Evolutionary Game Analysis of Construction Suspension Supervision under Persistent Disasters in the Greater Bay Area

Authors

  • Hanxiang Gong

    Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China

    Medical Affairs Department, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China
  • Jinghua Li

    School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
  • Xi Wang

    Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
  • Xuexian Fang

    Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China

    School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou 510180, China

DOI:

https://doi.org/10.30564/re.v8i5.13457
Received: 27 April 2026 | Revised: 16 July 2026 | Accepted: 23 July 2026 | Published Online: 3 September 2026

Abstract

This study aims to provide a methodological contribution by demonstrating how to incorporate exogenous environmental temporal dynamics into evolutionary game models. Unlike existing adaptive Evolutionary Game Theory (EGT) models that primarily capture endogenous behavioral feedback (e.g., adjusting penalties based on violation rates), this framework mathematically formalizes how external environmental duration structurally alters the payoff matrix over time. Using the persistent typhoons in the Greater Bay Area (GBA) purely as a narrative device, this study examines how disaster duration alters the cost-benefit structure of suspension supervision and identifies conditions to prevent regulatory failure. We built a tripartite evolutionary game model among government regulatory departments, construction units, and third-party subjects. A disaster persistence index (D) was explicitly embedded into compliance costs, accident losses, and governance benefits. We also treated detection probability as a dynamic variable. Replicator dynamics and numerical simulations were used to analyze equilibrium stability and parameter sensitivity. The system reaches an ideal safety equilibrium (compliant suspension, diligent performance, strict supervision) when compliance returns and diligence benefits outweigh violation and collusion gains. However, as disaster duration (D) extends, accumulating compliance costs quickly erode compliance incentives. This shift can drive the system into a dangerous state of "violation and dereliction" despite strict supervision. Sensitivity analysis reveals that compliance costs have a critical threshold effect. While higher fines promote compliance, their marginal utility diminishes. Furthermore, excessive collusion rent induces third-party dereliction, and rising regulatory costs threaten supervision sustainability. The model theoretically supports a shift toward dynamic governance. While mechanisms like duration-linked penalties or targeted compensation align with the mathematical conditions for safety equilibrium, these remain theoretical pathways requiring empirical validation before practical application.

Keywords:

Construction Safety; Evolutionary Game Theory; Disaster Persistence; Suspension Supervision; Guangdong–Hong Kong–Macao Greater Bay Area

References

[1] Wang, L., Gong, Z., Shi, L., et al., 2021. Knowledge mapping analysis of research progress and frontiers in integrated disaster risk management in a changing climate. Natural Hazards. 107(3), 2033–2052. DOI: https://doi.org/10.1007/s11069-020-04465-z

[2] Su, W., Gao, X., Jiang, Y., et al., 2021. Developing a construction safety standard system to enhance safety supervision efficiency in China: A theoretical simulation of the evolutionary game process. Sustainability. 13(23), 13364. DOI: https://doi.org/10.3390/su132313364

[3] Wang, Y., Liu, Z., 2023. Research on the regional cooperation innovation network of universities in the Guangdong–Hong Kong–Macao Great Bay Area. Sustainability. 15(12), 9838. DOI: https://doi.org/10.3390/su15129838

[4] Pamukcu-Albers, P., Ugolini, F., La Rosa, D., et al., 2021. Building green infrastructure to enhance urban resilience to climate change and pandemics. Landscape Ecology. 36(3), 665–673. DOI: https://doi.org/10.1007/s10980-021-01212-y

[5] Krelling, A.F., Wu, Y., Malik, J., et al., 2025. Thermal resilience of buildings and communities: A multistakeholder review of metrics and approaches. Annual Review of Environment and Resources. 50(1), 681–708. DOI: https://doi.org/10.1146/annurev-environ-013125-111914

[6] Kim, J.-M., Yum, S.-G., Adhikari, M.D., et al., 2024. A deep-learning approach to leveraging natural hazard indicators for improved safety on construction sites. Safety Science. 177, 106596. DOI: https://doi.org/10.1016/j.ssci.2024.106596

