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Economic Change and Restructuring

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19-01-2023

Energy, renewable and technical efficiency convergence: a global evidence

Authors: Eirini Stergiou, Nikos Rigas, Eftychia Zaroutieri, Konstantinos Kounetas

Published in: Economic Change and Restructuring | Issue 3/2023

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Abstract

Efficient energy consumption has become a top priority for countries in combating climate change while the contribution of renewable energy in the production process has increased over the recent decades. In this sense, the improvement of energy efficiency and promotion of renewable technologies without deteriorating economic performance are in the spotlight of policymakers. The aim of this study is to evaluate the energy, renewable and technical efficiency of 126 countries worldwide for the period 1990–2013. The total factor energy and renewable energy efficiency indices are estimated by applying the directional distance function (DDF). The incorporation of emissions as an undesirable output of the production process can actually eliminate any bias from the efficiency scores. The empirical results indicate that even though technical efficiency is low, both renewable and energy efficiency present high scores. Moreover, efficiency fluctuations are evident when income and continent classifications are employed. At the second stage, we further proceed with a convergence analysis for the corresponding efficiency measures. In order to become more efficient, countries should adapt their energy policies and technologies in accordance to other countries belonging to their convergence clubs.

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The two output sets are actually sub-vectors of the \(y^*\in {R_+^M}\) output set.

The axioms refer to (i) free disposability: if an output vector is feasible, then any smaller output vector is feasible (ii) null-jointness: even a small quantity of desirable outputs cannot be produced without simultaneous producing some undesirable (iii) weak disposability: it is impossible to decrease undesirable outputs without simultaneously reducing desirable outputs (iv) strong disposability: finite amounts of input can produce only finite amounts of output.

The advantage of nonparametric methods, such as the DDF, is that they do not impose any a priori functional form on technology, they do not require any information on input and output prices nor any restrictive assumptions regarding the units of measurement.

In this study, we present the efficiency scores for convenience reasons.

DEA constitutes a special case of the DDF approach.

The clustering algorithm is presented in the Appendix C Section. However, the utilization of distinct clustering methods, such as the K-means, would underline any (dis)similar formation of the convergence clubs. We owe this to an anonymous referee.

Although data are available until 2019 for the majority of variables, there are several missing values for many countries after 2013, especially for the variable of energy. Therefore, in order to incorporate the entire sample of countries employed in this study, the time period could not exceed 2013.

According to World Bank, they are exerted during the production process with the use of solid, liquid, gas fuels and gas flaring.

Africa, Asia, Australia, Europe, North and South America. Because our sample does not include any country from Antarctica, the specific continent is not present in the particular classification.

Table 9 displays the classification of countries into income groups.

An analytic demonstration of the countries of each club can be seen in Figs. 8, 9 and 10 for the technical, energy and renewable energy efficiency, respectively.

PS set c = 0 which ensures a high confidence of accuracy.

Babazadeh E, Pourmahmoud J (2018) A novel radial super-efficiency DEA model handling negative data. J Ind Eng Manag Stud 5(1):43–65. https://doi.org/10.22116/jiems.2018.66504CrossRef

Berk I, Kasman A, Kılınç D (2020) Towards a common renewable future: the System-GMM approach to assess the convergence in renewable energy consumption of EU countries. Energy Econ 87:103922. https://doi.org/10.1016/j.eneco.2018.02.013CrossRef

Berndt ER (1990) Energy use, technical progress and productivity growth: a survey of economic issues. J Prod Anal 2(1):67–83. https://doi.org/10.1007/BF00158709CrossRef

Bhattacharya M, Inekwe JN, Sadorsky P (2020) Consumption-based and territory-based carbon emissions intensity: determinants and forecasting using club convergence across countries. Energy Econ 86:104632. https://doi.org/10.1016/j.eneco.2019.104632CrossRef

Breidenich C, Magraw D, Rowley A, Rubin JW (1998) The Kyoto protocol to the United Nations framework convention on climate change. Am J Int Law 92(2):315–331. https://doi.org/10.2307/2998044CrossRef

Boyd GA, Pang JX (2000) Estimating the linkage between energy efficiency and productivity. Energy Policy 28(5):289–296. https://doi.org/10.1016/S0301-4215(00)00016-1CrossRef

Camarero M, Picazo-Tadeo AJ, Tamarit C (2013) Are the determinants of CO2 emissions converging among OECD countries? Econ Lett 118(1):159–162. https://doi.org/10.1016/j.econlet.2012.10.009CrossRef

Camioto FDC, Mariano EB, Santana NB, Yamashita BD, Rebelatto DADN (2018) Renewable and sustainable energy efficiency: an analysis of Latin American countries. Environ Prog Sustain Energy 37(6):2116–2123. https://doi.org/10.1002/ep.12877CrossRef

