Does renewable energy policy work? Evidence from a panel data analysis

Accepted Manuscript Does renewable energy policy work? Evidence from a panel data analysis

Wenfeng Liu, Xingping Zhang, Sida Feng PII:

S0960-1481(18)31468-X

DOI:

10.1016/j.renene.2018.12.037

Reference:

RENE 10912

To appear in:

Renewable Energy

Received Date:

20 March 2018

Accepted Date:

09 December 2018

Please cite this article as: Wenfeng Liu, Xingping Zhang, Sida Feng, Does renewable energy policy work? Evidence from a panel data analysis, Renewable Energy (2018), doi: 10.1016/j.renene. 2018.12.037

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Does renewable energy policy work? Evidence from a panel data

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analysis

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Wenfeng Liua, b, Xingping Zhanga, c, , Sida Fenga

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a

5

China

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b

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Power University), Changping Beijing, 102206, China

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c

9

Abstract

School of Economics and Management, North China Electric Power University, Beijing, 102206,

Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric

Research Center for Beijing Energy Development, Beijing 102206, China

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This paper develops a fixed effect model to evaluate the effect of renewable energy policy using

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a panel dataset covering 29 countries during the period of 2000 to 2015. The renewable energy

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policy system in this paper includes seven aggregate policies and twelve specific ones. The

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empirical results indicate that four of seven aggregate policies, including fiscal and financial

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incentives, market-based instruments, policy support, and research, development and deployment,

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are demonstrated to be significant to the improvement of renewable energy capacity. Meanwhile,

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three of twelve specific policies, including price policy, grants and subsidies, and strategic planning,

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have positive effects on renewable energy development. This paper assesses the aggregate and

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specific policies in the same analysis framework, and the empirical results determined that synergy

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effects existed among specific policies. The empirical results also indicate that the implementation

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of the Kyoto Protocol in 2005 has had positive impacts on renewable energy development in typical



Corresponding author: Tel.: +86 10 61773096; fax: +86 10 61773311.

E-mail address: [email protected] (X. Zhang). 1

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countries.

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Keywords: Renewable energy policy; Policy effect assessment; Panel data model

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1. Introduction

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Climate change has become more pronounced in recent years, as CO2 emissions have continued

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to grow worldwide. The countries researched in this paper emitted more than 70% of the entire

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world carbon emissions in 2016 [1], and they have set quantitative targets for renewable energy

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development, which is a key option to mitigate the climate change. Based on this background, public

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renewable policies have been components of national planning agenda for many developed and

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several large developing countries over the past several decades.

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There is broad consensus in the literature concerning the need for public policy to promote

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renewable energy development. First, the renewable energy policies are aimed at promoting the

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development and deployment of renewable energy, which provides a sustainable and stable

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domestic renewable energy market [2]. Second, the development of renewables that internalize the

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costs of no pollution deserves incentives from public policies. Third, reducing energy dependence

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is an important factor in introducing the public policy [3]. Finally, the compliance of international

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agreements, such as the Kyoto Protocol, European Union directives or a long-term development

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plan for renewable energy, are also indicated as other factors requiring the design of public policy.

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Hence, many countries and international organizations view renewables as important elements of

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energy security, dynamic economic development, environmental protection and greenhouse gas

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emissions reduction efforts [4]. There are a variety of incentives and regulations to choose to support

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renewables development; therefore, it is vital to assess the effectiveness of public policies for

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renewables. A majority of published studies have qualitatively or theoretically discussed the 2

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effectiveness of RE policies in the US or EU [5,6,7], while a minority present quantitative analyses.

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Menz and Vachon [8] was the pioneering study to quantitatively assess the effectiveness of different

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state policies that promote wind power, and several scholars have attempted to quantify the impacts

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of renewable energy policies in different directions subsequently. First, many studies have focused

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on analyzing the effectiveness of renewable energy policies at the state or provincial level. Several

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state level studies have reached a positive conclusion. For instance, Menz and Vachon [8] assessed

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the policies' relative effectiveness in terms of their impact on the level of wind capacity existing in

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states of the US, finding a positive relationship between the renewable portfolio standard (RPS) and

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wind power development. While others found mixed results, Carley [9] evaluated the effectiveness

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of state energy programs with an empirical investigation of the linkage between state RPS policy

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implementation and the percentage of renewable energy electricity generation across states in the

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US, and the results indicate that RPS implementation is not a significant predictor of the percentage

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of renewable energy generation in the total generation mix but increases the total amount of

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renewable energy generation. Against this background, researchers have started to question whether

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the effectiveness of renewable energy policies is conditional on the social, natural and institutional

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environment. For instance, Delmas and Montes-Sancho [10] argued that a large presence of non-

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governmental organizations, democratic representatives, and green residential customers facilitate

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the transmission of renewable energy policies in the US.