[7] Ran, R., Wang, S., Fang, J., et al., 2024. Safety risk analysis of urban viaduct construction based on dynamic weight. Buildings. 14(4), 1014. DOI: https://doi.org/10.3390/buildings14041014

[8] Wei, K., Li, H., Qin, J., et al., 2025. Estimation of long-term extreme responses of cable-stayed suspension bridge under wind and wave loadings via the extrapolation method. Structures. 74, 108631. DOI: https://doi.org/10.1016/j.istruc.2025.108631

[9] Karthick, S., Kermanshachi, S., Pamidimukkala, A., et al., 2024. Analysis of construction workers' health and safety in cold weather conditions. Journal of Cold Regions Engineering. 38(1), 04023022. DOI: https://doi.org/10.1061/JCRGEI.CRENG-687

[10] Yue, H., Ye, G., Yang, J., et al., 2025. Cost–benefit analysis of four-party construction safety supervision. Journal of Construction Engineering and Management. 151(2), 04024206. DOI: https://doi.org/10.1061/JCEMD4.COENG-15463

[11] Ye, Z., Ye, Y., Zhang, C., et al., 2023. A digital twin approach for tunnel construction safety early warning and management. Computers in Industry. 144, 103783. DOI: https://doi.org/10.1016/j.compind.2022.103783

[12] Ding, Z., Deng, S., Liu, Q., 2025. Insulator defect detection algorithm based on improved YOLO11s in snowy weather environment. Symmetry. 17(10), 1763. DOI: https://doi.org/10.3390/sym17101763

[13] Zhang, Y., Yi, X., Li, S., et al., 2023. Evolutionary game of government safety supervision for prefabricated building construction using system dynamics. Engineering, Construction and Architectural Management. 30(7), 2947–2968. DOI: https://doi.org/10.1108/ECAM-06-2021-0501

[14] Guo, F., Wang, J., Liu, D., et al., 2021. Evolutionary process of promoting construction safety education to avoid construction safety accidents in China. International Journal of Environmental Research and Public Health. 18(19), 10392. DOI: https://doi.org/10.3390/ijerph181910392

[15] Jafari, F., Isazade, V., Qasimi, A.B., et al., 2024. Exploring the effects of Urmia Lake's variability on adjacent cities and villages. Journal of the Indian Society of Remote Sensing. 52(7), 1561–1577. DOI: https://doi.org/10.1007/s12524-024-01886-2

[16] Li, H., Isazade, V., Pirasteh, S., et al., 2026. Spatiotemporal analysis of hydrological and environmental changes in Lake Urmia (2000–2024) using Google Earth Engine. Earth Science Informatics. 19(4), 41. DOI: https://doi.org/10.1007/s12145-026-02089-8

[17] Sun, S., Wang, Z., Hu, C., et al., 2021. Understanding climate hazard patterns and urban adaptation measures in China. Sustainability. 13(24), 13886. DOI: https://doi.org/10.3390/su132413886

[18] Wang, Z., Li, Z., Wang, Y., et al., 2024. Building green infrastructure for mitigating urban flood risk in Beijing, China. Urban Forestry & Urban Greening. 93, 128218. DOI: https://doi.org/10.1016/j.ufug.2024.128218

[19] Liu, P., Wang, Y., Han, T., et al., 2022. Safety evaluation of subway tunnel construction under extreme rainfall weather conditions based on combination weighting–set pair analysis model. Sustainability. 14(16), 9886. DOI: https://doi.org/10.3390/su14169886

[20] Xue, J., Shen, G.Q., Yang, R.J., et al., 2020. Dynamic network analysis of stakeholder conflicts in megaprojects: Sixteen-year case of Hong Kong–Zhuhai–Macao Bridge. Journal of Construction Engineering and Management. 146(9), 04020103. DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001895

[21] Yang, Y., Shao, B., Jin, L., et al., 2022. Collaborative governance of tower crane safety in the Chinese construction industry: A social network perspective. Buildings. 12(6), 836. DOI: https://doi.org/10.3390/buildings12060836

