The Impact of Wind Power Consumption on the Labor Market- A study of Ten Europe Union Member Countries
DOI:
https://doi.org/10.5380/rber.v8i1.53821Resumo
The impact of wind power consumption on the labor market was analyzed for a panel of ten European Union countries in a period from 1990 to 2015. The Autoregressive Distributed Lag Methodology was used in order to decompose the total effect of wind power consumption on the labor market in its short- and long-run components. The empirical results indicate that wind power consumption has a positive impact of 0.0191 on the labor market, and oil consumption does not cause any impact whatsoever.
Referências
APERGIS, N.; PAYNE, J.E. (2012) Renewable and non-renewable energy consumption-growth nexus: evidence from a panel error correction model. Energy Economics, v. 34, n. 3, p.733–738.doi: 10.1016/j.eneco.2011.04.007.
APERGIS,N.; DANULETIU, D.C. (2014) Renewable Energy, and Economic Growth: Evidence from the Sign of Panel Long-Run Causality. International Journal of Energy Economics and Policy, v.4, n.4, p.578-587. ISSN: 2146-4553.
BALTAGI, B.H. (2008) Econometric analysis of panel data. Fourth Edition, Chichester, UK: John Wiley &Sons. Available in: http://www1.tecnun.es/biblioteca/2009/ene/libmat1.pdf.
BREUSCH, T.S.; PAGAN, A.R. (1980) The lagrange multiplier test and its applications to model specification in econometrics. The Review of Economic Studies, v.47, n.1, p.239-253. Available in:< http://www.jstor.org/stable/2297111>.
BOBINAITE, V.; PRIEDITE, I. (2015) Assessment of impacts of wind electricity generation sector development: Latvian case. Procedia - Social and Behavioral Sciences, v.213, n.1,p.18-24.doi: 10.1016/j.sbspro.2015.11.397.
BOWDEN, N.; PAYNE, J.E. (2010) Sectoral analysis of the causal relationship between renewable and non- renewable energy consumption and real output in the U.S. Energy Sources, v. 5, p.400–408.doi: 10.1080/15567240802534250.
CHOI, I. (2001) Unit root test for panel data. Journal of International Money and Finance, v.20, n.1, p.249-272.doi:10.1016/S0261-5606(00)00048-6.
COSTA, H.; VEIGA, L. (2016) Gone with the Wind? Local employment impact of wind energy investment. Available in:<http://conference.iza.org/conference_files/environ _2016/costa_h24225.pdf>.
CLUDIUS, J.; FÖRSTER, H.; GRAICHEN, V. (2012) GHG mitigation in the EU: An overview of the current policy landscape. World Resources Institute , p.1-20. Available in:< http://www.wri.org/sites/default/files/pdf/ghg_mitigation_eu_policy_landscape.pdf>.
EJDEMO, T.; SODERHOLMN, P. (2015) Wind power, regional development and benefit-sharing: The case of Northern Sweden. Renewable and Sustainable Energy Reviews, v. 47, p.476–485.doi:10.1016/j.rser.2015.03.082.
FUINHAS, J.A.; MARQUES, A.C.: KOENGKAN, M.(2016) Are the renewable energy policies impaction on carbon dioxide emissions? the case of Latin America. Anales de Economia Aplicada XXX, p. 232 – 245. ISSN: 2174-3088.
GRANGER, C.W.J. (1960) Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, v.37, n.3, p.424-438. Available in:< http://www.jstor.org/stable/1912791>.
GKATSOU,S.;KOUNENOU,M.;PAPANAGITOU,P.;SEREMETI,D.;GEORGAKELLOS,D. (2014) The impact of green energy on employment: a preliminary analysis. International Journal of Business and Social Science, v. 5, n. 1, 2014. Available in: < http://ijbssnet.com/journals/Vol_5_No_1_January_2014/4.pdf>.
HAUSMAN, J. A. (1978) Specification tests in econometrics. Econometrica v.46, n.6 p.1251–1271. Available in:< http://links.jstor.org/sici?sici=0012-9682%2819781 ... O%3B2-X&origin=repec>.
JOUINI, J. (2015) Economic growth, and remittances in Tunisia: Bi-directional causal links. Journal of Policy Modelling, v.37, n.2, p.355–373.doi: 10.1016/j.jpolmod.2015.01.015.
KONDILI, E.;KALDELLIS,J.K. (2012) Environmental-social benefits/impacts of wind power, reference module in earth systems and environmental sciences- comprehensive. Renewable Energy, v.2, p.503-539.doi: 10.1016/B978-0-08-087872-0.00219-5.
LEVIN, A.; LIN, C-F.; CHU, C-S.J. (2012) Unit root test in panel data: Asymptotic and finite-sample properties. Journal of Econometrics, v.108, n.1, p.1-24.doi:10.1016/S0304-4076(01)00098-7.