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There are also studies analyzing the effectiveness of renewable energy policies at the country

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level. Johnstone et al. [11] examined the effect of environmental policies using panel data for 25

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OECD countries during the period of 1978 to 2003 and found that public policy plays a significant

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role in different renewable technological innovation. Based on this, Zhao et al. [12] evaluated the 3

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effects of renewable electricity policies on renewable electricity generation using a large panel

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dataset that covers 122 countries including developed, developing, and emerging market countries

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during the period of 1980 to 2010. The results suggest that renewable electricity policies play a

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crucial role in promoting renewable electricity generation, but their effectiveness is subject to

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diminishing returns as the number of policies increases. In addition, there are several cross-country

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studies investigating the relative effectiveness of different policy mechanisms. For instance, Dong

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[13] examined the relative effectiveness of feed-in tariff (FIT) and RPS in promoting wind capacity

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development using panel data covers 53 countries including the US, China, Germany, Spain, India

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etc. Fiscal and financial incentives were demonstrated to accelerate the development of renewable

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energy projects [14,15]; furthermore, mandatory requirements, quota, and obligation schemes do

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exhibit a positive influence on renewable energy application in the US [8].

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This study attempts to quantitatively assess the effectiveness of renewable energy public policies

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on renewable energy and extends previous studies in two ways. First, empirical analyses of the

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combination of aggregate and specific policies effectiveness remain scarce. The few existing studies,

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such as that of Zhao et al. [12], primarily focus on aggregate renewable energy policies, while Polzin

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et al. [16] mainly focus on specific renewable energy policies across OECD countries. In this paper,

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we assess the effectiveness of aggregate and specific policies in the same analysis framework, which

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can provide notable policy implications. Second, the Kyoto Protocol, a key international milestone,

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has experienced an important impacts on the formulation of renewable energy policies in various

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countries, we therefore incorporate it into model in the form of dummy variable. In addition, several

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control variables are introduced into the model to reduce the deviations of missing variables in this

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paper. 4

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The remaining sections of this paper are structured as follows: Section 2 presents the data and the

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analytical method. Section 3 presents the results and discussion, while section 4 presents the study’s

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conclusion and policy implications.

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2. Methods and Data

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2.1 Variables and data sources

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As dictated by the availability of data, 29 countries from the EU, the OECD, the signatories of

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the Kyoto Protocol and two developing countries (China and India), are the study sample in this

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paper. The reason for choosing these countries is that these countries have great support for

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renewable energy policies and the scale of renewable energy in these countries has increased stably.

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Fig. 1 shows the growing trend of renewables installation and the reduction of carbon dioxide

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emissions following the time that the Kyoto Protocol entry into force in the sample countries.

98 99

Fig. 1 Total installed and new added capacity of renewable energy 5

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Specifically, the sample countries in this paper include Australia, Austria, Belgium, Canada,

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China, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Ireland,

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Italy, Japan, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, South Korea, Spain,

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Sweden, Switzerland, Turkey, the United Kingdom and the United States of America. Many

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countries have generally focused on the question of the development of renewable energy from

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approximately 2000. Hence, we consider the data index of the stability and availability, and

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therefore choose 2000 as the starting year and 2015 as the ending year.

107 Policy variables

Dummy variable

The Kyoto dummy (d_k)

Fiscal and financial incentives (pv_1) Price policy (PP) Grants and subsidies (GS) Tax (T) Market-based instruments (pv_2) GHG emissions allowances (GA) Green certificates (GC) Direct investments (pv_3) Funds to sub-national governments (FSG) Infrastructure investments (II)

Dependent variable Total capacity installed-renewable energies (in MW) RE_capacity (re_c)

Policy support (pv_4) Institutional creation (IC) Strategic planning (SP) Regulatory instruments (pv_5) Codes and standards (CS) Obligation schemes (OS) Other mandatory requirements (OMR)

Control variables

108 109

Real Gross Domestic Product （cv_gdp）

Carbon dioxide emissions （cv_co2）

Electric power consumption （cv_epc）

High-technology exports (cv_hte)

Information and education (pv_6)

Research, development and deployment (pv_7)

Fig. 2 Framework of variables

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Fig. 2 shows the framework of variables. The data of dependent variable (renewable energy

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capacity) is from the International Renewable Energy Agency (IRENA), which is an

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intergovernmental organization that supports countries in their transition to a sustainable energy 6

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future and serves as the principal platform for international co-operation and a repository of policy,

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technology, resource and financial knowledge on renewable energy [17]. These data include

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information on installed electricity generating capacity, additions in renewable energy capacity and

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renewable energy power generation at the country level.

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We consider that the commitment to reduce greenhouse gas emissions and the emission-trading

118

scheme are substantial factors driving renewables development. The Kyoto Protocol signed in 1998

119

was a milestone in promoting carbon reduction. One of the key international steps in this direction

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was the implementation of the Kyoto Protocol in 2005. Countries that signed this protocol are

121

expected to have great commitment to renewables deployment, as shown by Popp et al. [18].

