What motivates developing countries to diversify sources of renewable energy?
Moises Neil V. Seriño1,2
1
Center for Southeast Asian Studies, Kyoto University, Kyoto, Japan2
Department of Economics, Visayas State University, Leyte, Philippines
Abstract
This paper investigates the determinants of diversification of nonhydro renewableenergy sources using a substantially larger data set covering 117 developing countries over theperiod 1980 – 2011. We use a novel variable capturing diversification of nonhydro renewableenergy sources (geothermal, solar, wind, waste and biomass) and explore several estimation
techniques such as two-part model, negative binomial and Poisson pseudo-maximum likelihood.
Results show that higher per capita income, implementation of policies promoting renewableenergy, advances in technological innovation and improvement in human capital promotes
diversification. Although the results are consistent with the literature, this study differentiates
itself by including the nonlinear effect of income. Results show that nonlinear effect of income
is evident across specifications suggesting non-monotonic changes in diversification as
developing countries get more affluent. In addition, concerns related to energy security such as
high dependence on foreign sources of fuel and increasing world market price for crude oil will
push developing countries to diversify sources of nonhydro renewable energy. In contrast, local
abundance of hydropower and availability of natural resource like oil impede diversification.
Finally, we find robust evidence that Kyoto Protocol positively influence diversification ofnonhydro renewable energy sources in developing countries but no conclusive results can beclaimed for financial sector development. This suggests that progressive integration ofdiversified renewables in developing countries’ energy mix can be hastened given relevant
policy mix and favorable economic conditions.
JEL classification: O13, Q42, Q48
Keywords: diversification index, nonhydro renewable energy, Kyoto Protocol, technology
diffusion

Corresponding author: Moises Neil V. Seriño, Visiting Researcher at Center for Southeast Asian Studies, Kyoto
University, 46 Shimoadachi-cho, Yoshida, Sakyo-ku, Kyoto 606-8501, Japan and Department of Economics,
Visayas State University, Visca, Baybay City, Leyte 6521, Philippines.
Email: serino@cseas.kyoto-u.ac.jp or moisesneil.serino@vsu.edu.ph11.

Introduction

The reliance on fossil fuels to support economic activities for the past century has
gravely threatened the Earth’s climate system. According to the Intergovernmental Panel for
Climate Change (IPCC) (2014) human influence on the recent climate change phenomenon isclear and this is largely attributed to the alarming and rising concentration of anthropogenic
green house gas emissions in the atmosphere. Despite a growing number of climate change
mitigation policies, annual carbon emissions have continued to increase at unprecedented levels
and recent report shows that green house gas emissions particularly carbon emissions arehighest in history (IPCC, 2014). The severity of threat associated with climate change onhumans and the environment cannot be ignored. Scientist and policy makers around the world
are making concerted efforts in averting catastrophic consequences from human induced
climate change.
The issue of climate change has created an enormous challenge for developing countries
to grow and prosper without further jeopardizing efforts in stabilizing the climate system(Seriño, 2017). It cannot be denied that increase energy access has contributed to theimprovement of lifestyle of billions of people particularly from developing countries. However,in 2016 approximately more than 1 billion people still do not have access to electricity(Renewable Energy Policy Network for the 21st Century [REN21], 2018). Increasing diffusionof renewal energy in developing countries can contribute to providing access to energy for all,boosting energy security, economic growth and reducing emissions (REN21, 2018). In addition,
the adoption of renewable energy in developing countries will not only slow down carbon
emissions but will also offer opportunity for them to leapfrog developed countries as a result ofharnessing energy from environment friendly sources before a lock in fossil fuel occurs (Popp,
2011; Watson and Sauter, 2011; Pfeiffer and Mulder, 2013).
Though conventional fossil fuels are still being the main source of energy worldwide,
renewables are steadily becoming part of the global energy mix. According to World Bank
(2018), the share of renewable energy consumption to total final energy consumption steadily
increased from 16.91% in 2007 to 18.05% 2015. These renewable energy consumption comesfrom the traditional biomass and modern renewables such as solar, wind, geothermal,hydropower, wave and biofuels for transport. As of 2016, the share of modern renewables tototal final energy consumption is approximately 10.4% (REN21, 2018). The combined share of
renewable energy (modern and traditional biomass) to total energy consumption in 2016 isaround 18.2% (REN21, 2018). Renewables have grown rapidly in recent years, accompaniedby sharp reductions in cost for solar photovoltaics and wind power (International Energy
Agency [IEA], 2018).
Renewable energy takes crucial role in transitioning to a less carbon-intensive economy
and more sustainable energy system (IEA, 2018). Positive development in renewable energy
surprisingly comes from developing countries. It used to be that in early 2000 the biggest shareof investment in renewables comes from developed countries but in 2015 and for the first timein history developing countries surpassed developed countries in terms of investment inrenewable energy (Frankfurt School, UNEP and BNEF, 2018). According to the 2018 reporton Global Trends in Renewable Energy investment jointly published by Frankfurt School ofFinance and Management, United Nations Environment Programme and Bloomberg New
Energy Finance, developing countries led by China, Brazil and India made up the majority ininvestment in renewable energy amounting to as much as 63% of the global investment while
the share of developed economies is just around 37%. China alone is responsible for over 40%
2

of global renewable capacity growth in 2017 making it the global leader in renewable energy
growth (IEA, 2018). If developing countries will pursue the growth trajectory in renewableenergy like China did, then this present an opportunity for sustainable development by growing
further without increasing emissions. However, despite the enormous environmental andeconomic benefits associated with renewable energy, global renewable energy transition indeveloping countries is still progressing far too slowly and unevenly (REN21, 2018). China,
India and Brazil accounted for more than half of the global investment in modern renewables
excluding hydro (Frankfurt School, UNEP and BNEF, 2018) while others are lagging behind.
To shed some light on this issue, this paper investigates various factors that enhances or
impedes diffusion of renewable energy in developing countries.
Further integration of renewable energy into other developing countries requires transferof these climate friendly technologies (Popp et al., 2011; Pfeiffer and Mulder, 2013). Although
there are already commercially available renewable technologies, still they account only alimited share of the total energy generation. This could be because of several market, economic,
institutional, technical and socio-cultural barriers that hinder developing countries in moving
away from conventional energy sources (Verbruggen et al., 2010; Dulal et al., 2013).
Correspondingly, several government policies like feed-in tariffs, tax credits, tradable
certificates, investment incentives and production quotas play an important role in promoting
the diffusion of renewable energy (IEA, 2014; Zhao et al., 2013). To aid policy decisions inaccelerating deployment of renewable energy, we investigate the issue of renewable energy
diversification. According to Brunnschweiler (2010) achieving a diversified and sustainableenergy supply for the future is one of the major challenges for today’s policymakers.
Diversification of renewable energy sources has direct implications to energy security. When acounty’s energy system relies heavily from one source, it becomes vulnerable to energy shocks.
Diversification of energy sources is an essential strategy for ensuring energy security.
Our study improves on the recent literature and takes a different approach byinvestigating what motivates developing countries to diversify sources of nonhydro renewableenergy. Our contribution to the literature is threefold. First, we use a novel variable in capturing
diversification. The usual practice in the literature is to aggregate the energy produced fromdifferent sources (see for example Brunnschweiler, 2010; Pfeiffer and Mulder, 2013; Zhao etal., 2013) or by counting the number of renewable energy sources (Seriño, 2018) to capture
diffusion of renewable energy in developing countries. We capture diversification by creating
a novel variable or index that will put weight to each source by using the share of energy
produced from a particular source to the total renewable energy generated. The index takes avalue of zero if a country has not invested in any nonhydro renewable technology and takes avalue of 1 if the energy generated is coming only from one particular source. A value of morethan one implies diversification. To the best of our knowledge, this is the first paper that uses
this approach in measuring diversification. Second, we focus only on modern nonhydrorenewable energy and exclude hydropower because hydropower is a mature technology(Pfeiffer and Mulder, 2013; Popp et al., 2011). In the last five years, the growth of renewableenergy mostly come from nonhydro sources such as solar, wind, geothermal, biomass and waste(IEA, 2018). Third, we use a substantially larger data set covering more than one hundreddeveloping countries spanning from 1980 to 2011. The empirical approach uses several
methods such as negative binomial regression, two-part model and Poisson pseudo-maximum
likelihood to account for large number of sample with zero observations and sample selection.
The remainder of the paper is structured as follows. Section 2 presents a brief literature
review followed by the importance of energy diversification for energy security in Section 3.
3

