Role of renewable, non-renewable energy consumption and carbon emission in energy efficiency and productivity change: Evidence from G20 economies

Abstract

The challenge of achieving sustainable economic development with a secure environmental system is a global challenge faced by both developed and developing countries. Energy Efficiency (EE) is crucial in achieving sustainable economic growth while reducing ecological impacts. This research utilizes the Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist-Luenberger Index (MLI) method to evaluate EE and productivity changes from 1995 to 2020 across G20 countries. The study uses four different input–output bundles to gauge the impact of renewable and non-renewable energy consumption and carbon emissions on EE and productivity changes. The study results show that including renewable energy consumption improves the average EE from 0.783 to 0.8578, but energy productivity declines from 1.0064 to 0.9988. Incorporating bad output (carbon emissions) in the estimation process enhances renewable EE and productivity change, resulting in an average EE of 0.6678 and MLI of 1.0044. Technological change is identified as the primary determinant of energy productivity growth in scenarios 1 and 2, while technical efficiency determines energy productivity change in scenarios 3 and 4. The Kruskal-Wallis test reveals a significant statistical difference between the mean EE and MLI scores of G20 countries.

Keywords

Renewable energy / DEA / Energy Efficiency / Productivity change / Carbon emissions

References

R. Akram, F. Chen, F. Khalid, Z. Ye, M.T. Majeed. Heterogeneous effects of energy efficiency and renewable energy on carbon emissions: Evidence from developing countries. J. Clean. Prod., 247 (2020), Article 119122,

M.K. Anser, W. Iqbal, U.S. Ahmad, A. Fatima, I.S. Chaudhry. Environmental efficiency and the role of energy innovation in emissions reduction. Environ. Sci. Pollut. Res., 27 (2020), pp. 29451-29463,

N. Apergis, G.C. Aye, C.P. Barros, R. Gupta, P. Wanke. The energy efficiency of selected OECD countries: A slacks-based model with undesirable outputs. Energy Econ., 51 (2015), pp. 45-53,

M. Azam, A.Q. Khan. Urbanization and environmental degradation: Evidence from four SAARC Countries—Bangladesh, India, Pakistan, and Sri Lanka. Environ. Prog. Sustain. Energy, 35 (2016), pp. 823-832,

C. Bampatsou, S. Papadopoulos, E. Zervas. Technical efficiency of economic systems of EU-15 countries based on energy consumption. Energy Policy, 55 (2013), pp. 426-434,

F. Bilgili, E. Koçak, Ü. Bulut. The dynamic impact of renewable energy consumption on CO2 emissions: A revisited Environmental Kuznets Curve approach. Renew. Sustain. Energy Rev., 54 (2016), pp. 838-845,

D. Campisi, P. Mancuso, S.L. Mastrodonato, D. Morea. Efficiency assessment of knowledge intensive business services industry in Italy: data envelopment analysis (DEA) and financial ratio analysis. Meas. Bus. Excell., 23 (2019), pp. 484-495,

D.W. Caves, L.R. Christensen, W.E. Diewert. The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50 (1982), pp. 1393-1414,

M.C. Chang. Applying the energy productivity index that considers maximized energy reduction on SADC (Southern Africa Development Community) members. Energy, 95 (2016), pp. 313-323,

T.P. Chang, J.L. Hu. Total-factor energy productivity growth, technical progress, and efficiency change: An empirical study of China. Appl. Energy, 87 (2010), pp. 3262-3270,

A. Charnes, W.W. Cooper, E. Rhodes. Measuring the efficiency of decision making units. Eur. J. Oper. Res., 2 (6) (1978), pp. 429-444,

Chen, Y., Du, J., 2015. Super-Efficiency in Data Envelopment Analysis. In: Zhu, D. (Ed.), Data Envelopment Analysis - A Handbook of Models and Methods. International Series in Operations Research and Management Science, pp. 381-414.

