Factors affecting energy sector efficiency in OECD countries

Year 2025, Volume: 54 Issue: 1, 143 - 155, 15.05.2025

Fatih Sağlam , Mervenur Sözen , Çağlar Sözen

https://doi.org/10.26650/ibr.2025.54.1267765

Abstract

Energy efficiency is an important concept for ensuring sustainable development and reducing energy consumption. Efficiency means using a minimal amount of energy to complete a process. Considering the amount of energy needed by OECD countries, we aimed to analyze the energy efficiency of 37 OECD countries in different sectors, such as transport, industry, trade, and agriculture for 2015. The analysis involves modeling the efficiency results obtained using multivariate adaptive regression splines to explore linear and nonlinear relationships. We examined the effects of various factors, such as Gross National Income (GNI), population density, Gross Fixed Capital Formation (GFCF), Gross Floor Area (GFA), CO2 density, and Total Primary Energy Supply (TPES). The results showed that the energy efficiency of OECD countries varies greatly between different sectors, with transport and industry being the least productive. In addition, this study revealed that the nonlinear effects of the GNI of the energy mix, population density, and CO2 density significantly affect energy efficiency. The findings show that policies aiming to promote energy efficiency in different sectors should consider the nonlinear effects of various factors that can lead to more efficient and sustainable energy use. In addition, increasing energy efficiency has many benefits, including environmental protection, cost savings, and improved economic performance.

Keywords

Data Envelopment Analysis , Second Stage Analysis , Energy Efficiency , OECD , MARS