Section 4 shows the empirical approach and the data used. Section 5 provides the results anddiscussions. Lastly, Section 6 presents the major highlights of the paper and concludes.

2.

Literature Review

According to Marques et al. (2010) there are increasing number of papers on renewableenergy but less attention has been given to the discussion on the determinants promoting
renewable energy. Also, the available empirical work is much more concentrated in developed
countries (see for example Menz and Vachon, 2006; Carley, 2009; Sadorsky, 2009; Marques etal., 2010; Ohler and Fetters, 2014; Upton and Snyder, 2017). However, investments inrenewable energy from developing countries have been rapidly increasing in the recent years(REN21 2018; IEA 2018). As growth in renewable energy accelerates, integrating this intodeveloping countries’ energy system becomes increasingly important (IEA, 2018).
Menz and Vachon (2006) analyzed the effectiveness of different state policies forpromoting wind power in the United States. Results show that state level policies are positively
related with wind power development. Key limitations of their study include limited sample
size and the possibility of omitted variable problem. Carley (2009) controls for these issues by
including a time dimension and estimated a model of fixed effects with vector decomposition
covering 50 states in the US for 9 years (1998 to 2006). However, results show that the policy
instrument (renewable portfolio standard) is not a significant predictor of renewable energy but
for each additional year that a state has a renewable energy policy, they are found to increase
the total amount of renewable energy generation. For the European case, Marques et al. (2010)
used panel data to investigate the factors driving renewable energy adoption among 24
European countries for the period 1990-2006 but they did not include policy variables in theiranalysis. This has been subsequently addressed by Marques and Fuinhas (2012), where theyfound evidence that policy measures promote a wider use of renewables. Zhao et al. (2013)
employed the methods of panel data analysis in evaluating the effect of renewable electricity
polices on renewable electricity generation covering developed, emerging and developing
countries. The results suggest that policies play a crucial role in promoting renewableelectricity generation but their effectiveness diminishes as the number of policy increases (Zhaoet al. 2013). Also, Aguirre and Ibikunle (2014) emphasized that certain government-backedenergy policies impede renewable energy investment implying failure in policy design. Despitesome failures in policy design, Dulal et al. (2013) argued that the government’s role iswarranted in the generation of renewable energy, especially in Asia where the increase inpopulation size should be met with dramatic increase in the energy supply.
Still focusing on the policy aspect, Polzin et al. (2015) examined the effectiveness of
public policy to induce investments in renewable energy in OECD countries. The effectivenessof policy varies according to the type of renewable energy technologies. The same findings
were highlighted by Kilinc-Ata (2016) when she evaluated the renewable energy policies
adopted by European countries and US states. Johnstone et al. (2010) examined the effect ofenvironmental polices not directly on renewable energy generation but on technologicalinnovation focusing on renewable energy. They used patent data as a proxy for technologicalinnovation on a panel of 25 countries and found that public policy plays a significant role indetermining patent applications and that those different policies have varied effects on
renewable energy sources. The work of Popp et al. (2011) is related to the study of Johnstone
et al. (2010) in the sense that they also use patents in assessing the impact of technologicalchange on investment in renewable energy capacity. They found evidence that technological4

advances in 26 OECD countries do lead to a greater investment in renewable energy, however
the effect is only small.
It is worth noting that the majority of the literature discussed above focuses ondeveloped countries. Only a handful of studies have been found to employ a moreheterogeneous sample of developing countries (Brunnschweiler, 2010; Freitas et al., 2012;
Pfeiffer and Mulder, 2013; Seriño, 2018). Though the literature of renewable energy indeveloping countries is limited, recent studies on this issue has been rapidly increasing.
Brunnschweiler (2010) studied the role of financial sector in renewable energy development
covering more than 100 developing and emerging economies. Results show that the influenceof financial sector has significant positive effect on the amount of renewable energy generationin developing countries. This impact is even larger when only non-hydro renewables are
considered such as wind, solar, geothermal and biomass (Brunnschweiler, 2010). Focusing only
on major developing countries, Freitas et al. (2012) examined the diffusion renewable energy
as influenced by Kyoto Protocol. Results suggest that the mechanisms of Kyoto Protocol may
only be supporting the spread of existing technologies rather than the development of anddiffusion of more sustainable variants of renewable energy technologies in the BRICS, i.e.
Brazil, Russia, India, China and South Africa. However, the paper of Pfeiffer and Mulder(2013) contradicts the results of Brunnschweiler (2010) and Freitas et al. (2012). Although
Pfeiffer and Mulder (2013) found a positive yet weak influence of the Kyoto Protocol on
nonhydro renewable energy diffusion, no solid evidence was presented for the influence offinancial sector development on the diffusion of renewable energy in developing countries.
Recently, Seriño (2018) also claimed that financial development showed no robust evidence on
its influence on nonhydro renewable energy adoption in developing countries but the influenceof Kyoto Protocol was not incorporated in their analysis. This current paper improves on the
recent literature and incorporate the influence of Kyoto Protocol on the diversification ofnonhydro sources of renewable energy in developing countries.

3. Diversification of renewable energy for energy security
In business, it is important to have a diversified portfolio to help manage risks andvolatility of investments. The saying “do not put all your eggs in one basket” is a common
expression encouraging diversification. When a country’s energy supply relies heavily on one
particular energy source it becomes extremely vulnerable to exogenous supply shock (van Hove,
1993). The energy crisis in 1970s had tremendous economic, political and social consequences
not just in developed countries but also in developing countries. Since then, policy makers have
paid increasing attention to energy security.
Li (2005) argued that diversification and localization of energy sources is essential for
future energy system because it promotes sustainable development as well as energy security.
Li (2005) stressed that the idea of diversified energy is good not just for the people but also forthe environment. He cited several analogies in other fields pointing out the advantage ofdiversification. For example, bio-diversity is a good strategy to prevent the spread of pests anddiseases, diversified portfolio will guarantee a better investment return and in governance, thesuccess of democracy has diversification of ideas at its core. Similarly, for renewable energy
diversifying its sources is viewed as an attractive option as it can helped stabilize energy supply.
The main disadvantage of renewable energy aside from its large capital cost is thereliability of energy supply. Most renewable energy relies on weather as its main source. For
5example, hydro power need rain to fill dams and keep the supply of water flowing, windturbines need wind to turn blades, solar panels need clear skies and sunshine to generate
electricity. These natural sources are somehow variable, unpredictable and inconsistent andwhen these sources are unavailable, the supply of energy will be affected. However,
diversification of these renewable energy sources will help allow for a steady and reliable
source of energy supply. For example, dry and sunny weather may not be good for generatinghydropower but will be great for generating electricity from solar panels; stormy weather may
reduce generation of solar energy but will be good for hydropower, wind energy and tidal
energy. Therefore, diversifying sources of renewable energy is essential to achieve energy
security through consistent and localized supply of energy (Li, 2005).