Z. Chen, P. Song, B. Wang. Carbon emissions trading scheme, energy efficiency and rebound effect – Evidence from China’s provincial data. Energy Policy, 157 (2021), Article 112507,

T. Chien, J.L. Hu. Renewable energy: An efficient mechanism to improve GDP. Energy Policy, 36 (8) (2008), pp. 3045-3052,

Y.H. Chung, R. Färe, S. Grosskopf. Productivity and undesirable outputs: A directional distance function approach. J. Environ. Manage., 51 (3) (1997), pp. 229-240,

G. Cui, S. Ren, B. Dou, F. Ning. Geothermal energy exploitation from depleted high-temperature gas reservoirs by recycling CO2: The superiority and existing problems. Geosci. Front., 12 (6) (2021), Article 101078,

R. Färe, S. Grosskopf, B. Lindgren, P. Roos. Productivity changes in Swedish pharamacies 1980–1989: A non-parametric Malmquist approach. J. Prod. Anal., 3 (1992), pp. 85-101,

F. Fidanoski, K. Simeonovski, V. Cvetkoska. Energy efficiency in oecd countries: A dea approach. Energies, 14 (4) (2021), p. 1185,

J. Freire-González, D. Font Vivanco. The influence of energy efficiency on other natural resources use: An input-output perspective. J. Clean. Prod., 162 (2017), pp. 336-345,

W. Gu, X. Zhao, X. Yan, C. Wang, Q. Li. Energy technological progress, energy consumption, and CO2 emissions: empirical evidence from China. J. Clean. Prod., 236 (2019), Article 117666,

X. Guo, C.C. Lu, J.H. Lee, Y.H. Chiu. Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China. Energy, 134 (2017), pp. 392-399,

P. He, Y. Sun, H. Shen, J. Jian, Z. Yu. Does environmental tax affect energy efficiency? An empirical study of energy efficiency in OECD countries based on DEA and Logit model. Sustainability, 11 (14) (2019), p. 3792,

F. He, Q. Zhang, J. Lei, W. Fu, X. Xu. Energy efficiency and productivity change of China’s iron and steel industry: Accounting for undesirable outputs. Energy Policy, 54 (2013), pp. 204-213,

J.L. Hu, S.C. Wang. Total-factor energy efficiency of regions in China. Energy Policy, 34 (17) (2006), pp. 3206-3217,

IRENA. Opportunities to accelerate national energy transitions through advanced deployment of renewables. https://www.irena.org/publications/2018/Nov/Opportunities-to-accelerate-national-energy-transitions-throughadvanced-deployment-of-renewables

N. Jalo, I. Johansson, F.M. Kanchiralla, P. Thollander. Do energy efficiency networks help reduce barriers to energy efficiency? -A case study of a regional Swedish policy program for industrial SMEs. Renew. Sustain. Energy Rev., 151 (2021), Article 111579,

E. Jebali, H. Essid, N. Khraief. The analysis of energy efficiency of the Mediterranean countries: A two-stage double bootstrap DEA approach. Energy, 134 (2017), pp. 991-1000,

A. Karasoy, S. Akçay. Effects of renewable energy consumption and trade on environmental pollution: The Turkish case. Manage. Environ. Qual., 30 (2019), pp. 437-455,

D. Kirikkaleli, E. Sofuoğlu, O. Ojekemi. Does patents on environmental technologies matter for the ecological footprint in the USA? Evidence from the novel Fourier ARDL approach. Geosci. Front., 14 (4) (2023), Article 101564,

M. Krarti, M. Aldubyan. Role of energy efficiency and distributed renewable energy in designing carbon neutral residential buildings and communities: Case study of Saudi Arabia. Energ. Buildings, 250 (2021), Article 111309,

K. Li, B. Lin. Metafroniter energy efficiency with CO2 emissions and its convergence analysis for China. Energy Econ., 48 (2015), pp. 230-241,

H. Liu, P. Yao, S. Latif, S. Aslam, N. Iqbal. Impact of Green financing, FinTech, and financial inclusion on energy efficiency. Environ. Sci. Pollut. Res., 29 (2022), pp. 18955-18966,

X. Liu, S. Zhang, J. Bae. The impact of renewable energy and agriculture on carbon dioxide emissions: Investigating the environmental Kuznets curve in four selected ASEAN countries. J. Clean. Prod., 164 (2017), pp. 1239-1247,

T. Ma, X. Cao. FDI, technological progress, and green total factor energy productivity: evidence from 281 prefecture cities in China. Environ. Dev. Sustain., 24 (2022), pp. 11058-11088,

MacFarland, T.W., Yates, J.M., 2016. Mann-Whitney U Test. In: MacFarland, T.W., Yates, J.M. (Eds.), Introduction to Nonparametric Statistics for the Biological Sciences Using R. Springer Nature, pp. 103–132. doi:10.1007/978-3-319-30634-6_4.

