Kaijing Xue

Dr. Kaijing Xue

Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
orcid

Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
Kaijing XueKaijing Xue
Kaijing Xue
Shili GuoShili Guo
Shili Guo
YLYi Liu
Yi Liu
Shaoquan LiuShaoquan Liu
Shaoquan Liu
Dingde XuDingde Xu
Dingde Xu
Individual perception of disaster risk is not only the product of individual factors, but also the product of social interactions. However, few studies have empirically explored the correlations between rural residents’ flat social networks, trust in pyramidal channels, and disaster-risk perceptions. Taking Sichuan Province—a typical disaster-prone province in China—as an example and using data from 327 rural households in mountainous areas threatened by multiple disasters, this paper measured the level of participants’ disaster-risk perception in the four dimensions of possibility, threat, self-efficacy, and response efficacy. Then, the ordinary least squares method was applied to probe the correlations between social networks, trust, and residents’ disaster-risk perception. The results revealed four main findings. (1) Compared with scores relating to comprehensive disaster-risk perception, participants had lower perception scores relating to possibility and threat, and higher perception scores relating to self-efficacy and response efficacy. (2) The carrier characteristics of their social networks significantly affected rural residents’ perceived levels of disaster risk, while the background characteristics did not. (3) Different dimensions of trust had distinct effects on rural residents’ disaster-risk perceptions. (4) Compared with social network variables, trust was more closely related to the perceived level of disaster risks, which was especially reflected in the impact on self-efficacy, response efficacy, and comprehensive perception. The findings of this study deepen understanding of the relationship between social networks, trust, and disaster-risk perceptions of rural residents in mountainous areas threatened by multiple disasters, providing enlightenment for building resilient disaster-prevention systems in the community.

10 August 2022 in Habitat International
Verified
Journal Article
Closed access
Kaijing XueKaijing Xue
Kaijing Xue
SCSha Cao
Sha Cao
YLYi Liu
Yi Liu
DXDingde Xu
Dingde Xu
Shaoquan LiuShaoquan Liu
Shaoquan Liu

2 February 2023 in ChemCatChem
Verified
Journal Article
Closed access
ZJZhe Jia
Zhe Jia
JSJiayin Shang
Jiayin Shang
Kaijing XueKaijing Xue
Kaijing Xue
XYXi Yang
Xi Yang
SWSumin Wang
Sumin Wang
QWQiguan Wang
Qiguan Wang

31 July 2019 in Sustainability
Verified
Journal Article
Open access
PDF file
Kaijing XueKaijing Xue
Kaijing Xue
Dingde XuDingde Xu
Dingde Xu
Shaoquan LiuShaoquan Liu
Shaoquan Liu
In recent years, the issue of employment quality for workers has received increasing attention from the government and academia. As a social resource, a social network can provide people with social support and help job seekers find better jobs by transmitting the information on job opportunities. However, currently, there are few empirical studies on employment quality from the perspective of social networks. Based on data from 194 samples from 400 households in Sichuan Province in 2015, this paper constructs an employment quality index system from the six dimensions of labor wages, working time, employment stability, employment environment, career development, and social security. In addition, from the perspective of the structural features and the overall characteristics of the social network, OLS (Ordinary Least Squares) and the path analysis model are used to quantitatively explore the mechanisms of action paths of the social network in terms of the non-agricultural employment quality of part-time peasants. The results show that: (1) the social network scale and the relative network of part-time peasants are found to positively affect employment quality. (2) Age, gender, and education level have indirect impacts on the employment quality loop through network heterogeneity and network scale. In addition, network heterogeneity and health status indirectly impact employment quality through a network scale. (3) By synthesizing the direct and indirect impacts, the comprehensive impacts of each factor on employment quality, in decreasing order, are: village–county distance > village terrain > family population > network scale > education level > skill > network heterogeneity > health status > age > gender. The results suggest that we should pay attention to the role of social network resources in improving employment quality, and should implement various measures to expand peasants’ social networks, so as to achieve high-quality employment.