Modelling the treatment effect of renewable energy policies on economic growth: Evaluation from MENA countries

Accepted Manuscript Modelling the treatment effect of renewable energy policies on economic growth: Evaluation from MENA countries Montassar Kahia, Mohamed Kadria, Mohamed Safouane Ben Aissa, Charfeddine Lanouar PII:

S0959-6526(17)30235-4

DOI:

10.1016/j.jclepro.2017.02.030

Reference:

JCLP 8956

To appear in:

Journal of Cleaner Production

Please cite this article as: Montassar Kahia, Mohamed Kadria, Mohamed Safouane Ben Aissa, Charfeddine Lanouar, Modelling the treatment effect of renewable energy policies on economic growth: Evaluation from MENA countries, Journal of Cleaner Production (2017), doi: 10.1016/ j.jclepro.2017.02.030 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.

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Modelling the treatment effect of renewable energy policies on economic growth: Evaluation from MENA countries Montassar Kahiaa,1,

Mohamed Kadriab, LAREQUAD & FSEGT, University of Tunis El Manar, Tunisia

Mohamed Safouane Ben Aissac,

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LAREQUAD & FSEGT, University of Tunis El Manar, Tunisia

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LAREQUAD & FSEGT, University of Tunis El Manar, Tunisia

Charfeddine Lanouard

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Department of Finance and Economics, College of Business and Economics, Qatar University, B.O. Box: 2713, DOHA, Qatar.

ABSTRACT

This paper investigates the effects of renewable energy (RE) policies on economic growth in MENA countries. Using the propensity score matching methodology, our empirical analysis, conducted on a sample of 24 economies (17 RE and 7 non-RE countries) from 1980 to 2012,

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shows that the treatment effect of RE policies has a significant and positive impact on stimulating and promoting economic growth in MENA economies that have implemented these energy policies. RE policies alone, however, are not sufficient. This change requires the collective long-term commitment of all stakeholders, including governments, citizens,

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financiers, private companies and international agencies. This would help impede policy overlaps and incoherence as well as consequently improve the overall policy effectiveness.

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Keywords: Renewable energy policies; economic growth; treatment effect evaluation; propensity score matching; MENA countries. JEL Classification: C21, C52, O44, Q2

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E-mail addresses: (M. Kahia) [email protected] (Corresponding author), (M. Kadria) [email protected], (M.S.B. Aïssa) [email protected], (C. Lanouar)

[email protected]. 1

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1. Introduction Evaluating the economic impact of renewable energy policies and their effectiveness remains a challenging task and of particular interest for governments, international institutions and local policymakers (Blazejczak et al., 2014). The principal objective behind this evaluation

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process is to help governments identify the efficacy of their implemented policies in terms of their positive effects on increasing renewable energy generation, reducing unemployment and increasing economic growth. In the energy economic literature, it is well recognized that the renewable energy sector and renewable energy (RE) sources play a vital role (Kahia et al.,

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2017) and represent promising responses to several challenges that face many countries and region around the world, including the challenges of energy mix diversification, energy security, and the economic diversification of oil producing countries (IRENA, 2016). Several

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studies have shown that promoting and investing in renewable energy can help in creating new jobs, enhancing economic growth and improving environmental quality (Farooq et al., 2013). In particular, the renewable energy sector provides a good opportunity for countries and regions whose budgets rely on energy exports, such the GCC countries and some other countries of the MENA region (Kung et al., 2017). For these countries, renewable energy sector and sources can offer a potentially significant supplement to the energy supply, e.g.,

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abundant low-cost electricity with lower levels of price volatility and less reliance on insecure trade flows, as well as opportunities for economic and social development, industrial diversification, electricity exports, better environmental and carbon footprints, increasing energy access, improving energy security, new value chain activities and an unprecedented

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opportunity to reduce the dependence on imports from regions experiencing political and economic uncertainty (REN21, 2012).

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The MENA region enjoys the high availability of natural resources needed to generate renewable energy, especially from solar, wind, biomass and hydropower. For instance, this region received the most important part of solar radiation in the world: for all MENA countries, the level of irradiance is above 1,800 kWh/m2/y, which is the highest level in the word (OECD, 2013).Despite the enormous potential for renewable energy in the region, the exploitation of these resources remains quite limited: only 1% of the MENA region’s primary energy mix is supplied with energy from renewable sources, compared to the world average of 20% (Jalilvand, 2012).

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In spite of their several environmental, economic and social advantages, however, the investment in renewable energy projects still faces major constraints in MENA countries. In reality, typical high production, mainly resulting from principally new or novel technologies, lead to soaring costs. These costs are born by the MENA governments that typically sustain

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the energy market in the MENA region by subsidizing energy prices (fuel oil and electricity) for end-users. As a consequence, it creates barriers for investors, ranging from market failure to domestic policy frameworks that continue favouring fossil fuels. Overall, interviews with private sector actors revealed three main consistent barriers to private investment in renewable energy in the MENA area: the lack of profitability of renewable energy projects, an

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insufficient positive cash flow to recover the high investment costs due to the long installation life of renewable energy projects, and, difficulties for investors in accessing financing

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(OECD, 2013).

To avoid the high number of failures and bankruptcies, the incentives applied to renewable energy projects must aim at overcoming the barriers mentioned (i.e., the high risks of such projects). The main support mechanisms for increasing renewable energy in the MENA region are divided into: (1) financial incentives including feed-in tariffs, power purchase agreements, and capital subsidies (grants, investment tax credits and training incentives); (2)

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regulatory incentives such as net metering; (3) market-based incentives including tradable clean development mechanisms (CDMs) and competitive bidding processes; and (4) fiscal incentives including various reductions in taxes (OECD, 2013). Additionally, these support mechanisms mentioned above can contribute to economic growth by ensuring that the

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resulting energy mix is delivered efficiently, at the least cost, sustainably, and securely (REN21, 2013). Consequently, a failure in any of these policies will create future economic and social costs that would certainly harm well-being, and so avoiding such failures can

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broadly foster economic growth and improve social well-being. A major question that arises at this level concerns the effectiveness of these renewable energy policies on the economic growth of these countries. Empirically, the question of examining the effectiveness of renewable energy policies is a new topic compared to others questions in the energy economic literature. However, there is a growing interest, in recent years, in determining the effects of these policies on some macroeconomics variables such as renewable energy generation, renewable energy investment among some others macroeconomic variables (see Zhang et al., 2017). Two mains remarks can be drawn from previous studies. Firstly, the majority of studies have focused on 3

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the direct effects of renewable energy policies on renewable energy generations and investments (see Zhao et al., 2013). Secondly, the overall literature has focused on using time series or panel data approaches (see Kilinc-Ata, 2016). From the energy economic literature, three large gaps can be identified. The first gap is