R. Maqbool. Efficiency and effectiveness of factors affecting renewable energy projects; an empirical perspective. Energy, 158 (2018), pp. 944-956,

A.C. Marques, J.A. Fuinhas. Is renewable energy effective in promoting growth?. Energy Policy, 46 (2012), pp. 434-442,

V. Moutinho, M. Madaleno, M. Robaina. The economic and environmental efficiency assessment in EU cross-country: Evidence from DEA and quantile regression approach. Ecol. Ind., 78 (2017), pp. 85-97,

Oil Change International and Friends of the Earth, 2021. G20 public finance institutions are still bankrolling fossil fuels (Issue October). https://1bps6437gg8c169i0y1drtgz-wpengine.netdna-ssl.com/wp-content/uploads/2021/10/OCI-G20-Report-04.pdf.

X. Ouyang, X. Wei, C. Sun, G. Du. Impact of factor price distortions on energy efficiency: Evidence from provincial-level panel data in China. Energy Policy, 118 (2018), pp. 573-583,

S.R. Paramati, U. Shahzad, B. Doğan. The role of environmental technology for energy demand and energy efficiency: Evidence from OECD countries. Renew. Sustain. Energy Rev., 153 (2022), Article 111735,

U.K. Pata, M.T. Kartal, T.S. Adebayo, S. Ullah. Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor. Geosci. Front., 14 (3) (2023), Article 101531,

M.G. Patterson. What is energy efficiency? Concepts, indicators and methodological issues. Energy Policy, 24 (1996), pp. 377-390,

M. Pulina, C. Detotto, A. Paba. An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach. Eur. J. Oper. Res., 204 (2010), pp. 613-620,

H. Qiao, F. Zheng, H. Jiang, K. Dong. The greenhouse effect of the agriculture-economic growth-renewable energy nexus: Evidence from G20 countries. Sci. Total Environ., 671 (2019), pp. 722-731,

J.S. Riti, Y. Shu. Renewable energy, energy efficiency, and eco-friendly environment (R-E5) in Nigeria. Energy, Sustain. Soc., 6 (2016), p. 13,

H.D. Saunders, J. Roy, I.M.L. Azevedo, D. Chakravarty, S. Dasgupta, D.u. De La Rue, S. Can, A. Druckman, R. Fouquet, M. Grubb, B. Lin, R. Lowe, R. Madlener, D.M. McCoy, L. Mundaca, T. Oreszczyn, S. Sorrell, D. Stern, K. Tanaka, T. Wei. Energy efficiency: what has research delivered in the last 40 years?. Annu. Rev. Env. Resour., 46 (2021), pp. 135-165,

M.Y. Shabalov, Y.L. Zhukovskiy, A.D. Buldysko, B. Gil, V.V. Starshaia. The influence of technological changes in energy efficiency on the infrastructure deterioration in the energy sector. Energy Rep., 7 (2021), pp. 2664-2680,

Y. Shen, S. Yue, S. Sun, M. Guo. Sustainable total factor productivity growth: The case of China. J. Clean. Prod., 256 (2020), Article 120727,

M. Song, J. Zhang, S. Wang. Review of the network environmental efficiencies of listed petroleum enterprises in China. Renew. Sustain. Energy Rev., 43 (2015), pp. 65-71,

H. Su, B. Liang. The impact of regional market integration and economic opening up on environmental total factor energy productivity in Chinese provinces. Energy Policy, 148 (2021), Article 111943,

P. Sun, L. Liu, M. Qayyum. Energy efficiency comparison amongst service industry in Chinese provinces from the perspective of heterogeneous resource endowment: Analysis using undesirable super efficiency SBM-ML model. J. Clean. Prod., 328 (2021), Article 129535,

M.A. Tachega, X. Yao, Y. Liu, D. Ahmed, H. Li, C. Mintah. Energy efficiency evaluation of oil producing economies in Africa: DEA, malmquist and multiple regression approaches. Clean. Environ. Syst., 2 (2021), Article 100025,

R. Tan, B. Lin. What factors lead to the decline of energy intensity in China’s energy intensive industries?. Energy Econ., 71 (2018), pp. 213-221,

Tang, L., He, G., 2021. How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China. Energy. https://doi.org/10.1016/j.energy.2021.121375.