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Although the Kyoto Protocol was signed in 1998, there was no legal effect at that time. The Kyoto

123

Protocol entry into force in 2005, it is the first time to restrict greenhouse gas emissions in the form

124

of regulations. Therefore, this paper takes the time of 2005 to set up variable. In this paper, dummy

125

variable is used: The Kyoto Protocol dummy (variable d_k) is 1 for the implementation countries

126

after 2005 and 0 otherwise. We expect a positive relationship between renewables capacity and the

127

Kyoto Protocol variable.

128

The policy variables are drawn from the IEA/IRENA policy and measure database. These

129

indicators have been used by previous scholars to analyze the impacts of policy instruments on

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renewable energy in OECD countries [16], Europe [3] and globally [4]. In this paper, we consider

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the following seven aggregate and fourteen specific policy instruments: (1) Fiscal and Financial

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Incentives, which designed to reducing the risk of investors, includes three specific instruments of

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price policy (PP), grants and subsidies (GS), and tax (T). (2) Market-based instruments, which

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provides a tool for trading and meeting renewable energy obligation among producers, and an open 7

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fair trading platform among market subjects, include specific instruments of greenhouse gas

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emissions allowances (GA) and green certificates (GC). (3) Direct investments, aimed at reducing

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the capital cost of renewable energy investment, includes funds to sub-national governments (FSG)

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and infrastructure investments (II). (4) Policy support, which seeks to define strategies and outlines

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specific programs to promote renewable capacity inside a country, includes institutional creation

140

(IC) and strategic planning (SP). (5) Regulatory instruments, which place a requirement on the

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minimum amount of electricity supply that comes from renewable sources, primarily include codes

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and standards (CS), obligation schemes (OS), and other mandatory requirements (OMR). (6)

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Information and education, which provides guidelines and obligations in regards to further

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deployment of renewable energy installations, and provides education and trainings for the

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workforce employed in the building sector on matters of energy efficiency and renewable energy

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installations. (7) Research, development and deployment (RD&D), which captures effect of

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technological progress on the promotion of renewable energy. The first 6 aggregate policy variables

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are measured by the number of active policies in a country per year, which previous studies call

149

“accumulated number of renewable energy policies and measures (ANPM)” [4,16]. Econometric

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model is a common method to quantitatively evaluate policy effectiveness of renewable energy,

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such as Marques and Fuinhas [3], Aguirre and Ibikunle [4] and Polzin et al. [16]. In the process of

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econometric analysis, a potential hypothesis is that the more policies of a specific type there are, the

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better it is for renewable energy capacity. Following this strand, we set up policy variables and

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evaluate the effectiveness of renewable energy policy in the framework of econometric models.

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Furthermore, there are factors delaying the investment process, such as the need to build a wind

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farm or solar park. With this analysis, we expand Polzin et al. [16] by providing more detailed 8

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policies for renewable installed equipment, considering the lagged reaction of policy variables to

158

the installed capacity of renewable energy.

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Control variables are drawn from the World Development Indicators database and BP Statistical

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Review Worlds Energy database, which includes Macroeconomic data and Energy data. Further,

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economic indicators that might drive renewables capacity include the GDP (cv_gdp). Electric power

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consumption (cv_epc) and CO2 emissions (cv_co2) are used respectively to account for energy

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consumption and CO2 emissions. To account for technological level, this paper includes High-

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technology exports (cv_hte).In Table 1, we present a summary of the variables used in this paper,

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along with their descriptive statistics.

166 Variable re_c

cv_gdp cv_co2 cv_epc cv_hte pv_1 PP GS T pv_2 GA GC pv_3 FSG

Table 1 Variables definition, sources and descriptive statistics Definition

Obs.

Mean

Std.dev.

Min

Max

Logarithm of the installed capacity of multiple renewable energy resources （Megawatts）

464

7.74

1.7

2.3

12.12

Real gross domestic product (in billions U.S. dollars，constant 2010)

464

17,005.66

28,510.70

1,066

165,974.50

464

7.5

4.63

0

20.21

464

6,858.25

5,399.26

0

25,590.69

464

16.3

8.46

0

47.84

ANPM (Fiscal and financial incentives)

464

5.38

5.49

0

31

ANPM (Price policy) ANPM (Grants and subsidies) ANPM (Tax)

464 464 464

1.08 1.74 0.41

1.78 2.19 0.71

0 0 0

12 12 3

ANPM (Market-based instruments)

464

0.53

1.11

0

7

464

0.15

0.45

0

2

464 464

0.27 0.91

0.64 1.74

0 0

5 9

464

0.20

0.52

0

3

Carbon dioxide emissions (metric tons per capita) Electric power consumption (kWh per capita) High-technology exports（% of manufactured exports）

ANPM (Greenhouse gas emissions allowances) ANPM (Green certificates) ANPM (Direct investments) ANPM (Funds to sub-national governments)

9

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ANPM (Infrastructure investments )

464

0.23

0.55

0

2

pv_4 IC SP pv_5 CS OS

ANPM (Policy support) ANPM (Institutional creation) ANPM (Strategic planning) ANPM (Regulatory instruments) ANPM (Codes and standards) ANPM (Obligation schemes)

