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Journals / Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2026 / Volume: 13 Issue: 1

Does Renewable Energy Enhance Firm Profitability? Insights from Dynamic Panel Estimation and Explainable Machine Learning

Serap Çoban(NEVSEHIR HACI BEKTAS VELI UNIVERSITY)0000-0002-6622-0327
Şükran Güngör Tanç(NEVSEHIR HACI BEKTAS VELI UNIVERSITY)0000-0002-0675-2479
Nurten Ulusay(NEVSEHIR HACI BEKTAS VELI UNIVERSITY)0000-0003-0972-9407
Pages
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Publication Date
2026-03-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These findings emphasize firm-level heterogeneity in the environmental–profitability link, offering valuable insights for aligning energy strategies with financial performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigates the impact of energy efficiency and energy intensity on firm profitability using econometric and machine learning methods.
📊
Key Findings & Data:Analyzing a panel dataset of 713 publicly listed firms from 2016 to 2022, we apply system-GMM and MMQR models to assess the linear effects of key variables on profitability.
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Conclusion & Impact:These findings emphasize firm-level heterogeneity in the environmental–profitability link, offering valuable insights for aligning energy strategies with financial performance.
🔬Design:Modeling & Simulation
📍Scope:2016 Verileri
⚙️Variables:Renewable energy · profitability · dynamic panel estimation

Abstract

This study investigates the impact of energy efficiency and energy intensity on firm profitability using econometric and machine learning methods. Analyzing a panel dataset of 713 publicly listed firms from 2016 to 2022, we apply system-GMM and MMQR models to assess the linear effects of key variables on profitability. We complement the analysis with SHAP and PDP visualizations from a panel structured LightGBM quantile model, capturing nonlinearities and marginal effects that are not addressed by econometric methods. Our results reveal a positive relationship between energy efficiency and profitability, especially among high-performing firms, indicating that scale benefits arise from adopting sustainable energy practices. Energy intensity shows nonlinear effects, with reversed U-shapes at lower profitability levels and U-shapes at higher levels. These findings emphasize firm-level heterogeneity in the environmental–profitability link, offering valuable insights for aligning energy strategies with financial performance.

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Cite This Article

Çoban, S., Tanç, Ş. G., Ulusay, N. (2026). Does Renewable Energy Enhance Firm Profitability? Insights from Dynamic Panel Estimation and Explainable Machine Learning. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Volume: 13 Issue: 1. https://doi.org/10.30798/makuiibf.1735957

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References (54)