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related to the sample of countries employed in most previous studies that focused their analysis on developed countries. The empirical literature shows that few number of studies have examined the effectiveness of renewable energy policies in emerging or developing countries and no studies have focused in the MENA region. The second gap concerns the

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macroeconomic indicator considered as a target to assess the effectiveness of renewable energy policies. For instance, the majority of previous studies have examined the effects of renewable energy policies on renewable energy generation, investment, employment, and

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CO2 emissions, while few studies have focused on the impact of renewable energy policies on economic growth. The third gap concerns the empirical methodology employed: the majority of previous studies have focused on using exploratory analysis approaches, case studies and econometric methods rather than using a non-parametric approach such as the propensity score matching (PSM) approach (see for instance, Romano et al., 2017). Seeking to fill the important gap that exists in the renewable energy literature by analysing the

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direct effects of renewable energy policies on economic growth, our study contributes to the empirical literature by investigating the case of the MENA region, which has rarely been explored in previous studies. This study also differs from previous studies in the way that it examines the effectiveness of renewable energy policies on economic growth. For instance,

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while previous studies have focused only on case studies and experimental data, this study uses a new methodological approach, the propensity score matching (PSM) approach. One of

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the major advantages of this approach is its ability to directly assess the effect of any policies implemented (renewable energy policies in our case) on the key variable (economic growth for our case). This method has recently captured the interest of many evaluators as a new tool to estimate causal treatment effects. To the best of our knowledge, it is the first time that such an approach is being taken for this type of analysis. The remainder of the paper is organized as follows. Section 2 presents the literature review related to the effectiveness of renewable energy on renewable energy generation, renewable energy investments, and on final economic indicators (economic growth and employment) and environment. Section 3 describes the data and methodology used. Section 4 discusses our 4

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econometric results. Section 5 highlights the main policy implications of our empirical findings. Finally section 6 concludes and presents limits and possible extensions. 2. Literature review

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Most of previous empirical studies investigating the effectiveness of renewable energy policies are recent and the majority of them have focused on the effects of these policies on promoting and stimulating renewable energy generation or renewable energy investments. A little of studies have investigated the effects of the renewable energy policies on the “final” economic indicators such as economic growth or employment. This paper proposes to classify

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this empirical literature on three main groups depending on the dependent variable of interest. These three groups are: (1) renewable energy generation effects, (2) renewable energy

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investments effects, and (3) effects on “final” macroeconomic and environmental variables effects.

The first group includes all studies that have examined the impacts of renewable energy policies on total renewable energy generation, renewable electricity generation and energy dependency. Among these studies, Menanteau et al. (2003) have investigated the effects of three types of instruments, namely feed-in-tariffs, a bidding system, and green certificate

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trading, on the development of renewable energy sources in the European states. Their results confirm that a system of feed-in tariffs is more efficient than the other policies. Using a similar sample of countries, Hubber et al. (2004) examined the effectiveness of the most promotional policies to increase the share of electricity generation from renewable energy.

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The authors show that there is no obvious support for a particular policy. Two others studies have examined the case of 25 EU countries, Held et al. (2006) and Ragwitz et al. (2007), who evaluated the effectiveness of different regulatory strategies and instruments to increase the

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deployment of renewable electricity generation. The authors suggest that a well-designed instrument and the long-term investment stability of any implemented policy are key criteria for policy efficiency and its effects on the deployment of renewable energy sources. Several others studies that fall into this first category include the works of Wiser et al. (2005), Menz and Vachon (2006), Carley (2009), and Yin and Powers (2010), who examined the United States. All these studies have focused on examining the effectiveness of renewable portfolio standards (RPS) in in-state renewable energy and electricity generation. The overall results are mixed. For instance, while Menz and Vachon (2006) and Yin and Powers (2010) found evidence that RPS have a significant impact on the deployment of renewable energy 5

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generation, Wiser et al. (2005) and Carley (2009) show that the effects of RPS on the development of renewable energy is not significant or state-dependent. Others studies include the work of Lund (2009), who examined the case of different geographical regions, which includes Europe countries, the U.S. and Japan. The author shows that renewable energy

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policies significantly contribute to the expansion of the renewable energy sector. Aslani et al. (2013) examine the Nordic countries and show that these countries represent a real success in terms of the effectiveness of the promotion of renewable energy policies. Zhao et al. (2013) and Wang et al. (2014) examine the effectiveness of renewable energy policies on renewable energy generation. The authors use a large sample of countries and find evidence that

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renewable energy policies play a vital role in the development of renewable energy generation. Zhao et al. (2013) show that the effectiveness of the policies is subject to

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diminishing returns as the number of policies increases.

The second group includes studies that have examined the effectiveness of renewable energy policies on investment in renewable energy. Among these studies, Sarzynski et al. (2012) have investigated the efficacy of state-level financial incentives on the market deployment of solar technology in the U.S. Using a cross-sectional time-series approach they show that states offering cash incentives, such as rebates or grants, had consistently stronger deployment

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of PV technology from 1997 to 2009 than states not offering cash incentives. Sánchez-Braza and Pablo-Romero (2014) use propensity score matching for the year 2010 and show that the tax bonus on the real estate tax (RET) was an effective instrument to promote solar thermal systems in those Andalusian municipalities that implemented the tax bonus compared with

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those that did not. In their study, Sánchez-Braza and Pablo-Romero (2014) examines 585 Spanish Andalusian municipalities. Liu et al. (2016) model the implemented Chinese FIT policies and analyse their impact on renewable energy investment in China’s power market

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using the open-loop model and show that Chinese FIT by cross control policy is a good alternative for controlling the investment in renewable technology and providing greater robustness in regulating the electricity spot market. Similarly, Romano et al. (2017) use the Panel Corrected Standard Errors (PCSE) model to evaluate the effectiveness of policy instruments (grants and/or incentives) adopted by developed and developing countries to promote renewable energy source generation and identify the differences between country groups. They show that all policies promote investment in renewable sources and their effectiveness depends on the stage of development of the countries.

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The third and last group corresponds to studies that have explored the effectiveness of renewable energy policies in terms of economics and environmental effects. Studies that fall into this group remains scare compared to the previous two groups. Among the studies that fall into this category is the study of Jaraitė et al. (2015), who examine the long-run effects of

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renewable energy support policies on economic growth and employment for the case of 15 European Union (EU) member states during the period from1990 to 2012. Their study shows that renewable energy policies raise the output and employment in the short-run. Yi (2013) also shows that clean energy policies have a positive effect on employment in the short-term for the case of U.S. metropolitan areas for the year 2006. Pérez de Arce et al. (2016) use

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fixed-effects regressions and linear demand-supply framework to examine the cost effectiveness of the RE policies in reducing CO2 emissions and show that policies are

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effective in reducing CO2 emissions.

