Analysis of the promotion of onshore wind energy in the EU: Feed-in tariff or renewable portfolio standard?

Accepted Manuscript Analysis of the promotion of onshore wind energy in the EU: feed-in tariff or renewable portfolio standard? María Teresa García-Álvarez, Laura Cabeza-García, Isabel Soares PII:

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DOI:

10.1016/j.renene.2017.03.067

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Renewable Energy

Please cite this article as: María Teresa García-Álvarez, Laura Cabeza-García, Isabel Soares, Analysis of the promotion of onshore wind energy in the EU feed-in tariff or renewable portfolio standard?, Renewable Energy (2017), doi: 10.1016/j.renene.2017.03.067 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Analysis of the promotion of onshore wind energy in the EU: feed-in tariff or renewable portfolio standard?

Highlights:

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An empirical evaluation of FIT and RPS in onshore wind energy is developed in the EU-28 ► FIT polices have significant impacts in terms of onshore wind installed capacity ► Contract duration and tariffs are essential concepts in successful FIT policies ► An exhaustive revision of the design elements in RPS is required in this technology

ACCEPTED MANUSCRIPT Title Analysis of the promotion of onshore wind energy in the EU: feed-in tariff or renewable portfolio standard? Author names and affiliations María Teresa García-Álvarez1 , Laura Cabeza-García2, Isabel Soares3 1

Corresponding Author. María Teresa García-Álvarez

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University of Coruna, Faculty of Economy, Department of Economic Analysis and Business Administration, Campus Elvina s/n, 15071, La Coruna, Spain. E-mail: [email protected], Telf.: (+0034) 981-167000. Ext. 2459. 2 University of Leon, Faculty of Economy, Department of Management and Economy, Campus Vegazana 24071, León, Spain. E-mail: [email protected] 3 Research Centre in Economics and Finance, University of Porto, Faculty of Economy, Rua Dr Roberto Frias s/n, 4200-464 Porto, Portugal. E-mail: [email protected]

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University of Coruna, Faculty of Economy, Department of Economic Analysis and Business Administration, Campus Elvina s/n, 15071, Coruna, Spain. E-mail: [email protected], Telf.: (+0034) 981167000. Ext. 2459.

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ANALYSIS OF THE PROMOTION OF ONSHORE WIND ENERGY IN THE EU:

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FEED-IN TARIFF OR RENEWABLE PORTFOLIO STANDARD?

4 ABSTRACT

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This paper provides an empirical evaluation of feed-in tariff and renewable portfolio standard

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policies applied to onshore wind power in the EU-28 over the period 2000-2014. It allows

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policy-makers to know: a) if these policies have actually increased onshore wind generation

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capacity compared to a scenario without regulatory supports, b) which policy led to most

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onshore wind generation capacity, c) the impact of policy design elements on onshore wind

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generation capacity. The results suggest that only feed-in tariff policies and their main policy

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design elements (contract duration and tariff price) have significant impacts in terms of

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installed capacity. The establishment of a more risk-free framework would be necessary in

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renewable portfolio standard policies in order to increase investor confidence.

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Keywords: wind energy, feed-in tariff policy, renewable portfolio standard, empirical

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assessment

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1. Introduction

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Solar and wind energy play a very important role in climate change and energy security

19

issues, therefore in the development pathway transition [1,2]. Wind energy is considered the

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most mature technology among new renewable technologies and is now a prominent player in

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the European Power System. It is able to supply 10.2% of European electricity needs [3].

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Indeed, the EU wind energy industry was an early-mover which is now quite active outside

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Europe. According to the European Commission [4] over 48% of European wind energy

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companies do business outside the EU.

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Regulatory options to encourage deployment of renewable energies (RES-E) may be divided

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into two large schemes: price-driven and quantity-driven [5]. Price-driven mechanisms

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include the feed-in tariff, which establishes a fixed payment per KWh of electricity produced

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from renewable energy. So, producers of renewable energy obtain remuneration based on a

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pre-set price per kilowatt-hour (feed-in tariff) or on the price of electricity established in the

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wholesale electricity market plus an incentive. On the other hand, quantity-driven

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mechanisms include bidding systems and tradable certificate systems. Through bidding

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systems, a call for renewable energy projects is made by governments for defined capacities.

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ACCEPTED MANUSCRIPT With this mechanism, the winners of contracts obtain a guaranteed tariff for a specific time

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period. In the case of negotiable green certificates, producers of renewable electricity receive

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a certificate for each pre-defined unit of electricity produced. Certificate prices are given by

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the market.

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Notwithstanding, Glachant and Henriot [6] recognise that the role of RES-E in the European

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electricity markets is evolving in two directions: either they are isolated from market prices so

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are receiving feed-in-tariffs, or they are integrated in the spot market and receive a premium

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on top of the spot price set by this market. To date, these price-based regulatory options have

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become far more popular than quantity-based strategies where renewable energy price is the

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outcome, not the assumption, of the RES-E support scheme.

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In this context, previous studies have mainly analysed the influence of price-based RES-E

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support policies (feed-in tariff) on RES-E development [7,8]. This paper tries to contribute to

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the empirical evidence by studying not only the effect of the feed-in tariff policy but also of

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quantity-based policies (quota system, also known as renewable portfolio standard) on RES-E

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development.

