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

Author

Listed:
  • Qinghua Pang

    (School of Economics and Finance, Hohai University, Changzhou 213022, China)

  • Xiaopeng Wei

    (School of Mathematics, Hohai University, Nanjing 211100, China)

  • Lina Zhang

    (School of Economics and Finance, Hohai University, Changzhou 213022, China)

  • Yanjie Shang

    (School of Management, Jiangsu University of Technology, Changzhou 213022, China)

Abstract

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.

Suggested Citation

  • Qinghua Pang & Xiaopeng Wei & Lina Zhang & Yanjie Shang, 2025. "Assessing the Impact of China’s Industry on Health: A Dynamic Three-Stage Bootstrap Model with an Endogenous Fuzzy Directional Distance Function," Sustainability, MDPI, vol. 17(2), pages 1-27, January.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:608-:d:1566985
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    References listed on IDEAS

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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
    2. Léopold Simar & Paul Wilson, 2011. "Inference by the m out of n bootstrap in nonparametric frontier models," Journal of Productivity Analysis, Springer, vol. 36(1), pages 33-53, August.
    3. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    4. Hirofumi Fukuyama & William Weber, 2015. "Measuring Japanese bank performance: a dynamic network DEA approach," Journal of Productivity Analysis, Springer, vol. 44(3), pages 249-264, December.
    5. Sebastián Lozano & Narges Soltani, 2018. "DEA target setting using lexicographic and endogenous directional distance function approaches," Journal of Productivity Analysis, Springer, vol. 50(1), pages 55-70, October.
    6. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    7. Krüger, Jens & Hampf, Benjamin, 2015. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77007, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    8. Simar, Léopold & Vanhems, Anne & Wilson, Paul W., 2012. "Statistical inference for DEA estimators of directional distances," European Journal of Operational Research, Elsevier, vol. 220(3), pages 853-864.
    9. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2008. "Asymptotics And Consistent Bootstraps For Dea Estimators In Nonparametric Frontier Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1663-1697, December.
    10. Dineswary Nadarajan & Saber Abdelall Mohamed Ahmed & Noor Fadiya Mohd Noor, 2023. "Seaport Network Efficiency Measurement Using Triangular and Trapezoidal Fuzzy Data Envelopment Analyses with Liner Shipping Connectivity Index Output," Mathematics, MDPI, vol. 11(6), pages 1-27, March.
    11. Shabani, Mohadeseh & Kordrostami, Sohrab & Jahani Sayyad Noveiri, Monireh, 2023. "Renewable energy performance analysis using fuzzy dynamic directional distance function model under natural and managerial disposability," Applied Energy, Elsevier, vol. 352(C).
    12. Benjamin Hampf & Jens J. Krüger, 2015. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 920-938.
    13. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    14. Zhang, Ren-Long & Liu, Xiao-Hong, 2021. "Evaluating ecological efficiency of Chinese industrial enterprise," Renewable Energy, Elsevier, vol. 178(C), pages 679-691.
    15. Hirofumi Fukuyama & William L. Weber, 2017. "Japanese Bank Productivity, 2007–2012: A Dynamic Network Approach," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 649-676, October.
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