Table 1 below reports a summary of previous studies related to the effectiveness of renewable energy policies. It classifies them in three mains categories based on the criteria’s presented above.

Insert Table 1 about here

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The present study falls into the last group of works presented above where our main variable of interest is economic growth. Moreover and unlike most of previous works, this study uses the propensity score matching method which is a recent approach that have been used recently in questions related to renewable energy policies. This propensity score matching technique

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will be presented in the following section. 3. Materials and Methods

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To investigate whether the adoption of renewable energy (hereafter RE) policies in MENA countries promotes the economic growth, we implement the propensity score matching (hereafter PSM) methodology initiated by Rubin (1977) and developed by Rosenbaum and Rubin (1983) and Heckman et al. (1998). This method is becoming increasingly popular and widely used in micro-econometrics as well as in different areas such as health and education. The PSM approach has nevertheless been recently employed in macroeconomic studies by, e.g., Kadria and Ben Aissa (2014).

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Our panel dataset consists of twenty four MENA economies over the period from 1980 to 2012. The data are drawn from various sources, including in particular the World Development Indicators (WDI).

Insert Table 2about here 3.1.Data 3.1.1. Sample

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The definition and sources of all variables are described in Table 2 below.

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This study uses annual frequency data of 17 are renewable energizers (hereafter REers), i.e., all MENA countries that have adopted RE policy (treatment group) and 7 are non-REers (control group), covering the period from 1980 to 2012. In addition, our control group was

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selected by relying on the criteria defined by Lin and Ye (2009), based on the level of economic development and the size of the country. Given these two criteria, the authors do not include in the control group countries with a GDP/capita at least as high as the poorest target country and with a population at least as large as the least populated RE country. Table 3 shows the sample of countries selected for this study, as well as the respective adoption dates for the REers.

3.1.2. Variables

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Insert Table 3about here

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i. Treatment versus outcome variables

In our study, the treatment variable is the RE (REit). It is considered as a dummy variable, taking the value of 1 if a country adopts an RE policy during the considered year, i.e., if the

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country implements any of the six policy instruments or RE policies, and 0 otherwise. Based on the database of public policies for RE compiled by the IEA, we consider the following six policy instruments used by Zhao et al. (2013) such as investment incentives; tax incentives; voluntary programs; production quotas; and tradable certificates. Moreover, the real GDP per capita growth (GDPpc_G) was used as outcome. ii. The conditioning variables The departure conditioning variables applied in our study to estimate the propensity scores (that are expected to affect both the outcome indicator and the treatment variable) are ten in 8

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number and which satisfy the required conditional independence hypothesis. These variables include the ratio of financial development (FD) measured by domestic credit for the private sector to GDP; the democracy indicator of polity IV (POLITY2); energy imports (EM) measured by the ratio of net energy imports to total energy consumption, i.e., this variable

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captures the degree of energy dependence on foreign countries; human capital (HK) measured by secondary school enrolment as a percentage of gross enrolment; foreign direct investment (FDI) measured by FDI net inflows as a proportion of GDP; the degree of trade openness (OPEN), which is measured by the sum of exports and imports as a percentage of GDP; the working age population (WAP) as a proportion of the total population; and the female variable

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(FEMALE) as the ratio of females to the total population. On the basis of several studies (see, e.g., Dong, 2012), a positive correlation was expected between these variables and the

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probability of RE adoption. The other conditioning variables that theoretically affect both RE and GDPpc_G variables and whose objective is to satisfy the conditional independence assumption are the CO2 intensity (CO2/TPES) measured by CO2 emissions per Total Primary Energy Source(TPES ) measured in Tera Jules (TJ)and the total public debt as a percentage of GDP (PUB_DEBT)(see, e.g., Al-Hinti et al., 2013). For the public debt, it is expected to have a negative effect on the probability of RE implementation. However, for the CO2 intensity

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there is no clear impact effect on the probability of RE implementation. 3.2.Econometric methodology

This subsection presents in details the treatment effect approach with PSM econometric methodology to be used to test the impact of the adoption of the RE policy on the real GDP

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per capita growth in MENA countries that have adopted this policy. Indeed, to estimate the average treatment effect on the treated (ATT), consider Equation (1)

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below,


 =  −  | = 1 =  | = 1 −  | = 1

(1)

With REi being the adoption variable of RE, which is a dummy variable of treatment;  is the value of the outcome variable for REeri, which corresponds within the framework of our study to the real GDP per capita growth, and  if not;  | = 1 is the value of the result that would have been observed if a REer has not adopted RE regime; and Yi1 |  = 1is the value of the result really observed in the same REer. The estimation of (ATT) poses a 9

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problem with the term  | = 1, of Equation (1), which is not observable; that is, in our case, the performance in terms of the economic growth of MENA REer countries is not observable if they did not apply this policy. Therefore, to counteract this problem, a common approach is estimating (ATT) by comparing the sample mean of the treatment group (REers)

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with that of the control group (non-REers). MENA countries, however, constitute a relatively heterogeneous group. Thus, such a statistical approach raises the question of selection bias, which can lead to an overestimation of the impact of RE adoption on economic growth. In response to this selectivity bias

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problem, Rosenbaum and Rubin (1983) have developed the PSM methodology. This is a nonexperimental method that consists of matching a treated observation with an untreated observation whose observable characteristics are comparable and considering the result  of

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the latter as the counterfactual of the treated observation. In other words, it accomplishes the matching of the REers with the non-REers that have the same observed characteristics so that the difference between the result of an adopter and the matching counterfactual can be attributed to the treatment (the adoption of RE). Therefore, the ATT can be estimated as follows:

(2)

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 =  | = 1,   −  | = 0,  

Where  | = 0 represents the economic growth observed in the counterfactual.  is the propensity score, which in our study means the probability for an MENA country i to adopt in year t an RE policy conditionally to the observable covariates  . The propensity

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score is noted as:

 =  |  =  = 1|

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(3)

In addition, the empirical validity of the PSM is based on two fundamental assumptions. The first is the conditional independence assumption, which implies that conditional on a set of observable characteristics  , the results variables Y0 and Y1 are independent from the treatment variable  . In other words, this means that the choice of adopting RE is independent of the potential outcomes in situations, adoption or no adoption. Consequently, this implies that the choice of switching to a RE strategy is solely based on observable countries’ characteristics and not on unobservable characteristics. Furthermore, in practical terms, this assumption implies the vector X should include all variables that influence

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treatment assignment and potential outcomes simultaneously (Lucotte, 2012). It implies that the effect of the treatment on the outcome is conditionally unconfounded. This assumption is expressed as follows:  ,  ⊥  |

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(4)

As shown by the theorem of Rosenbaum and Rubin (1983), however, compliance with the conditional independence assumption is essential because it allows us to match the treated and untreated observations on the basis of their propensity score  , and not on all the

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conditioning variables. This was the case with the matching method previously developed by Rubin (1977) to overcome the difficulty of matching  in the practical case that the number

This therefore means that:

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of covariates in these variables tends to increase.