464 464 464 464 464 464

4.21 0.75 2.07 3.90 1.02 0.55

5.59 1.32 3.02 4.77 1.81 1.08

0 0 0 0 0 0

50 11 28 29 15 5

OMR

ANPM (Other mandatory requirements)

464

0.65

1.21

0

8

pv_6

ANPM (Information and education)

464

1.51

3.01

0

19

pv_7

Research, development and deployment (Total RD&D in Million USD, 2015 price and exch. rates)

464

94.38

208.52

0

2,442.68

167 168

2.2 Model

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2.2.1 Variable intercept panel model

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A panel model can be categorized into three types: pooled regression model, variable intercept

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model and variable coefficient model [19]. A pooled regression model is a basic panel data model

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that assumes each individual in the sample has exactly the same regression equation; that is to say,

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the intercept and coefficient of the model would not change with individuals and time. A variable

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intercept model assumes the individual's regression equation has the same coefficient while different

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intercepts to capture the heterogeneity, and it means that the impact of unobserved effects can be

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absorbed into the intercept term. A variable coefficient model is suitable in the cases where there

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are changing economic structures or unobserved different socioeconomic and demographic

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background factors that imply that the response parameters of the included variables may be varying

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over time or may be different for different cross-sectional units [20]. Thus, it means that generally

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a variable intercept model or a pooled regression model is picked. Hence, this paper conducts an F-

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test to identify which method to use.

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Furthermore, given that it generally takes time to implement policies, the policy variables, except 10

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control variables, are l-period lagged in our research. The model to estimate is

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Ykt  const   i X ikt l    ' jCjkt  uk   kt

i

j

i 1

j 1

(1)

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where k is country and t is year. Ykt denotes the development of renewable energy. const is the

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national entity intercept. X is a vector of the i policy variables. βi is the coefficient of policy variable.

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C is a vector of the j control variables. β’j is the coefficient of control variable. uk+εkt is composite

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error term, unobservable random variable uk is intercept term of individual heterogeneity, εkt is a

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disturbance term that varies with time and individual.

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Table 2 presents the summary statistics for the results of model tests. F-test is to identify whether

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pooled regression model or variable intercept model is suitable; Hausman test and Auxiliary

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regression are to select the fixed effect model or the random effect model. Specifically, for F-test,

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“H0: all uk =0”, that is, a pooled regression model is acceptable. For Hausman test and Auxiliary

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regression test, “H0: no correlation between uk and X”, that is, random effect model is correct model.

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The p-value of F-test is 0.00 shown in Table 2, so the original hypothesis is therefore strongly

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rejected. Moreover, ρ represents the autocorrelation coefficient between the disturbance terms of

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different individuals in different periods. The greater the value of ρ is, the more important the

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individual effect part of the composite error term is. In Table 2, “ρ=0.97” indicates that the variance

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of composite error term is mainly due to the variation of individual effect. Consequently, the variable

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intercept model is adopted in the model evaluation.

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Table 2 Hausman test and F-test results

202 203

Test type

ρ

F-value

Chi-Square Statistic

P-value

F-test Hausman test Auxiliary regression

0.97 -

40.21 -

61.83 81.38

0.00 0.00 0.00

2.2.2 Fixed effect model and random effect model 11

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A variable intercept model can be further divided into a fixed effect (FE) model and a random

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effect model [19]. It is a basic problem to use the fixed effect model or the random effect model

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when we address the panel data. In a fixed effect model, the individual effect or time effect is

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interrelated to the independent variables, whereas in a random effect model, the individual effect or

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time effect is not relative to explanatory variables. In other words, the random-effects model allows

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one to estimate the coefficients of both time-varying and time-invariant variables while the fixed-

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effects model allows us only to estimate the coefficients of time-varying explanatory variables [20].

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When N (This paper is 29, which refers to the number of countries) is larger relative to T (This paper

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is 16, which refers to the time series), whether to treat the effects as fixed or random is not an easy

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question to answer. It can make a surprising amount of difference in the estimates of the parameters.

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Zhao et al. [21] used the Hausman test to identify the specific model of whether it is a fixed effect

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or a random effect. However, the traditional Hausman test assumes that the random effect is fully

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efficient, which means that both uk and εkt must be independently and identically distributed.

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Therefore, the traditional Hausman test is not applicable if the error of the clustering robust standard

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is quite different from that of the ordinary standard. To avoid this situation, in this paper, the

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Hausman test is done and the auxiliary regression with test is further conducted. It can be seen from

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the data in Table 2 that the original hypothesis is rejected for both p-values are 0.00 in Hausman test

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and Auxiliary regression test, meaning that a fixed effect is suitable.

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The variable intercept model with fixed effects is as Eq. (2). The variables are measured as

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logarithms, since the logarithm of the data will not change the nature and relationship, and

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simultaneously is helpful to heteroscedasticity elimination.