4.

Data and Methodology

4.1. Data sources and diversity index
The dataset used in this study covers 117 developing countries for 32 years spanningfrom 1980 to 2011. This dataset is compiled using four different sources: International Energy
Agency (IEA, 2014), World Bank’s World Development Indicators (WDI) (World Bank, 2014),
Energy Information Administration (EIA, 2014), and BP Statistical Review of World Energy(BP, 2014). Table 1 provides the data description and the source database for each variable.
Table 2 presents the descriptive statistics of the variables used in the analysis.
Our analysis focuses on the diversification of nonhydro sources of renewable energy indeveloping countries. These nonhydro sources include wind, solar, biomass, waste andgeothermal. Large hydropower is excluded in the current analysis because it is already a maturetechnology (Popp et al., 2011; Pfeiffer and Mulder, 2013). Our main source of data for thegeneration of renewable energy comes from IEA. Data from IEA on nonhydro power
generation can be considered comprehensive, however, it may have underestimated theelectricity generated as off-grid generation may not be included in the data set (Pfeiffer and
Mulder, 2013).
Table1. Data descriptions of the variables used and their sources
Variable
Definition
SourceDependent variables
Diversity index
Takes a value of 1 if a country adopts only 1
own
renewable source and a higher value if morecomputation
diversified. The index is weighted on the shareof each renewable energy source. If a country
did not invest in renewable energy, the value is
zero
Independent variables
GDP per capita
GDP per capita in constant 2005 USD (inWorld
logarithmic form)
Development
Indicator(WDI)
Energy import
Net energy imports in % of energy use
WDI
Population growth
Annual population growth in %WDI
Patents
Total patent application taken in log form
WDI
6

Financial development
Secondary enrollment
FDI
ODA
Crude oil price
Kyoto protocol
Renewable policy
Hydro energy
Oil production

Coal production
CoastalDomestic credit to private sector in % of GDP
Secondary school enrollment % grossForeign direct investment, net inflows in % of
GDP
Net official development assistance received
in % of GNI
Crude oil prices (West Texas intermediate)
Dummy variable taking value 1 from 1998
onwards and zero otherwise
Dummy variable taking value 1 from the yearof implementation of a renewable energy
policyTotal hydroelectric power generated (inthousand kilowatt-hours) per capita
Total oil production in thousands barrel / 1
million people
Total coal production in thousand tons / 1
million people
Dummy variable taking value 1 if a country
has coast

WDI
WDI
WDI
WDI
BP
International
Energy Agency
(IEA)
IEA
IEA
EnergyInformation
Administration
(EIA)
EIA
Google map(Google, 2014)

Table2. Descriptive statistics for developing countries from 1980 to 2010
Variablen
Mean
Std. Dev.
Min

Max

Dependent variables
Diversity index

3946

0.37

0.31

0

2.89

Independent variables
GDP per capita (in log)
Energy import
Population growth
Patents
Financial development
Secondary enrolment
FDI
ODA
Kyoto protocol
Renewable policy
Hydro energy
Oil production
Coal production
Crude oil priceCoastal

4090
2688
4727
1473
3708
2944
3849
3654
4752
4752
4070
3929
4144
4752
4752

7.17
-41.92
1.81
6.25
3.01
54.79
3.69
9.29
0.45
0.10
0.37
4281.3289337.7
37.66
0.78

1.07
195.54
1.35
1.93
0.87
30.03
11.79
12.95
0.50
0.30
0.89
14337.73
1012096
25.00
0.42

3.91
-1982.88-10.96
0.69
-0.58
2.48
-82.93
-2.70
0
0
0
0
0
14.39
0

9.60
99.96
11.18
13.17
5.12
122.20366.36242.291
1
10.08
216634.1
1.25e+07
100.06
1

Source: Author’s calculations based on the data described in Table 1

7

One of the innovations of this study is to come up with an indicator of diversification.
To do this, we use the share of renewable energy generated from each source to the total energy
generated from all nonhydro sources. The diversity index takes a value of zero if a country has
not invested in any nonhydro renewable technology and takes a positive value if a country has
invested in any of the nonhydro renewable technologies. Technically, the diversity index takes
a value of 1 if the energy generated is coming only from one particular source and if the country
is more diversified then the index is greater than 1. If each source carries the same share ofenergy generated, the index converges to the total number of nonhydro sources adopted bydeveloping countries. We propose, to estimate the diversity index as follows:

𝑑𝑑𝑑𝑑𝑑𝑑 = �1𝑛𝑛ℎ𝑟𝑟𝑟𝑟𝑗𝑗𝑗𝑗 2∑𝑛𝑛𝑖𝑖=1� 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 �

0𝑖𝑖𝑖𝑖 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 > 0(1)𝑖𝑖𝑖𝑖 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = 0

where dre captures the diversity of a developing country’s nonhydro renewable energy mix,
NHRE is the net generation of nonhydro-electricity measured in billion kilowatt-hours and thenhrejt is the individual net energy generation from either of the nonhydro renewable energy
sources (wind, solar, geothermal, biomass and waste) in country j in year t.
Pfeiffer and Mulder (2013) measures the variety of renewable energy technologies by
calculating the energy mix and uses this variable as a measure of diversification. Our proposed
calculation differs from Pfeiffer and Mulder (2013) because their index of diversification takes
a value of zero if a country produces its energy from only one source and converges to one
when a country is more diversified. We could have opted for this measure but this approachcannot accommodate zero observations or those developing countries that at a particular point
in time that did not invest in nonhydro renewable energy. Figure 1 below shows that our data
set has a substantial number of zero observations. Using Pfeiffer and Mulder’s (2013) index
will lead to a lot of missing observation because it will not compute for zero observation.
However, our proposed index handles this problem. For example, assuming there are three
countries named A, B and C. Country A and B produces energy from wind and solar while
Country C has not invested in any renewable energy. The index requires the amount of energy
generated from each source for us to do the calculation. For example, both country A and B
produces 10 gigawatts (GW) total renewable energy. Country A produces 5 GW from solar and
5 GW from wind energy while country B produces 7 GW from solar and only 3 GW from wind.
Applying our proposed formula for diversification, country A’s diversity is 2, Country B is 1.72
and Country C is zero. This implies that if there is equitable generation of energy from various
renewable sources, the diversity index converges to the number of renewable energy sources.