References

• Apergis, N., Aye, G. C., Barros, C. P., Gupta, R. and Wanke, P. (2015). Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics 51:45-53. google scholar
• Aydemir, Z. C. (2002). Bölgesel rekabet edebilirlik kapsamında illerin kaynak kullanım görece verimlilikleri: veri zarflama analizi uygulaması. DPT. google scholar
• Bertoldi, P. (2022). Policies for energy conservation and sufficiency: Review of existing policies and recommendations for new and effective policies in OECD countries. Energy and Buildings, 112075. google scholar
• Bowden, N., & Payne, J. E. (2009). The causal relationship between US energy consumption and real output: a disaggregated analysis. Journal of Policy Modeling 31(2):180-188. google scholar
• Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research 2(6):429-444. google scholar
• Cui, Q., & Li, Y. (2014). The evaluation of transportation energy efficiency: an application of three-stage virtual frontier DEA. Transportation Research Part D: Transport and Environment 29:1-11. google scholar
• Energy, (2020). In Merriam-Webster.com. https://www.merriam-webster.com/dictionary/energy. Accessed 21 December 2020. google scholar
• Fidanoski, F., Simeonovski, K., & Cvetkoska, V. (2021). Energy efficiency in OECD countries: A DEA approach. Energies, 14(4), 1185. google scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). Springer series in statistics, New York. google scholar
• Hamilton and Turton (2002) analyzes the sources of growth in energy-related greenhouse gas emissions for OECD countries over the period 1982–1997. google scholar
• He, P., Sun, Y., Shen, H., Jian, J., & Yu, Z. (2019). Does environmental tax affect energy efficiency? An empirical study of energy efficiency in OECD countries based on DEA and Logit model. Sustainability, 11(14), 3792. google scholar
• Hutter, C., & Weber, E. (2022). Russia-Ukraine war: Short-run production and labour market effects of the energy crisis (No. 10/2022). IAB-Discussion Paper. google scholar
• Jobert, T., & Karanfil, F. (2007). Sectoral energy consumption by source and economic growth in Turkey. Energy policy 35(11):5447-5456. google scholar
• Kazemzadeh, E., Fuinhas, J. A., Koengkan, M., Osmani, F., & Silva, N. (2022). Do energy efficiency and export quality affect the ecological footprint in emerging countries? A two-step approach using the SBM–DEA model and panel quantile regression. Environment Systems and Decisions, 42(4), 608-625. google scholar
• Kuzemko, C., Blondeel, M., Dupont, C., & Brisbois, M. C. (2022). Russia's war on Ukraine, European energy policy responses & implications for sustainable transformations. Energy Research & Social Science, 93, 102842. google scholar
• LeBlanc, M., & Crowley, J. (1999). Adaptive regression splines in the Cox model. Biometrics 55(1):204-213. google scholar
• Liu, Y. and Wang, K. (2015). Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis. Energy 93:1328-1337 google scholar
• Luptacik, M. (2010). Mathematical optimization and economic analysis. Springer, New York, pp 307. google scholar
• Paramati, S. R., Shahzad, U., & Doğan, B. (2022). The role of environmental technology for energy demand and energy efficiency: Evidence from OECD countries. Renewable and Sustainable Energy Reviews, 153, 111735. google scholar
• Park, Y. S., Lim, S. H., Egilmez, G. and Szmerekovsky, J. (2018). Environmental efficiency assessment of US transport sector: a slack-based data envelopment analysis approach. Transportation Research Part D: Transport and Environment 61:152-164. google scholar
• Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and buildings 40(3):394-398. google scholar
• Prisecaru, P. (2022). The war in Ukraine and the overhaul of EU energy security. Global Economic Observer, 10(1), 16-25. google scholar
• Ramanathan R., 2003. Data envelopment analysis, Sage Publications, New Delhi. google scholar
• Shi, G. M., Bi, J., & Wang, J. N. (2010). Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs. Energy policy 38(10):6172-6179. google scholar
• Sineviciene, L., Sotnyk, I., & Kubatko, O. (2017). Determinants of energy efficiency and energy consumption of Eastern Europe post-communist economies. Energy & Environment, 28(8), 870-884. google scholar
• Song, M. L., Zhang, L. L., Liu, W., & Fisher, R. (2013). Bootstrap-DEA analysis of BRICS’energy efficiency based on small sample data. Applied energy 112:1049-1055. google scholar
• Sözen, M., & Cengiz, M. A. (2022). Copula Approach to Multivariate Energy Efficiency Analysis. Asia-Pacific Journal of Operational Research, 39(06), 2150042. google scholar
• Wang, J. M., Shi, Y. F. and Zhang, J. (2017). Energy efficiency and influencing factors analysis on Beijing industrial sectors. Journal of Cleaner Production 167:653-664. google scholar
• Wang, Q., Zhao, Z., Zhou, P., & Zhou, D. (2013). Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Economic Modelling 35:283-289. google scholar
• Wang, Z., & Wang, X. (2022). Research on the impact of green finance on energy efficiency in different regions of China based on the DEA-Tobit model. Resources Policy, 77, 102695. google scholar
• Wang, Z., Liu, Q., & Zhang, B. (2022). What kinds of building energy-saving retrofit projects should be preferred? Efficiency evaluation with three-stage data envelopment analysis (DEA). Renewable and Sustainable Energy Reviews, 161, 112392. google scholar
• Wu, A. H., Cao, Y. Y., & Liu, B. (2014). Energy efficiency evaluation for regions in China: an application of DEA and Malmquist indices. Energy efficiency 7(3):429-439. google scholar
• Xie, B. C., Shang, L. F., Yang, S. B. and Yi, B. W. (2014). Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries. Energy, 74:147-157. google scholar
• Zachariadis, T. (2007). Exploring the relationship between energy use and economic growth with bivariate models: New evidence from G-7 countries. Energy economics, 29(6):1233-1253. google scholar
• Zhang, C., & Chen, P. (2022). Applying the three-stage SBM-DEA model to evaluate energy efficiency and impact factors in RCEP countries. Energy, 241, 122917. google scholar
• Zhu, Q., Chen, J., & Li, F. (2022). A comprehensive analysis of China’s regional energy and environment efficiency from supply chain perspective. International Journal of Logistics Research and Applications, 25(4-5), 709-724. google scholar
• Friedman, J. H. (1991). Multivariate adaptive regression splines. The annals of statistics, 19(1), 1-67. google scholar
• Sevimli, Y. (2009). Çok değişkenli uyarlanabilir regresyon uzanımlarının bir split-mouth çalışmasında uygulaması (Master's thesis, Marmara Universitesi (Turkey)). google scholar
• Zuo, Z., Guo, H., Li, Y., & Cheng, J. (2022). A two-stage DEA evaluation of Chinese mining industry technological innovation efficiency and eco-efficiency. Environmental Impact Assessment Review, 94, 106762. google scholar