An impact analysis of macroeconomic factors on South Asia’s renewable energy output

Abstract

Purpose
South Asia is one of the fastest-growing regions in the world. With its fast economic development, the energy requirement for the region has rapidly grown. As the region relies mainly on nonrenewable energy sources and is suffering from issues like pollution, the high cost of energy imports, depleting foreign reserves, etc. it is searching for those factors that can help enhance the renewable energy generation for the region. Thus, taking these issues into consideration, this paper aims to investigate the impact of macroeconomic factors that can contribute to the enhancement of renewable energy output in South Asia.

Design/methodology/approach
An autoregressive distributed lag methodology has been applied to examine the long-term effects of remittance inflows, literacy rate, energy imports, government expenditures and urban population growth on the renewable energy output of South Asia by using time series data from 1990 to 2021.

Findings
The findings indicated that remittance inflows have a negative and insignificant long-term effect on renewable electricity output. While it was discovered that energy imports, government spending and urban population growth have negative but significant effects on renewable electricity output, literacy rates have positive and significant effects.

Originality/value
Considering the importance of renewable energy, this is one of the few studies that has included critical macroeconomic variables that can affect renewable energy output for the region. The findings contribute to the body of knowledge that a high literacy level is crucial for promoting renewable energy output, while governments and policymakers should prioritize reducing energy imports and ensuring that government expenditures on renewable energy output are properly used. SAARC, the governing body of the region, also benefits from this study while devising the renewable energy output policies for the region.

Full text

An impact analysis of
macroeconomic factors on South
Asia’s renewable energy output
Imran Khan and Darshita Fulara Gunwant
Department of Humanities and Social Science,
Birla Institute of Technology and Science, Dubai, United Arab Emirates
Abstract
Purpose –South Asia is one of the fastest-growing regions in the world. With its fast economic
development, the energy requirement for the region has rapidly grown. As the region relies mainly on
nonrenewable energy sources and is suffering from issues like pollution, the high cost of energy imports,
depleting foreign reserves, etc. it is searching for those factors that can help enhance the renewable energy
generation for the region. Thus, taking these issues into consideration, this paper aims to investigate the
impact of macroeconomic factors that cancontribute to the enhancement of renewable energy output in South
Asia.
Design/methodology/approach –An autoregressive distributed lag methodology has been applied to
examine the long-term effects of remittance inflows, literacy rate, energy imports, government expenditures
and urban population growth on the renewable energy output of South Asia by using time series data from
1990 to 2021.
Findings –The findings indicated that remittance inflows have a negative and insignificant long-term
effect on renewable electricity output. While it was discovered that energy imports, government spending and
urban population growth have negative but significant effects on renewable electricity output, literacy rates
have positive and significant effects.
Originality/value –Considering the importance of renewable energy, this is one of the few studies that
has included critical macroeconomic variables that can affect renewable energy output for the region. The
findings contribute to the body of knowledge that a high literacy level is crucial for promoting renewable
energy output, while governments and policymakers should prioritize reducing energy imports and ensuring
that government expenditures on renewable energy output are properly used. SAARC, the governing body of
the region, also benefits from this study while devising the renewable energy output policies for theregion.
Keywords Remittances, Renewable energy output, Urban population growth,
Government expenditure, Literacy rate, Energy import, South Asia
Paper type Research paper
1. Introduction
Energy is a crucial concern related to many aspects of human life, whether it is a matter of
climate change, sustainable growth, employment, industrialization, export-import, etc. All
Funding: This is to confirm that the author has not received any financial assistance or funding in
relation to this research paper.
Declaration of interest statement: The authors whose names are listed immediately below certify
that they have NO affiliations with or involvement in any organization or entity with any financial
interest (such as honoraria; educational grants; participation in speakers’bureaus; membership,
employment, consultancies, stock ownership or other equity interest; and expert testimony or patent-
licensing arrangements) or non-financial interest (such as personal or professional relationships,
affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.
South Asia’s
renewable
energy output
Received 21 January2023
Revised 20 March 2023
18 April 2023
Accepted 24 April2023
International Journal of Energy
Sector Management
© Emerald Publishing Limited
1750-6220
DOI 10.1108/IJESM-01-2023-0013
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1750-6220.htm