LI, S-L.; CHANG,T-H.;CHANG,S-L. (2017) The policy effectiveness of economic instruments for the photovoltaic and wind power development in the European Union. Renewable Energy, v.101, p.660-666, 2017.doi: 10.1016/j.renene.2016.09.005.
MADDALA, G.S.; WU, S.A. (1999) Comparative study of unit root test with panel data a new simple test. Oxford Bulletin of Economics and Statistics, v.61, n.1, p.631-652doi:10.1111/1468-0084.0610s1631/abstract.
MEHARARA, M. (2007) Energy consumption and economic growth: the case of oil exporting countries. Energy Policy, v.35, n.5, p.2939–2945.doi: 10.1016/j.enpol.2006.10.018.
NASERI, S.F.; MOTAMEDI,S.; AHMADIAN, M. (2016) Study of Mediated Consumption Effect of Renewable Energy on Economic Growth of OECD Countries. Procedia Economics and Finance, v.36,p.502-509.doi: 10.1016/S2212-5671(16)30068-5.
O’BRIEN, R.M. (2007) A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, v.41, n.5, p.673- 690.doi:10.1007/s11135-006-9018-6.
OKKONEN, L.; LEHTONEN, O. (2016) Socio-economic impacts of community wind power projects in Northern Scotland. Renewable Energy, v.85,p.826-833.doi: 10.1016/j.renene.2015.07.047.
PESARAN, M. H. (2004) General diagnostic tests for cross section dependence in panels. University of Cambridge, Faculty of Economics. Cambridge Working Papers in Economics, n.0435. Available in:<http://www.econ.cam.ac.uk/research/ repec/cam/pdf/cwpe0435.pdf>.
PESARAN, M.H. (2007) A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, v.22, n.2, p.256-312.doi.: 10.1002/jae.951.
PESARAN, M.H.; SMITH, L.V.;YAMAGATA, T. (2013) Panel unit root tests in the presence of a multifactor error structure. Journal of Econometrics, v.175, n.2, p.94-115.doi:10.1016/j.jeconom.2013.02.001.
PESARAN, M.H.; SHIN, Y.; SMITH, R.P. (1999) Pooled mean group estimation of dynamic heterogeneous panels. Journal of American Statistical Association, v.94, n.446, p.621-634. Available in: <http://www.jstor.org/stable/2670182>.
RODRIGUES, T.P.;GOLÇALVES,S.L.;CHAGAS, A.L.S.(2016) Brazilian wind farms and its impacts in the labor market of the municipalities in northeast region. Department of Economics, FEA-USP working paper nº 2016-36. Available in: < http://www.repec.eae.fea.usp.br/documentos/Rodrigues_Goncalves_Chagas_36WP.pdf>.
SIMAS, M.; PACCA, S. (2014) Assessing employment in renewable energy technologies: A case study for wind power in Brazil. Renewable and Sustainable Energy Reviews, v.31, p.83-90.doi: 10.1016/j.rser.2013.11.046.
TUGCU, C.T.; OZLTURK, I.; ASLAIN, A. (2012) Renewable and non-renewable energy consumption and economic growth revisited: Evidence from G7 countries. Energy Economics, v. 34, n. 6, p.1942–1950.doi: 10.1016/j.eneco.2012.08.021.
TIWARI, A.K. (2011) Comparative performance of renewable and non-renewable energy source on economic growth and CO2 emissions of Europe and Eurasian countries: A PVAR approach. Economics Bulletin, v. 31, n. 3, p.2356–2372. Available in:< http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I3-P212.pdf>.
VERBEEK, M.A. (2008) Guide to Morden Econometrics. John Wily & Sons LTD, 3rd Edition. ISBN: 978-0-470-51769.7.
VALODKA, I.; VALODKIENE, G. (2015), The Impact of Renewable Energy on the Economy of Lithuania. Procedia - Social and Behavioral Sciences, n.213, p.123– 128.doi:10.1016/j.sbspro.2015.11.414.
WESTERLUND, J. (2007) Testing for error correction in panel data. Oxford Economics and Statistics, v.31, n.2, p.217-224.doi:10.1111/j.1468-0084.2007.00477. x.
WOOLDRIDGE, J.M. (2002) Econometric analysis of cross section and panel data. The MIT Press Cambridge, Massachusetts London, England, 2002.
WISER,R.;BOLINGER,M.;HEATH,G.;KEYSER,D.;LANTZ,E.;MACKNICK,J.;MAI,T.;MILLSTEIN,D.(2016) Long-term implications of sustained wind power growth in the United States: Potential benefits and secondary impacts. Applied Energy, v.179, p.146-158.doi: 10.1016/j.apenergy.2016.06.123.