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Therefore, this paper makes a double contribution. Firstly, the two main RES-E polices are

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considered together in the case of onshore wind power. This production technology is chosen

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because it has the largest installed base of any RES-E source [9]. Likewise, the feed-in tariff

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(FIT) and renewable portfolio standard (RPS) are chosen as the research focus because they

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represent the main policies for promoting RES-E in the EU [10,11]. However, other aspects

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that may influence RES-E development –the main policy design elements in both policies–

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are analysed in this paper. As far as we know, they have hardly been subject to quantitative

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analysis, with the exception of Jenner et al. [8] who only focus on FIT policies. Although the

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choice of design elements in each policy is at least as important for promoting RES-E as the

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choice of specific policies, the literature has focused on the study of RES-E support policies

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[12]. Secondly, our paper analyses these issues over a long and recent period of time in the

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EU-28, 2000-2014, and also allows for testing of a possible endogeneity problem, which is

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not taken into account in most of the previous studies. This approach offers robust and

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quantitative evidence in this research field.

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2. Literature review

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Few studies have assessed the effectiveness of RES-E policies [8,13]. Table 1 shows an

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overview of the literature that analyses this issue. Initially, the literature has mainly centred on

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ACCEPTED MANUSCRIPT both qualitative and theoretical approaches. In this context, Menanteau et al. [14] analysed the

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efficiency of RES-E support policies based on prices and quantities in the EU from a

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theoretical approach. Their results showed that the FIT was more efficient than bidding

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systems in the development of RES-E resources. Wüstenhagen and Bilharz [15] developed a

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qualitative analysis, for the German case, over the period 1990-2003, whose results indicated

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that the FIT was effective in new RES-E capacity as well as in cost reduction. Gan et al. [16]

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studied qualitatively the cases of Germany, the Netherlands, Sweden and the United States,

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over the period 2002-2005. They concluded that the success of RES-E support policies comes

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from the establishment of clear and coherent policies that involve an increase in the market

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share of RES-E in electricity generation and a reduction in RES-E costs –even when

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government intervention is eliminated.

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By means of case study methodology, Frondel et al. [7] found that the RES-E policy adopted

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in Germany (FIT) prevented the necessary market incentives from ensuring the development

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of cost-effective RES-E. Patlitzianas and Karagounis [17] studied the progress of RES-E in

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the new EU member states (Bulgaria, Cyprus, Czech, Estonia, Hungary, Latvia, Lithuania,

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Malta, Poland, Romania, Slovakia and Slovenia), over the period 2005-2009. The results of

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their case study showed that FIT seemed to be an effective instrument for developing RES-E

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in those member states. Liao [18] concluded that governments have to adopt flexible policies

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to achieve RES-E targets in different RES-E market phases (undeveloped, developing and

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developed markets) and therefore there was not an optimum RES-E support policy. Mignon

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and Bergek [19] showed that different investors are influenced by different institutional

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demands in Sweden and concluded that the design of a mix policy is essential in order to

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stimulate RES-E investment.

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The main conclusion obtained from the qualitative and theoretical literature is that RES-E

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policies are a main driver in the development of clean production technologies. Nevertheless,

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empirical assessment of the effectiveness of RES-E policies is still scarce. Carley [20] studied

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the impact of RES-E support policies on the contribution of RES-E to energy supply in 50 US

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states over the period 1998-2006 by means of a fixed-effect panel data methodology. The

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results indicated that RES-E support policies were not a significant predictor of the

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percentage of RES-E generation. Jenner et al. [8] analysed the effectiveness of FIT in

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developing onshore wind and photovoltaic solar power in 26 EU countries over the period

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1992-2008. Using the same kind of methodological approach, they concluded that the

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ACCEPTED MANUSCRIPT interaction of electricity prices and production costs as well as policy design were the most

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effective elements for developing RES-E.

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Likewise, there is scarce empirical literature analysing the impact of specific RES-E support

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policies (price versus quota mechanisms) on the development of these clean production

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technologies. Johnstone et al. [21] studied the effects of different public policies on

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innovation in RES-E in 25 OECD countries over the period 1978-2003. Their results showed

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that different policies are effective for developing different types of RES-E. Specifically, FIT

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tended to encourage innovation in more costly technologies, while quantity-based policies

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were needed to encourage innovation in more competitive technologies. Shrimali and Kniefel

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[22] analysed the effect of public policies on the development of various RES-E sources in 50

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US states over the period 1991-2007. By means of a fixed-effect panel data model, they found

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that clean energy funds and required green power options were the most effective instruments

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for developing all types of RES-E.

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In the case of the EU, Marques and Fuinhas [23] studied the effect of public policies

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supporting RES-E in 23 member states on the use of RES-E over the period 1990-2007. Using

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a panel corrected standard errors estimator, their results showed that policies of

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incentives/subsidies (including FIT) were significant drivers of the use of clean production

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technologies. Similar results were obtained by Dong [11] using a panel data for 53 countries

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over the period 2005-2009. Polzin et al. [24] analysed the influence of public measures on

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RES-E investments made by institutional investors in a variety of OECD countries over the

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period 2000-2011. Using panel data methodology, they found that FITs were especially

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relevant because they provided a more long-term and reliable signal to invest than quota

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policies.

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[Insert Table 1.Overview of the effectiveness of RES-E support policies in the EU. Source:

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Own elaboration]

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Finally, the majority of the previous empirical literature does not consider policy design

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features that influence the strength of RES-E support policies. Some studies used qualitative

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analysis, such as Masini and Menichetti [25], who used surveys to analyse behavioural factors

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influencing RES-E investment. Their results showed the relevance of FIT policies as well as

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their design features –tariff price and contract duration– for investment in RES-E. Similarly,

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Iychettira et al. [26] studied the design elements of different RES-E policies in the EU by

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ACCEPTED MANUSCRIPT means of three learning methods and concluded that these attributes needed to be harmonised

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at EU level. As an exception, Jenner et al. [8] considered tariff price, contract duration and

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digression rate in the FIT policies of 26 EU countries. Their results showed the importance of

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these variables for explaining the development of RES-E, but they do not consider RPS in

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their analysis. Our paper goes beyond this by quantifying and testing the significance of the

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two main RES-E support policies and their attributes in the EU.