 ,  ⊥  |  or else  ⊥  | 

(5)

The second hypothesis is the common support condition of propensity scores, whose importance for the application of PSM was emphasized by Heckman et al. (1998). This

countries.

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condition ensures the existence of some control countries comparable to each of the treated

Formally, the condition of common support can be written as: (6)

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0 <  < 1

Moreover, the process of estimating the average treatment effect on the treated includes four

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steps, referring in particular to Khandker et al. (2010). Indeed, the first step consists of estimating the propensity scores relying on the retained conditioning variables  . At this level it is important to note that the use of Probit/Logit/Tobit models, where the treatment variable is a dichotomous variable, provide almost the same results (see Caliendo and Kopeinig, 2008). Once the propensity scores are estimated, the next step is to proceed with the determination of the area of the common support densities of the two groups of countries’ propensity scores (REers and non-REers).Then, the “Min-Max” technique developed by Dehejia and Wahba (1999) and detailed by Smith and Todd (2005) will be used to calculate the ATT score. In our case and after estimating the score via a Probit model, sorting the data according to the estimated propensity score (from lowest to highest) and stratifying all 11

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observations in blocks such that in each block the estimated propensity scores for the treated and the controls are not significantly different. We started with five blocks of equal score range {0 - 0.2,…, 0.8 - 1} and tested whether the means of the scores for the treated and the control variables are statistically different in each block. After that, the balancing property

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was tested in all blocks for all covariates. Indeed, “The propensity score allows converting the multidimensional setup of matching into a one-dimensional setup. In that way, it allows reducing the dimensionality problem” (Rosenbaum and Rubin, 1983). Thus, the PSM approach solves the dimensionality problem. How? One condition for consistency of PSM is the balancing property of the propensity score model. “It ensures that observations with the

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same propensity score have the same distribution of observable covariates independently of treatment status; and for a given propensity score, assignment to treatment is “random” and

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therefore treatment and control units are observationally identical on average” (Huber, 2011). In other words, the balancing property of the propensity score states that conditional on the propensity score, the distributions of the covariates in the pools of treated and non-treated units must be equal, i.e., balanced. To check these properties, this study uses the DW test (see Dehejia and Wahba, 1999, 2002) and the regression test of Smith and Todd (2005). The third step is to estimate the ATT, specifically the average effect of the RE’s adoption on

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the economic growth of economies that have adopted this monetary policy framework. To do this, three out of the four propensity score matching methods(Nearest Neighbour Matching, Radius Matching, Local Linear Regression Matching and Kernel Matching) widely used in the empirical literature was selected to be sued in this study. The first method refers to the

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estimator of N nearest neighbour (nearest-neighbour matching) paired with replacement and consists of matching each treated observation with N control units (or the N non-treated observations) having the scores of the nearest propensity. Three values of N was used, N=1,

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N=2 and N=3. The second method is the local linear regression matching (LLRM) developed by Heckman et al. (1998). Finally, the method of kernel matching (Epanechnikov), which consists of retaining all untreated units (non-REers) belonging to the common support for the construction of the counterfactual. In other words, this method proposed by Heckman et al. (1998) allows matching a treatment unit (a REer) to all control units (non-REers) proportionally weighted as a function of their proximity in terms of propensity scores to the treated unit. The last step is to calculate the standard deviation, which allows the assessment of the statistical significance of the ATT using the bootstrap technique proposed by Lechner

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(2002) and detailed by Brownstone and Valletta (2001); please note that the retained number of replications is 1000. 4. Results

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4.1.Estimation of propensity scores The results of estimation of the Probit model presented in Table 4 show that apart from the financial development, the foreign direct investment, the degree of trade openness and the total public debt, the estimated coefficients associated with the other retained conditioning variables such as POLITY2, EM, HK, WAP, FEMALE and CO2intensity are statistically

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significant at the 1%, 5% and 10% levels, and these variables have the expected sign except for the democracy indicator. The positive sign of the CO2intensity can be explained by stating that greater pollution is accompanied by larger economic investment in renewable sources for

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electricity. In addition, the explanatory power of the model is medium, with a pseudo-R2 of McFadden equal to 40.41%. The p-value of the DW test is 0.044 and that of the regression test of Smith is 0.015. These results confirm that all the covariates included in the propensity score specifications satisfied the balancing property test, i.e., the distributions of the covariates in the pools of treated and non-treated units are balanced.

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Insert Table 4about here

To check the robustness of the results obtained from the Probit model, this study estimates also the Logit and the Tobit model. The results reported in table 4 columns 3-4 show that the results obtained from the Probit model does not change significantly when using the Logit

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and/or the Tobit model. This result confirm the findings of Caliendo and Kopeinig (2008) who shows that the use of Probit/Logit/Tobit models, where the treatment variable is a dichotomous variable, provide almost the same results, i.e., it does not significantly change

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the findings.

4.2.The results of matching Before examining the results of our estimates regarding the different matching methods reported in Table 5, it is important to analyse the results of the common support area. The results show that the procedure for determining the common support area has led to the elimination of 129 treated observations among the 167 initial ones, which is approximately 77.2% of the total sample. Concerning the estimation results, they are generally satisfactory and sufficient to observe a significant impact of the RE adoption on promoting the economic 13

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growth of economies that have implemented this energy policy, except for nearest-neighbour matching (N=1, 2). On average, this impact is of the order of 0.796 percentage points. Given this average value, the contribution of RE to the economic growth promotion can be rather important, as it enhances the real GDP per capita growth in MENA countries by at least 0.753

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(with kernel matching) and up to 0.827 (with LLRM) percentage points. Thus, our estimation results provide evidence that the promotion of RE policies can considerably stimulate the economic growth in the MENA region .Specifically, our empirical findings following that the RE policies have a positive impact in spurring economic growth, is in line with those obtained by Jaraitė et al. (2015) for the case of 15 European Union (EU) member states and by

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Blazejczak et al. (2014) for the case of Germany through using SEEEM (Sectoral EnergyEconomic Econometric Model). However, our empirical findings are in contradiction with

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that reached by Frondal et al. (2010) who found that the RE promotional instruments implemented by the German government was failed in spurring economic growth due to the massive amount of capital expenditure.