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ln Ykt  const   i ln Xikt l    ' j ln Cjkt  uk   kt

i

j

i 1

j 1

(2) 12

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Furthermore, we add the dummy variable of the Kyoto Protocol as a virtual variable for panel empirical study. i

j

i 1

j 1

ln Ykt  const   i ln Xikt l    ' j ln Cjkt   Dkt  uk   kt

229

Where Dkt =1 denotes the Kyoto Protocol is implemented.

230

To examine the effectiveness of specific policy, this paper set up model (4).

231

232 233

r

j

r 1

j 1

ln Ykt  const   1r ln X 'rkt l    ' j ln Cjkt   Dkt  uk   kt

(3)

(4)

where X ’is a vector of the r specific policy variables and β1r is the coefficient of specific policy. 3. Results and Discussion

234

The aim of this research is to uncover the influence of different policy instruments on the installed

235

capacity of renewable energy and conduct further discussion between cluster and specific policies

236

among typical countries. Table 3 presents the results based on Eq. (3) and Eq. (4). FE(1) is the

237

regression results of aggregate policy, and FE(2) is the deletion of the insignificant variable on the

238

basis of FE(1) to identify the robustness of our model. The results show that in terms of the positive

239

or negative effects of the regression on renewable energy capacity, both models generate very

240

similar results, including the R2 values. FE(3) is the regression results of specific policy on the basis

241

of FE(2). The empirical results of aggregate and specific policies are shown in Fig. 3, which present

242

the ranking of policies impacts and standard error bars.

13

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243 244

Fig. 3 The ranking of policies impacts

245

As is well-known, the Kyoto Protocol has played a vital role in the climate change mitigation and

246

renewable energy development. The results in Table 3 confirm its notable effect on renewables.

247

Furthermore, the ranking of aggregate policies impacts on renewable energy development shown in

248

Fig. 3 is market-based instruments, RD&D, fiscal and financial incentives, and policy support.

249

Specifically, our results imply that every 1 percentage point increase in these four aggregate policies,

250

respectively, is associated with an approximately 0.300, 0.210, 0.182, 0.163 percentage point

251

increase in renewable energy capacity at the 1% significance level. For the specific policy model,

252

PP, GS, SP are positive policies at the significance level of 1%, 1% and 5%, respectively. The

253

contribution to renewable energy is increased by 0.191, 0.163, 0.167 percentage point for every 1

254

percentage point increase in PP, GS, SP. Additionally, significant are the four control variables in

255

all models, specifically, GDP, High-technology exports, and Electric power consumption, which are

256

positively related to the renewables capacity, while the remaining variable of CO2 emissions 14

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negatively affects the dependent variable. In the following discussion, we highlight effective and

258

ineffective policy measures and relate our results to the following previous studies [3,4,11,16].

259

Table 3 Panel analysis results

Variables

FE(1)

　

FE(2)

　

coefficient

S.E.

coefficient

S.E.

cv_gdp cv_hte cv_co2 cv_epc

2.265** 0.361** -1.051** 0.210**

0.277 0.106 0.193 0.048

2.168** 0.369** -1.050** 0.209**

0.266 0.106 0.189 0.048

pv_1

0.182**

0.057

0.152**

0.052

pv_2

0.299**

0.088

0.286**

0.085

pv_3

-0.062

0.073

/

/

pv_4

0.163**

0.056

0.134**

0.051

pv_5 pv_6 pv_7 d_k cons R-sq

-0.063 -0.016 0.210** 0.522** -14.570** 0.801

0.060 0.070 0.046 0.072 2.393 　

/ / 0.213** 0.523** -13.740** 0.800

/ / 0.045 0.070 2.303 　

260

Note: Standard errors * p<0.05, ** p<0.01

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3.1 Fiscal and financial incentives

Variables cv_gdp cv_hte cv_co2 cv_epc PP GS T GA GC / IC SP / / pv_7 d_k cons R-sq

FE(3)

　

coefficient

S.E.

2.120** 0.420** -1.092** 0.221** 0.191** 0.163** -0.048 0.315 0.188 / 0.001 0.167* / / 0.191** 0.546** -13.320** 0.803

0.272 0.104 0.191 0.048 0.063 0.066 0.099 0.177 0.112 / 0.097 0.074 / / 0.047 0.068 2.354 　

262

It is found that fiscal and financial incentives have positive impacts on renewable energy

263

installation, which implies that the fiscal and financial incentives effects on renewable power

264

development is notably strong. In addition, the specific policies effects regressed in Eq. (4) show

265

that the coefficient of price policy, grants and subsidies are statistically significant, which means

266

they have played a substantial role in the fiscal and financial package and have large positive effects

267

on renewable energy promotion.

268

In the process of renewable energy development, each country provides renewable energy support

269

from the electricity price policy, feed-in tariffs (FIT) and Feed-in premium (FIP) are generally used. 15

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The FIT can be described as a premium paid for electricity that is supplied to an electricity grid from

271

a particular renewable energy generation source [22]. It is minimum price guaranteed by the national

272

governments for per unit of renewable electricity supplied to the grid for a long-term [23]. Grid

273

enterprises must pay the cost to renewable energy generation enterprises at such price. FIP is a type

274

of price-based policy instrument whereby eligible renewable energy generators are paid a premium

275

price, which is a payment in addition to the wholesale price. This premium can be fixed or floating.

