Yu Lin

Rapid urban development and human activities have led to drastic changes in land use, resulting to heightened ecological pressures and risks to ecosystems, especially in cities along the lower reaches of the Yellow River (CLRYR), China. However, the landscape pattern and associated ecological risks in the CLRYR in the past twenty years remain unclear. In this study, we employed the land use data to identify the primary landscape types and their transformations, providing an in-depth analysis of the prevailing landscape pattern and the landscape ecological risk (LER) in the CLRYR. Additionally, we explored the spatial distribution of LER and investigated the underlying driving forces behind these changes. The results reveal that: (1) Cropland is the main landscape type in the CLRYR region; however, the area of cropland decreases with the transition to impervious. (2) Due to human activities, landscape fragmentation and diversity have gradually increased in CLRYR, while aggregation has gradually decreased, until there was some improvement between 2015 and 2020. (3) The LER in the CLRYR region exhibits instability, with values of 0.1761, 0.1751, 0.1760, 0.1773, and 0.1751 displaying a fluctuating downward trend. Directional distribution analysis indicates a movement of the LER center of gravity towards the mouth of the Yellow River, accompanied by an increasing dispersion pattern. Analysis of driving forces suggests that natural factors hold greater explanatory power compared to social factors. Moreover, interaction detection results reveal that the combined effect of any two factors surpasses that of a single factor. The findings offer a theoretical foundation for enhancing planning policies aimed at striking a balance between environmental preservation and social advancement within the CLRYR region.

Cities along the lower reaches of the Yangtze River (CLRYR) have highlighted human-land conflicts with their rapid urban expansion. Fully assessing the landscape ecological risk (LER) of the region and its response in the context of urbanization is of great significance for regional sustainable development. To address this issue, this study explores the spatiotemporal evolution of LER in CLRYR from the perspective of “production-living-ecological” space (PLES), and evaluates the decoupling status of urbanization processes and LER in different cities, aiming to provide scientific reference for policymakers. The results indicate that the mean LER value increased from 0.2508 in 2000 to 0.2573 in 2020, with an increase in LER fluctuations. From a spatial distribution perspective, the proportion of medium risk is the highest, consistently above 30%; The lowest risk proportion is less than 3%. From 2000 to 2020, the Moran’s I values for LER in the CLRYR were 0.4773, 0.4014, 0.3326, 0.2462, and 0.4779, respectively, indicating a significant positive correlation. Through decoupling model analysis, it was found that only Wuxi, Suzhou, and Changzhou achieved strong decoupling between economic growth and LER between 2010 and 2020. The findings of this study provide an important scientific basis for a deeper understanding of the complex relationship between urbanization and ecological risks in CLRYR and also lay a theoretical foundation for promoting the implementation of green development strategies in the region.

As a critical resource in emergency response and a pivotal element in disaster prevention and risk reduction, shelters play a central role in the holistic continuum of rescue and relief efforts. However, existing research often overlooks the comprehensive assessment and enhancement of shelter resilience. This study proposes a novel safety–robustness–accessibility (SRA) model aimed at evaluating and enhancing the overall resilience of shelters in the face of disasters. Firstly, a resilience assessment system for shelters was established, leveraging multi-source data and encompassing diverse dimensions, including safety, robustness, and accessibility. Subsequently, the entropy weight method was utilized to determine the weights of the assessment indicators. The case study and comparative analysis were conducted on shelters situated in two urban areas, old and new, in Kunming City, China, namely Wuhua District and Chenggong District. The findings reveal a higher quantity of shelters in Wuhua District compared to Chenggong District; however, the overall resilience level is relatively low, predominantly categorized as “Mid-Low” grade, constituting a substantial 57.94%. Conversely, shelters in Chenggong District exhibit a relatively high resilience level, predominantly classified as “Medium” grade, accounting for 33.77%. This study furnishes valuable data references and specific strategies aimed at enhancing the resilience of urban shelters against disasters. It offers crucial insights for urban planning and management to strengthen shelter resilience, thereby contributing to the development of a more resilient and sustainable urban future.

The increase in the number and severity of seismic disasters has put communities in danger, especially in rapidly developing and densely populated areas. Traditional seismic vulnerability and risk assessment methods, including field investigation, are accurate at the building scale; however, their low-efficiency and high-cost characteristics limit the application of these methods in urban-scale regions with high-speed development and risk exposure. To address this issue, multisource remote sensing interpretation, support vector machine (SVM) and geographic information science (GIScience) technologies are combined to test the performance and efficiency of a urban-scale macroscopic seismic vulnerability and risk assessment method in the Lixia District of Jinan City, Shandong Province, China, which is characterized by rapid development, a variety of building types, and moderate-to-low seismic risk. First, a traditional field survey was conducted in Lixia District, and a building attribute information database was constructed. Second, the vulnerability proxies of building attribute information and building seismic vulnerability were estimated based on the EMS-98 standard and the SVM. Finally, vulnerability proxies established based on the RISK-UE model were applied to the Lixia database, and the vulnerability and risk assessment under different seismic intensities were estimated with the experimental accuracy verified. The results showed that the SVM method can obtain stable and accurate results in urban scale vulnerability assessment. The mean building vulnerability index in Lixia District is 0.43, which indicates that the overall seismic performance is good. Most of the area falls within the seismic intensity range of VII-X degrees and would experience slight to moderate damage. The results of the study contribute to enhancing the precision and efficacy of large-scale seismic risk assessment, and they can be used by relevant departments to create tailored emergency plans and reduce seismic hazard losses. Additionally, these results can aid in achieving the climate action goal (SDG13) of the United Nations Sustainable Development Goals (SDGs).