Determinants of villagers’ satisfaction with post-disaster reconstruction

Determinants of villagers’ satisfaction with post-disaster reconstruction: Evidence from surveys ten years after the Wenchuan earthquake

Ao, Yibin (Chengdu University of Technology)
Zhong, Jinglin (Chengdu University of Technology)
Zhang, Zijun (Chengdu University of Technology)
Han, Lili (Chengdu University of Technology)
Wang, Yan (Sichuan College of Architectural Technology)
Chen, Yunfeng (Purdue University)
Wang, T. (TU Delft Design & Construction Management)

2022

Residents’ satisfaction with post-disaster reconstruction in earthquake-stricken areas directly affects their quality of life, which cannot be ignored in post-disaster reconstruction. More than 10 years after the Wenchuan earthquake, we took ten randomly selected villages in the five areas hardest-hit by the Wenchuan earthquake as research objects and obtained 483 valid completed questionnaires. The villagers were randomly sampled and descriptive statistical analysis, factor analysis, and ordered logistic regression were used to explore the factors and relationships influencing villagers’ satisfaction with post-disaster reconstruction in Wenchuan earthquake-stricken areas. The results show that: 1) the more rural residents know about the post-disaster reconstruction, the greater their level of satisfaction; 2) the more the annual income of families increases after resettlement, the greater the satisfaction of rural residents with the post-disaster reconstruction; 3) six public factors, namely the village committee acts as, housing construction quality, public service, policy of benefiting farmers, cultural environment, and hardware environment, all significantly positively affect residents’ overall satisfaction with post-earthquake reconstruction. This study thus enriches the theory of residents’ satisfaction studies and the practice of post-earthquake reconstruction.

earthquake-stricken area
post-disaster reconstruction
satisfaction analysis
factorial analysis
ordered logistic regression

http://resolver.tudelft.nl/uuid:04f563e0-5093-4bf4-887b-5ed07c0c9b3a

https://doi.org/10.3389/fenvs.2022.952700

2296-665X

Frontiers in Environmental Science, 10

Institutional Repository

journal article

© 2022 Yibin Ao, Jinglin Zhong, Zijun Zhang, Lili Han, Yan Wang, Yunfeng Chen, T. Wang