276

Price policy proved particularly successful in countries such as China, Germany, and Spain. The

277

main features of this instrument are ensuring the stability of renewable energy projects and attracting

278

all types of social capital into the field of renewable energy. For investors, it is an important market

279

signal, and the adjustment of electricity price can also reflect the level of technological progress to

280

a certain extent. Thus, this research is in accordance with evidence by Del Río and Bleda [24], who

281

emphasize the advantages of price policy to spur development and to lower risks in renewable

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energy investment. From the perspective of policy evolution, the price policy was implemented

283

beginning with three countries in 2000, and it had been implemented in 21 countries of 29 sample

284

countries by 2015. It has played an important role in promoting the large-scale development of

285

renewable energy generation.

286

Grants and subsidies are the means by which the government provides financial aid or other forms

287

of income or price as the measures of support for the purpose of developing renewable energy

288

without direct capital gains or returns. From the perspective of evolution, this policy is a commonly

289

adopted policy in various countries. This policy, on the one hand, subsidizes production and

290

subsidies for renewable energy producers. On the other hand, subsidies are made for the output of

291

renewable energy equipment, which will increase production, reduce costs and improve economic 16

ACCEPTED MANUSCRIPT 292

efficiency.

293

However, tax instruments are not significant for promoting renewable energy development. This

294

is a surprising result since the primary objective of such an instrument is to financially enhance

295

renewables. Johnstone et al. [25] related this situation to the fact that investors may have little or no

296

confidence in policies that depend on public finance, since they are likely to be withdrawn abruptly

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with a change in administration. A good example is the Production Tax Credit for wind power in

298

the United States. This tax instrument has had a pattern of repeated expiration and short-term

299

renewal (usually no more than a couple of years), creating a boom-bust cycle in investment in this

300

technology over years, which is also believed to be damaging the industry's prospects due to the

301

uncertainty it creates (see [26]). In addition, although the industry agrees on the necessity of a five-

302

to ten-year incentive, legislators do not want to be associated with large spending programs, which

303

is why the Production Tax Credit is likely to remain as a two-year address option of renewal

304

indefinitely. Another source of uncertainty for tax incentive is economic downturn, particularly

305

during a financial crisis, when there are limited resources to support these measures. It would thus

306

be that the issue with the lack of positive reinforcement of renewable energy by tax instrument is a

307

result of the uncertainty of the policies. Gan et al. [5] also criticize how these measures do not

308

necessarily guarantee the achievement of their expected targets.

309

This paper confirms the findings reported by Marques and Fuinhas [3], who show that fiscal and

310

financial incentives policies are positive to increasing the contribution of renewables to the total

311

energy supply. In addition, this research confirms that a variety of policies comprising specific and

312

technology measures spur renewable energy technology deployment, higher income through grants

313

and subsidies, and lower capital costs through price policy support investors to increase renewable 17

ACCEPTED MANUSCRIPT 314

energy capacity. In sum, the price policy is a strong signal for investors, because it adjusts the risk

315

or reward structure to address capital market constraints. Grants and subsidies policy is proven to

316

be effective short-term measures to ease fiscal constraints. Tax incentives reduce the tax burden of

317

renewable energy enterprises and increase the competitiveness of renewable energy, but need a

318

stable policy environment.

319

3.2 Market-based instruments

320

This empirical research also provides evidence for market-based instruments including

321

greenhouse gas emissions allowances (GA), green certificates (GC) and white certificates (WC).

322

Many sample countries have not yet implemented the white certificates policy, only South Korea

323

and Italy introduced this instrument in 2007 and 2011, respectively (data from IEA/IRENA policies

324

and measures database); therefore, we mainly focus on greenhouse gas emissions allowances and

325

green certificates.

326

The separate policies of greenhouse gas emissions allowances or green certificates have no

327

significant effects in promoting renewable energy; however, the aggregate policy of market-based

328

instruments has a strong impact on renewable capacity expansion at the 1% significance level. This

329

indicates that synergy effects do exist between these two specific policies; that is to say, the

330

implementation of market-based instruments require the combination of greenhouse gas emissions

331

allowances and green certificates. On the one hand, greenhouse gas emissions allowances and green

332

certificates have different starting points: greenhouse gas emissions allowances is aimed at carbon

333

dioxide emission reduction and at solving the problem of greenhouse gas emissions, while the green

334

certificate is aimed at promoting the utilization of renewable energy and solving the problem of the

335

adjustment of energy structure. On the other hand, the two policies are essentially reducing 18

ACCEPTED MANUSCRIPT 336

greenhouse gas emissions by means of market-based instruments. The empirical analysis shows that

337

the combination of the two specific policies plays a very important role in the promotion of

338

renewable energy. Hence, the combination of greenhouse gas emissions allowances and green

339

certificates might be the most effective way to push the entire society to achieve energy

340

transformation with the least cost.