8

3000
2000
Frequency
1000
0
0

3
2
1
number of nonhydro renewable source of energy

4

Figure 1. Number of nonhydro sources of renewable energy adopted by developing countries.
4.2. Determinants of diversification
The main dependent variable captures diversity in nonhydro renewable technology.
Notably, our dependent variables contain a lot of number of zero observations or those countries
that did not invest in nonhydro renewable energy (Figure 1). Following Seriño (2018), we
specify our estimation regression in investigating the determinants that facilitates or impedes
diversification of nonhydro renewable energy as follows:
𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1 𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝛽𝛽2 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖𝑖𝑖 + 𝛽𝛽3 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖𝑖𝑖 + 𝛽𝛽4 𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑖𝑖𝑖𝑖 + 𝛿𝛿𝒁𝒁𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖 + 𝑣𝑣𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖(2)where dreit is the index of diversification for country i at year t, GDPit is the gross domestic
product capturing income, GDPsqit is the squared of GDP to incorporate nonlinear effect ofincome , policyit is a dummy variable capturing the implementation of renewable energy-related
policies, kyotoit is also a dummy variable capturing the potential impact of Kyoto Protocol on
the diffusion of renewable energy in developing countries, and Zit is the set of remaining
independent variables, ui is the country fixed effects used to capture time-invariant country
heterogeneity, vt is time fixed effects and εit is the remaining error. The covariates included in
the analysis are further discussed as follows:(1) Income as measured by GDP is positively correlated with adoption of renewable energy
as suggested by various authors (see for example Aguirre and Ibikunle, 2014; Marques
et al., 2010; Popp et al., 2011; Zhao et al., 2013, Seriño, 2018). We expect that countries
with higher affluence have the ability to diversify renewable energy sources. This study
differentiates itself from most literature by including the squared term of GDP toincorporate the nonlinear effect effect of income on renewable energy.
(2) Evidence on the influence of policy in stimulating adoption of renewable energy isabundant in the literature ( see for example Menz and Vachon, 2006; Carley, 2009;
Johnstone et al., 2010; Marques and Fuinhas, 2012; Zhao et al., 2013; Aguirre andIbikunle, 2014; Polzin et al., 2015; Kilinc-Ata, 2016) Based on IEA’s database, there
several policy types related to renewable energy. This includes economic instruments,
9information and education, policy support, regulatory instruments, research anddevelopment and voluntary approaches (IEA, 2014) . We used a dummy variable if anyof those policies are implemented by developing countries.
(3) One of the first key steps in attempting to control global carbon emission was the
adoption of Kyoto protocol in the late 1997. We control the impact of Kyoto protocol
by introducing a time dummy from 1998 onward. This marks a greater environmental
awareness which is not just confined to developed countries (Brunnschweiler, 2010;
Johnstone et al., 2010; Popp et al., 2011; Aguirre and Ibikunle, 2014). Freitas et al.
(2012) examined whether the Kyoto mechanisms have stimulated the diffusion ofrenewable energy technologies in the BRICS, i.e. Brazil, Russian, India China andSouth Africa.
(4) Energy imports capture how dependent countries are on external sources of energy.
Bigger energy imports may stimulate investment on renewable energy as a measure forenergy security (Aguirre and Ibikunle, 2014; Dong, 2012; Marques et al., 2010; Popp
et al., 2011; Zhao et al., 2013).
(5) Population growth is an important indicator for energy demand. Most studies available
in the literature did not include population growth in their analysis. The average
population growth in developing countries is close to 2 (Table 2). Such high growth will
put pressure on energy demand in the future.
(6) Popp et al. (2011) showed that diffusion of renewable technologies in developing
countries can be facilitated by the degree of technological advancement. The number ofpatents is used as proxy for technological advancement (Johnstone et al., 2010).
(7) The role of financial development in stimulating renewable energy growth is a bit
ambiguous in the literature. Brunnschweiler (2010) found out that financial sector
development has significant positive effect on the amount renewable energy produced
but Pfeiffer and Mulder (2013) showed no evidence from financial sector developmenton the diffusion of nonhydro renewable energy in developing countries. This paper will
provide evidence on this aspect and may help clear the ambiguity of financial
development.
(8) Human capital as proxied by secondary enrollment has important implications in the
adoption of renewable energy (Pfeiffer and Mulder, 2013; Zhao et al., 2013).
(9) FDI and ODA are two external sources of funding which may potentially influence
diversification of renewable energy in developing countries (Brunnschweiler, 2010;
Pfeiffer and Mulder, 2013; Zhao et al., 2013).
(10) Another control variable is the amount of electricity generated from hydropower.
Countries having large hydro power may not be as keen in investing in new renewable
as opposed those who do not have (Popp et al., 2011; Pfeiffer and Mulder, 2013).
(11) Production of coal and crude oil are included in the analysis to control for conventionalsources of energy (Marques et al., 2010). Countries abundant with these energy sources
may be reluctant to invest in renewable energy. If renewable energy complements
conventional sources, then an increase in coal and oil production is positively associatedwith renewable energy (Seriño, 2018).
(12) We include the in the analysis crude oil prices to control for global market fluctuations
in fuel prices. If developing countries are price sensitive, then investments in renewableenergy can augment their energy demands (Aguirre and Ibikunle, 2014).
10

(13) Natural endowment is captured by including a coastal dummy. This variable takes avalue of 1 if a country has coastal area and 0 otherwise. This serves as a proxy of
production potential of renewables.
(14) To account for heterogeneity of developing countries, regional dummy variables areincluded in the analysis.
(15) Lastly, we control for time variations by including year dummies in the regression.
4.3. Empirical approach
For the empirical approach, two-stage estimation technique is used to model the choice
whether to adopt or not adopt nonhydro sources of renewable energy. And then conditional on
adopting, we examine the factors influencing diversification of nonhydro sources of renewableenergy. Take note, we have a large number of zero values in our dependent variable consistingalmost 68% of the total observation (Figure 1). However, we assume that these zero
observations are true zero or actual outcome. These zero observations reflect countries who
have not invested in nonhydro renewable energy sources 1. To address this issue, the two-stage
estimation method using two-part model pioneered by Duan et al. (1983) is used. Two-partmodel is a methodological strategy designed to deal with large percentage of zero value
observations (Pfeiffer and Mulder, 2013). The two-part model decomposes an observed randomvariable into two observed variables. The methodology consists of estimating in the first stage
the observation with zero values and positive outcome and in the second stage we estimate thesubset with positive outcome. We explicitly assumed that the decision to adopt a nonhydrorenewable source and whether to diversify it or invest in different types of nonhydro resources
are independent of each other.
The two-part model (2PM) consist of two equations, the first equation estimates theentire sample and the second equation focuses on the subset of the sample with positive
outcomes. The positive outcomes refer to the computed diversity index. We specify the firststage equation as follows:Pr(𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖∗ = 1| 𝑍𝑍𝑖𝑖𝑖𝑖 ) = Pr[𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖 > 0 | 𝑍𝑍𝑖𝑖𝑖𝑖 ] = 𝛺𝛺 (𝛽𝛽𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 )(3)

𝑑𝑑𝑑𝑑𝑑𝑑𝑖𝑖𝑖𝑖 = 𝛽𝛽𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜔𝜔𝑖𝑖𝑖𝑖

(4)

where dre*it takes a value of 1 if the index of diversification is greater than zero and zerootherwise. Zit is the vector of control variables, β is the associated coefficient, Ω the standard
normal distribution and εit is the remaining error term. In the second equation, we use thediversity index defined in equation 1 as the dependent variable. Diversifying sources ofnonhydro renewable resources on the condition that a country invests on nonhydro renewableenergy is defined as:

where dreit captures how diversified is a particular country i at a given time t, Zit captures our
independent variables and ωit is the remaining error. We estimate equations 3 and 4 using
method of logit and ordinary least squares, respectively.
For robustness check, we use the method of negative binomial to model countobservation. The negative binomial probability distribution is presented as follows:
See for example Pfeiffer and Mulder (2013) and for further discussion on model selection with true zero andpotential zero observation, please refer to Dow and Norton (2003).

