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Renewable and non-renewable energy consumption—impact on economic growth and CO2 emissions in five emerging market economies

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Abstract

In a neo-classical aggregate production and Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) modeling framework, the paper attempts to explore the relationship between disaggregated energy consumption, economic growth, and carbon dioxide emissions in case of five emerging market economies—Brazil, Russia, China, India, and South Africa (BRICS) over the period 1992 to 2016. The study applied the robust unit root, cointegration, and long-run elasticity estimation methods like Pooled Mean Group and differenced panel generalized method of moments for empirical exercise. Having detected the panel heterogeneity and cross-sectional dependence, the cointegration tests documented the evidence of a long-run association among the variables. In the long-run, capital, labor, and non-renewable energy consumption are found to affect the economic growth positively. On the contrary, the impact of renewable energy consumption on the economic growth is found be positive but statistically insignificant. Moreover, population, per-capita income, and non-renewable energy consumption are found to increase the emissions whereas renewable energy consumption decreases them. Therefore, along with a proper emissions controls, BRICS countries should design and implement effective support policies so as to ensure the economic growth along with environmental sustainability.

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Notes

  1. Nobuo Tanaka, Executive Director (IEA 2009b), emphasized this prognosis as follows: “The message is simple and stark: if the world continues on the basis of today’s energy and climate policies, the consequences of climate change will be severe. Energy is at the heart of the problem—and so must form the core of the solution (Apergis et al. 2010).

  2. Renewable energy is projected to be the fastest growing world energy source (International Energy outlook 2010).

  3. Renewable Energy Policy Network for the twenty-first century.

  4. Like renewable energy tax credits, installation rebates for renewable energy systems, renewable energy portfolio standards, and the creation of markets for renewable energy certificates.

  5. The time period between 1992 and 2016 covers the period when most of the renewable initiatives have been implemented across countries.

  6. https://en.wikipedia.org/wiki/List_of_countries_by_carbon_dioxide_emissions

  7. https://renewablesnow.com/news/renewables-to-reach-438-of-brazils-energy-mix-in-2017-563742/

  8. According to Ozturk (2010), using different data sets, alternative econometric methodologies and different country’s characteristics are the main reasons of the conflicting result.

  9. Refer to Ozturk (2010) for a detailed survey.

  10. In 2015, the combined nominal GDP of BRICS countries equals around US$16.6 trillion, equivalent to approximately 22% of the gross world product.

  11. Moroney (1992: 337) rightly argues: “It is one thing to correctly cite energy’s small cost share in GNP, but an error to conclude, on this account, that energy plays a secondary role. Its role is primary, coequal with capital formation.”

  12. Diesendorf (2002) and Roca (2002), however, criticized the inclusion of behavior factor (B) on account of its existing representation in each term on the right hand side of Eq. 3 and difficulty in quantification.

  13. Technically cross-sectional dependence may arise due presence of common shocks and unobserved components that ultimately become part of the error term, spatial dependence, and idiosyncratic pairwise dependence in the disturbances with no particular pattern of common components or spatial dependence (De Hoyos and Sarafidis 2006).

  14. if there is sufficient cross-sectional dependence in the data and this is ignored in estimation, the decrease in estimation efficiency can become so large that, in fact, the pooled (panel) least-square estimator may provide little gain over the single-equation ordinary least squares (Phillips and Sul, 2003).

  15. The test is usually applied where T < N, a panel situation where the LM test statistic enjoys no desirable statistical properties in that it exhibits substantial size distortions (Pesaran 2004). In addition, the test can be applied in both balanced and unbalanced panels.

  16. To conserve space, the details of the panel unit root and stationarity tests have been omitted.

  17. In these test statistics, autoregressive coefficients are pooled across different countries to check for the stationarity or otherwise of estimated residuals by taking cognizance of common time factors and heterogeneity of cross sections.

  18. These statistics are based on averages of the individual autoregressive coefficients associated with the unit root tests of the residuals for each country in the panel.

  19. Conversely, the large positive values reject the null of no cointegration in case of v test. For further details, refer to Pedroni (1999, 2004).

  20. There are three dynamic estimators of this family available in the literature like dynamic fixed effects (DFE), mean group (MG) given by Pesaran and Smith (1995), and Pooled Mean Group (PMG) developed by Pesaran and Smith, 1995, Pesaran et al., 1999. The first one completely avoids the heterogeneity and only intercepts, and error variances are allowed to vary. In the second one, intercepts, slope coefficients (both short and long run), and error variances are allowed to vary. And finally, PMG estimator allows the intercepts, error variances, and short-run slope coefficients to vary across groups; however, the long-run parameters are assumed to be the same. The choice for the appropriate estimator is decided by the Hausman 1978 test. As reported in Tables 5 and 7, Hausman test favors the null of “difference in long-run coefficients not systematic,” and hence, PMG is applied in both the cases of economic growth and CO2 emission analysis.

