A two-stage analytical approach to assess sustainable energy efficiency

Beyzanur Cayir Ervural, Selim Zaim, Dursun Delen

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Administrators and policymakers at regional, national and global level are well aware of the necessity and undeniable benefits of renewable energy for long-term sustainability. In this study, we developed a two-stage analytical methodology to assess the efficiency of energy sources (a combination of various energy sources, mostly based on renewable sources), and Turkey, a country with a variety of renewable energy potential because of its favorable geographic and climatic conditions, was used as an illustrative case. Specifically, in the first stage, we utilized a nonparametric method and a powerful benchmarking tool—Data Envelopment Analysis (DEA)—to analyze energy efficiencies for each province. In the second stage, we employed the Ordinary Least Square (OLS) regression and Tobit regression models to investigate the environmental factors affecting energy efficiency. And then, we used the Charnes-Cooper-Rhodes (CCR) DEA and Tobit regression combination to perform a validation of the findings. The tandem utilization of DEA, OLS, and Tobit regression models allowed us to overcome some of the shortcomings of these methods when they are utilized individually. The results revealed the factors that have direct and positive influence/effect on the efficiencies, which included gross domestic product per-capita, population size, and the amount of energy production from renewable energy sources. The findings also suggested that starting the investments at the less-efficient provinces result in a better overall nationwide technical efficiency. These results can potentially help decision makers to develop and manage energy investment strategies.

Original languageEnglish
Pages (from-to)822-836
Number of pages15
StatePublished - 1 Dec 2018
Externally publishedYes


  • Data envelopment analysis (DEA)
  • Efficiency analysis
  • Ordinary least square (OLS)
  • Renewable energy
  • Super-efficiency
  • Tobit regression


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