Insert Table 5 about here 5. Policy implications

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The evidence for the positive impact of renewable energy policies on economic growth has strong policy implications. First, because the empirical results show that promoting renewable energy sources is a major stimulus of economic growth, it is consequently viable for the MENA economies to continue promoting this sector to better benefit from its positive impact

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on economic growth and unemployment reduction. Policymakers in these countries, however, will be faced with several constraints and challenges that are mainly due to the lack of expertise and absence of high human capital qualification. In addition, there are many other

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challenges that are specific to the MENA region due to the high level of temperature, dry weather, and presence of dust, especially in the GCC countries (Kahia et al., 2016). All these factors can also influence the promotion of the renewable energy sector and should therefore be taken into consideration when designing renewable energy policies. Our recommendations can be divided into three mains groups: financially and fiscally oriented

recommendations,

legislatively

oriented

recommendations,

technologically and environmentally oriented recommendations.

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and

finally,

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5.1.Financially and fiscally oriented recommendations The financially and fiscally oriented recommendations include the gradual removal of subsidies and reorientation of support towards the renewable energy producers to increase the share of renewable energy in the total energy mix. To help in removing these subsides, the

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government and policymakers should create a competitive market for the renewable energy sector. The impact of removing subsides should be thoroughly studied, however, in order not to affect the segment of population with lower income.

Moreover, financial incentives for producers should be well-planned, well-oriented and well-

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designed in terms of time, target and type. Additionally, the government and policymakers can help in providing aid and incentives in terms of simplifying the process of implementing

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renewable energy investors. New regulations and financial support is also needed and new forms of financial support, such as the Sukuk and Green Sukuk schemes (see Moghul and Safar-Aly, 2015), should be studied.

Regarding the fiscally oriented recommendations, policies should be stable and not subject to uncertainty to promote the development of renewable energy (see Abdmouleh et al., 2015). For instance, following GEDB (2013), the uncertainty regarding the possible expiration of the

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production tax credit (PTC) for wind in the U.S in 2012 has decreased investment in wind power. A clear and stable strategy is needed to make investors confident. 5.2.Legislatively oriented recommendations

The literature related to renewable energy sources shows that three major legislative systems

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have been implemented globally to promote renewable energy generation. Following the study of GEDB (2013) and Abdmouleh et al. (2015), most countries around the world use the

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Feed-In-Tariffs (FIT) system (approximately 71 countries and 28 states or provinces). The second legislative strategy is the Renewable Portfolio Standard (RPS), which has been implemented by 22 countries and 54 states or provinces, and the third legislative strategy is Net Metering, which has been implemented by 37 countries and 51 states or provinces. For the MENA countries, it is important to thoroughly investigate which type of legislative strategy will be more suitable as it will depend on the individual country’s economic structure and its characteristics.

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5.3.Technologically and environmentally oriented recommendations Technologically oriented recommendations are related to the local, regional and international collaborations that can strengthen global efforts to accelerate the adoption of renewable energy policies by assessing their compatibility through effective regulatory mechanisms.

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This would help impede policy overlaps and incoherence and improve the overall policy effectiveness. Although there are several collaboration initiatives regionally and internationally, there is a need to centralize these initiatives to better target the needs of these countries.

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Regarding the environmentally oriented recommendations, it is important for the MENA countries to benefit from several incentives such as the Clean Development Mechanism

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(CDM). The CDM offers the MENA region enormous potential benefits due mainly to the potential of this region in generating renewable energy. For instance, by implementing several renewable energy projects, the countries of the MENA region can benefits from the CDM because it allow the generation of revenues from reducing GHG emissions. For instance, a one ton reduction in CO2 emissions is equivalent to one credit, and those credits can be traded on international markets (see Mezher et al., 2011).

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Finally, it is strongly recommend for the MENA policymakers to learn the best policy practice from successful countries to correctly identify the policies most suitable for implementation in MENA countries to gain the greatest benefit. This will also both maximize the positive impact of renewable energy policies on economic growth and help avoid the

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negative effect of “policy crowdedness”, which diminishes the considerable impacts of renewable energy policies on economic growth. For instance, Zhao et al. (2013) show that the effectiveness of renewable energy policies is subject to diminishing returns as the number of

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policies increases.

6. Conclusion, limits and possible extension The present study has examined the effectiveness of renewable energy policies adopted in promoting economic growth in MENA economies. Unlike previous studies, it implements a non-parametric approach, propensity score matching, to investigate the issue mentioned above. More specifically, the study explores the effectiveness of RE policy instruments on the promotion of economic growth for a set of 27 MENA countries (17 RE and 7 non-RE countries) from 1998 to 2012 by means of a number of conditioning variables. The estimation 16

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matching results robustly suggest that renewable energy policies have significant positive impacts on stimulating and promoting the economic growth of MENA economies that have implemented these energy policies. To expand the renewable energy sector and therefore effectively spur economic growth overall, the MENA countries should maintain the

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promotion of this sector by considering the design specificity of each RE policy instrument. This study presents two limitations that must be noted. First, this study presents a comprehensive overview of the effectiveness of RE policies in the MENA region, and the results cannot be applicable to a particular locale because each individual country possesses

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dissimilar economic, social, political and environmental characteristics, suggesting that the effectiveness of RE policies for each country are based on the abovementioned factors. Second, it is well known that the effect of RE polices are different in terms of technology and

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the regulation type. With the use of dummy variables, one shortcoming of the regression model presented here consists of ignoring the heterogeneity of the policy type. Future investigations should carefully examine those distinctions and develop longitudinal data sets for economic growth as well as empirically examine the dynamic treatment effects of RE policy instruments in promoting economic growth in MENA countries.

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Acknowledgments

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We would like to express our deep thanks to the editor and anonymous referee for their most valuable comments. Any errors or omissions are borne by ours.

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References Abbas, S.A., Belhocine, N., ElGanainy, A., Horton, M., 2010. A historical public debt database. IMF Working Paper 10, 245, Washington, District of Columbia (D.C.), USA.

RI PT

Abdmouleh, Z., Alammari, R.A.M., Gastli, A., 2015. Recommendations on renewable energy policies for the GCC countries. Renewable and Sustainable Energy Reviews 50, 1181– 1191. Al-Hinti, I., Al-Ghandoor, A., Sakhrieh, A., Akash, B., Abu-Nada, E., 2013. A comparative analysis of energy indicators and CO2 emissions in 15 Arab countries. International Journal of Environment and Waste Management 11, 129-147.

SC

Aslani, A., Helo, P., Naaranoja M., 2014. Role of renewable energy policies in energy dependency in Finland: System dynamics approach. Applied Energy 113, 758–765.