1

11

Pr(𝑌𝑌𝑖𝑖 = 𝑗𝑗) =1𝛼𝛼Γ�𝑗𝑗+ �1
Γ(𝑗𝑗+1)Γ� �𝛼𝛼

1(𝛼𝛼𝜆𝜆𝑖𝑖 )𝑗𝑗 [1 + 𝛼𝛼𝜆𝜆𝑖𝑖 ]−(𝑗𝑗+𝛼𝛼)(5)where 𝛼𝛼 > 0 is a nuisance parameter to be estimated along with ß, 𝛤𝛤 is the gamma function, i.e.
a discrete probability density function for j. The negative binomial can be derived from a
Poisson distribution in which the 𝜆𝜆 is distributed as a gamma random variable. The first two
moments of the negative binomial distribution are given byandE(Yi|Xi) = λi = exp(Xiß)(6)

Var(Yi|Xi) = λi(1 + αλi)(7)wherein the conditional variance exceeds the conditional mean of the distribution. The negative
binomial model is employed as a functional form to deal with overdispersion problem (Greene,2008).
In addition, we also explored the methods of Poisson pseudo-maximum likelihood(PPML) estimation technique as additional robustness check. PPML is suitable for modelling
large proportion of zero observations. Santos Silva and Tenreyro (2010) did a pioneering workon Poisson pseudo-maximum likelihood (PPML) estimation technique. Using the usual OLS
estimation will yield large bias given that the dependent variable has a large number of zero
observations while PPML gives consistent estimates in the presence of large zero observations.
This was based on the simulation conducted by Santos Silva and Tenreyro (2010). In addition,
PPML also effectively handles heteroskedasticity by using robust covariance matrix.

5.

Results and Discussions

5.1. Baseline results using two-part model estimation
The derived diversity index is used as the dependent variable in our baseline
specification. By using this index, we use the share of electricity generated from each source tothe total nonhydro electricity as weights for each nonhydro source. Table 3 presents the baseline
results from different specifications while controlling for time and regional fixed effects.
Results show the marginal effects from the two part model estimation 2.
While the effect of income on renewable energy has been well documented in theliterature, none of the studies explored the nonlinear effect of income (Vachon and Menz, 2006;
Marques et al., 2010; Pfeiffer and Mulder, 2013; Zhao et al., 2013; Aguirre and Ibikunle, 2014).
Results shows that while income plays a positive significant role in explaining thediversification, the nonlinearity of its effect is strongly evident. Higher income makes
developing countries more capable of diversifying nonhydro sources but the effect is nonmonotonous. Our results showed evidence of U-shaped kind of relationship between income
and diversification. This implies that as developing countries grow, diversification of renewableenergy sources tends to decline, but as their economies become more affluent diversification
increases. Results suggest that as countries develop demand for renewable energy is substituted
2

Details of the two-part model estimation with the logit and OLS results can be found in Appendix A.
12

by conventional energy sources to fuel a growing economy but as their economies mature it
will address environmental concerns through the use of cleaner sources of energy.
The nonlinear effect of income on diversification of renewable energy presents
additional evidence on the relevance of environmental Kuznets curve (EKC). The EKC
hypothesis that for a given society environmental problems will worsen until reaching its peak
and then further increase in income will translate to a reduction in environmental problems associety takes initiatives in cleaning the environment (Stern 2004; Seriño and Klasen 2015;
Irfany and Klasen, 2017). Table 3 shows that in model 3 and 4 the log of GDP per capita is
negative but the squared term is positive. This associated sign reflects the environmental
Kuznets curve suggesting that at initial stage of development countries choose to fuel economic
activities with unclean energy but as the countries accumulate more wealth then they take careof the environment by using clean sources of energy.
The other determinants such as population growth, energy imports and oil price arepositively correlated with diversification (Table 3). A growing population means rising energy
demand. Countries that are more dependent on foreign energy sources are more likely todiversify nonhydro renewable energy. Similarly, higher world price for crude oil pricefacilitates diversification of nonhydro renewable energy sources. Developing countries are
sensitive to price increases in fuel because this can influence higher inflation and hamper
economic growth. Renewable energy presents an attractive option in dealing with energy
security, hence, diversifying sources of renewable energy is a feasible action for dealing withfluctuations in oil prices. While Marques et al. (2010) and Aguirre and Ibikunle (2014) suggestthat energy prices are not relevant factors in explaining the diffusion of renewable energy, we
argued the opposite. Our results are consistent with Chang et al. (2009) and Seriño (2018) that
increases in fossil fuel prices are associated with increases in renewable energy use.
The associated relationship of policy variable, number of patents for technologicaladvancements and improved human capital through secondary enrollment are as expected(Table 3). Implementing policies that facilitates diffusion of renewable energy is positively
correlated with diversification. However, Zhao et al. (2013) warn about policy crowdedness,wherein the effectiveness of policies diminish as more renewable energy policies are put inplace. Technological improvements can facilitate diversification. This quite plausible because
modern nonhydro renewable energy are technology dependent. This implies that adopting avariety of renewable energy requires a certain grasp of the related technology. This result iscomplemented by the evidence showing that accumulation of human capital as measured bysecondary enrollment positively contributes to the diversification. Our results largely confirmed
previous findings on the influence of policy (Johnstone et al., 2010; Zhao et al., 2013),
technology (Popp et al., 2011), and human capital (Pfeiffer and Mulder, 2013) on renewableenergy. However, the effect of financial development to diversification is not solid. Given that
renewable energy requires a high level of financing, we would have expected robust results.
Painuly and Wohlgemuth (2006) noted that absence of well-developed financial intermediaries
and the consequent financing difficulties impede the development of renewable energy indeveloping countries. The paper of Pfeiffer and Mulder (2013) claimed that financial
development has no influence on renewable energy diffusion in developing countries but
Brunnschweiler (2010) and Freitas et al. (2012) argued the opposite. Further studies showed bydone to clarify this issue.
Developing countries that are already generating renewable energy from hydropower
do not show evidence of diversification. This suggest that developing countries are reluctant toinvest in nonhydro sources if they have hydropower. This reflects the claim that countries with13

relatively low carbon intensity in their energy system are likely to diminish incentives ininvesting in other renewables (Popp et al., 2011; Pfeiffer and Mulder, 2013). Similarly,
abundance of natural resources such as oil decreases the attractiveness in investing in othersources of energy. According to Popp et al. (2011) local oil production reduces concern ofenergy security, increases the relative price of other renewable energy and undermines support
for reducing emissions making investment in renewable energy unattractive. However, theeffect of coal production is inconclusive.
Table3. Average marginal effects from two-part model estimation (ATPM) with diversity
index as dependent variable
(1)
(2)
(3)
(4)
Variables
ATPM
ATPM
ATPM
ATPM
Log GDP per capita
Log GDP per capita_sq
Energy import
Population growth
Oil price
Policy on renewables
No. of patents
Financial development
Hydro energy