Chambers RG, Fāure R, Grosskopf S (1996) Productivity growth in APEC countries. Pac Econ Rev 1(3):181–190. https://doi.org/10.1111/j.1468-0106.1996.tb00184.xCrossRef

Chatzistamoulou N, Kounetas K, Tsekouras K (2019) Energy efficiency, productive performance and heterogeneous competitiveness regimes. Does the dichotomy matter? Energy Econ 81:687–697. https://doi.org/10.1016/j.eneco.2019.05.005CrossRef

Chen W, Geng W (2017) Fossil energy saving and CO2 emissions reduction performance, and dynamic change in performance considering renewable energy input. Energy 120:283–292. https://doi.org/10.1016/j.energy.2016.11.080CrossRef

Comin D, Hobijn B (2010) An exploration of technology diffusion. Am Econ Rev 100(5):2031–59. https://doi.org/10.1257/aer.100.5.2031CrossRef

Costantini V, Mazzanti M, Montini A (2013) Environmental performance, innovation and spillovers. Evidence from a regional NAMEA. Ecol Econ 89:101–114. https://doi.org/10.1016/j.ecolecon.2013.01.026CrossRef

Duro JA, Alcántara V, Padilla E (2010) International inequality in energy intensity levels and the role of production composition and energy efficiency: an analysis of OECD countries. Ecol Econ 69(12):2468–2474. https://doi.org/10.1016/j.ecolecon.2010.07.022CrossRef

Falavigna G, Ippoliti R, Manello A, Ramello GB (2015) Judicial productivity, delay and efficiency: a directional distance function (DDF) approach. Eur J Oper Res 240(2):592–601. https://doi.org/10.1016/j.ejor.2014.07.014CrossRef

Ghisetti C, Quatraro F (2017) Green technologies and environmental productivity: a cross-sectoral analysis of direct and indirect effects in Italian regions. Ecol Econ 132:1–13. https://doi.org/10.1016/j.ecolecon.2016.10.003CrossRef

Halkos GE, Tzeremes NG (2013) Renewable energy consumption and economic efficiency: evidence from European countries. J Renew Sustain Energy 5(4):041803. https://doi.org/10.1063/1.4812995CrossRef

Hu JL, Wang SC (2006) Total-factor energy efficiency of regions in China. Energy Policy 34(17):3206–3217. https://doi.org/10.1016/j.enpol.2005.06.015CrossRef

Huang J, Yu Y, Ma C (2018) Energy efficiency convergence in China: catch-up, lock-in and regulatory uniformity. Environ Resour Econ 70(1):107–130. https://doi.org/10.1007/s10640-017-0112-0CrossRef

Honma S, Hu JL (2008) Total-factor energy efficiency of regions in Japan. Energy Policy 36(2):821–833. https://doi.org/10.1016/j.enpol.2007.10.026CrossRef

Jorgenson DW (1984) The role of energy in productivity growth. Energ J. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol5-No3-2CrossRef

Jorgenson DW (1996) Technology in growth theory. In: Conference Series-Federal Reserve Bank of Boston, Vol. 40, pp. 45-77

Kounetas KE (2018) Energy consumption and CO2 emissions convergence in European Union member countries. A tonneau des Danaides? Energy Econ 69:111–127. https://doi.org/10.1016/j.eneco.2017.11.015CrossRef

Lee H, Choi Y (2018) Greenhouse gas performance of Korean local governments based on non-radial DDF. Technol Forecast Soc Change 135:13–21. https://doi.org/10.1016/j.techfore.2018.07.011CrossRef

Mahlberg B, Sahoo BK (2011) Radial and non-radial decompositions of Luenberger productivity indicator with an illustrative application. Intern J Prod Econ 131(2):721–726. https://doi.org/10.1016/j.ijpe.2011.02.021CrossRef

Mulder P, De Groot HL, Pfeiffer B (2014) Dynamics and determinants of energy intensity in the service sector: a cross-country analysis, 1980–2005. Ecol Econ 100:1–15. https://doi.org/10.1016/j.ecolecon.2014.01.016CrossRef

Nilsson LJ (1993) Energy intensity trends in 31 industrial and developing countries 1950–1988. Energy 18(4):309–322. https://doi.org/10.1016/0360-5442(93)90066-MCrossRef

Oh DH (2010) A meta Frontier approach for measuring an environmentally sensitive productivity growth index. Energy Econ 32(1):146–157. https://doi.org/10.1016/j.eneco.2009.07.006CrossRef

Page SE (2006) Path dependence. Quart J Polit Sci 1(1):87–115. https://doi.org/10.1561/100.00000006CrossRef

Pan X, Pan X, Jiao Z, Song J, Ming Y (2019) Stage characteristics and driving forces of China’s energy efficiency convergence-an empirical analysis. Energy Effic 12(8):2147–2159. https://doi.org/10.1007/s12053-019-09825-8CrossRef