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The literature shows that there are important differences in both RES-E development and the

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characteristics of RES-E policies between countries and over time. Therefore, key issues for

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policy-makers are to know: a) if RES-E policies have actually increased RES-E generation

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capacity compared to a scenario without regulatory supports, b) which RES-E policy (based

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on quantities or prices) led to most RES-E generation capacity, c) the impact of policy design

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on RES-E generation capacity. Our contribution to the literature is an empirical assessment of

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those three questions in the case of onshore wind power for a long period of time in the EU-

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28.

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2.1. Statement of hypotheses

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With the aim of promoting the development of wind energy, governmental intervention has

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been essential to protect this clean production technology from competition by conventional

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production technologies. Menanteau et al. [14], Patlitzianas and Karagounis [17] and Marques

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and Fuinhas [14], among others, show the importance of FIT policies to promote RES-E –

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including wind energy– in the EU in terms of installed capacity and electricity generation. On

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the other hand, Shrimali and Kniefel [22] and Ederer [27] emphasize the importance of RPS

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based on its effectiveness to incentivise RES-E in the form of profit and certainty.

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Taking into consideration the above-mentioned arguments, both FIT and RPS would provide

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incentives to develop innovative technological solutions in wind energy that can be

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economically viable. Thus, the following first hypothesis is presented:

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Hypothesis 1. Wind support policies (FIT and RPS) result in the installation of onshore wind

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capacity.

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However, there may be remarkable differences in policy design elements within the same

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RES-E promotion policy. Therefore, the success or failure of a specific policy might stem

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from its particular design elements [13]. Masini and Menichetti [25] and Iychettira et al. [26]

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ACCEPTED MANUSCRIPT indicate that a positive impact of tariff price and contract duration is expected on RES-E

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installed capacity (including wind energy) as enough incentives in terms of remuneration and

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maintenance of contract duration are essential for ensuring expansion. Thus, a positive

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relationship between tariff price and contract duration was found by Jenner et al. [8] in FIT

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policies, using panel data methodology.

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Considering this argument, the following hypotheses are proposed:

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Hypothesis 2. The higher the tariff price in FIT, the greater the onshore wind power installed

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capacity.

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Hypothesis 3. The longer the contract duration in FIT, the greater the onshore wind power

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installed capacity.

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Due to the scarce empirical evidence regarding policy design elements of RPS, research has

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been mainly based on qualitative analysis. Masini and Menichetti [25] and Iychettira et al.

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[26] identify certificate prices and certificate validity lifetime as the main design elements of

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this policy. They show that these variables should involve a positive impact on RES-E

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installed capacity as they reduce risk and therefore increase the profitability of these

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generation plants. In addition, Fischlein and Smith [28] also indicate the importance of

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considering the certificate award rate as this positively affects the expected profitability of

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wind generation plants.

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Thus, the following hypotheses are tested:

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Hypothesis 4. The higher the certificate prices in RPS, the greater the onshore wind power

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installed capacity.

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Hypothesis 5. The higher the award rates in RPS, the greater the onshore wind power

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installed capacity.

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Hypothesis 6. The longer the certificate validity lifetime in RPS, the greater the onshore wind

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power installed capacity.

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3. Methodology

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This section discusses the sample, the variables and the methodology used in the empirical

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assessment.

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ACCEPTED MANUSCRIPT 3.1. Sample

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In order to carry out the empirical analysis, the Eurostat database was examined as well as

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reports about the state of RES-E support policies in the EU [29,30] over the period 2000-2014

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(28 countries, 449 observations). The analysis starts in 2000 as most of RES-E support

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policies in the EU were adopted in the first years of that decade. In order to avoid missing

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values in the estimates and to have the same sample size in all models, those cases for which

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there was not information on any of the variables were not considered. As a result, an

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unbalanced panel of 27 countries and 284 observations was obtained.

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3.2. Variables

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The literature shows RES-E installed capacity and contribution of RES-E energy to electricity

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supply as the main dependent variables for measuring RES-E development [23,31]. In this

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study, wind installed capacity is considered as the dependent variable because a key issue for

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policy-makers is to know if wind energy support policies have actually increased wind

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capacity beyond what would have happened in their absence [8]. More specifically, this

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variable is measured as the electricity capacity of onshore wind generators (in Mw)

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(WIND_CAPACITY).

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As RES-E support policies may influence wind installed capacity, this effect is taken into

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account by creating three dummy variables: RES-E promotion policy1 (the non-existence of a

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specific promotion policy in wind energy) (RES-E_POL1), RES-E promotion policy2 (RPS in

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wind energy) (RES-E_POL2), and RES-E promotion policy3 (FIT in wind energy) (RES-

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E_POL3) [11,13].

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Likewise, the effects of the main policy design elements are considered in FIT and RPS

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policies. Regarding FIT policies, tariff price and contract duration variables were included in

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the model. Tariff price refers to the price obtained by a wind producer for electricity sold to

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the grid (in Euros/MWh) in FIT observations and 0 otherwise (TARIFF_PRICE): in the case

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of premium tariffs, it is the bonus; for fixed-price tariffs, it is the amount of the tariff.

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Contract duration is the duration of a FIT contract in years and 0 otherwise

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(TARIFF_DURATION).

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Certificate prices, award rate and validity lifetime are considered the main design elements in

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the model for RPS policy. Certificate prices are the prices that are generally obtained through

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ACCEPTED MANUSCRIPT a market mechanism (in Euros/MWh) in RPS policy for wind energy and 0 otherwise

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(QUOTA_PRICE). Award rate refers to the number of certificates awarded for wind

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generators (in MWh) (QUOTA_AR). Certificate validity lifetime is the duration of a RPS

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contract in years and 0 otherwise (QUOTA_DURATION).

