Behavioral Strategies of Stakeholders in Green Building Performance Insurance: A Tripartite Evolutionary Game Analysis

The development of green buildings is essential to the low-carbon transition of the construction industry. As an important financial instrument linking green insurance with green building development, green building performance insurance (GBPI) addresses the “performance gap” between the actual operational performance of buildings and design targets. However, it remains at the pilot stage, and stakeholders’ willingness to participate remains limited. Based on evolutionary game theory, this study establishes a tripartite evolutionary game model involving the local government, insurance companies, and construction enterprises. Through theoretical derivation and numerical simulation, the study examines how initial probabilities and key parameters influence the evolution of the three stakeholders’ behavioral strategies. Monte Carlo analysis is used to assess the robustness of the main evolutionary outcomes under parameter uncertainty. Sobol sensitivity analysis further examines parameter interactions. The model results indicate that the local government plays a pivotal role in promoting green building performance insurance. Higher premium subsidy rates are associated with a greater fiscal burden for the local government, whereas higher actual premium rates are associated with lower payoffs for construction enterprises. These findings suggest that effective promotion requires a dynamic balance among the local government’s fiscal capacity, the operational sustainability of insurance companies, and the affordability of premiums for construction enterprises.

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Publication Details

Journal
Buildings
Published
2026-09-09
DOI
https://doi.org/10.3390/buildings16183599
Primary Topic
Sustainable Building Design and Assessment
Type
article
Field-Weighted Citation Impact
0.00
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Behavioral Strategies of Stakeholders in Green Building Performance Insurance: A Tripartite Evolutionary Game Analysis

Yannuo Liu, Dian Xu, Xinyu Cao, Jinli Duan et al.
Buildings
Sustainable Building Design and Assessment
article

Behavioral Strategies of Stakeholders in Green Building Performance Insurance: A Tripartite Evolutionary Game Analysis

Yannuo Liu, Dian Xu, Xinyu Cao, Jinli Duan, Zhenyue Wang
article en

Abstract

The development of green buildings is essential to the low-carbon transition of the construction industry. As an important financial instrument linking green insurance with green building development, green building performance insurance (GBPI) addresses the “performance gap” between the actual operational performance of buildings and design targets. However, it remains at the pilot stage, and stakeholders’ willingness to participate remains limited. Based on evolutionary game theory, this study establishes a tripartite evolutionary game model involving the local government, insurance companies, and construction enterprises. Through theoretical derivation and numerical simulation, the study examines how initial probabilities and key parameters influence the evolution of the three stakeholders’ behavioral strategies. Monte Carlo analysis is used to assess the robustness of the main evolutionary outcomes under parameter uncertainty. Sobol sensitivity analysis further examines parameter interactions. The model results indicate that the local government plays a pivotal role in promoting green building performance insurance. Higher premium subsidy rates are associated with a greater fiscal burden for the local government, whereas higher actual premium rates are associated with lower payoffs for construction enterprises. These findings suggest that effective promotion requires a dynamic balance among the local government’s fiscal capacity, the operational sustainability of insurance companies, and the affordability of premiums for construction enterprises.

BuildingsVol. 16(18)
Chongqing University of Science and Technology (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 14%
Sustainable Building Design and Assessment
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