Risk Assessment of Green Renovation Projects for Aging Residential Communities Based on Fuzzy Bayesian Networks
- DOI
- 10.2991/978-94-6239-758-3_24How to use a DOI?
- Keywords
- Aging Residential Communities; Green Renovation; Risk Assessment; Fuzzy Bayesian Networks; Noisy-OR Model
- Abstract
In response to the characteristics of green renovation projects in old residential communities, such as multiple participants and phased and dynamic risk transmission, this paper constructs a risk assessment system with 16 indicators in four stages of the entire life cycle. It integrates combined weighting, triangular fuzzy numbers and Bayesian networks to establish an assessment model, and introduces the Noisy-or model to simplify the parameters. Using a project in a city in East China as an empirical case, the results show that the project is generally at a medium-to-high risk level (posterior probability 0.62), with the most prominent risks in the decision-making and construction stages; financing difficulties, lack of cooperation from residents, and insufficient experience in green property management are the key risk factors.
- Copyright
- © 2026 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Baorui Wang PY - 2026 DA - 2026/09/08 TI - Risk Assessment of Green Renovation Projects for Aging Residential Communities Based on Fuzzy Bayesian Networks BT - Proceedings of the 2026 7th International Conference on Management Science and Engineering Management (ICMSEM 2026) PB - Atlantis Press SP - 241 EP - 247 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6239-758-3_24 DO - 10.2991/978-94-6239-758-3_24 ID - Wang2026 ER -