M AN U

Aslani, A., Naaranoja, M., Wong K.F.V., 2013. Strategic analysis of diffusion of renewable energy in the Nordic countries. Renewable and Sustainable Energy Reviews 22, 497 505. Blazejczak, J., Braun, F.G., Edler, D., Schill, W.P., 2014. Economic effects of renewable energy expansion: A model-based analysis for Germany. Renewable and Sustainable Energy Reviews 40, 1070–1080. Caliendo, M., Kopeinig, S., 2008. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys 22, 31–72.

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Carley, S., 2009. State renewable energy electricity policies: an empirical evaluation of effectiveness. Energy Policy 37, 3071-3081.

EP

Chang,Y., Fang, Z., Li, Y., 2016. Renewable energy policies in promoting financing and investment among the East Asia Summit countries: Quantitative assessment and policy implications. Energy Policy, 95, 427-436.

AC C

Dehejia, R., Wahba, S., 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94, 1053-1062. Dehejia, R., Wahba, S., 2002. Propensity-score-matching methods for non experimental causal studies. The Review of Economics and Statistics 84, 151-161. Dong, C.G., 2012. Feed-in tariff vs. renewable portfolio standard: an empirical test of their relative effectiveness in promoting wind capacity development. Energy Policy 42, 476485. Fagiani, R., Richstein, J.C., Hakvoort, R., De Vries, L., 2014. The dynamic impact of carbon reduction and renewable support policies on the electricity sector.Utilities Policy 28, 28– 41.

18

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Farooq, M.K., Kumar,S., Shrestha, R.M., 2013. Energy, environmental and economic effects of Renewable Portfolio Standards (RPS) in a Developing Country.Energy Policy 62, 989–1001.

RI PT

GEDB, Global Economy and Development at Brookings, Doha carbon and energy forum 2013. Briefing Paper Solar Energy and Other Alternative Energy, the Brookings Institution, Washington, District of Columbia (D.C.), USA. Heckman, J.J., Ichimura, H., Todd, P., 1998. Matching as an econometric evaluation estimator. Review of Economic Studies 65, 261–294.

SC

Held, A., Haas, R., Ragwitz, M., 2006. On the success of policy strategies for the promotion of electricity from renewable energy sources in the EU. Energy & Environment 17(6), 849-868.

M AN U

Huber, C., Faber, T., Haas, R., Resch, G., Green, J., Ölz, S., et al., 2004. Action plan for deriving dynamic RES-E policies report of the project Green-X. Vienna University of Technology, Institute of Power Systems and Energy Economics, Austria. Huber, M., 2011. Testing for covariate balance using quantile regression and resampling methods. Journal of Applied Statistics 38, 2881–2899. IRENA, 2016. Renewable Energy Benefits: Measuring the Economics. The International Renewable Energy Agency (IRENA), Abu Dhabi, UAE.

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Jalilvand, D.R., 2012. Renewable Energy for the Middle East and North Africa: Policies for a Successful Transition. Friedrich-Ebert-Stiftung Department for Near/ Middle East and North Africa, Berlin. Jaraitė, J., Karimu, A., Kažukauskas, A., Kažukauskas, P., 2015. Renewable Energy Policy, Economic Growth and Employment in EU Countries: Gain without Pain? CERE Working Paper, Umeå University, Sweden.

EP

Kadria, M., Ben Aissa, M.S., 2014. Implementation of Inflation Targeting and Budget Deficit Performance in Emerging Countries: A Treatment Effect Evaluation. Journal of Applied Business Research 30 (4), 1077-1090.

AC C

Kahia, M., Ben Aïssa, M.S., Charfeddine, L., 2016. Impact of renewable and non-renewable energy consumption on economic growth: New evidence from the MENA Net Oil Exporting Countries (NOECs). Energy 116, 102-115. Kahia, M., Ben Aïssa, M.S., Charfeddine, L., 2017. Renewable and non-renewable energy use - economic growth nexus: The case of MENA Net Oil Importing Countries. Renewable and Sustainable Energy Reviews 71, 127–140. Khandker, S.R., Koolwal, G.B., Samad, H.A., 2010. Handbook on Impact Evaluation: Quantitative Methods and Practices. The World Bank, Washington, District of Columbia (D.C.), USA. Kilinc-Ata, N., 2016. The evaluation of renewable energy policies across EU countries and US states: An econometric approach. Energy for Sustainable Development 31, 83–90.

19

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Kung, C-C., Zhang, L., Chang, M-S., 2017. Promotion policies for renewable energy and their effects in Taiwan. Journal of Cleaner Production 142 (2), 965-975.

RI PT

Lean, H. H., Smyth, R., 2013. Will policies to promote renewable electricity generation be effective? Evidence from panel stationarity and unit root tests for 115 countries. Renewable and Sustainable Energy Reviews 22, 371–379. Lechner, M., 2002. Some practical issues in the evaluation of heterogeneous labor market programs by matching methods. Journal of the Royal Statistical Society 165 (1), 59-82.

SC

Lin, S., Ye, H., 2009. Does inflation targeting make a difference in developing countries? Journal of Development Economics 89, 118–123, In: Brito, R.D., Bystedt, B., 2010. Inflation targeting in emerging economies: Panel evidence. Journal of Development Economics 91, 200-201.

M AN U

Liu, C., Li, N., Zha, D., 2016. On the impact of FIT policies on renewable energy investment: Based on the solar power support policies in China's power market. Renewable Energy 94, 251–267. Lucotte, Y., 2012. Adoption of inflation targeting and tax revenue performance in emerging market economies: An empirical investigation. Economic Systems 387 (20), 609-628. Lund, P.D., 2009. Effects of energy policies on industry expansion in renewable energy. Renewable Energy 34, 53-64. Menanteau, P., Finon, D., Lamy, M.L., 2003. Prices versus quantities: choosing policies for promoting the development of renewable energy. Energy Policy 31, 799-812.

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Menz, F.C., Vachon, S., 2006. The effectiveness of different policy regimes for promoting wind power: experiences from the states. Energy Policy 34, 1786-1796. Mezher, T., Dawelbait, G., Abbas, Z., 2011. Renewable energy policy options for Abu Dhabi: drivers and barriers. Masdar Institute of Science and Technology, Abu Dhabi, UAE.

EP

Moghul, U.F., Safar-Aly, S.H.K., 2015. Green Sukuk: the introduction of Islam’s environmental ethics to contemporary Islamic finance. Georgetown International Environmental Law Review (GIELR) 27(no1).