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Control variables are grouped in two categories: (i) political factors and (ii) socioeconomic

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factors.

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(i) Political factors. Empirical literature shows that the institutional framework as well as the

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manner in which policy-makers operate have an impact on environmental policy adoption and

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on the achievement of their expected results [20,23,31,32]. In this context, the variables of

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dependency on energy imports and government commitment toward environmental policy are

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introduced.

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Dependency on energy imports refers to the extent to which an economy relies upon imports

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in order to meet its energy needs (information obtained from Eurostat). It is calculated as net

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imports divided by gross inland energy consumption (in %) (IMPORTS). It is expected that a

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country’s dependency on energy imports results in higher investment in its own renewable

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sources in order to substitute energy imports with local resources [33, 34].

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Regarding government commitment toward environmental policy, environmental investment

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made by the government (in Euros per capita) (GOV_POLICY) is considered. This variable is

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expected to involve a greater green energy commitment and therefore higher wind energy

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deployment [20,35].

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(ii) Socioeconomic factors. These factors are possible explanatory variables for the type of

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energy sources [36]. In this context, we introduce price of oil, contribution of oil to electricity

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generation, carbon dioxide emissions per capita, and electricity energy consumption.

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Price of oil is the Brent crude petroleum price by barrel obtained from the BP Statistical

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Review of World Energy (Euros/barrel) (PRICE_OIL). Higher prices of conventional

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production technologies, such as oil, are expected to lead to greater use of RES-E [37,38] and

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therefore to greater wind energy development.

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The variable contribution of oil to electricity generation shows the percentage of oil in total

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gross electricity supply (in %) from Eurostat (CONTRIBUTION_OIL). In order to determine

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the impact of this variable on RES-E development, it is necessary to consider that the power

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ACCEPTED MANUSCRIPT of interest groups associated with fossil energy sources may be an obstacle for the

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development of RES-E [39]. Therefore, the contribution of oil to electricity generation is

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expected to have negative effects on wind energy development.

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Regarding carbon dioxide emissions per capita, this variable includes total emissions related

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to the Kyoto Protocol (carbon dioxide -CO2-, methane -CH4-, nitrous oxide -N2O-, and the F-

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gases hydro fluorocarbons, per fluorocarbons and sulphur hexafluoride -SF6-) divided by

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population (tonnes of CO2 equivalent per capita) from Eurostat (EMISSIONS). Greenhouse

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gas emissions are largely responsible for climate change. Therefore it is expected that greater

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amounts of greenhouse gas emissions means more incentives for RES-E investments and

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therefore more wind energy development [40,41].

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Finally, electricity consumption per capita (MWh per capita) obtained from Eurostat was

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considered (ELECT_CONSUMPTION). Electricity consumption shows the energy needs of a

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country. As these needs can be met either by RES-E or by traditional fossil fuel sources or a

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mix of traditional and clean production technologies, electricity consumption may have a

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positive or a negative impact on RES-E (and particularly wind) development [36,39].

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3.3. Model

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In order to test the hypotheses proposed in the theoretical background, pooled OLS

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regressions clustered at firm level are used with the STATA12 program1. In addition, in order

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to control for endogeneity problems in the models proposed, explanatory and control

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variables are lagged by one year. Initially, the possibility of employing a panel data

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methodology, such as the two-step difference GMM model drawn up for dynamic panel data

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models by Arellano and Bond [43], was considered. However, as our number of countries is

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not so large, this methodology was not applied because the results would not be reliable as the

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number of instruments would be larger than the number of countries.

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The two proposed models are as follows:

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Wind_Capacity i = a0 + β X i +

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1

2014

∑D

t = 2000

t

+ εi

The cluster option also implies the estimation of robust standard errors. 10

[Model 1]

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WIND_CAPACITYi = a 0 + β1 TARIFF_PRICE i + β 2 TARIFF_DURATION + β 3 QUOTA_PRICE i + i 2014

β 4 QUOTA_AR i + β 5 QUOTA_DURA TION + ∑ Dt + ε i t =2000

[Model 2]

∑D is a set of time dummy variables and εi is the error term.

2014

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where

t =2000

t

4. Results

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Table 2 shows the descriptive statistics while Table 3 lists the correlation coefficients of the

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variables used in the regression analyses. Although some of the variables show a statistically

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significant correlation, analysis of the variance inflation factors (VIF) revealed no evidence of

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multicollinearity as all of them remained under 10 [44].

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[Insert Table 2.Descriptive statistics. Source: Own elaboration]

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Table 3 summarises the results of the regression analyses. Model 1 considers the effect of

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RES-E promotion policies on the contribution of wind energy to electricity supply, while

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Model 2 focuses on different policy design elements. As explained in the variables section,

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RES-E_POL is a qualitative variable that places RES-E support polices in three possible

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categories; thus, to make it operative, three dummy variables are defined. However, in the

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regression models it is only possible to add k-1 dummies (in our case 2) because in the other

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case the parameters cannot be estimated. Therefore, the results are presented by combining

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the dummies in pairs to understand what their coefficients really mean. It is sufficient to state

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the results of the combination of dummy RES-E_POL2 (RPS in wind energy) and RES-

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E_POL3 (FIT in wind energy) because the results of the remaining combinations can be

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deduced from the previous one.