  21. If φi = 0, then there is an evidence of no cointegration.

  22. There are two versions of panel GMM—first differenced and system GMM. The first differenced GMM uses the entire data in first differences in a single equation framework. However, system GMM uses the level equation to obtain a system of two equations: one differenced and one in levels. By adding the second equation, additional instruments can be obtained. Because system GMM uses more instruments than the difference GMM (to gain more efficiency), it may not be appropriate to use system GMM with a data set with a small number of countries, which in this study is only 5. So difference GMM is applied instead of system GMM.

  23. “The Indian energy sector is predominantly coal-based (69%), with 5% non-hydro renewables and 12% hydropower. Financing and coordination between renewable resource–rich states (Tamil Nadu, Gujarat, and Rajasthan) and the rest of the country are major challenges for grid-integration purposes,” Bhattacharya et al. 2016. Similarly, Brazil, Russia, and South Africa are also underdeveloped so far as the relative percentage of REC in total energy consumption is concerned. However, china has been moving quite increasingly towards renewable energy adoption. Since China was home to more than one-quarter of the world’s renewable power capacity in 2016 (REN21 2017).

  24. The sign of N changed from positive to negative, and this may be due to diminishing returns to variable factor which in the short-run is labor.

  25. Calculated as the inverse of the absolute value of the error correction term (ECT), (Apergis et al. 2010)

  26. The test for AR (1) process, however, in first differences usually rejects the null hypothesis of no autocorrelation (Mileva 2007).

  27. In this case, five instruments were used.

  28. Except for GDP per-capita, with an impact of only 0.02 (positive) but statistically insignificant

  29. In this case, as well, number of instruments used equals to the number of cross-sectional units involved in the analysis.

References

  • http://www.ren21.net/wp-content/uploads/2017/06/17-8399_GSR_2017_Full_Report_0621_Opt.pdf

  • https://yearbook.enerdata.net/renewables/renewable-in-electricity-production-share.html

  • Adamantiades A, Kessides I (2009) Nuclear power for sustainable development: current status and future prospects. Energy Policy 37(12):5149–5166

    Google Scholar 

  • Al-mulali U, Fereidouni HG, Lee JY, Sab CNBC (2013) Examining the bi-directional long run relationship between renewable energy consumption and GDP growth. Renew Sust Energ Rev 22:209–222

    Google Scholar 

  • Alper A, Oguz O (2016) The role of renewable energy consumption in economic growth: evidence from asymmetric causality. Renew Sust Energ Rev 60:953–959

    Google Scholar 

  • Anderson TW, Hsiao C (1981) Estimation of dynamic models with error components. J Am Stat Assoc 76(375):598–606

    Google Scholar 

  • Apergis N, Payne JE (2010a) Renewable energy consumption and growth in Eurasia. Energy Econ 32(6):1392–1397

    Google Scholar 

  • Apergis N, Payne JE (2010b) Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy 38(1):656–660

    Google Scholar 

  • Apergis N, Payne JE (2011a) A dynamic panel study of economic development and the electricity consumption-growth nexus. Energy Econ 33(5):770–781

    Google Scholar 

  • Apergis N, Payne JE (2011b) The renewable energy consumption–growth nexus in Central America. Appl Energy 88(1):343–347

    Google Scholar 

  • Apergis N, Payne JE (2012) Renewable and non-renewable energy consumption-growth nexus: evidence from a panel error correction model. Energy Econ 34(3):733–738

    Google Scholar 

  • Apergis N, Payne JE (2014) The causal dynamics between renewable energy, real GDP, emissions and oil prices: evidence from OECD countries. Appl Econ 46(36):4519–4525

    Google Scholar 

  • Apergis N, Payne JE (2015) Renewable energy, output, carbon dioxide emissions, and oil prices: evidence from South America. Energy Sources, Part B: Economics, Planning, and Policy 10(3):281–287

    CAS  Google Scholar 

  • Apergis N, Payne JE, Menyah K, Wolde-Rufael Y (2010) On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol Econ 69(11):2255–2260

    Google Scholar 

  • Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58(2):277–297

    Google Scholar 

  • Beaudreau BC (2005) Engineering and economic growth. Struct Chang Econ Dyn 16(2):211–220