these activities are, in one way or another, linked with energy. Furthermore, energy
consumption and output are inextricably linked to economic progress and human
development (Gozgor et al.,2018;Mazur, 2011;Tao et al.,2020). According to the
International Energy Agency’s, 2021 report, global energy consumption is expected to rise
by 4.6% in 2021, more than compensating for a 4% decline in 2020 and driving demand
0.5% higher than in 2019. About 70% of the predicted increase in global energy
consumption is expected to come from emerging and developing markets, where demand is
expected to be 3.4% higher than in 2019. (Energy Agency, 2021). On the other hand, energy
demand is expected to rise by more than 1,000 TWh, or 4.5%, in 2021. It is about five times
larger than the drop in 2020, and more importantly, about 80% of the expected expansion in
2021 is in emerging and developing markets. By 2021, renewable energy would account for
about 30% of total energy production. This is the greatest proportion since the beginning of
the Industrial Revolution, and it represents an increase from less than 27% in 2019 (Energy
Agency, 2021). The International Energy Agency also predicts that the world will require
US$44tn in new investments in world energy supply, including US$23tn in energy
efficiency, to meet energy demand by 2040 (Katherine, 2016).
Furthermore, as per the Energy Progress Report, which monitors and evaluates progress
toward the global goal of universal access to affordable, reliable, sustainable and modern
energy highlights that the proportion of the worldwide population with access to electricity
has steadily increased from 83% in 2010 to 90% in 2019. Remarkable electrification
initiatives delivered connectivity to 1.1 billion people globally between 2010 and 2019,
reducing the number of individuals without electricity from 1.2 billion in 2010 to 759 million
in 2019 (IEA, 2020b). It was a great achievement to enhance electrification at a global level,
as several studies have found that insufficient access to electricity causes an economy to
slow down, implying that electricity availability and consumption are essential factors in
determining economic growth and industrialization (Akçay and Demirtas,2015). Plenty of
studies have also indicated that energy consumption leads to economic development
(Balitskiy et al., 2014).
These waves of global electrification initiatives also reached the regional level. One such
region that has achieved success in electrification is South Asia. As per Figure 1, over the
past 28 years, the South Asian region has managed to provide electricity to 93.61% of the
population, while at the same time, electricity has reached 99.72% and 95.74% of the urban
and rural populations, respectively. This significant increase in electricity reach has caused
an increase in the energy demand that the region has fulfilled by using both renewable and
non-renewable energy sources. As per Figure 2, in the year 1986, the electricity output by
non-renewable sources was 67.03%, which had climbed to a level of 79.97% by the end of
2015, while during the same period, the electricity output from renewable sources had
reached 4.76% by the end of 2015 from a start of 0.0008% in the year 1986.
Even though the South Asian region has succeeded in increasing the amount of energy
that comes from renewable sources, this change is minimal when taking into account the
achievement of sustainable renewable energy output (World Bank, 2021). Moreover,
traditional energy usage leads to global warming either directly or indirectly (Niamir et al.,
2020). Hence, the available alternative is either to reduce the intake of fossil fuels or increase
the use of renewable energy to minimize greenhouse gas emissions (Mazur, 2011). However,
reducing energy consumption is unrealistic, particularly in a developing region like South
Asia, where energy consumption has already increased due to recent economic expansion,
urbanization, industrial development and population growth (Vidyarthi, 2015). Thus, the
only alternative left is to enhance the output of renewable energy sources. Noting that
renewable energy is essential for a clean environment, its output is hampered due to a
IJESM

shortage of domestic financial sources (Gozgor et al.,2018). As a result, developing
economies are looking for alternative sources of funding that can help them create an
infrastructure for renewable energy (Wang et al., 2021). One such source of foreign funds
that has gained worldwide attention is remittance inflows, which emerged as a rescue to fill
these fund requirements for the developing countries and are regarded as the primary
source of finance for clean energy in developing economies (Tao et al., 2020). According to
Figure 2.
Electricity generation
from renewable vs
non-renewable
sources
67.0338
79.9772
0.0008
4.7679
0.00
1.00
2.00
3.00
4.00
5.00
6.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Renewable vs Non-renewable energy production (In %)
Electricity producon from oil, gas and coal sources (% of total)
Electricity producon from renewable sources, excluding hydroelectric (% of total)
Source: World Bank
Figure 1.
Electricity access in
South Asia
39.58
93.61
81.55
99.72
46.74
95.74
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electrification in South Asia (In %)
Access to electricity, rural (% of rural populaon)
Access to electricity, urban (% of urban populaon)
Access to electricity (% of populaon)
Source: World Bank
South Asia’s
renewable
energy output