[22] Hinsberg, K., Nadesan, M., Lamanna, A., 2024. Communicative framework development for construction risk governance: An analysis of risk and trust perception for organizational sustainability. Sustainability. 16(13), 5794. DOI: https://doi.org/10.3390/su16135794

[23] Gong, S., Gao, X., Li, Z., et al., 2021. Developing a dynamic supervision mechanism to improve construction safety investment supervision efficiency in China: Theoretical simulation of evolutionary game process. International Journal of Environmental Research and Public Health. 18(7), 3594. DOI: https://doi.org/10.3390/ijerph18073594

[24] Huang, S., Hu, J., Xu, W., et al., 2025. Different strategy choices analysis based on stochastic evolutionary game model for construction safety supervision with/without smart site technology. Buildings. 15(4), 603. DOI: https://doi.org/10.3390/buildings15040603

[25] Ning, X., Qiu, Y., Wu, C., et al., 2022. Developing a decision-making model for construction safety behavior supervision: An evolutionary game theory-based analysis. Frontiers in Psychology. 13, 861828. DOI: https://doi.org/10.3389/fpsyg.2022.861828

[26] Feng, Y., Teo, E.A.L., Ling, F.Y.Y., et al., 2014. Exploring the interactive effects of safety investments, safety culture and project hazard on safety performance: An empirical analysis. International Journal of Project Management. 32(6), 932–943. DOI: https://doi.org/10.1016/j.ijproman.2013.10.016

[27] Jiang, X., Sun, H., Lu, K., et al., 2023. Using evolutionary game theory to study construction safety supervisory mechanism in China. Engineering, Construction and Architectural Management. 30(2), 514–537. DOI: https://doi.org/10.1108/ECAM-03-2020-0182

[28] Guo, H., Yu, Y., Skitmore, M., 2017. Visualization technology-based construction safety management: A review. Automation in Construction. 73, 135–144. DOI: https://doi.org/10.1016/j.autcon.2016.10.004

[29] Wu, F., Xu, H., Sun, K.-S., et al., 2022. Analysis of behavioral strategies of construction safety subjects based on the evolutionary game theory. Buildings. 12(3), 313. DOI: https://doi.org/10.3390/buildings12030313

[30] Chen, C., Tang, X., 2025. Strategies of metaverse safety training in highway construction projects: A tripartite evolutionary game. Buildings. 15(22), 4083. DOI: https://doi.org/10.3390/buildings15224083

[31] Zhang, F., Cao, J., Wu, Z., et al., 2025. Evolutionary game analysis of construction worker safety supervision based on complex network. Buildings. 15(6), 907. DOI: https://doi.org/10.3390/buildings15060907

[32] Chen, Z., Xia, L., Su, Y., et al., 2025. Research on the evolutionary game of safety behavior of EPC consortium members based on prospect theory. Journal of Asian Architecture and Building Engineering. 24(3), 1606–1624. DOI: https://doi.org/10.1080/13467581.2024.2329359

[33] Peng, J., Zhang, Q., Feng, Y., et al., 2023. Optimization of construction safety resource allocation based on evolutionary game and genetic algorithm. Scientific Reports. 13(1), 17097. DOI: https://doi.org/10.1038/s41598-023-44262-9

[34] Wen, Z., Mo, M., 2025. The impact of environmental regulations and digital empowerment on agri-food supply chains under stochastic market demand conditions. AIMS Mathematics. 10(9), 22731–22768. DOI: https://doi.org/10.3934/math.20251011

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How to Cite

Gong, H., Li, J., Wang, X., & Fang, X. (2026). Evolutionary Game Analysis of Construction Suspension Supervision under Persistent Disasters in the Greater Bay Area. Research in Ecology, 8(5), 1–23. https://doi.org/10.30564/re.v8i5.13457

Issue

Vol.8, Iss.5 (October 2026): In Progress

Article Type

Article

License

Copyright © 2026 Hanxiang Gong, Jinghua Li, Xi Wang, Xuexian Fang

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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