E. Theodorsson-Norheim. Kruskal-Wallis test: BASIC computer program to perform nonparametric one-way analysis of variance and multiple comparisons on ranks of several independent samples. Comput. Methods Programs Biomed., 23 (1986), pp. 57-62,

K. Tone. A slacks-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res., 143 (2002), pp. 32-41,

Tone, K., 2003. Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. GRIPS Research Report Series.

A. Vishwakarma, M. Kulshrestha, M. Kulshreshtha. Efficiency evaluation of municipal solid waste management utilities in the urban cities of the state of Madhya Pradesh, India, using stochastic frontier analysis. Benchmarking (2012),

Z.H. Wang, H.L. Zeng, Y.M. Wei, Y.X. Zhang. Regional total factor energy efficiency: An empirical analysis of industrial sector in China. Appl. Energy, 97 (2012), pp. 115-123,

J. Wen, C.V. Okolo, I.C. Ugwuoke, K. Kolani. Research on influencing factors of renewable energy, energy efficiency, on technological innovation. Does trade, investment and human capital development matter?. Energy Policy, 160 (2022), Article 112718,

P. Wilkinson, K.R. Smith, S. Beevers, C. Tonne, T. Oreszczyn. Energy, energy efficiency, and the built environment. Lancet, 370 (2007), pp. 1175-1187,

J. Witajewski-Baltvilks, E. Verdolini, M. Tavoni. Induced technological change and energy efficiency improvements. Energy Econ., 68 (2017), pp. 17-32,

C. Woo, Y. Chung, D. Chun, H. Seo, S. Hong. The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries. Renew. Sustain. Energy Rev., 47 (2015), pp. 367-376,

World Bank database, 2022. Renewable energy consumption (% of total final energy consumption). https://www.bing.com/search?q=renewable+enrrgy+consumption+wdi&form=QBLH&sp=-1&pq=renewable+enrrgy+consumption+w&sc=7-30&qs=n&sk=&cvid=66ADE537940E4358AC565D1B4D6DDF30&ghsh=0&ghacc=0&ghpl=.

Worldometers, 2022. worldometers. https://www.worldometers.info/world-population/southern-asia-population/.

J. Wu, P. Yin, J. Sun, J. Chu, L. Liang. Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspective. Eur. J. Oper. Res., 254 (3) (2016), pp. 1047-1062,

Z. Yang, X. Wei. The measurement and influences of China’s urban total factor energy efficiency under environmental pollution: Based on the game cross-efficiency DEA. J. Clean. Prod., 209 (2019), pp. 439-450,

N. Zhang, Y. Choi. Environmental energy efficiency of China’s regional economies: A non-oriented slacks-based measure analysis. Soc. Sci. J., 50 (2) (2013), pp. 225-234,

Y.J. Zhang, Y.F. Sun, J. Huang. Energy efficiency, carbon emission performance, and technology gaps: Evidence from CDM project investment. Energy Policy, 115 (2018), pp. 119-130,

C. Zhao, H. Zhang, Y. Zeng, F. Li, Y. Liu, C. Qin, J. Yuan. Total-factor energy efficiency in BRI countries: An estimation based on three-stage DEA model. Sustainability, 10 (1) (2018), p. 278,

Z. Zheng. Energy efficiency evaluation model based on DEA-SBM-Malmquist index. Energy Rep. (7) (2021), pp. 397-409,

P. Zhou, K.L. Poh, B.W. Ang. A non-radial DEA approach to measuring environmental performance. Eur. J. Oper. Res., 178 (1) (2007), pp. 1-9,