World Bank statistics, remittances from expatriate workers have grown from US$10bn in
1975 to US$646bn by the end of 2020. In contrast, during the same time, remittances to
South Asia have climbed from US$438m to US$147bn (World Bank, 2021). By the end of
2020, remittances to South Asia accounted for 22.7% of total global remittances, while in
1975, the region’s contribution to total world remittances was just 4.30% (World Bank,
2021). Taking these statistical figures into account, the question arises: can remittance
inflows be one of the main sources of foreign funds that can positively affect the production
of renewable energy?
Aligning with the importance of foreign fund inflows, it is also the support of the
government that plays a very important role in renewable energy output (Gielen et al.,2019).
Government expenditure is one such factor that describes how much the government of an
economy spends to fulfill the requirements of the nation. As a result, a government’s share
of total expenditure on renewable energy output describes the extent to which it is serious
about building the nation from a long-term perspective (Kim, 2021). Since the COVID-19
pandemic ended, governments all over the world have realized how important renewable
energy is and have increased their spending on it by about 50%, spending US$710 more
than they did before the pandemic. It is also expected that by the end of 2023, developed
economies will have spent about US$370bn. However, in developing economies, government
spending on renewable energy is still a concern as they spend only one-tenth compared to
developed economies, which amounts to US$52bn globally (IEA, 2022). As a result, the
question arises: are government expenditures influencing renewable energy output in South
Asia?
In addition to government expenditures, it is also the energy availability in an economy
that defines how an economy fulfills its energy needs (Nag and Sarkar, 2018). Developing
countries, in particular, that lack local energy sources, rely heavily on energy imports to
meet their energy needs (Asif and Muneer, 2007). This imported energy mainly comes in the
form of non-renewable energy, the reason being that for a developing economy, constructing
the facilities to generate renewable energy costs more than importing non-renewable energy
from other energy-rich nations. Hence, they prefer the import of non-renewable energy over
generating renewable energy. Taking these ablaze arguments into account, the question
arises: does the import of energy cause any negative effect on its renewable energy output?
Furthermore, it is generally assumed that as the literacy level of adults improves, it
brings many positive changes to society (Lusardi, 2019). These include more awareness
about the sustainable environment and clean energy to preserve the national resources of
the economy for their future generations. By the end of the year 2020, the adult literacy rate
in South Asia was at a level of 73% of the total population (World Bank, 2022). With such a
moderate level of adult literacy, it raises the question of whether these improvements in
adult literacy positively affect the renewable energy output in the region?
Finally, urbanization is an important factor that is thought to be directly related to
energy consumption. People who live in cities consume more energy than those who live in
rural areas of the economy (Sun et al.,2021). While, at the same time, it is assumed that those
sections of the population who live in cities are more aware in comparison to those who live
in the rural parts of the country in terms of understanding the need for renewable energy
(Sharma et al.,2021). With an annual urban population growth rate of 2.4% in South Asia
(World Bank, 2018), the question is whether this urban population growth has a positive
impact on the region’s renewable energy output.
Hence, based on the preceding discussions, this study will provide a novel contribution to
the existing plethora of literature in at least four different aspects.
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First, as per our understanding and investigation of the literature, it has been found that
there is no such study available in the context of the South Asian region. Hence, it will be the
first inference. Second, the majority of the studies related to renewable energy generation
have been conducted for the developed nations, G7 countries, etc. Consequently, there is a
dearth of available studies for developing nations, which is being addressed by the analysis
of our study. Third, the majority of the past studies have examined the influence of
monetary policies, fiscal policies, environmental policies, financing and energy aid on
renewable energy generation. While the impact of macroeconomic factors on renewable
energy generation has been largely overlooked. Our study has included five macroeconomic
variables to assess this relationship. Fourth, the studies for developing countries have not
focused on the factors that are instrumental in enhancing the generation of renewable
energy, but rather on the factors that promote economic growth. In contrast to this, our
study has focused on those factors that enhance the generation of renewable energy, which
were entirely absent in past studies.
Taking into account the assumption that remittance inflows, energy imports,
government spending, literacy rate and urbanization are the major macroeconomic factors
that can influence South Asian renewable energy output, the authors finds four main
reasons that motivated them to conduct this study, which will also contribute to the existing
literature. First, as per statistical figures, as of now the South Asian region has achieved
little success in enhancing its renewable energy output, and this concern is also highlighted
by the World Bank. Hence, the findings of the study will help the regional energy
policymakers take into consideration those factors that are affecting renewable energy
output in the region. Second, it is the first inference for South Asia that will add insights on
renewable energy output by using five different macroeconomic variables. Hence, it adds
value to the existing literature. Third, because the macroeconomic variables used in this
study is a combination of economic, financial and social indicators, it will provide a broad
picture to the energy policymakers so they can specifically target the concerned area.
Fourth, SAARC, the governing body of the South Asian region, can also use the findings of
the study while implementing regional-level renewable energy policies. This research study
will try to answer five research questions. They are as follows:
RQ1. Remittance inflows positively affect the renewable energy output.
RQ2. The literacy rate positively affects the renewable energy output.
RQ3. Energy imports negatively affect the renewable energy output.
RQ4. Government expenditures positively affect the renewable energy output.
RQ5. Urban population growth positively affects the renewable energy output.
There are five sections in this research study. Section 2 contains a thorough overview of the
literature. Section 3 discusses data and technique, whereas Section 4 discusses empirical
findings. Section 5 expands on the summary and conclusion. Finally, Section 6 explains the
limitations and the future scope of the study.
2. Literature review
Whereas many academics have looked into the impact of remittances on economic growth
and other social aspects, little has looked into the link between remittances and energy
output. As remittances increase a person’s wealth, they may directly increase energy
consumption if the money is spent to make living conditions better. Furthermore,
remittances may indirectly boost energy consumption through high economic growth and
South Asia’s
renewable
energy output

