ASSESSMENT OF THE RENEWABLE ENERGY EFFICIENCY OF THE G20 COUNTRIES BY BALANCED PERFORMANCE WEIGHTS AND DATA ENVELOPMENT ANALYSIS
Öz
Renewable energy has been a too much used term for sustainable development and environmental studies in recent years. The aim of this study is to reveal the renewable energy performance of the G20 countries by means of data envelopment analysis (DEA) and balanced performance weights. DEA is a nonparametric and powerful technique which is used for performance measurement. Determining the input and output weights in DEA is an important step that can greatly change the result of the study. Several methods have been developed to determine the weights. In this study, balanced performance weights method, that is developed by Alp (2016) and calculated by using the correlations between inputs and outputs, and the classical CCR method are used and the results are compared. In the analysis, energy intensity and labor force are used as input, and per capita national income, CO2 emission amount and the percentage of electricity generated by renewable energy sources in total electricity generated are used as output. According to the CCR results 7 countries are effective, and 1 country is effective in the result of analysis using balanced weights calculated with the help of correlations. It has been observed that the results of the analysis made with the balanced weights model give more discriminating results than the classical model.
Anahtar Kelimeler
Data envelopment analysis,Renewable energy,Balanced performance weights,G20
Kaynakça
- Alp, İ. (2016). Another way to determine weights of balanced performance evaluations. Dumlupi-nar University Journal of Social Science, ICEBSS Özel Sayısı, 151-161.
- Banker, R.D., Charnes, A., Cooper, W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30, 1078– 1092.
- Chien, T., Hu, J.L. (2007). Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Policy, 35(7), 3606-3615.
- Charnes, A., Cooper, W.W., Rhodes, E. (1978). Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 429–44.
- Dünya ve Türkiye Enerjive Tabii Kaynaklar Görünümü. (2017). Erişim Adresi http://www.enerji.gov.tr/Resources/Sites/1/Pages/Sayi_15/mobile/index.html#p=1.
- IEO2017. (2017). Erişim Adresi https://www.eia.gov/outlooks/ieo/pdf/0484(2017).pdf.
- IRENA. (2017). REthinking Energy 2017. Erişim Adresi http://www.irena.org/documentdownloads/publications/irena_rethinking_energy_2017.pdf.
- Menegaki, A.N. (2013). Growth and renewable energy in Europe: Benchmarking with data enve-lopment analysis, Renewable Energy, 60, 363-369.
- Sözen, A., Alp, İ., Kılınç, C. (2012). Efficiency Assensment of the Hydro-power in Turkey by Using Data Envelopment Analysis. Renewable Energy, 46, 192-202.
- Wang, H. (2015). A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator. Energy, 80, 114-122.