With the acceleration of global climate change and urbanization, urban green infrastructure, as a key means to enhance ecological resilience and environmental quality, continues to attract the attention of policymakers and academia. Environmental Social Governance(ESG) performance covers multiple dimensions, such as environmental protection, social benefits, and governance mechanisms, and has become an effective indicator system for measuring the sustainability of green infrastructure. However, most of the existing ESG performance evaluations use static and linear methods, which makes it difficult to reveal the dynamic causal relationship and uncertainty between indicators. In addition, in the face of incomplete evaluation data and a complex and changing policy environment, the evaluation results are often out of touch with reality, making it difficult to provide strong support for green infrastructure investment and management.
This paper takes Chengdu, China’s first “park city” pilot city, as the research object, and constructs an ESG performance evaluation framework for green landscape projects based on Bayesian networks. This framework realizes the dynamic modeling of the fluctuation range and risk transmission path of ESG indicators under different policy and environmental scenarios by systematically constructing a conditional probability table (CPT) and combining it with the Monte Carlo simulation method. It has significant advantages over existing methods in uncertainty modeling and policy scenario integration. At the same time, key driving factors are identified through sensitivity analysis to provide support for optimizing performance management strategies.
The results of empirical analysis show that this method can effectively reveal the complex causal relationship and uncertainty transmission mechanism between ESG performance indicators and accurately reflect the dynamic evolution characteristics of green infrastructure performance. The proposed evaluation framework not only provides a scientific basis for risk management and decision-making of green infrastructure projects in Chengdu, but also provides analytical tools and practical paths for other cities and even countries in urban planning, sustainable investment, and ESG information disclosure.
Author Contributions
Conceptualization, Z.Z. and F.P.G.; validation, Z.Z. and F.P.G.; formal analysis, Z.Z.; data curation, Z.Z.; writing—original draft preparation, Z.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.
Conflicts of Interest
The authors declare no conflicts of interest.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).