human capital formation (Adams and Cuecuecha, 2013). Murshed et al. (2021), in their study
of four South Asian nations, have suggested that the inflow of foreign funds, mainly
remittances, can promote the usage of renewable energy consumption, which further can be
the reason for generating renewable energy. Gong (2011) has found that in China, every 1%
rise in remittances results in a modest (0.09%) drop in firewood usage in the communities of
origin, which is an indicator that remittances help curtail the usage of non-renewable energy
sources. Baniya and Giurco (2021), in their study of five least-developed countries in Asia,
found that remittance inflows are critical for the success of the economies’renewable energy
output programs, suggesting that remittance inflows should be taken seriously. In the case
of Nigeria, by taking a data set over a period of 1990–2020, Ojapinwa and Lawani (2022)
have found out that remittance inflows do contribute to renewable energy output, the reason
being that the related policies have not been implemented that can channel remittance into
renewable energy output. Ma and Wang (2023), in their study of the global economy, have
taken a data set for the period 1990–2020. The findings of their study suggested that
remittance inflows are critical for the sustainable development of the world economy,
especially in the context of the renewal energy sector and for the generation of renewal
energy. With all these findings, which mostly support that remittance inflows have a
positive effect on renewable energy output. Hence, the question arises: whether remittance
inflows have a positive impact on renewable energy output in South Asia or not?
Besides remittance inflows across numerous mechanisms, urbanization has a substantial
impact on energy usage. Industrialization brings with it the formation of new industrial
facilities that provide job opportunities, causing people to migrate from rural areas to urban
areas, resulting in an increase in the urban population, which in turn leads to increased
energy demand and, as a result, higher CO
2
emissions (Sinha et al.,2019). Furthermore,
urbanization has an impact on the environment and the share of using renewable energy
sources in a variety of ways, encompassing transportation, vehicle traffic, global trade,
medical services, landscape architecture and economic output (Wang et al., 2019). As of now,
minimal empirical research has looked at the impact of urbanization on non-renewable
energy output. In one such study of 29 developed nations, Shahzad et al. (2021) have taken a
data set for a period of 1990–2018 and confirmed that urbanization positively affects the
renewable energy output for the developed economies, highlighting the fact that the
developed economies have better existing energy infrastructure that helps them transform
from non-renewable energy sources to renewable energy output options. Wang et al. (2021),
in their study of the top renewable energy countries, took a data set over a period of 1980–
2014. The findings of the study, which included a Granger causality test, confirmed that
urbanization is causing the generation of renewable energy, which means because of
urbanization, the energy requirement increases and to fulfill these needs, the economies shift
toward renewable energy generation options. With all of these findings that indicate
urbanization enhances renewable energy output, it will be fascinating to find out: does
urbanization positively affect renewable energy production in South Asia?
Education has become a necessity for sustainable growth in the twenty-first century. It
helps individuals develop their learning skills and values through formal and informal
means, allowing for the development of an environmentally sustainable society (Tamsila,
2021). Education and literacy are crucial for understanding issues that may influence the
country and the environment to achieve long-term growth (Walter, 2010). The researchers
have indicated that environmental education, which begins at the elementary level, can have
a long-term influence on the environment. As a result, the higher the educational level of the
population and the higher the literacy rate, the greater the impact on the country’s
environment by expanding possibilities for people to work on technological elements that
IJESM

