Assessing the Impact of China’s Industry on Health: A Dynamic Three-Stage Bootstrap Model with an Endogenous Fuzzy Directional Distance Function

Evaluating the industrial eco-system is essential for promoting resource efficiency, environmental development, and public health protection. However, traditional Data Envelopment Analysis (DEA) methods are prone to biases and rely on precise numerical values, which pose significant challenges when evaluating the industrial ecosystem in China, particularly given the prevalent data uncertainties and environmental complexities. Additionally, DEA models are typically static, fail to capture the long-term trends and dynamic characteristics of the industrial ecosystem. To address these issues, this study proposes a dynamic three-stage model with an endogenous fuzzy directional distance function. The model integrates the generalized smooth bootstrap method and fuzzy comparison techniques to correct errors and account for data uncertainties, improving the accuracy and scientific validity of eco-efficiency assessments. Chinese enterprises face challenges such as excessive resource consumption, environmental pollution, and health risks, necessitating a more comprehensive and flexible evaluation system to adapt to the complex and dynamic nature of the industrial ecosystem. By focusing on industrial production, environmental governance, and health threats (IPEGHT) in China, the research aims to provide a robust framework for enhancing industrial eco-efficiency in a dynamic and uncertain environment. The results show: (1) The proposed method for selecting endogenous direction vectors is applied to the industrial production stage, thereby identifying the subsequent improvement directions for industrial production in various provinces. (2) The idea of integration is adopted to introduce the generalized smooth bootstrap method and fuzzy mathematics, to conduct a precise and comprehensive evaluation of the IPEGHT industrial eco-system. (3) From 2011 to 2021, China’s IPEGHT industrial eco-system efficiency showed a step-like distribution, and attention needs to be paid to environmental governance. The proposed method’ application can facilitate the integration of DEA and fuzzy mathematics, and fosters a more sustainable industrial eco-system.