0.059**

0.103***-1.745***-1.856***(0.0290)(0.0327)(0.3751)(0.6467)

0.003***

0.000(0.0249)(0.0425)(0.0002)(0.0003)(0.0003)(0.0003)(0.0257)(0.0246)(0.0243)(0.0376)(0.0022)(0.0027)(0.0028)(0.0032)(0.0440)(0.0474)(0.0480)(0.0453)(0.0098)(0.0110)(0.0112)(0.0142)(0.0251)(0.0272)(0.0262)(0.0329)(0.02184)(0.0234)(0.0409)(0.0035)(0.0036)(0.0039)(0.0147)(0.0148)(0.0158)

0.114***
0.009***
0.185***
0.084***
0.004

Oil production

0.124***
0.012***
0.080*
0.105***
0.017-0.104***-0.021***

Coal production-0.021

Secondary enrolment

0.123***
0.000

0.116***
0.012***
0.049

0.114***
0.001-0.113***-0.025***-0.0060.175**
0.009***
0.072
0.132

-0.095***-0.295***-0.031***0.078***
0.007***-0.015***(0.0056)

ODA

Regional dummies
Time dummies
Observations

0.001***(0.002)

FDI

Coastal dummy

0.126***-0.0120.245***

0.150***

0.117**(0.0494)(0.0534)(0.0532)

yes
yes
1,216

Note: Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

yes
yes
1,091

yes
yes
1,091

(0.0072)

0.194**(0.0862)

yes
yes
732

14

For external sources of funding such as foreign direct investment (FDI) and the official
development assistance (ODA), the associated correlation with diversification is negative.
While these two external sources of funding are important for technology transfers, neither ofthem facilitates the diversification of nonhydro renewable energy sources. The same findings
were reported by Pfeiffer and Mulder (2013). This result adds to the huge literature on casting
doubt about the effectiveness of aid in developing countries. Seriño (2018) presented somepossible explanation citing that ODA variable is aggregated value and does not capture theenergy specific projects. Similarly, FDI does not promote diversification of nonhydrorenewable energy sources in developing countries because most FDI projects are related toconventional sources of fuel.
To control for regional fixed effects, the developing countries were divided into six
regions following World Bank’s regional aggregation, namely: (1) Latin America and the
Caribbean, (2) Europe and Central Asia, (3) Middle East and North Africa, (4) Sub-Sahara
Africa, (5) South Asia and (6) East Asia and Pacific. Aside from regional fixed effects, we also
control for geographic locations of countries taking a value of 1 if a country has a coastline.
This controls the geographic advantage of some countries in harnessing renewable energy andalso captures the ease of access to trade. Results show that countries with coastal areas are more
likely to diversify sources of renewable energy as compared to landlocked countries. We also
incorporate in our specifications, year dummies to control for variations in renewable energy
associated with time 3.
5.2. Does Kyoto Protocol facilitate diversification of renewable energy in developing
countries?
The adoption of Kyoto Protocol in late 1997 marks a significant shift in global climate
policy. We incorporate the potential impact of Kyoto Protocol on nonhydro renewable energy
diversification by including a time dummy from 1998 onwards 4. Although Kyoto Protocol did
not place a heavy burden among developing countries on reducing emission, results in Table 4
consistently show a strong positive and significant effect of Kyoto Protocol on thediversification of nonhydro sources of renewable energy. This suggests that greater
environmental awareness has led to a greater diffusion of renewable energy in developing
countries. The probability of diversifying sources of renewable energy has been 0.2 points
higher since the adoption of Kyoto Protocol in late 1997. These results align well with theprevious findings in the literature (Brunnschweiler, 2010; Johnstone et al., 2010; Pfeiffer and
Mulder, 2013; Aguirre and Ibikunle, 2014). Brunnschweiler (2010) found that most of theincrease in renewable energy did not just come from hydropower but also a 27-fold increase in
the electricity generated from nonhydro sources after the adoption of Kyoto Protocol.
Focusing on the coefficients of Kyoto Protocol in Table 4, it is observed that there aredifferences in the magnitudes of the estimates between two-part model and negative binomial.
The difference can be explained by the type of data used in the estimation. For the negative
binomial, the dependent variable is a count data capturing the number of nonhydro sources ofrenewal energy adopted by each country while for the two-part model, we used the computed
diversity index. Though the magnitudes differ, the associated relationship is consistent acrossseveral specifications. This shows that Kyoto Protocol has a significant and positive influence
Estimates of regional and time dummies are not shown to save space but are available upon request.
We drop our time dummies when we introduce Kyoto Protocol in the specification to avoid collinearity since italso captures time effect.

3
4

15

on the diversification of nonhydro sources of renewable energy in developing countries. As tothe other covariates, results are as expected and quite similar with the previous results.
Table 4. Effect of Kyoto Protocol on the diversification of nonhydro sources.
(5)
(6)
(7)
(8)
ATPM
ATPM
ATPM
NegBinVariable
Log GDP per capita
log GDP per capita_sq
Energy import
Population growth
Oil price
Policy on renewables
No. of patents
Financial development
Kyoto Protocol
Hydro share
Oil production
Coal production
Secondary enrolment

0.108***-1.669***-1.602**0.191***-1.507**(0.0333)(0.380)(0.6873)(0.0587)(0.6781)(0.0251)(0.0454)0.001***

0.002***(0.0003)(0.0003)(0.0003)(0.0004)(0.0007)(0.0240)(0.0236)(0.0374)(0.0365)(0.0628)(0.0009)(0.0009)(0.0009)(0.0010)(0.0012)(0.0466)(0.0474)(0.0453)(0.0706)(0.0735)(0.0110)(0.0111)(0.0148)(0.0192)(0.0267)(0.0254)(0.0243)(0.0318)(0.0476)(0.0555)(0.0367)(0.0368)(0.0421)(0.0703)(0.0838)(0.0209)(0.0222)(0.0415)(0.0682)(0.0973)(0.0035)(0.0036)(0.0039)(0.0065)(0.0148)(0.0151)(0.0154)(0.0149)(0.0338)(0.0439)0.000
0.096***
0.003***
0.117**
0.102***
0.030

0.260***-0.087***-0.021***-0.019(0.118***0.000

0.086***
0.003***
0.089*
0.109***
0.016

0.262***-0.096***-0.024***-0.003

FDI

Regional dummies
Constant
Observation

0.109**

0.148***
0.001

0.119***
0.133***-0.095***0.137***-0.280***-0.032***0.083***
0.008***

0.039

0.003***
0.292***
0.208***
0.108**
0.574***-0.377***-0.026***0.018

0.101*(0.0530)(0.0522)

yes

1,091

yes

1,091

(0.0423)

