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A machine learning-enhanced three-stage SBM-DEA model with undesirable outputs for measuring green total factor productivity on a global scale

Author

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  • He, Junlin
  • Lau, Wee-Yeap
  • Ng, Kok-Haur

Abstract

This study proposes a novel machine learning-enhanced three-stage slack-based measure-data envelopment analysis framework, integrated with the global Malmquist productivity index to measure green total factor productivity (GTFP) globally. The framework applies the least absolute shrinkage and selection operator regression to select different sets of environmental factors for each input slack variable and employs a long short-term memory model to estimate slacks by mitigating omitted variables, multicollinearity, linearity constraints, and distributional assumptions while reducing statistical noise. Using data from 150 countries spanning 2011–2023, the proposed framework significantly reduces estimation bias affected by external influences in 129 countries. Results reveal uneven GTFP performance between developed and developing economies.

Suggested Citation

  • He, Junlin & Lau, Wee-Yeap & Ng, Kok-Haur, 2025. "A machine learning-enhanced three-stage SBM-DEA model with undesirable outputs for measuring green total factor productivity on a global scale," Finance Research Letters, Elsevier, vol. 86(PD).
    • Handle: RePEc:eee:finlet:v:86:y:2025:i:pd:s1544612325018653
    DOI: 10.1016/j.frl.2025.108611
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    1. Liu, Yang & Wang, Jianda & Dong, Kangyin & Taghizadeh-Hesary, Farhad, 2023. "How does natural resource abundance affect green total factor productivity in the era of green finance? Global evidence," Resources Policy, Elsevier, vol. 81(C).
    2. David E. Bloom & David Canning, 2004. "Global demographic change : dimensions and economic significance," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, issue Aug, pages 9-56.
    3. Ma, Xiaoyan & Zhou, Aimin & Chi, Chuenyu, 2025. "ESG performance and green total factor productivity," Finance Research Letters, Elsevier, vol. 73(C).
    4. Xiong Wang & Yazhi Zhu & Xiaohang Ren & Giray Gozgor, 2023. "The impact of digital inclusive finance on the spatial convergence of the green total factor productivity in the Chinese cities," Applied Economics, Taylor & Francis Journals, vol. 55(42), pages 4871-4889, September.
    5. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    6. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    7. Krugman, Paul, 1980. "Scale Economies, Product Differentiation, and the Pattern of Trade," American Economic Review, American Economic Association, vol. 70(5), pages 950-959, December.
    8. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    9. Harold Fried & Shelton Schmidt & Suthathip Yaisawarng, 1999. "Incorporating the Operating Environment Into a Nonparametric Measure of Technical Efficiency," Journal of Productivity Analysis, Springer, vol. 12(3), pages 249-267, November.
    10. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    11. Costanza, Robert & d'Arge, Ralph & de Groot, Rudolf & Farber, Stephen & Grasso, Monica & Hannon, Bruce & Limburg, Karin & Naeem, Shahid & O'Neill, Robert V. & Paruelo, Jose, 1998. "The value of the world's ecosystem services and natural capital," Ecological Economics, Elsevier, vol. 25(1), pages 3-15, April.
    12. Yang, Senmiao & Wang, Jianda & Dong, Kangyin & Jiang, Qingzhe, 2023. "A path towards China's energy justice: How does digital technology innovation bring about a just revolution?," Energy Economics, Elsevier, vol. 127(PA).
    13. Zhishuo Zhang & Yao Xiao & Huayong Niu, 2022. "DEA and Machine Learning for Performance Prediction," Mathematics, MDPI, vol. 10(10), pages 1-23, May.
    14. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    15. Xie, Rui & Yao, Siling & Han, Feng & Zhang, Qi, 2022. "Does misallocation of land resources reduce urban green total factor productivity? An analysis of city-level panel data in China," Land Use Policy, Elsevier, vol. 122(C).
    16. Xiao, Biyun & Li, Haibo, 2025. "Financial efficiency, green innovation and green total factor productivity," Finance Research Letters, Elsevier, vol. 76(C).
    17. Lei, Xiao & Shen, Z.Y. & Štreimikienė, Dalia & Baležentis, Tomas & Wang, Guang & Mu, Yunguo, 2024. "Digitalization and sustainable development: Evidence from OECD countries," Applied Energy, Elsevier, vol. 357(C).
    18. Jiang, Yankun & Han, Guanghe & Yu, Dan, 2024. "Digital finance and agricultural green total factor productivity: the mediating role of digital village development," Finance Research Letters, Elsevier, vol. 67(PB).
    19. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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