Rapid urbanization in China has strengthened the connection and cooperation among cities and has also led urban residents to be more vulnerable in adverse environmental conditions. Vulnerability research has been an important foundation in urban risk management. To make cities safe and resilient, it is also necessary to integrate the connection among cities into a vulnerability assessment. Therefore, this paper proposed a new conceptual framework for urban social vulnerability assessment based on network theory, where a new dimension of social vulnerability (connectivity) was added into the framework. Using attribute data, the traditional social vulnerability index of a city (SVI_node) was calculated via the projection pursuit cluster (PPC) model. With the relational data retrieved from the Baidu search index, a new dimension (connectivity) of social vulnerability (SVI_connectivity) was evaluated. Finally, an integrated social vulnerability index (SVI_urban) was measured combined with SVI_node and SVI_connectivity. This method was applied in the Yangtze River Delta region of China, where the top three high values of SVI_node belonged to the cities of Taizhou (Z), Jiaxing, and Huzhou. The three lowest cities were Hangzhou, Nanjing, and Shanghai. For SVI_urban, the social vulnerability of cities in different hierarchies behaved differently. For Hierarchies 2 and 3, when compared to SVI_node, the SVI_urban was significantly reduced. However, the variation between SVI_node and SVI_urban in Hierarchy 4 was slight. Furthermore, an increase for the city of Taizhou (J) in its social vulnerability was achieved after connecting to the network. Huzhou, in Hierarchy 5, increased its social vulnerability the most when adding connectivity in the social vulnerability assessment. Based on the results of our case study, a conclusion was drawn that network connectivity had an influence on social vulnerability. However, when connectivity was strong enough, it could help cities to mitigate their traditional social vulnerability, whereas a loose connection in the network aggregated their traditional social vulnerability. Hence, the latter should be emphasized in future urban risk management.

Many frequent and severe air pollution incidents have emerged across the vast parts of China recently. The identification of factors and mapping social vulnerability has become extremely necessary for environmental management and sustainable development. However, studies associating social vulnerability with air pollution remain sparse. With reference to research achievements of social vulnerability, this study made a new trial regarding social vulnerability assessment to air pollution. With the projection pursuit cluster (PPC) model, the top three factors contributing to social vulnerability index (SVI) were discovered and SVI and SVI dimensions (susceptibility, exposure, and adaptability) were evaluated. Results revealed that adaptability values are higher than susceptibility and exposure values. SVI is in a poor condition as, for the whole region, most values belong to the high-medium level. High SVI values mainly appear in the northern and the southern ends of study area. SVI in Shanghai is lower than in Jiangsu and Zhejiang provinces. On the scale of prefecture-level city, it can be found that the low-value centers of SVI always occurred in urban core areas. The spatial variation and inequality in social vulnerability provide policy-makers a scientific basis for air pollution prevention and sustainable management.

This research offers a new framework for assessing social vulnerability to climate change. A social vulnerability assessment trial was carried out for Chinese coastal cities at the county level. First, the 10 factors having the most influence on social vulnerability were identified. They are “House with no lavatory”, “House with no bath facilities”, “Employees in primary industry”, “Houses with no tap water”, “GDP in primary sector”, “Children”, “House with no kitchen”, “Rate of natural increase (RNI), “Employees in management sector”, and “Highly educated”. Second, indexes of social vulnerability, exposure, sensitivity, and adaptability were evaluated and mapped to examine their spatial pattern. The results demonstrate that the distribution of exposure index (EI) is similar to that of social vulnerability index (SVI): many counties are categorized at the medium level while a few counties belong to the high or low categories. The distribution of adaptability proves that it should be paid more attention, as 30.14% of its counties belong to the lowest level. After calculating the Getis-Ord Gi* statistic of SVI, two cold spots and two hot spots are identified. Third, the relationship between urban development and social vulnerability are discussed. During urbanization, there are evident differences of SVI between urban and rural areas. Urbanization can help city districts reduce social vulnerability, while creating more social vulnerability in the coastal counties. For the districts, more adjustment strategies and work should be applied in the dimension of exposure during urbanization. For the counties, the prominent problem to be faced is an increase in sensitivity.

This paper explored a new approach regarding social vulnerability to climate change, and measured social vulnerability in three parts: (1) choosing relevant indicators of social vulnerability to climate change; (2) based on the Hazard Vulnerability Similarity Index (HVSI), our method provided a procedure to choose the referenced community objectively; and (3) ranked social vulnerability, exposure, sensitivity, and adaptability according to profiles of similarity matrix and specific attributes of referenced communities. This new approach was applied to a case study of the Yangtze River Delta (YRD) region and our findings included: (1) counties with a minimum and maximum social vulnerability index (SVI) were identified, which provided valuable examples to be followed or avoided in the mitigation planning and preparedness of other counties; (2) most counties in the study area were identified in high exposure, medium sensitivity, low adaptability, and medium SVI; (3) four cities, Shanghai, Nanjing, Suzhou, and Hangzhou were much less vulnerable than others due to their high adaptability; (4) to some extent, there were geographical similarities of SVI, exposure index, sensitivity index, and adaptability index; and (5) the indicator of “Employees in primary industry” related to SVI the most, the indicator of “Renter” related to exposure index (EI) most, the indicator of “Elderly” related to sensitivity index (SI) the most, and the indicator of “Urban residents” relates to adaptability index (AI) most. These results can help decision makers find the focus of their mitigation works, where the efficient of practices will then be improved.