ESG introduction mechanism of construction firms based on Bayesian network coupled with machine learning: Evidence from Zhengzhou

Chinese construction enterprises are at a pivotal point in their transition to sustainable development, with Environmental, Social, and Governance (ESG) emerging as a key driver. However, limited understanding of ESG mechanisms hampers effective management strategies. To address this challenge, this study constructs an ESG introduction mechanism framework based on Bayesian networks and machine learning algorithms. Using Zhengzhou, a major city in China, as a case study, the research employs quantitative methods to identify key factors influencing ESG introduction. The findings reveal that attitudes towards ESG-related construction products, the establishment of corporate brand image, and ESG readiness are the three primary factors driving ESG introduction among construction enterprises. Further scenario simulation analyses indicate that a positive attitude towards ESG significantly enhances the likelihood of successful ESG introduction. Additionally, fostering and expanding local ESG consulting and service agencies and strengthening regulatory measures markedly improve the success rate of ESG introduction. This framework provides construction enterprise managers with a practical tool to analyze ESG introduction mechanisms and offers critical decision-making support for policymakers in designing policies to promote ESG introduction.