can minimize pollution and protect the environment from deterioration (Hasan Kaya and
Elster, 2018). Furthermore, modern education related to renewable energy has become a
need of the hour, although it is suffering from the effects of non-availability of trained
teachers, an unstructured renewable energy curriculum and financial issues, among others
(Kandpal and Broman, 2014). Alawin et al. (2016), in their survey-based study in Jordan,
found that there is a lack of awareness about renewable energy output among students, and
the subjects are not structured in such a way as to clearly describe the importance of
renewable energy for the nation. These findings suggest that traditional energy education
should be updated with modern sustainability and renewable energy synergy programs
that can reach a larger global audience and help create awareness about reducing carbon
emissions and promoting renewable energy generation (Jennings, 2009). Because past
literature provides evidence that education levels, specifically those related to renewable
energy, can help promote renewable energy output. Hence, does it imply that education has
the same impact in South Asia on renewable energy output?
Energy demand economic literature dates back to the 1950s. Plenty of research studies
have been conducted involving diverse energy sources, timeframes, evaluation
methodologies and nations (Adewuyi, 2016). However, the need for energy importsis far less
well understood. Some of the researchers have linked energy imports with crude oil imports
from most developing countries, such as Turkey (Ozturk and Arisoy, 2016), China (Roberts
and Rush, 2012), Barbados (Moore, 2011), India (Ghosh, 2009), Indonesia (Atty Mardiana
et al., 2013), or South Africa (Ziramba, 2010), Korea (Kim and Baek, 2013) and the USA
(Camacho-Guti
errez, 2010). While other research studies concluded that oil prices and
income are perhaps the significant drivers of energy demand that result in energy imports,
such as in developing nations, there is literature available related to Ghana (Adom, 2013),
India (Filippini and Pachauri, 2004), Indonesia (Sa’ad, 2009), Iran (Pourazarm and Cooray,
2013), Nigeria (Iwayemi et al., 2010), Pakistan (Jamil and Ahmad, 2011), Sri Lanka
(Amarawickrama and Hunt, 2008) and Turkey (Halicioglu, 2007). However, for the
developed nations, most of the studies focused on the USA (Luchansky and Monks, 2009;
Wadud et al.,2010). As per the conclusion of these research studies, energy demand is
frequently inelastic to changes in both factors, but results vary substantially between
countries and periods. With all these findings, we may conclude that energy imports further
lead to energy demand, which further leads to energy demand. But the question arises: Does
this energy import affect renewable energy output in South Asia?
Finally, the development of renewable energy technology is critical to solving climate
change and sustainable energy issues. As a result, many policymakers, researchers and
scientists have urged for significant increases in government R&D investments in the
renewable energy area to meet current global climate protection commitments (Reichardt
and Rogge, 2014). Many governments have taken appropriate action. For example, at the
2015 Paris climate summit, 20 nations agreed to “The Mission Innovation,”pledging to
double government renewable energy R&D investment to more than US$30bn by 2021
(Sanchez and Sivaram, 2017). Nevertheless, even though government funding for renewable
energy R&D has risen significantly over the last two decades, particularly in Europe and
among OECD nations (IEA, 2020a), it is still meager when we look at the government
spending on renewable energy in emerging economies like South Asia. As a result, it is
critical to determine whether the government’s efforts to promote renewable energy output
in South Asia are adequate.
A summary of the available literature related to renewable energy output is as follows:
To summarize, there is enough evidence to evaluate the influence of remittance inflows
on renewable energy output. The literature piques the interest of economists, researchers,
South Asia’s
renewable
energy output

academicians and environmentalists who are always looking for innovative ways to create a
sustainable environment as well as efficient and cost-effective energy solutions. As a result,
it will be fascinating to examine how remittance inflows, the literacy rate, energy imports,
government expenditures and urban population growth, affect South Asia’s renewable
energy output. According to the literature, no significant studies have been conducted to
investigate this relationship, motivating theauthors to perform this investigation.
3. Data and methodology
The study investigates the impact of macroeconomic variables on renewable electricity
energy output using yearly time series data from 1990 to 2021. Data on renewable energy
(REN), remittance inflows (RE), literacy rate (LR), energy imports (EI), government
expenditure (GX) and urban population growth (UP) were extracted from the World Bank
Development Indicators Database (World Bank, 2021). The Eviews10 software is used to
examine the data. The description of the selected variable is mentioned in Table 1.
While the general model formulation is given in Eq. (1). The following function has been
incorporated:
Table 1.
Summary of related
literature
Author Title Period of the study Finding
Xue (2015) Effectiveness of Federal and State Government
Polices On Renewable Energy Generation
1990–2012 Positive
Pastor (2020) The effects of renewables portfolio standards
on renewable energy generation
2001–2016 Positive
Sun et al. (2022) An analysis of the impact of fiscal and
monetary policy fluctuations on the
disaggregated level renewable energy
generation in the G7 countries
2000–2018 Positive
Razmi et al. (2021) Time-varying effects of monetary policy on
Iranian renewable energy generation
1984–2016 Positive
Osiolo (2021) Impact of cost, returns and investments:
Towards renewable energy generation in Sub-
Saharan Africa
1990–2014 Positive
Liu et al. (2021) Nexus between green financing, renewable
energy generation, and energy efficiency:
empirical insights through DEA technique
2016–2020 Negative
Haque and Rashid (2022) Effects of cumulative energy aid projects on
renewable energy generation capacity
2000–2013 Positive
Fotio et al. (2022) Financing renewable energy generation in SSA:
Does financial integration matter?
1980–2017 Mixed
Fatima et al. (2019) Analysing long-term empirical interactions
between renewable energy generation, energy
use, human capital, and economic performance
in Pakistan
1990–2016 Mixed
An et al. (2021) Analysis of Energy Projects Financial
Efficiency and Renewable Energy Generation
in Russia
2012–2019 Negative
Shahzad et al. (2021) Do Environment-Related Policy Instruments
and Technologies Facilitate Renewable Energy
Generation? Exploring the Contextual Evidence
from Developed Economies
1994–2018 Positive
Source: Author creation
IJESM

