Xuesong Guo

Globally, climate-related disasters (CRDs) have become a common phenomenon in the last few decades, increasing vulnerability and causing disaster mortality. To manage disaster risk, the global Sendai Framework for disaster risk reduction (SFDRR) provides a solid action plan for disaster risk reduction (DRR) considering all the dimensions of vulnerability. However, countries are still less receptive at the local level to adopting the SFDRR. Also, the global research community did not explore vulnerability features based on the Sendai Framework nor integrated such features with the four priorities of SFDRR. Based on the case of Pakistan, this study seeks to bridge the gap through three objectives. The first objective of the study is to identify the vulnerability features cited in the SFDRR. In addition, the research aims to develop a new vulnerability model by integrating SFDRR’s four priorities. As a second objective, it is intended to develop a decision support framework for ranking and evaluating each of the SFDRR priority action areas under local conditions. A third objective is to assess the impact of climate-induced environmental, socioeconomic and political vulnerabilities on climate-driven mortality. To meet these three different objectives, the authors have adopted a multi-method approach by using multi-criteria decision analysis (MCDA) and binary logistic regression method (BLRM). Based on the assessment, the authors suggested some policy implications.

With the purpose of limiting the drastically increasing quantity of travel volumes and the sustainability of urban traffic systems, many cities have implemented urban traffic restriction (UTR) policies. However, insufficient stakeholder involvement in UTR policy assessment often leads to a lack of social and political support, in addition to a lack of acceptability with respect to policy implementation. In this background, participatory system dynamics modeling (PSDM) is presented as a methodology to engage stakeholders in UTR policy assessment. By the proposed methodology, a comprehensive case study of UTR policy in the city of Xi’an was illustrated. Based on the assessment results, the leverage point influencing the performance of the UTR policy was identified and discussed, with different policy scenarios simulated and tested. The simulation results show that developing public transportation while implementing UTR policy was indicated as the most reasonable solution at the present stage, and the annual growth rate of private cars should be monitored in the future. The results confirmed that the PSDM methodology can facilitate the system thinking of stakeholders, which is important to reach consensus-based assessment results and to enhance the social support for UTR policy.

The paper examines stakeholder participation of social stability risk assessment for mega projects in China from a network perspective, with participatory decision-making in a political system discussed. From this analysis, we developed and tested hypotheses on stakeholder participation in social stability risk assessment. Using data obtained through content analysis, we established network on each compulsory procedure in social stability risk assessment to test the hypotheses. Additional impactful factors were discussed using singular value decomposition method in the study. We also provided practical implications and suggestions for policy and practice in the article.

Corporate credit‐rating prediction using statistical and artificial intelligence techniques has received considerable attentions in the literature. Different from the thoughts of various techniques for adopting support vector machines as binary classifiers originally, a new method, based on support vector domain combined with fuzzy clustering algorithm for multiclassification, is proposed in the paper to accomplish corporate credit rating. By data preprocessing using fuzzy clustering algorithm, only the boundary data points are selected as training samples to accomplish support vector domain specification to reduce computational cost and also achieve better performance. To validate the proposed methodology, real‐world cases are used for experiments, with results compared with conventional multiclassification support vector machine approaches and other artificial intelligence techniques. The results show that the proposed model improves the performance of corporate credit‐rating with less computational consumption.