    Google Scholar 

  • Bhattacharya M, Paramati SR, Ozturk I, Bhattacharya S (2016) The effect of renewable energy consumption on economic growth: evidence from top 38 countries. Appl Energy 162:733–741

    Google Scholar 

  • Bildirici ME (2012) The relationship between economic growth and biomass energy consumption. Journal of Renewable and Sustainable Energy 4(2):023113

    Google Scholar 

  • Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87(1):115–143

    Google Scholar 

  • Breusch TS, Pagan AR (1980) The Lagrange multiplier test and its applications to model specification in econometrics. Rev Econ Stud 47(1):239–253

    Google Scholar 

  • Chien T, Hu JL (2007) Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Policy 35(7):3606–3615

    Google Scholar 

  • De Hoyos RE, Sarafidis V (2006) Testing for cross-sectional dependence in panel-data models. Stata J 6(4):482

    Google Scholar 

  • De Vries BJ, Van Vuuren DP, Hoogwijk MM (2007) Renewable energy sources: their global potential for the first-half of the 21st century at a global level: an integrated approach. Energy Policy 35(4):2590–2610

    Google Scholar 

  • DeCanio SJ (2009) The political economy of global carbon emissions reductions. Ecol Econ 68(3):915–924

    Google Scholar 

  • Diesendorf M (2002) I=PAT or I=PBAT. Ecol Econ 42:3–6

    Google Scholar 

  • Dietz T, Rosa EA (1994) Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review 1(2):277–300

    Google Scholar 

  • Dietz T, Rosa EA (1997) Effects of population and affluence on CO2 emissions. Proc Natl Acad Sci 94(1):175–179

    CAS  Google Scholar 

  • Dogan E (2015) The relationship between economic growth and electricity consumption from renewable and non-renewable sources: a study of Turkey. Renew Sust Energ Rev 52:534–546

    Google Scholar 

  • Dogan E, Seker F (2016a) Determinants of CO2 emissions in the European Union: the role of renewable and non-renewable energy. Renew Energy 94:429–439

    CAS  Google Scholar 

  • Dogan E, Seker F (2016b) The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries. Renew Sust Energ Rev 60:1074–1085

    Google Scholar 

  • Ehrlich PR, Holdren JP (1971) Impact of population growth. Science 171(3977):1212–1217

    CAS  Google Scholar 

  • Fan Y, Liu LC, Wu G, Wei YM (2006) Analyzing impact factors of CO2 emissions using the STIRPAT model. Environ Impact Assess Rev 26(4):377–395

    Google Scholar 

  • Fang Y (2011) Economic welfare impacts from renewable energy consumption: the China experience. Renew Sust Energ Rev 15(9):5120–5128

    Google Scholar 

  • Ghali KH, El-Sakka MI (2004) Energy use and output growth in Canada: a multivariate cointegration analysis. Energy Econ 26(2):225–238

    Google Scholar 

  • Gnansounou E (2008) Assessing the energy vulnerability: case of industrialised countries. Energy Policy 36(10):3734–3744

    Google Scholar 

  • Harrison, P., & Pearce, F. (2000). AAAS atlas of population & environment. Univ of California Press

  • Hausman JA (1978) Specification tests in econometrics. Econometrica: Journal of the econometric society 46:1251–1271

    Google Scholar 

  • Hedenus F, Azar C, Johansson DJ (2010) Energy security policies in EU-25—the expected cost of oil supply disruptions. Energy Policy 38(3):1241–1250

    Google Scholar 

  • Hirschl B (2009) International renewable energy policy—between marginalization and initial approaches. Energy Policy 37(11):4407–4416

    Google Scholar 

  • Holdren JP, Ehrlich PR (1974) Human population and the global environment: population growth, rising per capita material consumption, and disruptive technologies have made civilization a global ecological force. Am Sci 62(3):282–292

    CAS  Google Scholar 

  • Holtz-Eakin D, Newey W, Rosen HS (1988) Estimating vector autoregressions with panel data. Econometrica: Journal of the Econometric Society 56:1371–1395

    Google Scholar 

  • Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74

    Google Scholar 

  • International Energy Agency, 2009a. World energy outlook, 2009, Paris, France

  • International Energy Agency, 2009b. Press Release, October 6, Bangkok

  • Jäger-Waldau A (2007) Photovoltaics and renewable energies in Europe. Renew Sust Energ Rev 11(7):1414–1437

    Google Scholar 

  • Kao C (1999) Spurious regression and residual-based tests for cointegration in panel data. J Econ 90(1):1–44