AC C

OECD, 2013. Renewable Energies in the Middle East and North Africa: Policies to Support Private Investment, OECD Publishing. www.oecd-ilibrary.org/finance-andinvestment/renewable-energies-in-the-middle-east-and-north-africa_9789261 83704-en (accessed 20.01.2017). Pérez de Arce, M., Sauma, E., Contreras, J., 2016. Renewable energy policy performance in reducing CO2 emissions. Energy Economics 54, 272–280. Prasad, M., Munch, S., 2012. State-level renewable electricity policies and reductions in carbon emissions. Energy Policy 45, 237–242. Ragwitz, M., Held, A., Sensfuss, F., Huber, C., Resch, G., Faber, T., et al., 2007. OPTRESassessment and optimization of renewable support schemes in the European electricity market, Final report. Karlsruhe, Baden-Württemberg, Germany. 20

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REN21, 2012. Renewable Energy Policy Network for the 21st Century. Renewables 2012 Global Status Report. REN21 Secretariat, Paris, France. REN21, 2013. Renewable Energy Policy Network for the 21st Century. Renewables 2013 Global Status Report. REN21 Secretariat, Paris, France.

RI PT

Romano, A.A., Scandurra, G., Carfora, A., Fodor, M., 2017. Renewable investments: The impact of green policies in developing and developed countries. Renewable and Sustainable Energy Reviews 68, 738–747. Rosenbaum, P., Rubin, D., 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55.

SC

Rubin, D., 1977. Assignment to Treatment Group on the Basis of a Covariate. Journal of Educational Statistics 2, 1-26.

M AN U

Sánchez-Braza, A., Pablo-Romero, M.P., 2014.Evaluation of property tax bonus to promote solar thermal systems in Andalusia (Spain). Energy Policy 67, 832–843. Sarzynski, A., Larrieu, J., Shrimali, G., 2012. The impact of state financial incentives on market deployment of solar technology. Energy Policy 46, 550-557. Schmid, G., 2012. The development of renewable energy power in India: Which policies have been effective? Energy Policy 45, 317–326. Smith, J.A., Todd, P., 2005. Does matching overcome LaLonde’s critique of nonexperimental estimators. Journal of Econometrics 125 (1-2), 303-353.

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Wang, T., Gong, Y., Jiang, C., 2014. A review on promoting share of renewable energy by green-trading mechanisms in power system. Renewable and Sustainable Energy Reviews 40, 923-929.

EP

Wiser, R., Porter, K., Grace, R., 2005. Evaluating experience with renewables portfolio standards in the United States. Mitigation and Adaptation Strategies for Global Change 10, 237–263. Yi, H., 2013. Clean energy policies and green jobs: An evaluation of green jobs in U.S. metropolitan areas. Energy Policy 56, 644–652.

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Yin, H., Powers, N., 2010. Do state renewable portfolio standards promote in-state renewable generation? Energy Policy 38, 1140–1149. Zhang, Y-J., Peng, Y-L., Ma, C-Q., Shen, B., 2017. Can environmental innovation facilitate carbon emissions reduction? Evidence from China. Energy Policy 100, 18–28. Zhao, Y., Tang, K.K., Wang, L.L., 2013. Do renewable electricity policies promote renewable electricity generation? Evidence from panel data. Energy Policy 62, 887-897.

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Period/Year

First category: renewable energy generation Kilinc-Ata (2016) 1990–2008

Country/countries

Methodology

27 EU countries and 50 US states

Panel regression tests

Dependent variable

Results

Ratio of renewable electricity capacity in total electricity supply from non-hydro renewable sources in country Renewable energy generation

Renewable energy policy instruments play an important role in promoting renewable energy capacities, but their effectiveness differs by the type of renewable energy policy instruments.

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Authors

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Table 1: summary of previous studies related to the effectiveness of renewable energy policies

1990-2012

Different countries

Review Case studies/ Simulation models

Zhao et al. (2013)

1980–2010

122 countries

The Poisson pseudomaximum likelihood estimation technique

Lean and Smyth (2013)

1980–2008

115 countries

Aslani et al. (2013)

1973-2011

Nordic countries

Panel unit root and stationarity tests Conceptual analysis

Schmid (2012)

2001–2009

9 Indian States

Yin and Powers (2010)

1993-2006

50 states of US

Carley (2009)

1998-2006

50 states of US

A variant of a standard fixed effects model

Ragwitz et al. (2007)

1990-2020

EU 25 countries

Prospective analysis based on the model Green-X/Simulation model

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Wang et al. (2014)

Fixed-effects regressions/ Linear demand-supply framework. Panel data approach

1

Electricity generation from non-hydro renewable sources as a share of total electricity generation Renewable electricity generation

Renewable energy power

The percentage of generating capacity in a state that is non-hydro renewable Renewable energy percentage of electricity generation per year. Renewable electricity deployment

RE policies can successfully raise the proportion of renewable energy power and diversify the renewable energy resources. Renewable electricity policies play a vital role in promoting renewable electricity generation. The effectiveness of policies depends on the type of renewable electricity policy and the energy source. RE policies are effective in promoting renewable electricity generation. The results show that the policies and decisions of renewable energy promotion in the Nordic countries have presented a successful case to be followed by other developed and developing countries. The RE policies play a crucial role in promoting the renewable energy power development.

Results advocate that renewable portfolio standards policies have had a significant and positive effect on in-state renewable energy development. Results point out that renewable portfolio standard (RPS) implementation is not a significant analyst of the proportion of renewable energy generation. They indicate that the continuity and long term investment stability of any implemented policy is a key criterion for the stable growth of renewable energy markets as well as for getting targets of

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1998–2003

United States

Ordinary least squares (OLS) methods

Wind development indices

Held et al. (2006)

1998-2005

EU 25 countries

Analyses based on the criteria of effectiveness and Economic efficiency

US

Three broad categories of criteria : Outcome criteria/ Policy design criteria/ Market context criteria

Effectiveness indicator for Renewable Energy Source technology/Levelized profit per unit electricity generated Renewable energy development

EU 15 Member States

Evaluation criteria (Minimize generation costs/ Lower producer profits)/The computer programme Green-X Price-based approaches/ quantitybased approaches

Menanteau et al. (2003)

European States

2014

China

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Chang et al. (2016)

the year 2013

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Second category: renewable energy investments Romano et al. (2017) 2004–2013 Developing and developed countries (56 countries)

Liu et al. (2016)

16 East Asian Summit countries

The share of electricity generation

Renewable energy generation

The results confirm that system of feed-in tariffs is more efficient than the other policies.

Panel Corrected Standard Errors (PCSE) model

Renewable investments (The share of Renewable Electricity Net Generation)

The open-loop model

Renewable energy investment

They confirm that not all policies promote the investments in renewable sources and their effectiveness depends by the stage of development of the countries. Chinese FIT by cross control policy is a good alternative to well control the investment in renewable technology and provide higher robustness in regulating the electricity spot market.