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[Insert Table 3.Correlation matrix. Source: Own elaboration]

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The results of Model 1 support Hypothesis 1 that establishing that wind power support

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policies (FIT and RPS) result in the installation of onshore wind capacity. Feed-in tariff

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(RES-E_POL3) significantly increases onshore wind capacity in comparison to the non-

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existence of a specific promotion policy for wind energy (RES-E_POL1). These findings are

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in line with other previous studies [11,17,23] that indicate greater RES-E development with

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FIT. However, the pairwise comparisons suggest that there are no significant differences

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between RPS (RES-E_POL2) and the non-existence of a specific promotion policy

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(RES_POL1). RPS (RES-E_POL2) increases but the increase in onshore wind capacity is not

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statistically significant in comparison with the non-existence of a specific wind energy 11

ACCEPTED MANUSCRIPT promotion policy (RES-E_POL1). An explanation might lie in the higher risk for investors as

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they do not have fixed payments so might refrain from investment [20,24,45]. Thus, this first

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hypothesis is only confirmed partially.

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However, contrary to Hypothesis 2 which states that the higher the tariff price in FIT, the

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greater the onshore wind power installed capacity, the results suggest that a higher tariff price

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obtained by an onshore wind generator when power is sold to the grid (TARIFF_PRICE)

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reduces onshore wind capacity (β= -0.477; p = 0.002). The explanation might lie in the

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regulatory uncertainty concerning this design element [5,19]. Tariffs are frequently changed

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in the annual legislation of member states on climate change and energy efficiency issues. In

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addition, if wind power is considered a mature production technology in the EU, investors

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may have lower expectations in terms of maintaining high tariff prices.

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On the other hand, the results support Hypothesis 3 which establishes that the longer the

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contract duration in FIT, the greater the onshore wind power installed capacity. Thus, longer

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duration of a FIT contract (TARIFF_DURATION) favours onshore wind capacity (β= 0.454

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p = 0.000). Although not shown, this policy design element is more stable with a duration

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between 15 and 20 years for this production technology in the EU. Similar results are

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obtained using qualitative approaches [25,26].

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Finally, the results do not support Hypothesis 4 (the higher the certificate prices in RPS, the

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greater the onshore wind power installed capacity), Hypothesis 5 (the higher the award rates

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in RPS, the greater the onshore wind installed capacity nor Hypothesis 6 (the longer validity

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lifetime for certificates in RPS, the greater onshore wind installed capacity). They must be

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rejected as certificate prices (QUOTA_PRICE), award rate (QUOTA_AR) and the duration of

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an RPS contract (QUOTA_DURATION) are not statistically significant. These results

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suggest that RPS design might not reduce investor risk or increase investor confidence in the

325

expected income stream [45].

326

Regarding control variables, political factors such as IMPORTS and GOV_POLICY do not

327

seem to significantly influence the capacity of onshore wind generators. However, some

328

socioeconomic factors seem to matter. More specifically, in line with other studies [37,39],

329

the results indicate that PRICE_OIL is positively associated with onshore wind energy

330

capacity (in Model 1 and 2, respectively, β= 0.045 p = 0.023; β= 0.054 p = 0.057). Therefore,

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12

ACCEPTED MANUSCRIPT 331

the analysis suggests that higher oil prices provide incentives to substitute this traditional

332

energy source with onshore wind energy.

333

In

334

(ELECT_CONSUMPTION) has a negative impact on onshore wind installed capacity (in

335

Models and 1 and 2, β= -7.028 p = 0.063; β= -6.828 p = 0.005). This result suggests that

336

larger electricity energy consumption needs are not supplied by wind energy but by other

337

alternative resources.

338

The analysis also indicates a negative effect of the percentage of oil in total gross electricity

339

supply (CONTRIBUTION_OIL) on wind capacity but only in Model 2 and at a 10 per cent

340

level (β= -0.032 p = 0.063). Therefore, the existence of industrial lobbying (especially

341

relevant in the case of oil) might make it difficult to develop onshore wind energy [23].

342

In addition, the Model 1 suggests that the higher the carbon dioxide emissions per capita

343

(EMISSIONS), the larger the capacity of onshore wind generators (β= 0.336 p = 0.097). This

344

result is in line with Jenner et al. [8]. Greenhouse gas emissions might encourage wider use of

345

cleaner production technologies, such as onshore wind energy.

346

Finally, regarding annual effects, dummy proxies for years 2001 (in both models) and 2007

347

(in Model 1) are negative and significant. This means that, ceteris paribus, in those cases the

348

specific year influenced the dependent variable in a different and negative way in comparison

349

with the situation existing in the reference year 2000.

findings

indicate

that

electricity

consumption

per

capita

RI PT

the

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addition,

[Insert Table 4. Linear regression analysis. Source: Own elaboration]

EP

350

Robustness of model results

352

In order to establish the robustness of our results, our estimations are repeated using

353

additional measures, considering the FIT and RPS sub-samples separately, and additional

354

estimations.

355

First, Hypothesis 1 was tested by considering individually the FIT and RPS, respectively. It

356

must be emphasized that the number of observations in both sub-samples is not large and

357

consequently it is necessary to be cautious when interpreting the results. In any case, the

358

results are quite similar to those shown in Model 2 in Table 4 for the whole sample of

359

countries.

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ACCEPTED MANUSCRIPT Second, the models proposed (summarised in Table 4) were estimated by considering the

361

contribution of wind energy to electricity supply, as a percentage of total gross electricity

362

supply (WIND_CONTRIBUTION), as the dependent variable, instead of the installed

363

capacity of wind energy. The results are similar.

364

Third, when the estimations (summarised in Table 4) are repeated by considering IMPORTS,

365

CONTRIBUTION_OIL, and EMISSIONS only as endogenous variables (in which the

366

endogeneity problem is clearer), the results did not vary significantly.

367

Fourth, as it might be necessary to consider all remuneration in order to analyse the expansion

368

of onshore wind energy [27], the estimations were repeated defining TARIFF_PRICE as

369

electricity market price plus bonus in the case of premium tariffs and QUOTA_PRICE as

370

electricity market price plus certificate prices. The new variable related to TARIFF_PRICE

371

turns out to be negative and significant at the 5 percent level (β= -0.155, p-value = 0.046),

372

while the new variable related to QUOTA_PRICE presents a positive coefficient (β= 0.066)

373

but is not statistically significant (p-value = 0.445). Thus, the results are the same as those

374

shown in Table 4 concerning policy prices.