RENt¼fRE
t;EIt;GXt;UPt;LRt
ðÞ
(1)
Where tis time trend, REN
t
,RE
t
,EI
t
,GX
t
,UP
t
,LR
t
The empirical model of the study is as follows:
RENt¼
a
0þ
a
1REtþ
a
2EItþ
a
3GXtþ
a
4UPtþ
a
5LRtþ
«
t(2)
Where
a
0
is the constant term, while
a
1
,
a
2
,
a
3
,
a
4
,
a
5
are the coefficients of remittance
inflow, energy import, government expenditure, urban population, literacy rate,
respectively; and
«
t
is the error term.
The main objective of this research is to find out the impact of remittance inflow, energy
import, government expenditure, urban population and literacy rate on the renewable
energy generation in South Asia. To achieve this objective, we have applied the
Autoregressive Distributed Lag model, which involves the steps beginning from unit root
testing, optimal lag length formation, selection of an appropriate ARDL model, cointegration
test and finally diagnostic testing.
3.1 Unit root test and optimum lag length selection
The unit root test is used to check the stationarity of the time-series data. As in time series
modeling, we often come across data that shows trends over time. However, the time series data
must not be of such a time-trending nature. As has been highlighted by Gujarati (2003),itis
critical to avoid spurious regression that makes accurate forecasting difficult. Once the
stationarity of the variables has been confirmed, the next step is to select the optimal lag length
for our stationary data series. In empirical research that is based on an autoregressive model, it is
important to select the appropriate lag length for each series. Including too many lags while
specifying the model can reduce the degree of freedom and consequently lead to multicollinearity.
Jeffrey (2012) mentioned that we should keep the small lag length up to two lags not to lose the
degree of freedom. There are many criteria, like Akaike information criterion (AIC), BIC, SIC and
HQ, that can be used to determine the appropriate lag length. By using these criteria, we are
required to select the model that gives the lowest values for these information criteria.
3.2 ARDL model formation and bound testing
Once the optimum lag length has been obtained, we have to find the appropriate ARDL
model among many models that may contain different lag lengths for different variables.
The model is to be selected based on the lowest value of information criteria, which has been
obtained based on optimal lag length. In comparison to conventional cointegration
procedures, the ARDL bounds cointegration methodology has several advantages (Pesaran
et al., 2001). The first advantage is that it accepts mixed-order variables in the form of I(0)
and I(1) but not I(2) or higher-order variables (Muhammad and Faridul, 2011). Second, the
ARDL constrains cointegration techniques by allowing them to be applied to small and
finite samples (N 50), whereas conventional cointegration techniques require a large sample
observation. Third, the ARDL bounds cointegration method considers fractionally
integrated variables. The key aspect is that the ARDL bounds technique is capable of
addressing omitted variables and serial correlation concerns, as well as any endogeneity
issue, because it offers unbiased estimates in the long-run model. Finally, the ARDL bounds
cointegration technique has the advantage of estimating long-run and short-run dynamics
simultaneously in a single reduced form of the equation (Harris and Sollis, 2003). The model
formulation is similar to that of Zaman et al. (2021), but the variables included in the model
are different. The ARDL model specification of this study is as follows:
South Asia’s
renewable
energy output

