Research Article
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Year 2021, Volume: 11 Issue: 1, 136 - 146, 30.06.2021
https://doi.org/10.37094/adyujsci.755048

Abstract

References

  • [[1] Çapik, M., Present situation and potential role of renewable energy in Turkey; Renewable Energy, 46, 01-13, 2012.
  • [2] Song, M.L., Zhang, L.L., Liu, W., Fisher, R., Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data, Applied Energy, 112, 1049-1055, 2013.
  • [3] Menegaki, A.N., Growth and renewable energy in Europe: benchmarking with data envelopment analysis, Renewable Energy, 60, 363-369, 2013.
  • [4] Kupeli, M., İhsan, A., G20 Ülkelerinin yenilenebilir enerji etkinliğinin dengeli performans ağirliklari ve veri zarflama analizi ile değerlendirilmesi, Uluslararası İktisadi ve İdari İncelemeler Dergisi, 207-218, 2018.
  • [5] Wang, H., A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator, Energy, 80, 114-122, 2015.
  • [6] Sozen, A., Mirzapour, A., Cakır, M.T., İskender, Ü., Çipil, F., Selecting best location of wind plants using DEA and TOPSIS approach in Turkish cities, Gazi J. Eng. Sci, 1, 174-193, 2016.
  • [7] Lin, B., Long, H., A stochastic frontier analysis of energy efficiency of China's chemical industry, Journal of Cleaner Production, 87, 235-244, 2015.
  • [8] Honma, S., Hu, J.L., A panel data parametric frontier technique for measuring total-factor energy efficiency: an application to Japanese regions, Energy, 78, 732-739, 2014.
  • [9] Zhou, P., Ang, B.W., Zhou, D.Q., Measuring economy-wide energy efficiency performance: a parametric frontier approach, Applied Energy, 90(1), 196-200, 2012.
  • [10] Hsiao, W. L., Hu, J. L., Hsiao, C., Chang, M. C., Energy efficiency of the Baltic Sea Countries: an application of stochastic frontier analysis, Energies, 12(1), 104, 2019.
  • [11] Filippini, M., Hunt, L.C., Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach, The Energy Journal, 59-80, 2011.
  • [12] Lin, B., Du, K., Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: an application to Chinese energy economy, Energy, 76, 884-890, 2014.
  • [13] Jin, T., Kim, J., A comparative study of energy and carbon efficiency for emerging countries using panel stochastic frontier analysis, Scientific Reports, 9(1), 6647, 2019.
  • [14] Coelli, T.J., Rao, D.S.P., O'Donnell, C.J., Battese, G.E., An introduction to efficiency and productivity analysis, 2nd ed, Springer, New York, 2005.
  • [15] Aigner, D.J., Lovelly, C.A.K., Schmidt, P.J., Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6, 1977.
  • [16] Battese, G.E., Corra, G.S., Estimation of a production frontier model: with application to the pastoral zone of Eastern Australia', Australian Journal of Agricultural Economics, 21, 169-179, 1977.
  • [17] Meeusen, W., Van den Broeck, J., Efficiency estimation from Cobb Douglas production functions with composed error, International Economic Review,18, 435–444, 1977.
  • [18] Kumbhakar, S.C., Ghosh S., McGuckin J.T., A generalized production frontier approach for estimating determinants of inefficiency in U.S. dairy farms, Journal of Business and Economics Statistics, 9(3), 279-286, 1991.
  • [19] Huang, C. J., Liu, J.T., Estimation of a non-neutral stochastic frontier production function, Journal of Productivity Analysis, 5(2), 171-180, 1994.
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  • [26] Deniz, E., Akbilgic, O., Howe, J.A., Model selection using information criteria under a new estimation method: Least squares ratio, Journal of Applied Statistics, 38 (9), 2011.
  • [27] Koç, H., Dünder, E., Gümüştekin, S., Koç, T., Cengiz, M.A., Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria, Communications in Statistics-Theory and Methods, 47(21), 5298-5306, 2018.
  • [28] Moritz, S., Bartz-Beielstein, T, ImputeTS: time series missing value imputation in R, The R Journal, 9(1), 207-218, 2017.
  • [29] Croissant, Y., Millo, G., Panel data econometrics in R: The plm package, Journal of Statistical Software, 27(2), 1-43, 2008.
  • [30] Coelli, T., Henningsen, A., Henningsen, M.A., Package ‘frontier’, Available in ftp://gnu.cs.pu.edu. tw/network/CRAN/web/packages/frontier/frontier. pdf. Accessed, 2017.

Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria

Year 2021, Volume: 11 Issue: 1, 136 - 146, 30.06.2021
https://doi.org/10.37094/adyujsci.755048

Abstract

Renewable energy is a sustainable energy source that can be produced repeatedly by using the resources that exist in nature's own evolution. Renewable energy sources occupy an important place in the world and our country due to their renewability, minimal environmental impact, low operating and maintenance costs, and their national qualifications, and reliable energy supply features. In this study renewable energy efficiency levels for the BRICS countries and Turkey were examined. In the study covering the period 2006-2015, we used the SFA method for efficiency analysis in input selection. We used information complexity criteria to decide which input set is the best on renewable energy efficiency process. The selection results pointed out to the CO2 emission and Energy intensity as the most explanatory inputs. We observed that the selected inputs have significant effect on the renewable energy efficiencies. According to results, the renewable energy efficiency values follow approximately the same pattern for each country and do not vary significantly between the years. When comparing the renewable energy efficiencies among the countries, Brazil has the best performance with approximately 97% efficiency level, and Russia has the worst one. The efficiency level of Turkey is rather weak, but it is not the worst and the average efficiency is very close to China.

References

  • [[1] Çapik, M., Present situation and potential role of renewable energy in Turkey; Renewable Energy, 46, 01-13, 2012.
  • [2] Song, M.L., Zhang, L.L., Liu, W., Fisher, R., Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data, Applied Energy, 112, 1049-1055, 2013.
  • [3] Menegaki, A.N., Growth and renewable energy in Europe: benchmarking with data envelopment analysis, Renewable Energy, 60, 363-369, 2013.
  • [4] Kupeli, M., İhsan, A., G20 Ülkelerinin yenilenebilir enerji etkinliğinin dengeli performans ağirliklari ve veri zarflama analizi ile değerlendirilmesi, Uluslararası İktisadi ve İdari İncelemeler Dergisi, 207-218, 2018.
  • [5] Wang, H., A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator, Energy, 80, 114-122, 2015.
  • [6] Sozen, A., Mirzapour, A., Cakır, M.T., İskender, Ü., Çipil, F., Selecting best location of wind plants using DEA and TOPSIS approach in Turkish cities, Gazi J. Eng. Sci, 1, 174-193, 2016.
  • [7] Lin, B., Long, H., A stochastic frontier analysis of energy efficiency of China's chemical industry, Journal of Cleaner Production, 87, 235-244, 2015.
  • [8] Honma, S., Hu, J.L., A panel data parametric frontier technique for measuring total-factor energy efficiency: an application to Japanese regions, Energy, 78, 732-739, 2014.
  • [9] Zhou, P., Ang, B.W., Zhou, D.Q., Measuring economy-wide energy efficiency performance: a parametric frontier approach, Applied Energy, 90(1), 196-200, 2012.
  • [10] Hsiao, W. L., Hu, J. L., Hsiao, C., Chang, M. C., Energy efficiency of the Baltic Sea Countries: an application of stochastic frontier analysis, Energies, 12(1), 104, 2019.
  • [11] Filippini, M., Hunt, L.C., Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach, The Energy Journal, 59-80, 2011.
  • [12] Lin, B., Du, K., Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: an application to Chinese energy economy, Energy, 76, 884-890, 2014.
  • [13] Jin, T., Kim, J., A comparative study of energy and carbon efficiency for emerging countries using panel stochastic frontier analysis, Scientific Reports, 9(1), 6647, 2019.
  • [14] Coelli, T.J., Rao, D.S.P., O'Donnell, C.J., Battese, G.E., An introduction to efficiency and productivity analysis, 2nd ed, Springer, New York, 2005.
  • [15] Aigner, D.J., Lovelly, C.A.K., Schmidt, P.J., Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6, 1977.
  • [16] Battese, G.E., Corra, G.S., Estimation of a production frontier model: with application to the pastoral zone of Eastern Australia', Australian Journal of Agricultural Economics, 21, 169-179, 1977.
  • [17] Meeusen, W., Van den Broeck, J., Efficiency estimation from Cobb Douglas production functions with composed error, International Economic Review,18, 435–444, 1977.
  • [18] Kumbhakar, S.C., Ghosh S., McGuckin J.T., A generalized production frontier approach for estimating determinants of inefficiency in U.S. dairy farms, Journal of Business and Economics Statistics, 9(3), 279-286, 1991.
  • [19] Huang, C. J., Liu, J.T., Estimation of a non-neutral stochastic frontier production function, Journal of Productivity Analysis, 5(2), 171-180, 1994.
  • [20] Stevenson, R.E., Likelihood function for generalized stochastic frontier estimation, Journal of Econometrics, 13, 57-66, 1980.
  • [21] Greene, W.M., The econometric approach to efficiency analysis, the measurement of productive efficiency: techniques and applications, published in Harold O. Fried, Lovell, C.A.K. and Schmidt, S.S. (eds.), Oxford University Press: 68–119, 1993.
  • [22] Battese, G.E., Broca, S.S., Functional forms of stochastic frontier production functions and models for technical inefficiency effects: a comparative study for wheat farmers in Pakistan, Journal of Productivity Analysis, 8(4), 395-414,1977.
  • [23] Bozdogan, H., Akaike’s information criterion and recent developments in information complexity, Journal of Mathematical Psychology, 44 (1), 2000.
  • [24] Bozdogan, H., Intelligent statistical data mining with information complexity and genetic algorithms, Statistical Data Mining and Knowledge Discovery, 15-56, 2004.
  • [25] Pamukçu, E., Bozdogan, H., Çalık, S., A novel hybrid dimension reduction technique for undersized high dimensional gene expression data sets using information complexity criterion for cancer classification, Computational and Mathematical Methods in Medicine, 2015(2015).
  • [26] Deniz, E., Akbilgic, O., Howe, J.A., Model selection using information criteria under a new estimation method: Least squares ratio, Journal of Applied Statistics, 38 (9), 2011.
  • [27] Koç, H., Dünder, E., Gümüştekin, S., Koç, T., Cengiz, M.A., Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria, Communications in Statistics-Theory and Methods, 47(21), 5298-5306, 2018.
  • [28] Moritz, S., Bartz-Beielstein, T, ImputeTS: time series missing value imputation in R, The R Journal, 9(1), 207-218, 2017.
  • [29] Croissant, Y., Millo, G., Panel data econometrics in R: The plm package, Journal of Statistical Software, 27(2), 1-43, 2008.
  • [30] Coelli, T., Henningsen, A., Henningsen, M.A., Package ‘frontier’, Available in ftp://gnu.cs.pu.edu. tw/network/CRAN/web/packages/frontier/frontier. pdf. Accessed, 2017.
There are 30 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences, Applied Mathematics
Journal Section Mathematics
Authors