Quantitative assessment based on the Index of renewable energy

Index of renewable energy policies by using five mostly employed criteria:

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1990 -2004

They confirm that a well-designed instrument (especially Feed in tariff) ensures the fastest deployment of power plants using Renewable Energy Sources (RES) at the lowest cost to society. The results confirm that both successes and failures with the aforementioned policies; some state RPS policies are positively impacting renewable energy development, while others have been inadequately planned and will do little to move on renewable energy markets. There is no obvious favorite support policy; each instrument has its pro and cons.

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Huber et al. (2004)

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Menz and Vachon (2006)

Wiser et al. (2005)

renewable energies in the electricity sector at low costs. The results confirm that Our finding that renewable portfolio standards (RPS) are effective in promoting the development of wind capacity.

2

RE Policies help creating a market for renewable energy, take advantage of potential profits, lessen risks concerning the investment, build up and

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1997-2009

585 Andalusia municipalities (Spain)

The propensity matching

states of US

Cross-sectional timeseries approach

Third category: final economics and environmental effects Zhang et al. (2017) 2000–2013 30 provinces of China

The propensity matching

1990-2012

Fagiani et al. (2014)

2012–2050

Aslani et al. (2014)

2011–2020

Finland

Prasad and Munch (2012) Source: Authors’ tabulation

2006 1997 –2008

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Yi (2013)

score

US metropolitan areas

39 states of United States

The annual amount of solar technology installed

Carbon emissions

Oligopolistic Cournot competitive framework

CO2 emissions

Panel-data time-series econometric techniques Simulation approach/ system-dynamics methodologies

Economic growth employment CO2 emissions

System dynamics approach and simulation model Two stage Probit least squares model

Energy dependency

Fixed-effectsregression model

CO2 emissions

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Jaraitė et al. (2015)

Power market where generation firms compete à la Cournot 15 European Union (EU) member states Europe

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Pérez de Arce et al. (2016)

score

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Sarzynski et al. (2012)

2010

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Sánchez-Braza and PabloRomero (2014)

market, uncertainty, profitability , technology, and financial resources The number of new square meters of installed solar panels during 2010.

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implement new technologies, and improve the access to financial resources.

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policies

Employment

The tax bonus on Real estate tax (RET) was an effective instrument to promote solar thermal systems in those Andalusian municipalities that implemented the tax bonus compared with those that did not. Results indicate that states offering cash incentives, such as rebates or grants, had consistently stronger deployment of PV technology from 1997 to 2009 than states not offering cash incentives.

The policy of carbon emission trading is found insufficient to reduce the huge carbon emissions in China. RE policies are effective in reducing CO2 emissions. and

The renewable energy policies engender a rise in output and employment in the short-run. They confirm that both carbon reduction and renewable support policies are indispensable for improving the sustainability of the electricity sector and reducing CO2 emissions. Energy dependency on imported sources will drop off between 1% and 7% rely on the defined scenarios. The clean energy policies have a positive effect on employment in the short-term. RE policies are robust in reducing CO2 emissions.

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Table 2: Variables definitions and sources.

FD POLITY2 EM HK FDI

Indicator of democracy taking values from -10 (very Polity IV Project autocratic) to +10 (very democratic). Ratio of net energy imports to total energy consumption. WDI Secondary school enrollment as a proportion of gross WDI enrollment (Human capital). FDI net inflows as a proportion of GDP. WDI

WAP

Trade openness (as the sum of exports and imports of WDI goods and services as a share of GDP). Working-age population a proportion of total population. WDI

FEMALE

Ratio of female to total population.

CO2intensity

CO2 emissions per Total Primary Energy Source measured EIA in TeraJules (TJ). Total public debt as a share of GDP. International Monetary Fund (IMF); Abbas et al. (2010)

PUB_DEBT

WDI

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OPEN

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GDPpc_G

Definitions Sources Dummy variable, coded as 1 if any of renewable energy International Energy Agency (IEA) policies are adopted, 0 otherwise. Real GDP per capita growth. World Development Indicators (WDI) Domestic credit to private sector ratio in % of GDP. WDI

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Variables RE

Table 3: List of the sample countries with dates of renewable energy adoption.

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RE countries

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Algeria Armania Cyprus Egypt Iran Israel Jordan Kuwait Libya Malta Morocco SaudiArabia Syrian Tunisia Turkey United ArabEmirates Yemen

Source: International Energy Agency (IEA).

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Adoption’s dates 1999 2001 2003 2008 2005 1980 2005 2009 2007 2000 2008 2008 2009 1995 2001 2004 2010

Non-RE Countries

Bahrain Georgia Iraq Lebanon Mauritania Oman Qatar

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Table 4: Probit, Logit and Tobit estimates of propensity scores.

HK FDI OPEN WAP FEMALE CO2intensity PUB_DEBT

(0.001)

-0.053**

-0.066**

-0.016

(0.014)

(0.026)

(0.012)

0.001*** (0.000)

0.0007

0.0006

(0.000)

(0.000)

0.019***

0.038***

0.023***

(0.004)

(0.008)

(0.004)

-0.002

-0.003

-0.01

(0.016)

(0.028)

(0.014)

-0.007

-0.014*

-0.006*

(0.004)

(0.007)

(0.003)

0.147***

0.265***

0.125***

(0.02)

(0.036)

(0.017)

0.168***

0.291***

0.133***

(0.024)

(0.044)

(0.02)

0.001***

0.0003***

0.0007

(0.143)

(0.000)

(0.000)

-0.001

-0.002

0.003**

(0.001)

(0.004)

(0.001)

762 0.4041 -238.765

762 0.4059 -238.04

762 0.2745 -392.76

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Number of observations Pseudo-R2 Loglikelihood

0.0006

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EM

0.001 (0.003)

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POLITY2

0.001 (0.002)

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FD

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Renewable energy (RE) Probit Logit Tobit

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Note: Values in parentheses are standard deviations. ***, **, * represent respectively the statistical significance at threshold of 1%, 5% and 10%.

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Table 5 Matching estimates of RE’s treatment effect on economic growth. Algorithms of matching Nearest-neighbor matching N=1 N=2

LLRM Kernel matching (Epanechnikov) (Epanechnikov)

N=3

1.122 (1.558)

Nb. of treated units on common support

129

129

129

Nb. of treated units off common support

38

38

Nb. of untreated observations

595

595

0.827** (1.199)

38

38

38

595

595

595

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129

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Note: Bootstrapped standard errors on the basis of 1000 replications are in parentheses. **, * represent respectively the statistical significance at threshold of 5% and 10%.

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0.753* (1.244)

129

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(2) AverageTreatment on Treated (ATT)

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1.525 (1.691)

RE 0.808* (1.44)