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the ordinary least squares method, they reported a positive relationship between quota policy instruments and development of wind power. Other researchers (Neuhoff et al. 2008; Smith and Urpelainen, 2014; Yin and Powers, 2010) have also found positive and significant relationships between quotas and the capacity of RE deployment. In this paper, section 2 will describe the methods and the data used in this study. This includes presenting the model and describing determinants of the variables in detail. Section 3 will present the empirical findings and discussion, and section 4 will provide the conclusions and discuss policy implications. 2. Methods and Data This study uses panel regression tests, resulting in a comprehensive analysis of the links between RE growth and policy trends. I assemble a country-level in the EU and in the US panel data set spanning 1990-2008 and employ a country/state model with regression test for reliability of the results of panel data models. Panel data controls for country heterogeneity using EU and US provide more information than analysing them separately. It is also crucial to consider the reliability of the present work undertaken to confirm appropriate interpretation of the regression test results. To this aim the things that may have an impact on test results will be accounted for (Khandker, 2005). One can use a fixed-effects estimator, since the unobserved heterogeneity is constant over time. A fixed-effect panel specification is used for testing unobserved heterogeneity and all the variables are expressed as deviations from their mean values (Waldfogel, 1997). In other words, panel data are used to examine the hypothesis that renewable electricity capacity is related to observable and unobservable characteristics influencing renewable electricity capacity. As the unobserved sources of renewable heterogeneity are relatively constant over time, this paper can treat these
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Crenel (Ratio of renewable electricity capacity) Natural logarithm of the contribution of renewable electricity capacity to total installed capacity 1463 .032 .054 0 .618 Independent variables Renewable Policy Variables FIT (Dummy) Feed-in-Tariffs as main policy for RES-E deployment 1463 .125 .330 0 1 Quota (Dummy) as main policy for RES-E deployment 1463 .186 .389 0 1 Tender (Dummy) Tendering as main policy for RES-E deployment 1463 .205 .404 0 1 Tax Tax and subsidies incentives as main policy for RES-E deployment 1463 .226 .418 0 1 Substitute Variables Thermal Share of thermal electricity generation (oil, gas and coal) from total 1463 .652 .266 .000 1.000 Nuclear Share of nuclear electricity generation from total 1463 .194 .212 -.007 .878 Security Variables Energy import The ratio of net energy imports to total energy consumption 1463 .244 .300 -.528 1.138 Electricity import The ratio of net energy imports to total electricity consumption 1463 .026 .143 -.881 1.015 Economics Variables GDP per capita Income per capita, PPP (constant 2005 international $) 1463 3.204 18.642 -527.6 119.938 Electricity consumption TOE (Tons of Oil Equivalent) 1463 4.17e 5.54e 3.49e Coal price Coal price Northwest Europe Market Price (US$ per tone, 1995) 1463 28.797 23.928 0 149.78 Gas Natural gas price European Union CIF (US$ per million Btu, 1995) 1463 5.646 3.254 1.42 36.73 Environmental Variables CO2PC emission growth CO2 emissions per inhabitant in the EU and US 1463 -.018 .795 -6.594 5.529 Table 5A provides descriptive data on observation, mean, standard deviation, minimum, and maximum for each of these measures of renewable energy capacity for the response 6
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That is, it is important to understand how different factors affect RE investment decisions not only policies, but also technology, economic factors, and knowledge of the RE operations. 2.3. Linking RE Investment and Policies The RE policy literature has rarely incorporated the investors' viewpoint. The policy literature has usually focused on the economics of energy technologies and market efficiency. The economic evaluation for investment choices does not entirely explain how investors arrange capital or how they choose the competing RE technologies. The literature suggests that RE investors use social and psychological perspectives in the analysis of investment choices (Masini and Menichetti, 2013). Furthermore, the installed RE capacity instead of RE investment has been used as a dependent variable to look at the effectiveness of RE policies. For example, a large number of country/state level case studies have been carried out across different geographies, RE policy instruments, and RE sources (Aguirre and Ibikunle, 2014; Breukers and Wolsink, 2007; Carley, 2009; Lipp, 2007; Song, 2011; Zhao et al., 2013). Much of this literature suggests that renewable policy instruments are effective drivers to invest RE sources. On the other hand, diversity in RE policy outcomes is strongly affected by variations in the level of risk those different policies involve for renewable investors (Wüstenhagen and Menichetti, 2012). RE policy instruments that effectively reduce the risk for investors are more likely to encourage investment in large-scale deployment of RE (Luthi and Wüstenhagen, 2011; Masini and Menichetti, 2012). The importance of policy instruments is significant for developing renewable sectors. For example, Miranda (2010) argues that RE policies are
particularly stimulating research in other country contexts, possibly further advancing and testing the model, which has been proposed in this paper. 6. Acknowledgement I am indebted to the Turkish Ministry of Higher Education for the grant to carry out this study, Dr. Markus Kittler, Dr. Ian Lange, and Prof. Dr. Frans De Vries for their immense contributions and their positive feedbacks. I also would like to thank everyone interviewed as part of this research for generously offering their time and insight. 7. References Agterbosch, S. Vermeulen, W. and Glasbergen, P. (2004), “Implementation of wind energy in the Netherlands: the importance of the social–institutional setting”, Energy Policy 32(18): 2049-2066. Aguirre, M. and Ibikunle, G. (2014), “Determinants of renewable energy growth: A global sample analysis”, Energy Policy 69: 374-384. Alvarez, G.C. Jara, R.M. and Julian, J.R.R. (2009), of the effects on employment of public aid to renewable energy sources”, http://www.juandemariana.org/pdf/090327-employment-public-aid-renewable.pdf, [01.08.2014]. Barradale, M.J. (2010), “Impact of public policy uncertainty on renewable energy investment: Wind and the production tax credit”, Energy Policy 38(12): Bergek, A. Mignon, I. and Sundberg, G. (2013), “Who invests in renewable electricity production? Empirical evidence and suggestions for further research”, Energy Policy 56: 568-581.
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