RENt¼
b
0þX
p
i¼0
b
1iRENtiþX
p1
i¼0
b
2iREtiþX
p2
i¼0
b
3iEItiþX
p3
i¼0
b
4iGXti
þX
p4
i¼0
b
5iUPtiþX
p5
i¼0
b
6iLRtiþ
d
7RENt1þ
d
8REt1þ
d
9EIt1þ
d
10GXt1
þ
d
11UPt1þ
d
12LRt1þ
«
t
(3)
In equation (3);p, p1, p2, p3, p4 and p5 are the optimal lag length;
«
t
is the error term;
b
0
is
constant;
b
1i
,
b
2i
,
b
3i
,
b
4i
,
b
5i
,
b
6i
are the short-run coefficients and
d
7
,
d
8
,
d
9
,
d
10
,
d
11
,
d
12
are
the long-run coefficients. In the unrestricted error correction model (ECM) specified in
equation (4), the null hypothesis asserts that there is no long-term equilibrium relationship
between the H
0
variables, whereas the alternative hypothesis states that there is a long-term
homogeneous H
1
relation between the variables:
H0:
d
7¼
d
8¼
d
9¼
d
10 ¼
d
11 ¼
d
12 ¼0
H1:
d
76¼
d
86¼
d
96¼
d
10 6¼
d
11 6¼
d
12 6¼ 0
By comparing the obtained bound testing-F-test statistic values to the critical values given
by Pesaran et al. (2001), the long-run link between the variables used in this investigation
was validated. Pesaran et al. (2001) analyzed the long-run relationship between the variables
by comparing two sets of alternative critical values at each significance level. One set of
critical values reflects the critical value at the lower bound when all regressors are I(0). In
contrast, another set represents the critical value at the upper bound when all regressors are
I(1). The null hypothesis of no long-term relationship can be rejected if the computed F-
statistic is greater than the critical value, irrespective of the time series integration orders.
On the other hand, the null hypothesis cannot be rejected if the F statistic is smaller than the
critical value. But if the test statistics fall between the lower and higher critical levels, no
conclusion on cointegration can be drawn. If cointegration occurs among the variables in
this investigation, the following ECM will be used to approximate the variables’short-run
dynamics. The ECM requirements for this investigation are as follows:
RENt¼
b
0þX
p
i¼0
b
1iRENtiþX
p1
i¼0
b
2iREtiþX
p2
i¼0
b
3iEItiþX
p3
i¼0
b
4iGXti
þX
p4
i¼0
b
5iUPtiþX
p5
i¼0
b
6iLRtiþ
l
ECTt1þ
«
t(4)
In which
l
denotes the coefficient of error correction term which should be in negative and
statistically significant to achieve the long-run equilibrium.
3.3 Diagnostic tests
Finally, the diagnostic tests have been applied to ensure that the regression model is
correctly specified for the included regressors. We have applied the Jarque-Bera test for
normality to confirm that the data is normally distributed. The Breusch–Godfrey serial
IJESM

correlation LM test has been applied to check for autocorrelation in the errors in a regression
model. The Breusch-Pagan test is used to determine whether or not heteroscedasticity is
present in a regression model to confirm that the residuals are distributed with equal
variance. For model stability and to detect the structural change in the series, the CUSUM
and CUSUM of the square test have been applied.
4. Empirical results and discussion
For examining the integration of the variables, we have applied the Augmented Dickey-
Fuller (ADF) and Phillips-Perron unit tests (Dickey and Fuller, 1979;Phillips and Perron,
1988), and their results are reported in Table 2. The remittance inflow (RE) is stationary at
level, while other variables like renewable energy (REN), energy imports (EI), government
expenditure (GX), urban population growth (UP) and literacy rate (LR) become stationary
only at the first difference. Thus, RE is found to be I (0), and the other variables are found to
be an integration of order one, I (1). As a result, the variables used in this study have mixed
stationarity attributes, comprising stationary at level and first difference.
Table 3 summarizes the test results for determining the optimal lag length that will be
used for the analysis. Like other studies analyzing the data using the ARDL approach, this
study also uses the AIC to choose an optimal lag length. The analysis results showed a lag
length of 2 as the most suitable lag length as per AIC criteria. Hence, we have selected it for
further analysis. Additionally, as seen in Figure 3, the ARDL (1, 1, 0, 2, 0, 0) model has a
lower lag than the other models. As a result, this study uses the ARDL (1, 1, 0, 2, 0, 0).
Table 4 reports the analysis of the cointegration test between the variables used in this
study. As shown in Table 4, the calculated F-statistic is 4.61, which is greater than the upper
Table 2.
Variables description
Variable Description
Energy imports (EI) Energy imports, net (% of energy use)
Government expenditure (GX) General government final consumption expenditure (% of GDP)
Literacy rate, adult (LR) Literacy rate, adult total (% of people ages 15 and above)
Remittances (RE) Personal remittances, received (% of GDP)
Share of renewable electricity (REN) Renewable electricity output (% of total electricity output)
Urban population growth (UP) Urban population growth (annual %)
Source: World Bank
Table 3.
Unit root test
Variable
ADF test Philip–Perron test
At level At 1st difference At level At 1st difference
t-stats p-value t-statistic p-value t-stats p-value t-statistic p-value
REN 2.4400 0.1400 4.5100 0.0000 2.4400 0.1400 4.5100 0.0000
RE 3.0100 0.0500 4.4300 0.0000 3.6400 0.0100 6.8400 0.0000
EI 1.9100 0.3200 4.0600 0.0100 1.9100 0.3200 4.1000 0.0000
GX 2.5300 0.1200 3.9600 0.0100 2.2500 0.2000 3.9600 0.0100
UP 1.1800 0.6600 3.1000 0.0400 1.2100 0.6500 2.9900 0.0500
LR 1.3400 0.5900 4.7600 0.0000 1.4000 0.5700 4.7600 0.0000
Source: Author calculation via Eviews10 software
South Asia’s
renewable
energy output

