Promoting Multi-Agent Collaborative Governance of Construction Safety Risks: Considering Strategic Heterogeneities of Projects with Different Costs

Numerous safety hazards in construction projects can readily cause safety accidents. While collaborative governance among stakeholders is vital for construction safety, it is hampered by interest-related factors. Evolutionary game theory is an excellent tool for analyzing participants’ behavioral decisions based on interest factors, and it is employed in this study to explore strategies for promoting collaborative governance. However, existing studies rarely mention the concept of collaborative governance of construction safety risks, seldom focus on construction payment disputes between owners and contractors, and barely take into account the differences in interests and decisions faced by stakeholders under projects of varying costs. Based on this, an evolutionary game model among the government, owner and contractor is established by taking China’s construction industry as an example, and MATLAB numerical simulation is conducted. First, the heterogeneity of the laws of strategy evolution under different cost levels was verified. Subsequently, cost levels were divided into two major categories and four subcategories based on strategy evolution results, and sensitivity analysis was conducted for each corresponding scenario. It was found that rewards for owners and contractors are barely effective, while cutting government regulatory costs and boosting positive governmental incentives generally play a positive role. The effects of penalties for inadequate safety investment and safety accidents on collaboration differ across project costs. Nevertheless, collaborative governance can be achieved via reasonable parameter optimization. This study addresses the critical issue of interest factors hindering collaborative governance, and provides a critical perspective for promoting construction safety and the sustainability of the construction industry. Cost-stratified analysis reduces overly definitive suggestions, offering valuable insights for both theory and practice.