375

Fifth, as the expansion of wind farm development might involve delays, two lags instead of

376

one were considered for the explanatory and control variables in order to have a longer period

377

of time. The results remained the same.

378

Finally, the initial models were repeated by considering alternatively different proxies for the

379

control variables and, in all three cases, for the main explanatory variables the results

380

remained the same. More specifically, the oil price and oil contribution variables were

381

substituted by the price of imported natural gas in Europe (average import border price)

382

according to the World Bank (Euros/million BTU) (PRICE_GAS), and the contribution of gas

383

to electricity generation variable that shows the percentage of gas in total gross electricity

384

supply (in %) (CONTRIBUTION_GAS), respectively. Moreover, regarding government

385

commitment toward environmental policy, the GOV_POLICY variable was changed to

386

environmental protection expenditure that considers not only environmental investments but

387

also environmental current expenditure and subsidies (in Euros per capita) (GOV_POLICY2).

388

Finally, household electricity prices referring to electricity prices charged to final consumers

389

(in Euro/MWh) were considered instead of electricity consumption (ELECT_PRICES).

390

5. Discussion

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14

ACCEPTED MANUSCRIPT RES-E is an essential concept for promoting a low carbon economy in the EU. In this context,

392

Directive 2009/28/EC [47] sets an overall policy for the production and promotion of energy

393

from clean production technologies. It establishes as its objective that at least 20% of total

394

energy needs in the EU can be fulfilled by RES-E by 2020. In order to reach this target, each

395

member state has to meet individual national targets that are established by the Directive in

396

terms of the starting-point and overall potential for RES-E. Likewise, this legislation allows

397

member states to choose the RES-E support policy that they consider most suitable (price

398

mechanisms –FIT– or quota mechanisms –RPS, among others) depending on their

399

characteristics.

400

In this paper, the relative effectiveness of the two main RES-E support policies (FIT and RPS)

401

for promoting wind capacity in the EU is compared. The results show that FIT would give

402

better results than RPS policy in terms of installed wind capacity. FIT seems to involve a

403

more stable governmental commitment than RPS policy, resulting in higher reductions in

404

price and volume and a less framework involving less risk, with the consequent incentive to

405

invest in onshore wind energy [48].

406

However, any support policy can fail if it does not have certain design elements. In this

407

context, the relative effectiveness of these issues in both policies is studied. The results of this

408

paper suggest the importance of tariff price and contract duration in FIT for developing wind

409

energy capacity. A consistent commitment by government in the long-term is essential for

410

successful development of wind energy.

411

Nevertheless, the results indicate that neither RPS nor its policy design elements are

412

significant for the development of wind energy capacity. Uncertainty about the certificate

413

value affects the financial situation of investors who might refrain from investments [45].

414

Some strategic issues related to RES-E still need to be solved in the EU: the harmonisation of

415

national support policy,and infrastructure limits.

416

support policies largely depends on the credibility of these instruments for potential investors.

417

A first action to promote such credibility might be the establishment of a stable regulatory

418

framework to secure continuity of development in both promotion schemes. This would

419

require the same contract duration and certificate validity in FIT and RPS respectively in each

420

member state [49]. Therefore, joint efforts for similar framework conditions might allow the

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15

In this context, the success of RES-E

ACCEPTED MANUSCRIPT convergence of the different RES-E support policies into an optimal strategy based on the best

422

element design.

423

Another possible option might be to harmonise FITs as they are effective and cost-efficient

424

instruments for increasing RES-E generation and for achieving environmental and security

425

targets [45]. Taking the successful factors of this support policy as their basis, Muñoz et al.

426

[50] identify the incorporation of a modular premium that provides information and

427

transparency as an essential concept for creating policy robustness and eliminating political

428

uncertainty.

429

Finally, a certain tendency towards “bottom-up” convergence is being seen in the EU related

430

to how policy-makers design RES-E support policies [51]. However, specific considerations

431

are necessary regarding not only the RES-E potential of each country but also their

432

infrastructure limits. Various member states do not have the required grid infrastructure to

433

correctly allow RES-E to develop and compete on fair terms with conventional power

434

sources. Grid infrastructures have to consider the special characteristics of some RES-E plants

435

compared to traditional plants, such as intermittent power output (as with wind power),

436

decentralisation or smaller plant size [52].

437

6. Conclusions

438

The development of RES-E is relevant for meeting energy security, energy efficiency and

439

greenhouse gas emission reduction targets in the EU. Therefore, it is essential to establish

440

suitable RES-E support policies in the member states to achieve the overall aims laid out in

441

the 2020 and 2050 EU Strategies.

442

This paper provides an empirical evaluation of FIT and RPS applied to onshore wind power

443

in the EU. Therefore, it is intended to contribute to the decision making process to design

444

RES-E support policies.

445

Our results suggest that FIT gave better results in terms of onshore wind installed capacity in

446

the EU-28 over the period 2000-2014. The analysis indicates that its main policy design

447

elements (contract duration and tariff price) have significant impacts on the development of

448

that clean production technology. However, policy-makers should consider reducing

449

regulatory uncertainty about the tariff price when enacting such policies.

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16

ACCEPTED MANUSCRIPT RPS and its main design elements (certificate prices, award rate and validity lifetime) do not

451

seem to have a significant impact on the development of onshore wind energy in our analysis.

452

Policy-makers should consider establishing a more risk-free framework to increase investor

453

confidence in their expected income stream.