Patterson MG (1996) What is energy efficiency?: concepts, indicators and methodological issues. Energy Policy 24(5):377–390. https://doi.org/10.1016/0301-4215(96)00017-1CrossRef

Phillips PC, Sul D (2003) Dynamic panel estimation and homogeneity testing under cross section dependence. Econ J 6(1):217–259. https://doi.org/10.1111/1368-423X.00108CrossRef

Picazo-Tadeo AJ, Reig-Martinez E, Hernandez-Sancho F (2005) Directional distance functions and environmental regulation. Resour Energy Econ 27(2):131–142. https://doi.org/10.1016/j.reseneeco.2004.07.001CrossRef

Romero-Ávila D (2008) Convergence in carbon dioxide emissions among industrialised countries revisited. Energy Econ 30(5):2265–2282. https://doi.org/10.1016/j.eneco.2007.06.003CrossRef

Schurr SH (1982) Energy efficiency and productive efficiency: some thoughts based on American experience. Energy J. https://doi.org/10.5547/ISSN0195-6574-EJ-Vol3-No3-1CrossRef

Schurr SH (1983) Energy efficiency and economic efficiency: an historical perspective. In: Schurr SH (eds) Energy, productivity and economic growth, Sonebulum and Wood Oelgeschlager, Gunn and Hain Publishers

Shao S, Yang L, Yu M, Yu M (2011) Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994–2009. Energy Policy 39(10):6476–6494. https://doi.org/10.1016/j.enpol.2011.07.049CrossRef

Soukiazis E, Proenca S, Cerqueira PA (2019) The interconnections between renewable energy, economic development and environmental pollution: a simultaneous equation system approach. Energy J. https://doi.org/10.5547/01956574.40.4.esouCrossRef

Stergiou E, Kounetas KE (2021) Eco-efficiency convergence and technology spillovers of European industries. J Environ Manage 283:111972. https://doi.org/10.1016/j.jenvman.2021.111972CrossRef

Stergiou E, Kounetas K (2021b) European industries’ energy efficiency under different technological regimes: the role of CO2 emissions, climate, path dependence and energy mix. Energy J https://doi.org/10.5547/01956574.42.1.este

Stern DI (2012) Modeling international trends in energy efficiency. Energy Econ 34(6):2200–2208. https://doi.org/10.1016/j.eneco.2012.03.009CrossRef

Wang Q, Li S, Pisarenko Z (2020) Heterogeneous effects of energy efficiency, oil price, environmental pressure, R &D investment, and policy on renewable energy-evidence from the G20 countries. Energy 209:118322. https://doi.org/10.1016/j.energy.2020.118322CrossRef

Wang Q, Zhan L (2019) Assessing the sustainability of renewable energy: an empirical analysis of selected 18 European countries. Sci Total Environ 692:529–545. https://doi.org/10.1016/j.scitotenv.2019.07.170CrossRef

Wang Q, Zhao Z, Zhou P, Zhou D (2013) Energy efficiency and production technology heterogeneity in China: a meta-frontier DEA approach. Econ Model 35:283–289. https://doi.org/10.1016/j.econmod.2013.07.017CrossRef

Wang Z, Feng C, Zhang B (2014) An empirical analysis of China’s energy efficiency from both static and dynamic perspectives. Energy 74:322–330. https://doi.org/10.1016/j.energy.2014.06.082CrossRef

Watanabe M, Tanaka K (2007) Efficiency analysis of Chinese industry: a directional distance function approach. Energy Policy 35(12):6323–6331. https://doi.org/10.1016/j.enpol.2007.07.013CrossRef

Wei C, Shen MH (2007) Energy efficiency and its influencing factors: an empirical analysis based on DEA. Manag World 8:66–76

Wu H, Hao Y, Ren S (2020) How do environmental regulation and environmental decentralization affect green total factor energy efficiency: evidence from China. Energy Econ 91:104880. https://doi.org/10.1016/j.eneco.2020.104880CrossRef

Wu H, Mentel U, Lew G, Wang S (2022) What drives renewable energy in the group of seven economies? Evidence from non-parametric panel methods. Econ Res https://doi.org/10.1080/1331677X.2022.2092525

Zhang XP, Cheng XM, Yuan JH, Gao XJ (2011) Total-factor energy efficiency in developing countries. Energy Policy 39(2):644–650. https://doi.org/10.1016/j.enpol.2010.10.037CrossRef

Title: Energy, renewable and technical efficiency convergence: a global evidence
Authors: Eirini Stergiou
Nikos Rigas
Eftychia Zaroutieri
Konstantinos Kounetas
Publication date: 19-01-2023
Publisher: Springer US
Published in: Economic Change and Restructuring / Issue 3/2023
Print ISSN: 1573-9414
Electronic ISSN: 1574-0277
DOI: https://doi.org/10.1007/s10644-022-09479-0

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