0.002***
0.173***-0.0020.313***
0.194***-0.092*0.326***-0.983***-0.075***0.150***
0.019***(0.0027)(0.0057)(0.0133)-0.031**-0.0100.132**

0.109***(0.0016)-0.013**ODA
Coastal dummy(9)
NegBin(0.0066)-0.084***(0.0291)0.217**

0.477***(0.0908)(0.1470)(0.1777)(0.3925)(2.7602)

yes

732

yes
-4.359***1,086

0.419**
yes
2.564
727

Note: The dependent variable when using negative binomial regression is the number of nonhydro sources ofrenewable energy while the dependent variable when using two-part model is the derived diversity index ofnonhydro renewable energy or simply the weighted number of nonhydro sources.
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
16Freitas et al. (2012), on the other hand, is a bit skeptical on the global influence of Kyoto
Protocol in influencing diffusion of cleaner variants of renewable energy in developing
countries. Their results suggest that mechanisms under Kyoto Protocol are not creating
incentives for the use of sustainable variants of renewable energy but are merely encouraging
increased capacity of existing technologies (Freitas et al., 2012). However, our results add tothe strand of literature supporting evidence that global climate policy such as Kyoto Protocol
can encourage developing countries to seek out cleaner sources of energy.

5.3. Extended analysis and robustness check
Instead of conducting two-stage estimation using the diversity index as dependent
variable, we check the robustness of our results by pooling all the observation and run an OLS
regression using similar specifications. Since many developing countries have yet to invest in
renewable energy, our dependent variable, which is the diversity index, has a large proportionof zero observations. To deal with this problem, we follow the suggestion of Zhao et al. (2013)
to use the method of Poisson pseudo-maximum likelihood (PPML) estimation technique. Using
the usual OLS estimation will yield a large bias given that our dependent variable has morethan 60% zero observations (Figure 1). In addition, PPML also effectively handles
heteroskedasticity by using a robust covariance matrix 5. The PPML approach gives consistentestimates regardless how the data is distributed (Santos Silva and Tenreyro, 2011; Zhao et al.,2013). We wanted to control for country fixed effects but as PPML fails to converge with theinclusion of too many country dummies we instead control for regional fixed effects. We also
control for time dummies in regressions 10 to 11 and then in regressions 12 to 13 we include
Kyoto Protocol.
Table 5 presents the estimation results using Poisson pseudo-maximum likelihood
estimation. Results of PPML generally complement our previous findings. Higher income isassociated with diversification and the nonlinear effect of income still holds. Higher
dependence on external sources of energy as proxied by energy imports contributes positively
to diversification of nonhydro sources of renewable energy. Higher crude oil prices andgrowing population is associated with increasing diversification. Diversification of nonhydrosources of renewable energy is further supported by the adoption of policies related torenewable energy, technological innovation as proxied by number of patents and improvementin human capital. In contrast, oil production and abundance of hydropower is negatively
associated with diversification. As with our previous results, FDI and ODA do not support
diversification. The effect of Kyoto Protocol is still positive and significant.
One surprising results for Table 5 is the influence of financial development on
diversification. There is somehow no consensus in the literature as to the effect of financial
development on renewable energy. While Painuly and Wohlgemuth (2006) cautioned thatabsence of well-developed financial intermediaries and the consequent financing difficulties
may impede the development of renewable energy in developing countries, Pfeiffer and Mulder(2013) claimed that financial development has no influence on renewable energy diffusion indeveloping countries. Our result is also a bit puzzling because the baseline specification
presented no conclusive evidence on the influence of financial development but our extended
analysis showed significant positive correlation of financial development and diversification of
5

Details of the PPML estimation could be found in Santos Silva and Tenreyro (2011).
17

renewable energy. Perhaps further empirical studies can be done evaluating the influence offinancial development on renewable energy.
Table 5. Estimation results using the Poisson pseudo-maximum likelihood estimation.
(10)
(11)
(12)
(13)
VARIABLES
PPML
PPML
PPML
PPML
Log GDP per capita
log GDP per capita_sq
Energy import
Population growth
Oil price
Policy on renewables
No. of patents
Financial development
Kyoto Protocol
Hydro energy
Oil production
Coal production
Secondary enrolment

0.102**(0.0451)0.000(0.0003)
0.076*(0.0413)0.015***(0.0033)
0.097(0.0652)
0.066***(0.0094)
0.291***(0.0430)-0.330***(0.0684)-0.004(0.0038)-0.017(0.0235)

FDI
ODA
Coastal dummy
Constant
Regional dummies
Time dummies
Observation
R-squared

Note: Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.305***(0.0911)-4.749***(0.4441)
yes
yes
1,908
0.393

-2.077***(0.4686)
0.133***(0.0305)
0.001*(0.0007)0.175***(0.0439)0.013***(0.0036)
0.113(0.0741)0.041***(0.0128)0.254***(0.0551)-0.724***(0.0635)-0.022**(0.0106)
0.091**(0.0427)0.011***(0.0024)-0.047**(0.0207)-0.020*(0.0105)
0.897***(0.1560)
3.397*(1.8648)
yes
yes
1,242
0.521

0.106**(0.0455)
0.000(0.0003)
0.050(0.0434)
0.003***(0.0010)0.122*(0.0651)
0.063***(0.0093)0.287***(0.0424)0.438***(0.0617)-0.315***(0.0685)-0.004(0.0038)-0.015(0.0231)0.299***(0.0923)-3.914***(0.3505)
yes
no
1,908
0.386

-2.043***(0.4833)
0.131***(0.0313)0.001*(0.0007)0.153***(0.0504)
0.000(0.0011)0.129*(0.0749)
0.046***(0.0125)0.232***(0.0531)0.444***(0.0780)-0.724***(0.0647)-0.022**(0.0103)0.084**(0.0406)
0.013***(0.0024)-0.040**(0.0177)-0.016(0.0110)0.898***(0.1532)
4.027**(1.8910)
yes
no
1,242
0.501

18

6.