454

Therefore, the EU should encourage the use of FIT in the development of onshore wind

455

energy as it seems to be the most successful method for achieving RES-E targets. Another

456

alternative would be to make a thorough, urgent review of the design elements of RPS as

457

onshore wind technology is already mature in the EU. It is essential to detect the main

458

weaknesses of this policy and correct them in order to achieve energy efficiency and security

459

targets.

460

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569

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20

ACCEPTED MANUSCRIPT Table 1. Overview of effectiveness of RES-E support policies in the EU RESULTS

COUNTRY/PERIOD

Menanteau et al. [14]

FIT policies are more efficient than bidding policies

Theoretical analysis

Wüstenhagen and Bilharz [15]

Effectiveness of FIT policies

Germany/1990-2003

Gan et al. [16]

Clear and coherent policies are essential to obtain sucessful RES-E support policies

Germany, the Netherlands, Sweden and the US/2002-2005

QUALITATIVE AND THEORETICAL APPROACHES

RI PT

CASE STUDY Frondel et al. [7]

Effectiveness of FIT policies

Germany

Patlitzianas and Karagounis [17]

Effectiveness of FIT policies

Liao [18]

There is not an optimum RES-E support policy

Main countries in RES-E/1976-2010

Mignon and Bergek [19]

It is necessary the development of a mix policy

Sweden

Carley [20]

RES-E support policies are not a significant predictor of RES-E development

50 US states/1998-2006

Johnstone et al. [21]

There is not an optimum RES-E support policy

25 OECD countries/1978-2003

Shrimali and Kniefel [22]

Effectiveness of clean energy funds and green power options

50 US states/1991-2007

Marques and Fuinhas [23]

Effectiveness of incentives/subsidies policies

23 EU memberstates/1990-2007

Jenner et al. [8]

Electricity prices, production costs and policy design are more effective elements in developing RES-E

26 EU countries /1992-2008

Polzin [24]

Effectiveness of FIT policies

OECD/2000-2011

Recent EU states/2005-2009

M AN U

SC

EMPIRICAL ASSESSMENT

AC C

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Source: Own elaboration

ACCEPTED MANUSCRIPT Table 2: Descriptive statisticsa

Maximum

Minimum

Std. Dev.

WIND_CAPACITY

2.824

20.857

0

4.356

TARIFF_PRICE

4.559

23.73

0

TARIFF_DURATION

8.651

25

0

8.236

QUOTA_PRICE

0.894

11

0

2.200

QUOTA_AR

0.197

2

0

0.448

QUOTA_DURATION

2.345

15

0

5.161

IMPORTS

56.309

GOV_POLICY

32.126

PRICE_OIL

50.165

CONTRIBUTION_OIL

8.902

EMISSIONS

10.430

ELECT_CONSUMPTION

0.581

RES-E_POL1 RES-E_POL2

TE D

Other explanatory variables

a

AC C

n = 284

EP

RES-E_POL3

SC

Mean

M AN U

Variables

RI PT

WIND_CAPACITY, denotes onshore wind installed capacity; TARIFF_PRICE, is tariff of onshore wind generators with feed-in tariff policy; TARIFF_DURATION, is the contract duration of onshore wind generators with feed-in tariff policy; QUOTA_PRICE, prices obtained generally through a market mechanism in quota policy in onshore wind energy; QUOTA_AR, denotes the number of awarded certificates for onshore wind generators; QUOTA_DURATION, is the duration of a quota contract; IMPORTS, is the extent to which an economy relies upon imports in order to meet its energy needs; GOV_POLICY, is the environmental investment made by government; PRICE_OIL, is Brent crude petroleum price by barrel; CONTRIBUTION_OIL, is the percentage of oil in total gross electricity supply; EMISSIONS; are total emissions related to Kyoto Protocol divided by population; ELECT_CONSUMPTION, is electricity energy consumption per capita; RES-E_POL1, refers to non-existence of a specific promotion policy in onshore wind energy; RES-E_POL2, denotes the existence of renewable portfolio standard in onshore wind energy; RES-E_POL3, is the feedin tariff policy in onshore wind energy.

4.328

101.3

-24.1

27.112

212.036

0.422

32.549

78.833

16.742

19.549

100

0

22.821

28.39

4.77

4.138

1.406

0.164

0.227

% (number of observations = 1) 22.18% (63) 17.61 (50) 60.56 (172)

ACCEPTED MANUSCRIPT

Table 3: Correlation matrixa

Variables

1

1. WIND_CAPACITY

1

2

3

4

5

6

7

SC

RI PT

WIND_CAPACITY, denotes onshore wind installed capacity; TARIFF_PRICE, is tariff of onshore wind generators with feed-in tariff policy; TARIFF_DURATION, is the contract duration of onshore wind generators with feed-in tariff policy; QUOTA_PRICE, prices obtained generally through a market mechanism in quota policy in onshore wind energy; QUOTA_AR, denotes the number of awarded certificates for onshore wind generators; QUOTA_DURATION, is the duration of a quota contract; IMPORTS, is the extent to which an economy relies upon imports in order to meet its energy needs; GOV_POLICY, is the environmental investment made by government; PRICE_OIL, is Brent crude petroleum price by barrel; CONTRIBUTION_OIL, is the percentage of oil in total gross electricity supply; EMISSIONS; are total emissions related to Kyoto Protocol divided by population; ELECT_CONSUMPTION, is electricity energy consumption per capita; RESE_POL1, refers to non-existence of a specific promotion policy in onshore wind energy; RES-E_POL2, denotes the existence of renewable portfolio standard in onshore wind energy; RES-E_POL3, is the feed-in tariff policy in onshore wind energy.

