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).

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

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.