Haydar Koç 0000-0002-8568-4717

Publication Date June 30, 2021
Submission Date June 19, 2020
Acceptance Date May 14, 2021
Published in Issue Year 2021 Volume: 11 Issue: 1

Cite

APA Koç, H. (2021). Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria. Adıyaman University Journal of Science, 11(1), 136-146. https://doi.org/10.37094/adyujsci.755048
AMA Koç H. Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria. ADYU J SCI. June 2021;11(1):136-146. doi:10.37094/adyujsci.755048
Chicago Koç, Haydar. “Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria”. Adıyaman University Journal of Science 11, no. 1 (June 2021): 136-46. https://doi.org/10.37094/adyujsci.755048.
EndNote Koç H (June 1, 2021) Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria. Adıyaman University Journal of Science 11 1 136–146.
IEEE H. Koç, “Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria”, ADYU J SCI, vol. 11, no. 1, pp. 136–146, 2021, doi: 10.37094/adyujsci.755048.
ISNAD Koç, Haydar. “Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria”. Adıyaman University Journal of Science 11/1 (June 2021), 136-146. https://doi.org/10.37094/adyujsci.755048.
JAMA Koç H. Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria. ADYU J SCI. 2021;11:136–146.
MLA Koç, Haydar. “Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria”. Adıyaman University Journal of Science, vol. 11, no. 1, 2021, pp. 136-4, doi:10.37094/adyujsci.755048.
Vancouver Koç H. Assessing the Renewable Energy Efficiency Levels of BRICS Countries and Turkey Using Stochastic Frontier Analysis and Information Complexity Criteria. ADYU J SCI. 2021;11(1):136-4.

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