341

3.3 Direct investments

342

The class of policy “Direct investments” includes funds to sub-national governments (FSG),

343

infrastructure investments (II). The results from empirical analysis revealed that this type of

344

instrument is ineffective in promoting renewable energy capacity. We hereby confirm earlier works

345

(based on Multiple renewable energy analyses) that resulted in that this form of direct investment

346

did not spur renewable energy development [16]. This stands in contrast to previous literature that

347

highlights the grid expansion as conducive to renewable energy deployment and investment [27].

348

Funds to sub-national governments are a direct investment of federal money, with regional, local or

349

municipal level entities as targets. Infrastructure investment to provide grid access seem ineffective

350

for channeling investors’ money into renewable energy technologies, according to the paper [16].

351

The reasons for non-significant results in this paper might lie in the different structure of different

352

renewable energy technologies, which have different regional focuses and scales of power plants.

353

3.4 Policy support

354

Certain policies and measures seek to define strategies and outline specific programs to promote

355

renewable capacity within a country. These measures are tracked as policy support, which includes

356

institutional creation (IC) and strategic planning (SP). The empirical results indicate that policy

357

support instruments accelerated the capacity additions in the renewable energy sector effectively in 19

ACCEPTED MANUSCRIPT 358

the period of 2000 to 2015.

359

From the perspective of specific policy, institutional creation is insignificant to promoting

360

renewables capacity, while strategic planning has positive effects on the development of renewable

361

energy, as investors favor a long-term stable policy environment.

362

As shown in Fig. 3, the coefficient of aggregate policy (That is, the combination of institutional

363

creation and strategic planning policy) has no significant differences than that of the strategic

364

planning instrument. While it can be seen from the data in Table 3 that the aggregate policy group

365

(Significant at 1% level) reported more significance than the specific policy group (Significant at

366

5% level). Indicating that the combination of institutional creation and strategic planning policy

367

might promotes the effects of strategic planning policy. Institutional creation, such as the

368

implementation of an energy agency, might accelerates the deployment of strategic planning

369

effectively.

370

With this analysis, the empirical results are in support of the strong role that institutional creation

371

and a long-term policy commitment play in an effective policy mix. Commitment, creation, stability,

372

reliability and predictability are all elements that increase confidence of market actors, reduce

373

regulatory risks, and reduce the cost of capital. Thus, the results of this study confirm empirical

374

works that indicate clear strategic long-term economic instruments are conductive to renewable

375

energy development. Therefore, long-term policies for the stable environment through strategic

376

planning and probably through institutional creation will support the long-term development of

377

renewable energy.

378

3.5 Regulatory instruments and information and education

379

According to Table 3, the regulatory instrument is insignificant in the stage of renewable energy 20

ACCEPTED MANUSCRIPT 380

development. Gan et al. [5] state that regulatory instruments appear to have been the most effective

381

in promoting renewable energy development, while the empirical results in this paper are

382

inconsistent with the this view reported by Gan et al. (2007). Furthermore, there are cases in the

383

previous literature where even more details about the policies do not generate the expected results.

384

For example, Polzin et al. [16] note that regulatory mechanisms and the institutionalization of

385

markets in the form of regulatory instruments also attract institutional investors, while the empirical

386

results are not significant. Aguirre and Ibikunle [4] did not find significance for regulatory

387

instruments. This result is aligned with those from Marques and Fuinhas [3]. Furthermore, measures

388

and policies belonging to the “Information and education”, such as Advice/Aid in Implementation,

389

Information provision, do not yet seem to be relevant in stimulating the renewable deployment.

390

3.6 Research, development and deployment (RD&D)

391

To capture the effect of RD&D to renewable energy installment, we use Detailed Country RD&D

392

Budgets of renewable energy as a proxy, taken from the IEA's Energy Technology Research and

393

Development Database. In the 1970s, the renewables policy framework was dominated by Research

394

and Development programs [4]. In this paper, we evaluate how effective and significant the policy

395

instruments are in promoting renewable energy deployment. The results suggest that RD&D is an

396

important factor to facilitate renewables in typical countries. This is reflected by a high level of

397

statistical significance, at the 1% significance level, and positive relationship between the RD&D

398

budget variable and the dependent variable. As renewable technologies are still expensive and thus

399

cannot compete with fossil fuel technologies, RD&D was needed to reduce costs and create more

400

advanced renewable energy technologies.

401

3.7 Non-policy variables 21

ACCEPTED MANUSCRIPT 402

Besides the above policy variables, GDP, high-technology exports, CO2 emissions, electric power

403

consumption, and the Kyoto Protocol are statistically significant non-policy variables at the 1%

404

significance level. Out of these non-policy variables, CO2 emissions negatively related to the

405

dependent variable, while the remaining variables positively affected renewables deployment. It is

406

worth noting that the implement of Kyoto Protocol in 2005 was a key international milestone in

407

carbon emission, which shocked the policymakers in their minds and promoted the transformation

408

of the world development philosophy on low-carbon economy. Although some countries failed to

409

achieve the goal of reducing emissions because of the high cost of low carbon development in the

410

short term, a large number of countries are committed to search for a low-carbon development path

411

in the long run. It is undeniable that the low carbon development philosophy will promote the

412

renewable energy deployment in the long term, which is an effective way to emission reduction.