Summary and Conclusion

In this paper, we investigate what motivates developing countries to diversify modern
sources of renewable energy despite its huge upfront investment cost. In contrast to most studies
in the literature that uses the amount of energy generated from renewable energy, we develop
a new method capturing diversification by using the quantity of energy generated from eachsource as weights. To the best of our knowledge this is the first paper that uses this kind of
measure for diversification. The analysis is focused only on the modern sources of renewableenergy such as solar, wind, geothermal, biomass and waste excluding hydropower. Although
there is a substantial number of developing countries not investing in nonhydro sources ofrenewable energy, we observed that there has been a rapid diversification of since the mid1990s.
Based on a substantially larger data set, we assess a wider range of potential
determinants of renewable energy diversification across 117 developing countries spanningfrom 1980 to 2011. Though we observed a rising trend in diversification, the dependent variable
has still a lot of zero observation reflecting those countries that did not invest in any nonhydrosources. To address this methodological issue (large observation with zero values), we explore
several empirical techniques such as two-part model, negative binomial and Poisson-pseudo
maximum likelihood estimation. The two-part model is used to investigate whether to adopt or
not renewable energy and conditional on adopting, do they diversify or rely only on source of
renewable energy. The negative binomial model is used for count observation and Poisson
pseudo-maximum likelihood estimation is used to model the aggregate electricity generated
from nonhydro renewable sources giving attention to large number of zero observations.
We find that income has a non-monotonous effect on diversification. While mostavailable studies in the literature do not include the nonlinear effect of income, our results showrobust evidence that income has a significant nonlinear effect portraying a U-shaped kind of
relationship with nonhydro renewable energy. This implies that as society develops demand forrenewable energy is declining because it is replaced by conventional energy to fuel a growing
economy but as the economy accumulates more wealth, it will address environmental concerns
through the use of various sources of clean and renewable energy. Results shows the relevanceof environmental Kuznets curve suggesting that greater environmental awareness comes withincreasing affluence.
Holding other factors constant, countries that are more dependent on foreign energy
sources are more likely to diversify nonhydro renewable energy. Energy security is a major
policy agenda in developing countries. Diversification is one of the feasible approaches tolocalize energy supply and improve energy security. This result is complemented by the effectof higher world price for crude oil. Developing countries are sensitive to price increases in fuelbecause it can trigger higher inflation and hamper economic growth. Renewable energy
presents an attractive option in dealing with energy security and diversifying its sources canhelp minimize the risks related to fluctuations in world market prices for oil.
Results show that the coefficient of policy variable, patents and secondary enrolment
are as expected. This means that implementation of renewable energy policies, technologicalinnovation and accumulation of human capital positively contributes to the diversification ofnonhydro sources of renewable energy in developing countries. Several policies provide
incentives for promoting the development of renewable energy, however, Zhao et al. (2013)
cautioned about the potential crowding out effect of these several policies. The significant
19positive coefficient of patents suggests that diversification of nonhydro renewable energy isfacilitated by the advances in technology. Similarly, improvement in human capital as proxiedby enrollment in secondary education do facilitates diversification of renewable energy.
However, financial development posted a mix result. There is also no consensus in the literatureas the effect of financial development on renewable energy. While Brunnschweiler (2010)
reported a robust effect, Pfeiffer and Mulder (2013) showed no evidence for any influence
resulting from financial sector development.
On the other hand, abundance of hydro power impedes diversification. Maybe
developing countries are less enthusiastic in utilizing other sources of renewable energy if hydropower is already part of their energy mix. Also, abundance of oil reduces incentives to diversify
sources of nonhydro renewable energy because they don’t have to worry about issues
concerning energy security. In addition, abundance of oil will make renewable energy more
expensive making it less attractive. There is also no conclusive evidence that shows FDI andODA promotes diversification of nonhydro renewable energy. Lastly, the effect of Kyoto
Protocol on the diversification is positive and significant. This suggests that global
environmental awareness has led to a greater diffusion of renewable energy in developing
countries. This can also be associated by the growing number of clean development mechanism(CDM) projects in developing countries because of Kyoto Protocol.
Our empirical investigations identified drivers and barriers to adoption of a morediversified sources of renewable energy. Results presents various dimensions where policy
makers can promote renewable energy. Although there is rising trend of renewable energy
adoption in developing countries particularly led by China, still it has a long way to go inintegrating renewables in the energy system. According to REN21(2018), the share of modern
renewable energy (excluding traditional biomass) to total final energy consumption is onlyaround 10.4% in 2016 a little higher from its 2015 levels. This shows that there is progress in
integrating renewable energy to the countries’ energy system, albeit slow. Given the strong link
between economic development and energy consumption, future growth of developing
countries will heavily rely on massive energy use (Jakob et al., 2014). In the last decade, weare seeing continued economic growth from developing countries and this could be theiropportunity to transform its economy and integrate a more diversified sources of cleaner energy.
Our empirical analysis shows that one of the primary drivers of behind rising diversification isgreater environmental concern. This is manifested by the positive and significant correlation of
Kyoto Protocol on renewable energy diversification. By using cleaner sources of energy,
developing countries can transition to a low-carbon economy while ensuring energy security
and sustainability.

7.

References

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23

Appendix A. Estimation using two-part model with diversity index as dependent variable.
Variables
Logit
OLS
Logit
OLS
Logit
OLS
Logit
OLS
Log GDP pc

0.022

0.101***

0.167

0.144*** -5.042**-1.89***-8.600-2.03***(0.1538)(0.0285)(0.1883)(0.0356)(2.1088)(0.4417)(6.9972)(0.3954)0.358**

0.132***

0.557

0.141***(0.1444)(0.0280)(0.4612)(0.0255)

Log GDP pc_sq
Energy import
Pop growth
Oil price
Policy on ren
Patents
Finance dev

0.013*** 0.001***

0.004**-0.0000.004*-0.0000.013***

0.000**(0.0013)(0.0002)(0.0020)(0.0002)(0.0020)(0.0002)(0.0035)(0.0002)0.138

0.166***

0.207

0.173***

0.187

0.162***

0.879**

0.181***(0.1247)(0.0297)(0.1301)(0.0297)(0.1326)(0.0282)(0.3444)(0.0439)0.045***

0.003**

0.070***

0.004**

0.072***

0.004**

0.067*

0.005***(0.0131)(0.0016)(0.0180)(0.0018)(0.0191)(0.0018)(0.0354)(0.0019)0.772*** 0.113***0.176

0.100***

0.094

0.064*

0.569

0.043

(0.2515)(0.0356)(0.3031)(0.0388)(0.3097)(0.0385)(0.4380)(0.0469)

0.511***

0.005

0.664***

0.023**

0.699*** 0.030*** 1.017*** 0.082***(0.0591)(0.0092)(0.0800)(0.0095)(0.0855)(0.0090)(0.1717)(0.0132)-0.0390.018-0.001

0.030-0.0670.018-0.726**-0.060**(0.1409)(0.0211)(0.1726)(0.0232)(0.1692)(0.0208)(0.3418)(0.0296)-0.624***-0.032*-0.65***-0.041**-2.66***-0.120**(0.1518)(0.0162)(0.1626)(0.0178)(0.3648)(0.0563)-0.104*** -0.01***-0.12***-0.02***-0.20***-0.03***(0.0235)(0.0249)(0.0026)(0.0456)(0.0032)

Hydro energy
Oil prod'n
Coal prod'n(0.0027)-0.071-0.021-0.0460.000

0.335**

0.090***(0.0927)(0.0127)(0.0965)(0.0115)(0.1504)(0.0168)

0.087***-0.000(0.0197)(0.0015)

Sec enrolment
FDI
ODA
Coastal dummy-0.060-0.018**(0.0492)(0.0068)-0.123*-0.002(0.0669)(0.0079)

1.521***

0.009

1.013***

0.017

0.841**-0.0002.966***-0.109(0.2800)(0.0466)(0.3211)(0.0529)(0.3301)(0.0475)(0.7434)(0.1130)

Regional dummies

yes

yes

yes

yes

yes

yes

yes

yes

Time dummies

yes

yes

yes

yes

yes

yes

yes

yes

-7.93***-0.412*-10.55*** -0.90***

8.546

7.00***

17.8137.488***(1.4138)(0.2378)(1.5355)(0.2688)(7.6957)(1.7118)(26.6973)(1.5401)

Pseudo R2/ R2

0.38820.29970.43410.34400.43890.39350.61650.5282

Observations

1,216

672

1,091

602

1,091

602

732

404

Constant

Note: Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

24