    Google Scholar 

  • Kaygusuz K (2007) Energy for sustainable development: key issues and challenges. Energy Sources, Part B: Economics, Planning, and Policy 2(1):73–83

    CAS  Google Scholar 

  • Kaygusuz K, Yüksek Ö, Sari A (2007) Renewable energy sources in the European Union: markets and capacity. Energy Sources, Part B: Economics, Planning, and Policy 2(1):19–29

    Google Scholar 

  • Lee CC, Chang CP (2008) Energy consumption and economic growth in Asian economies: a more comprehensive analysis using panel data. Resour Energy Econ 30(1):50–65

    Google Scholar 

  • Lee CC, Chang CP, Chen PF (2008) Energy-income causality in OECD countries revisited: the key role of capital stock. Energy Econ 30(5):2359–2373

    Google Scholar 

  • Levin A, Lin CF, Chu CSJ (2002) Unit root tests in panel data: asymptotic and finite-sample properties. J Econ 108(1):1–24

    Google Scholar 

  • Lewis JI, Wiser RH (2007) Fostering a renewable energy technology industry: an international comparison of wind industry policy support mechanisms. Energy Policy 35(3):1844–1857

    Google Scholar 

  • Liddle B (2011) Consumption-driven environmental impact and age structure change in OECD countries: a cointegration-STIRPAT analysis. Demogr Res 24:749–770

    Google Scholar 

  • Lütkepohl H (1982) Non-causality due to omitted variables. J Econ 19(2–3):367–378

    Google Scholar 

  • Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data and a new simple test. Oxf Bull Econ Stat 61(S1):631–652

    Google Scholar 

  • Martinot E, Wiser R, Hamrin J, 2005. Renewable energy policies and markets in theUnited States http://www.resource-solutions.org/lib/librarypdfs/IntPolicy-RE.policies.markets.US.pdf2005

  • Menyah K, Wolde-Rufael Y (2010) CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6):2911–2915

    CAS  Google Scholar 

  • Mileva, E. (2007). Using Arellano-Bond dynamic panel GMM estimators in Stata. Economics Department, Fordham University, 1–10

  • Moroney JR (1992) Energy, capital and technological change in the United States. Resour Energy 14(4):363–380

    Google Scholar 

  • Narayan PK, Smyth R (2008) Energy consumption and real GDP in G7 countries: new evidence from panel cointegration with structural breaks. Energy Econ 30(5):2331–2341

    Google Scholar 

  • Oh W, Lee K (2004) Energy consumption and economic growth in Korea: testing the causality relation. J Policy Model 26(8–9):973–981

    Google Scholar 

  • Ozturk I (2010) A literature survey on energy–growth nexus. Energy Policy 38(1):340–349

    Google Scholar 

  • Ozturk I, Bilgili F (2015) Economic growth and biomass consumption nexus: dynamic panel analysis for sub-Sahara African countries. Appl Energy 137:110–116

    Google Scholar 

  • Paramati SR, Sinha A, Dogan E (2017) The significance of renewable energy use for economic output and environmental protection: evidence from the next 11 developing economies. Environ Sci Pollut Res 24(15):13546–13560

    CAS  Google Scholar 

  • Payne JE (2010a) Survey of the international evidence on the causal relationship between energy consumption and growth. J Econ Stud 37(1):53–95

    Google Scholar 

  • Payne JE (2010b) A survey of the electricity consumption-growth literature. Appl Energy 87(3):723–731

    Google Scholar 

  • Pedroni P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxf Bull Econ Stat 61(S1):653–670

    Google Scholar 

  • Pedroni P (2004) Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20(3):597–625

    Google Scholar 

  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels

  • Pesaran MH, Smith R (1995) Estimating long-run relationships from dynamic heterogeneous panels. J Econ 68(1):79–113

    Google Scholar 

  • Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94(446):621–634

    Google Scholar 

  • Phillips PC, Sul D (2003) Dynamic panel estimation and homogeneity testing under cross section dependence. Econ J 6(1):217–259

    Google Scholar 

  • Poumanyvong P, Kaneko S (2010) Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecol Econ 70(2):434–444

    Google Scholar 

  • Reddy BS, Assenza GB (2009) The great climate debate. Energy Policy 37(8):2997–3008

    Google Scholar 

  • Roca J (2002) The IPAT formula and its limitations. Ecol Econ 42:1–2

    Google Scholar 

  • Saad W, Taleb A (2018) The causal relationship between renewable energy consumption and economic growth: evidence from Europe. Clean Techn Environ Policy:1–10

