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Investment Efficiency Analysis and Evaluation of Power Grids in China: A Robust Dynamic DEA Approach Incorporating Time Lag Effects

Author

Listed:
  • Yan Li

    (Economic and Technological Research Institute, Development Division of State Grid Gansu Electric Power Company, Lanzhou 730050, China)

  • Sha Yan

    (State Grid Tianshui Electric Power Supply Company, Tianshui 741000, China)

  • Yongyan Sun

    (Economic and Technological Research Institute, Development Division of State Grid Gansu Electric Power Company, Lanzhou 730050, China)

  • Lihong Liu

    (State Grid Baiyin Electric Power Supply Company, Baiyin 730900, China)

  • Zhiying Zhang

    (School of Engineering, Sichuan Normal University, Chengdu 610101, China)

  • Yuhong Shuai

    (School of Management, Chongqing University of Science and Technology, Chongqing 401331, China)

Abstract

Effective assessment of power grid investment efficiency is crucial for optimizing resource allocation and improving operational performance. However, existing evaluation methods typically fail to account for two critical factors: inherent uncertainties in input–output data and temporal delays in investment returns. To address these limitations, this study introduces an integrated evaluation framework combining robust optimization techniques for uncertain variables with a time-lag Data Envelopment Analysis (DEA) approach to capture the multi-period dynamics and ensure resilience against external shocks and data perturbations. An empirical analysis conducted on panel data from 31 provincial power grid enterprises in China (2015–2023) reveals significant regional disparities in efficiency, particularly between coastal and resource-rich provinces. The findings highlight that excluding time-lag effects leads to systematic underestimation of efficiency and employing robust optimization yields more resilient efficiency scores amidst data uncertainties. The study contributes methodologically by advancing DEA frameworks to better reflect the complexities of power grid investments and empirically provides valuable insights for policymakers seeking to enhance investment strategies and achieve sustainable development goals.

Suggested Citation

  • Yan Li & Sha Yan & Yongyan Sun & Lihong Liu & Zhiying Zhang & Yuhong Shuai, 2025. "Investment Efficiency Analysis and Evaluation of Power Grids in China: A Robust Dynamic DEA Approach Incorporating Time Lag Effects," Energies, MDPI, vol. 18(18), pages 1-28, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4962-:d:1752449
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    References listed on IDEAS

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    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    3. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    4. Wang, Baixun & Zhao, Wenjuan, 2022. "Interplay of renewable energy investment efficiency, shareholder control and green financial development in China," Renewable Energy, Elsevier, vol. 199(C), pages 192-203.
    5. Yongxiu He & Wenya Liu & Jie Jiao & Jie Guan, 2018. "Evaluation method of benefits and efficiency of grid investment in China: A case study," The Engineering Economist, Taylor & Francis Journals, vol. 63(1), pages 66-86, January.
    6. Zhou, Sheng & Tong, Qing & Pan, Xunzhang & Cao, Min & Wang, Hailin & Gao, Ji & Ou, Xunmin, 2021. "Research on low-carbon energy transformation of China necessary to achieve the Paris agreement goals: A global perspective," Energy Economics, Elsevier, vol. 95(C).
    7. Azadeh, A. & Ghaderi, S.F. & Omrani, H., 2009. "A deterministic approach for performance assessment and optimization of power distribution units in Iran," Energy Policy, Elsevier, vol. 37(1), pages 274-280, January.
    8. Liu, Liyun & Zhao, Zhenzhi & Zhang, Mingming & Zhou, Dequn, 2022. "Green investment efficiency in the Chinese energy sector: Overinvestment or underinvestment?," Energy Policy, Elsevier, vol. 160(C).
    9. Jingqi Sun & Nuermaimaiti Ruze & Jianjun Zhang & Haoran Zhao & Boyang Shen, 2019. "Evaluating the Investment Efficiency of China’s Provincial Power Grid Enterprises under New Electricity Market Reform: Empirical Evidence Based on Three-Stage DEA Model," Energies, MDPI, vol. 12(18), pages 1-17, September.
    10. Sadjadi, S.J. & Omrani, H., 2008. "Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies," Energy Policy, Elsevier, vol. 36(11), pages 4247-4254, November.
    11. Özgür Özpeynirci & Murat Köksalan, 2007. "Performance evaluation using data envelopment analysis in the presence of time lags," Journal of Productivity Analysis, Springer, vol. 27(3), pages 221-229, June.
    12. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    13. Hatami-Marbini, A. & Arabmaldar, A., 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138965, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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