Renewable Energy Use and Energy Productivity: A Panel Data Analysis


  • Gonca YILMAZ Istanbul Gelisim University
  • Esat DAŞDEMİR Istanbul Gelisim University


Energy, Renewable Energy, Energy Productivity, Sustainability, Economic Growth


This study emphasizes renewable energy source use's effects on productivity, competitiveness, and growth. The hypothesis of the study was tested with panel data analysis on European and Central Asian countries. Panel data analysis covered 15 Eurasian countries with data from 1990-2014. In our hypothesis, renewable energy use increases income by increasing efficiency in production. The model established reveals the difference between various energy types and renewable energy. Analysis outputs support the study hypothesis. According to the results, the increase in the share of renewable energy use in total energy use affects per capita income positively. On the other hand, an increase in the share of fossil fuels decreases per capita income. Our study suggests that countries aiming for better economic growth should increase their use of renewable energy.


AY, İ. C. (2021). Air Pollution, Health and Economic Growth: A Panel Data Analysis for Countries with the Highest Co2 Emission. Akademik Hassasiyetler, 8(15), 269–288.

BALTAGI, B. H., & WU, P. X. (1999). Unequally Spaced Panel Data Regressions with AR(1) Disturbances. Econometric Theory, 15(6), 814–823.

BROWN, M. B., & FORSYTHE, A. B. (1974). Robust Tests for the Equality of Variances. Journal of the American Statistical Association, 69(346), 364–367.

CETIN, M., & YILMAZ, G. (2017). Organıc agrıculture practıces in Turkey as a value chaın creatıng model of agrıcultural productıon. PressAcademia Procedia, 4(1), 11-19.

CHIEN, T., J.L. HU, Renewable energy and macroeconomic efficiency of OECD and non-OECD economies, Energy Policy, 35 (2007), 3606–3615

DAŞDEMİR, E. N. (2018). Bölüşüm Üzerine: Ülkelerarası Rekabet Gücü İle Yurtiçi Bölüşüm İlişkisi. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi. 20(2), 456–469.

DRISCOLL, J. C., & KRAAY, A. C. (1998). Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data. The Review of Economics and Statistics, 80(4), 549–560.

DURBIN, J., & WATSON, G. S. (1971). Testing for Serial Correlation in Least Squares Regression. III. Biometrika, 58(1), 1–19.

European Commission (2010). EUROPE 2020. A strategy for smart, sustainable and inclusive growth. Communication from the Commission, COM (2010) 2020 final, 8-33.

FREES, E. W. (2004). Longitudinal and Panel Data. Cambridge University Press. Accessed 14 July 2020.

FRIEDMAN, M. (1937). The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32(200), 675–701.

HAUSMAN, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46(6), 1251–1271.

HORBACH, J.,(2008). Determinants of environmental innovation—new evidence from German panel data sources. Resource Policy, 37 (1), 163-173.

HUANG, Q. (2020). Insights for global energy interconnection from China renewable energy development. Global Energy Interconnection, 3(1), 1-11.

KARAKOSTA, C., PAPPAS, C., MARINAKIS, V., & PSARRAS, J. (2013). Renewable energy and nuclear power towards sustainable development: Characteristics and prospects. Renewable and Sustainable Energy Reviews, 22, 187-197.

LEVENE, H. (1960). Robust Tests for Equality of Variances. In I. OLKIN, G. S. GHURYE, W. HOEFFDING, G. W. MADOW, & B. H. MANN (Eds.), Contributions to Probability and Statistics. Stanford University Press, 2, 278-292.

MA, L., HOSSEINI, M. R., JIANG, W., MARTEK, I., & MILLS, A. (2018). Energy productivity convergence within the Australian construction industry: A panel data study. Energy Economics, 72, 313-320.

MASSON-DELMOTTE V., ZHAI P., PÖRTNER H-O, ROBERTS D, SKEA J, SHUKLA P.R., et al. (2018). Global warming of 1.5 C. An IPCC Special Report on the Impacts. IPCC Special 2018 Report, 601-610.

MENYAH, K., & WOLDE-RUFAEL, Y. (2010). CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy, 38(6), 2911-2915.

OZTURK I., BILGILI F., (2015). Economic growth and biomass consumption nexus: Dynamic panel analysis for Sub-Sahara African countries. Applied Energy, (137), 110-116

PARAMATI, S. R., SINHA, A., & DOGAN, E. (2017). The significance of renewable energy use for economic output and environmental protection: evidence from the Next 11 developing economies. Environmental Science and Pollution Research, 24(15), 13546-13560.

PARK, S. H., JUNG, W. J., KIM, T. H., & LEE, S. Y. T. (2016). Can renewable energy replace nuclear power in Korea? An economic valuation analysis. Nuclear Engineering and Technology, 48(2), 559-571.

PESARAN, M. H. (2004). General Diagnostic Tests for Cross Section Dependence in Panels (Issue 0435). Faculty of Economics, University of Cambridge. Accessed 15 July 2020.

RENNKAMP, B., HAUNSS, S., WONGSA, K., ORTEGA, A., & CASAMADRID, E. (2017). Competing coalitions: The politics of renewable energy and fossil fuels in Mexico, South Africa and Thailand. Energy Research & Social Science, 34, 214-223.

SAIDI, K., & OMRI, A. (2020). Reducing CO2 emissions in OECD countries: Do renewable and nuclear energy matter? Progress in Nuclear Energy, 126, 103425.

VALADKHANI A., NGUYEN J., (2019). Long-run effects of disaggregated renewable and non-renewable energy consumption on real output. Applied Energy, 255- 113796.

WAHEED, R., SARWAR, S., WEI, C. (2019). The survey of economic growth, energy consumption and carbon emission. Energy Report, 5, 1103-15.

XUE, X., WU, H., ZHANG, X., DAI, J., & SU, C. (2015). Measuring energy consumption efficiency of the construction industry: the case of China. Journal of Cleaner Production, 107, 509-515.




How to Cite

YILMAZ, G., & DAŞDEMİR, E. (2021). Renewable Energy Use and Energy Productivity: A Panel Data Analysis. Journal of Sustainable Economics and Management Studies, 1(1), 73–82. Retrieved from