  • Sadorsky P (2009) Renewable energy consumption and income in emerging economies. Energy Policy 37(10):4021–4028

    Google Scholar 

  • Sadorsky P (2009a) Renewable energy consumption, CO2 emissions and oil prices in the G7 countries. Energy Econ 31(3):456–462

    Google Scholar 

  • Salim RA, Hassan K, Shafiei S (2014) Renewable and non-renewable energy consumption and economic activities: further evidence from OECD countries. Energy Econ 44:350–360

    Google Scholar 

  • Sarafidis V, Robertson D (2009) On the impact of error cross-sectional dependence in short dynamic panel estimation. Econ J 12(1):62–81

    Google Scholar 

  • Sari R, Soytas U (2007) The growth of income and energy consumption in six developing countries. Energy Policy 35(2):889–898

    Google Scholar 

  • Schulze PC (2002) I=PBAT. Ecol Econ 40:149–150

    Google Scholar 

  • Sebri M, Ben-Salha O (2014) On the causal dynamics between economic growth, renewable energy consumption, CO2 emissions and trade openness: fresh evidence from BRICS countries. Renew Sust Energ Rev 39:14–23

    Google Scholar 

  • Shafiei S, Salim RA (2014) Non-renewable and renewable energy consumption and CO2 emissions in OECD countries: a comparative analysis. Energy Policy 66:547–556

    CAS  Google Scholar 

  • Silva S, Soares I, Pinho C (2012) The impact of renewable energy sources on economic growth and CO2 emissions-a SVAR approach. European Research Studies 15(4):133

    Google Scholar 

  • Sims REH (2004) Renewable energy: a response to climate change. Sol Energy 76(1–3):9–17

    Google Scholar 

  • Sovacool BK (2009) Rejecting renewables: the socio-technical impediments to renewable electricity in the United States. Energy Policy 37(11):4500–4513

    Google Scholar 

  • Soytas U, Sari R (2006) Energy consumption and income in G-7 countries. J Policy Model 28(7):739–750

    Google Scholar 

  • Stern, P. (1992). Global Environment Change. Understanding the human dimensions. National Research Council. Washington, DC. M. LUDEVID (1996).-El cambio global en el medio ambiente. Introducción a sus causas humanas. Marcombo. Boixareu Editores. Bar celona, 25

  • Stern DI (2000) A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Econ 22(2):267–283

    Google Scholar 

  • Stern, N. (2007). The stern review: economics of climate change

  • Tiwari AK (2011a) A structural VAR analysis of renewable energy consumption, real GDP and CO2 emissions: evidence from India. Econ Bull 31(2):1793–1806

    Google Scholar 

  • Tiwari AK (2011b) Comparative performance of renewable and nonrenewable energy source on economic growth and CO2 emissions of Europe and Eurasian countries: a PVAR approach. Economics Bulletin 31(3):2356–2372

    Google Scholar 

  • Tugcu CT, Ozturk I, Aslan A (2012) Renewable and non-renewable energy consumption and economic growth relationship revisited: evidence from G7 countries. Energy Econ 34(6):1942–1950

    Google Scholar 

  • Waggoner PE, Ausubel JH (2002) A framework for sustainability science: a renovated IPAT identity. Proc Natl Acad Sci 99(12):7860–7865

    CAS  Google Scholar 

  • Wolde-Rufael Y (2009) Energy consumption and economic growth: the experience of African countries revisited. Energy Econ 31(2):217–224

    Google Scholar 

  • York R, Rosa EA, Dietz T (2002) Bridging environmental science with environmental policy: plasticity of population, affluence, and technology. Soc Sci Q 83(1):18–34

    Google Scholar 

  • York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46(3):351–365

    Google Scholar 

  • Yuan JH, Kang JG, Zhao CH, Hu ZG (2008) Energy consumption and economic growth: evidence from China at both aggregated and disaggregated levels. Energy Econ 30(6):3077–3094

    Google Scholar 

  • Zeb R, Salar L, Awan U, Zaman K, Shahbaz M (2014) Causal links between renewable energy, environmental degradation and economic growth in selected SAARC countries: progress towards green economy. Renew Energy 71:123–132

    Google Scholar 

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    Javed Ahmad Bhat

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Bhat, J.A. Renewable and non-renewable energy consumption—impact on economic growth and CO2 emissions in five emerging market economies. Environ Sci Pollut Res 25, 35515–35530 (2018). https://doi.org/10.1007/s11356-018-3523-8

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