413

4. Conclusions

414

This paper focuses on a panel of 29 typical countries for the time span 2000-2015 to characterize

415

the effectiveness of public policies regarding renewables. This research adds empirical evaluation

416

of specific policies on renewables to uncover the effects of different policies on renewables

417

development and detail function of specific instruments in aggregate policies.

418

The analysis demonstrates that certain aggregate public policies have been drivers toward the

419

development of renewables. On the one hand, four of seven aggregate measures have produced the

420

desired effect of increasing renewable energy power capacity in the period analyzed. The ranking

421

of aggregate policies impacts is, market-based instruments, research, development and deployment

422

(RD&D), fiscal and financial incentives, and policy support. Specifically, regressions indicate that

423

for a 10 percentage point increase in these four policies, countries will install respectively 3.00%, 22

ACCEPTED MANUSCRIPT 424

2.10%, 1.82%, 1.63% more renewable energy capacity per year on average. However, the other

425

three aggregate measures, including direct investments, regulatory instruments, information and

426

education, have not yet been effective in fostering renewable energy use in these target countries.

427

For the specific policies, price policy, grants and subsidies are the stable instruments of fiscal and

428

financial incentives to promote the development of renewable energy. For policy support

429

instruments, strategic planning was the main effect policy to spur renewable energy development

430

while institutional creation was not. We find that for a 10 percentage point increase in price policy,

431

grants and subsidies, and strategic planning, countries will install 1.91%, 1.63%, 1.67% more

432

renewable energy capacity per year on average.

433

This paper assesses the aggregate policy and their specific ones in the same analysis framework,

434

and the empirical results found that synergy effects do exist among specific policies, so it is vital

435

for policy makers to incorporate specific mix and economic conditions into the policy design. For

436

example, separate policies of greenhouse gas emissions allowances or green certificates have an

437

insignificant effect while their combination (that is, the aggregate policy of market-based incentives)

438

has a positive influence on renewable energy capacity at the 1% significance level. It is worth noting

439

that market-based instruments should be given special attention in promoting renewable power

440

development in the future. In the formulation of market-oriented policies, integrating greenhouse

441

gas emissions allowances and green certificates policies may be a better solution to avoid policy

442

failure. On the one hand, countries can directly control greenhouse gas emissions through

443

greenhouse gas emissions allowances policy, such as setting a cap on the permitted amount of

444

emissions and distributing a corresponding number of allowances globally. On the other hand, they

445

set indirect energy means through green certificates instrument to increase the proportion of 23

ACCEPTED MANUSCRIPT 446

renewable energy consumption and reduce greenhouse gas emissions. In addition, the specific

447

policy of institutional creation has insignificant effects while the specific policy of strategic planning

448

has positively effects (at the 5% significance level) on the development of renewable energy. It is

449

worth noting that their combination has a more significant effect (at the 1% significance level) on

450

the development of renewable energy, which indicates that institutional creation promotes the

451

effects of strategic planning.

452

The empirical results highlighted that implementation of the Kyoto Protocol in 2005 has positive

453

impact on renewable energy deployment in typical countries. In fact, due to the entry into force of

454

the Kyoto Protocol, many countries have introduced renewable energy development policies to

455

promote renewable energy development. The empirical results also indicate that GDP, electric

456

power consumption, and high-technology exports, which are control variables in the model, are

457

confirmed to have positive effects on the continuous development of renewables. However, CO2

458

emissions, another control variable in the model, have a negative impact on the deployment of

459

renewables.

460

Overall, the empirical results suggest that the development of renewables has been established

461

essentially upon direct subsidies, strategy plans and RD&D while market-oriented policy

462

instruments should be the greenhouse gas emissions allowances and green certificates mixes. With

463

the increase of government subsidies and expenditure, the further direction of renewables is market-

464

driven logic instead of policy-driven logic; therefore, the appropriate design of market-oriented

465

policy instruments need to be discussed further.

466

Acknowledgements

467

The authors would like to thank the anonymous referees and the editor of this journal. The authors 24

ACCEPTED MANUSCRIPT 468

also gratefully acknowledge the financial support of the Major Program of the National Social

469

Science Fund of China (Grant No. 15ZDB165).

470

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HIGHLIGHTS 

Panel data study on the effectiveness of renewable energy policies to promote RE development.



Four of seven aggregate policies are positively significant to RE capacity increase especially Market-based instruments and RD&D.



Three of twelve specific policies have significant positive effects on RE capacity including price policy, grants and subsidies, and strategic planning.



Synergy effects do exist among specific policies in renewable energy increase.