facing geopolitical tend to prioritize economic efficiency over environmental concerns, particularly those with high levels of maritime trade, economic development, and carbon intensity. We further validated these shifts by analyzing carbon dioxide emissions of global vessel navigation. This study highlights the urgent need to bridge geopolitical divides to achieve the ambitious target of zero-carbon emissions in global shipping by mid-century.

Scrub typhus has emerged as a life-threatening and increasingly prevalent vector-borne disease. While the spatial and temporal distributions of scrub typhus have been studied by the research community, the main driving factors that influence the spatiotemporal patterns of the disease remain under investigation. Using Guangdong Province as a case study, we combined monthly scrub typhus case data from 2010 to 2019 and environmental and vector-related datasets with a Bayesian hierarchical mixed model to elucidate the spatiotemporal characteristics of the disease. This study revealed that the most highly endemic areas of scrub typhus are concentrated in the western and southern parts of Guangdong Province. A distinct bimodal pattern of scrub typhus was observed, with peaks typically occurring from May to July and October to November. The fitted model indicated that forest, cropland, and chigger mites were positively associated with scrub typhus transmission. Furthermore, climate and vectors were identified as key factors shaping the bimodal seasonal patterns of scrub typhus. Despite data-related limitations, including the treatment of population as a time-invariant variable and the binary simplification of vector suitability, sensitivity analyses confirmed the robustness of the model, offering valuable insights for scrub typhus prevention in Guangdong.

Invasive species like Ageratina adenophora significantly threaten biodiversity, ecosystem stability, and economic resources in China. This study assesses the current and future potential invasion risk of A. adenophora across China, focusing on the provinces of Yunnan, Sichuan, Guizhou, and Guangxi—areas identified as highly susceptible to invasion. Utilizing both climate and human activity data, our results reveal that population density and temperature seasonality are dominant national‐scale factors that affect the invasion risk of A. adenophora , while regional drivers exhibit significant variation. Projections show a northward and altitudinal shift in invasion risk by the 2060s, highlighting new potential invasion hotspots driven by future changes in climate and human activities. These findings underscore the importance of region‐specific management strategies and establish a foundation for adaptive measures to mitigate the ecological and economic impacts of A. adenophora .

Although the immediate impact of the COVID-19 pandemic has been alleviated, its long-term effects continue to shape global health and public safety. Policymakers should prepare for potential future health crises and direct urban planning toward more sustainable outcomes. While numerous studies have examined factors influencing the risk of COVID-19, few have investigated the spatial spillover effects of urban form and green space. In this study, we quantified urban form using landscape pattern indices, represented population mobility with the Baidu Migration Scale Index, and assessed the role of key influencing factors on the epidemic through STIRPAT and spatial Durbin models. Our findings reveal that population migration from Wuhan had a significant local impact on the spread of COVID-19. These factors not only intensified local transmission, but also triggered positive spatial spillover effects, spreading the virus to neighboring regions. We also found that green space connectivity (pc₅) plays a crucial role in reducing the spread of the virus, both locally and in surrounding areas. High green space connectivity helps mitigate disease transmission during an epidemic. In contrast, the spatial configuration and unipolarity of urban areas (pc₁) contributed to the increased spread of the virus to neighboring cities. Ultimately, balancing building density with green space distribution is essential for enhancing urban resilience. This research provides new insights into sustainable urban planning and helps us understand the impact of the spillover effects of gray–green space forms on public health and safety.