8

9

10

11

12

13

-0.303*** (0.000)

1

3. RES-E_POL2

-0.073 (0.214)

-0.246*** (0.000)

1

4. RES-E_POL3

0.311*** (0.000)

-0.644*** (0.000)

-0.572*** (0.000)

1

5. TARIFF_PRICE

0.226*** (0.000)

-0.563*** (0.000)

-0.488*** (0.000)

0.851*** (0.000)

1

6. TARIFF_DURATION

0.529*** (0.000)

-0.561*** (0.000)

-0.486*** (0.000)

0.849*** (0.000)

0.740*** (0.000)

1

7. QUOTA_PRICE

-0.065 (0.273)

-0.217*** (0.000)

0.881*** (0.000)

-0.504*** (0.000)

-0.429*** (0.000)

-0.428*** (0.000)

1

-0.068 (0.264)

-0.235*** (0.000)

0.952*** (0.000)

-0.546*** (0.000)

-0.464*** (0.000)

-0.463*** (0.000)

0.820*** (0.000)

1

8. QUOTA_AR 9. QUOTA_DURATION

-0.068 (0.253)

-0.243*** (0.000)

0.984*** (0.000)

-0.564*** (0.000)

-0.480*** (0.000)

-0.478*** (0.000)

0.813*** (0.000)

0.953*** (0.000)

1

-0.053 (0.370)

-0.001 (0.979)

-0.143* (0.015)

0.115† (0.060)

0.095 (0.109)

0.085 (0.149)

0.034 (0.568)

-0.177** (0.002)

-0.209*** (0.000)

1

10. IMPORTS 11. GOV_POLICY

-0.089 (0.133)

-0.058 (0.324)

-0.056 (0.346)

0.099† (0.093)

0.090 (0.128)

0.082 (0.163)

0.007 (0.904)

-0.064 (0.277)

-0.069 (0.241)

0.345*** (0.000)

1

0.274*** (0.000)

-0.482*** (0.000)

0.112† (0.057)

0.312*** (0.000)

0.376*** (0.000)

0.248*** (0.000)

0.090 (0.128)

0.133* (0.024)

0.126* (0.034)

-0.059 (0.316)

0.004 (0.942)

1

-0.109† (0.065)

0.453*** (0.000)

-0.004 (0.943)

-0.021 (0.717)

1

-0.101† (0.088)

0.154** (0.009)

0.688*** (0.000)

-0.166** (0.004)

-0.167 (0.004)

TE D

EP

AC C

12. PRICE_OIL

M AN U

2. RES-E_POL1

14

13. CONTRIBUTION_OIL

-0.103† (0.083)

0.110† (0.062)

-0.106† (0.072)

-0.013 (0.821)

-0.070 (0.236)

-0.058 (0.324)

-0.057 (0.332)

-0.106† (0.073)

14. EMISSIONS

-0.077 (0.196)

0.063 (0.289)

-0.073 (0.214)

0.003 (0.957)

-0.014 (0.809)

0.075 (0.204)

-0.035 (0.549)

-0.111† (0.060)

1

15

ACCEPTED MANUSCRIPT

15. ELECT_CONSUMPTION

-0.162** (0.006)

-0.057 (0.332)

-0.024 (0.676)

0.066 (0.263)

0.018 (0.757)

0.083 (0.165)

-0.073 (0.217)

-0.039 (0.507)

-0.028 (0.642)

-0.284*** (0.000)

a

AC C

EP

TE D

M AN U

SC

RI PT

n = 284 (P-value) † p< 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001

0.165** (0.005)

-0.061 (0.302)

-0.403*** (0.000)

0.507*** (0.000)

1

ACCEPTED MANUSCRIPT Table 4: Linear regression analysisa

Model 1

RES-E_POL2

0.804 (0.734)

RES-E_POL3

2.998* (1.187)

-0.477** (0.139)

TARIFF_PRICE

QUOTA_PRICE QUOTA_AR QUOTA_DURATION

0.454*** (0.085)

M AN U

TARIFF_DURATION

Model 2

SC

Variables

RI PT

WIND_CAPACITY, denotes onshore wind installed capacity; TARIFF_PRICE, is tariff of onshore wind generators with feed-in tariff policy; TARIFF_DURATION, is the contract duration of onshore wind generators with feed-in tariff policy; QUOTA_PRICE, prices obtained generally through a market mechanism in quota policy in onshore wind energy; QUOTA_AR, denotes the number of awarded certificates for onshore wind generators; QUOTA_DURATION, is the duration of a quota contract; IMPORTS, is the extent to which an economy relies upon imports in order to meet its energy needs; GOV_POLICY, is the environmental investment made by government; PRICE_OIL, is Brent crude petroleum price by barrel; CONTRIBUTION_OIL, is the percentage of oil in total gross electricity supply; EMISSIONS; are total emissions related to Kyoto Protocol divided by population; ELECT_CONSUMPTION, is electricity energy consumption per capita; RES-E_POL1, refers to non-existence of a specific promotion policy in onshore wind energy; RES-E_POL2, denotes the existence of renewable portfolio standard in onshore wind energy; RES-E_POL3, is the feed-in tariff policy in onshore wind energy.

0.067 (0.162) -0.650 (0.842) 0.069 (0.123) -0.007 (0.024)

GOV_POLICY

-0.036 (0.021)

-0.023 (0.016)

PRECIO OIL

0.045* (0.019)

0.054† (0.027)

CONTRIBUTION_OIL

-0.031 (0.023)

-0.032† (0.016)

EMISSIONS

0.336† (0.195)

0.193 (0.120)

-7.028† (3.623)

-6.828** (2.229)

AC C

EP

TE D

-0.008 (0.039)

IMPORTS

ELECT_CONSUMPTION

Annual effect considered

Yes

Yes

R2

0.247

0.505

F

10.99***

11.67***

Number of countries

27

27

Number of observations

284

284

a

Standardised coefficients with robust standard error in brackets

b

There are significant annual effect in the models

† p< 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001