  1. Apan, M., & İslamoğlu, M. (2018). Determining the impact of financial characteristics on firm profitability: An empirical analysis on Borsa Istanbul energy firms. WSEAS Transactions on Business and Economics, 15, 547-559.
  2. Apergis, N., & Sorros, J. (2014). The role of R&D expenses for profitability: Evidence from US fossil and renewable energy firms. International Journal of Economics and Finance, 6(3), 8-15.
  3. Arellano, M., & Bond, S. (1998). Dynamic panel data estimation using DPD98 for GAUSS: A guide for users. Institute for Fiscal Studies.
  4. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51.
  5. Bai, R., Lin, B., & Liu, X. (2021). Government subsidies and firm-level renewable energy investment: New evidence from partially linear functional-coefficient models. Energy Policy, 159, 112587.
  6. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99– 120.
  7. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143.
  8. Choi, B., Park, W., & Yu, B. K. (2017). Energy intensity and firm growth. Energy Economics, 65, 399–410.
  9. Cleary, S. (2006). International corporate investment and the relationships between financial constraint measures. Journal of banking & finance, 30(5), 1559-1580.
  10. Dang, V. A., Gao, N., & Lin, H. (2024). Do environmental regulations affect firms’ investment decisions? Evidence from renewable energy policy. Journal of Sustainable Finance and Accounting, 2.
  11. Das, K. C., & Mahalik, M. K. (2023). Renewable energy use and export performance of manufacturing firms: Panel evidence from six industries in India. Energy Economics, 125, 107637.
  12. DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 48(2), 147-160.
  13. Donaldson, T., & Preston, L. E. (1995). The stakeholder theory of the corporation: Concepts, evidence and implications. Academy of management Review, 20(1), 65-91.
  14. Dopierała, Ł., Mosionek-Schweda, M., Laskowicz, T., & Ilczuk, D. (2022). Financial performance of renewable energy producers: A panel data analysis from the Baltic Sea Region. Energy Reports, 8, 11492-11503.
  15. Dorigoni, S., & Anzalone, G. A. (2024). Production of energy from renewable sources and financial performance of European utilities: A panel-data analysis. Energy Policy, 194, 113178.
  16. Fan, L. W., Pan, S. J., Liu, G. Q., & Zhou, P. (2017). Does energy efficiency affect financial performance? Evidence from Chinese energy-intensive firms. Journal of Cleaner Production, 151, 53–59.
  17. Feng, Q., & Wei, H. (2023). Import competition and firm energy intensity: Evidence from China. Economic Analysis and Policy, 78, 543–560.
  18. Freeman, R. E. (2010). Strategic management: A stakeholder approach. Cambridge university press.
  19. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189-1232.
  20. Gozgor, G., Ho, T., Li, J., & Mousavi, M. M. (2024). The impact of energy diversification on firm performance: The moderating role of corporate social responsibility. International Review of Financial Analysis, 96, 102875.
  21. Halkos, G. E., & Tzeremes, N. G. (2012). Analyzing the Greek renewable energy sector: A Data Envelopment Analysis approach. Renewable and sustainable energy reviews, 16(5), 2884-2893.
  22. Hart, S. L., & Dowell, G. (2011). Invited editorial: A natural-resource-based view of the firm: Fifteen years after. Journal of Management, 37(5), 1464–1479.
  23. Hazarika, N. (2021). R&D intensity and its curvilinear relationship with firm profitability: Perspective from the alternative energy sector. Sustainability, 13(9), 5060.
  24. Henriques, S. T., Sharp, P., Tsoukli, X., & Vedel, C. (2024). Adaptability, diversification and energy shocks: A firm- level productivity analysis. Energy Economics, 139, 107721.
  25. Herman, R., Nistor, C., & Jula, N. M. (2023). The influence of the increase in energy prices on the profitability of companies in the European union. Sustainability, 15(21), 15404.
  26. Hulshof, D., & Mulder, M. (2020). The impact of renewable energy use on firm profit. Energy Economics, 92, 104957.
  27. Joaqui-Barandica, O., Manotas-Duque, D. F., & Uribe, J. M. (2024). Common factors in the profitability of energy firms. The Energy Journal, 46(2), 171-200. https://doi.org/10.1177/01956574241280779
  28. Kažukauskas, A., & Jaraitė, J. (2012, May). The profitability of power generating firms and policies promoting renewable energy. In 2012 9th International Conference on the European Energy Market (pp. 1-8). IEEE.
  29. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W.,. & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
  30. Khan, Y., & Liu, F. (2023). Consumption of energy from conventional sources: A challenge to the green environment—Evaluating the role of energy imports and energy intensity in Australia. Environmental Science and Pollution Research, 30(9), 22712–22727.
  31. Koenker, R., Hallock, K.F., 2001. Quantile regression. Journal of Economic Perspectives, 15(4), 143-156.
  32. Kolk, A., & Pinkse, J. (2005). Business responses to climate change: Identifying emergent strategies. California Management Review, 47(3), 6–20.
  33. Lu, M., & Wang, K. (2024). The direct energy rebound effects for manufacturing and service sectors in China: Evidence from firm-level estimations. Journal of Cleaner Production, 474, 139243.
  34. Lukic, R. (2021). Energy efficiency and profitability in the service sector: A comparative study. Journal of Energy and Environmental Policy Options, 4(4), 8–14.
  35. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  36. Machado, J. A., & Silva, J. S. (2019). Quantiles via moments. Journal of Econometrics, 213(1), 145–173.
  37. Makeeva, E., Popov, K., & Teplova, O. (2024). Patenting for profitability: Green energy innovations and firm performance in BRICS countries. Frontiers in Environmental Science, 12, 1332792.
  38. Morina, F., Balomenou, C., & Ergün, U. (2021). Determinants of profitability: Evidence of renewable energy companies. In Global, regional and local perspectives on the economies of Southeastern Europe: Proceedings of the 11th International Conference on the Economies of the Balkan and Eastern European Countries (EBEEC) in Bucharest, Romania, 2019 (pp. 243–256). Springer International Publishing.
  39. Nguyen, P. H., Nguyen, L. A. T., Pham, H. A. T., & Pham, M. A. T. (2023). Breaking ground in ESG assessment: Integrated DEA and MCDM framework with spherical fuzzy sets for Vietnam's wire and cable sector. Journal of Open Innovation: Technology, Market and Complexity, 9(3), 100136.
  40. Oliver, C. (1997). Sustainable competitive advantage: combining institutional and resource‐based views. Strategic management journal, 18(9), 697-713.
  41. Porter, M., & Van der Linde, C. (1995). Green and competitive: Ending the stalemate. In The dynamics of the eco-efficient economy: Environmental regulation and competitive advantage (pp. 120–134).
  42. Ruggiero, S., & Lehkonen, H. (2017). Renewable energy growth and the financial performance of electric utilities: A panel data study. Journal of Cleaner Production, 142, 3676–3688.
  43. Sadorsky, P. (2012). Modeling renewable energy company risk. Energy Policy, 40, 39-48.
  44. Shapley, L. S. (1953). A value for n-person games.
  45. Shin, H., Ellinger, A. E., Nolan, H. H., DeCoster, T. D., & Lane, F. (2018). An assessment of the association between renewable energy utilization and firm financial performance. Journal of Business Ethics, 151, 1121-1138.
  46. Sirin, S. M., & Yilmaz, B. N. (2024). Energy transition and non-energy firms’ financial performance: Do markets value capability-based energy transition strategies? Energy Economics, 136, 107658.
  47. Sun, J., Fujita, H., Zheng, Y., & Ai, W. (2021). Multi-class financial distress prediction based on support vector machines integrated with the decomposition and fusion methods. Information Sciences, 559, 153-170.
  48. Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.
  49. Westerman, W., De Ridder, A., & Achtereekte, M. (2020). Firm performance and diversification in the energy sector. Managerial Finance, 46(11), 1373–1390.
  50. Yao, Y., Feng, J., & Yang, Q. (2022). Impact of financing constraints on firm performance: Moderating effect based on firm size. Computational Intelligence and Neuroscience, 2022(1), 1954164.
  51. Zhang, D., & Kong, Q. (2022). Renewable energy policy, green investment and sustainability of energy firms. Renewable Energy, 192, 118-133.
  52. Zhang, K. Q., Tang, L. Z., & Chen, H. H. (2021). The impacts of environmental performance and development of financing decisions on economic sustainable performance: From the view of renewable and clean energy industry. Clean Technologies and Environmental Policy, 23, 1807-1819.
  53. Zhou, P., Song, F. M., & Huang, X. (2023). Environmental regulations and firms' green innovations: Transforming pressure into incentives. International Review of Financial Analysis, 86, 102504.
  54. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W.,. & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.