bound critical value of 3.38 at the 5% level of significance. At the 5% level of significance,
the null hypothesis that there is no long-run relationship between renewable energy output
and macroeconomic variables to South Asia is rejected. As a result, it may be stated that
there is a long-run relationship between renewable energy output and macroeconomic
variables to South Asia across the research period.
Table 5 displays the estimated long-run coefficients of the variables used in this
investigation. Remittance inflows have indicated a negative and non-significant impact on
Figure 3.
ARDL Model
selection graph
Table 4.
ARDL model
selection criteria
Lag length selection
Lag LogL LR FPE AIC SC HQ
0 198.7205 NA 2.12E-15 16.7583 16.46208 16.6838
1 309.1608 153.6562* 3.70e-18* 23.23138 21.15786* 22.70989*
2 346.0363 32.06567 7.50E-18 23.30751 19.4567 22.339
Source: Author calculation via Eviews10 software
Table 5.
ARDL bound test
bound test
Bound test
F-statistic Value = 4.615520 k = 5
Critical value bound
Significance 1(0) bound 1(1) bound
10% 2.08 3
5% 2.39 3.38
2.50% 2.7 3.73
1% 3.06 4.15
Source: Author calculation via Eviews10 software
IJESM

renewable energy output. This finding was aligned with the past finding of Ojapinwa and
Lawani (2022), in which they also confirmed that remittance inflows do not support
investment in renewable energy. It is worth noting that although the South Asian region is
the highest remittance-receiving participant, it cannot be ascertained that it will positively
contribute to renewable energy outputunless a suitable measure is taken.
The findings related to energy imports indicated a significant negative impact on the
share of renewable energy output, as every 1% increase in energy imports leads to a
2.53% decrease in the share of renewable energy output. This finding is also aligned with
the past finding of Bulut and Muratoglu (2018), in which they found that energy imports
were one of the reasons hampering the production of renewable energy in Turkey.
However, in our case of the South Asian region, it happens mainly because the major
share of energy imports comes from non-renewable sources, e.g. oil imports. Hence, the
region is required to reduce its dependence on non-renewable sources and gradually shift
toward renewable energy sources. Furthermore, the results of government expenditures
also indicated similar conclusions. It has been discovered that with every 1% increase in
government expenditure, there is a 2.61% decrease in the share of generating renewable
energy, which is statistically significant, indicating that the government is not spending
appropriately on the use of renewable energy and is instead focusing on non-renewable
sources. This finding is corroborated by the case study based in Iran, in which it was
concluded that government spending does not specifically provide enough attention to
renewable energy output. However, it does create synergy in the whole ecosystem of
renewable energy generation and attract people to focus on renewable energy (Xuan Ao
et al., 2022).
Moreover, the results further reveal that with the growth of the urban population,
there is a negative impact on generating renewable energy, as with every 1% increase in
urban population growth, there is a decline of 3.51% in renewable energy output. These
results are contradictory to the previous research in this area. Baye et al. (2021) in their
study of 32 SSA countries have concluded that urbanization leads to improvements in
renewable energy generation as urbanization enhances energy needs, which the
government fulfills by using renewable energy sources. However, in South Asia,
urbanization is causing a negative impact on renewable energy, indicating that
renewable energy generation has not yet received enough attention. Moreover, the youth
literacy rate has a significant positive long-run impact on the share of people using
renewable energy. Every 1% increase in literacy level leads to a 4.58% increase in the
generation of renewable energy. It is an indicator that the more people get educated, the
more they will understand the importance of renewable energy and act accordingly. Osei
et al. (2022) in their comparative analysis study of Asian and European economies have
Table 6.
Long run results
Long run result
Variables Coefficient Std. error t-statistic Prob.
RE 0.40532 0.26451 4.53234 0.1494
El 2.533 0.74757 3.38831 0.0049
GX 2.61894 0.61457 0.4.26141 0.0009
UP 3.51418 0.81226 4.3264 0.0008
LR 4.58271 1.72898 2.65053 0.02
C 2.25305 4.22705 0.53301 0.603
Source: Author calculation via Eviews10 software
South Asia’s
renewable
energy output

also suggested that, as the literacy level improves, it positively affects the renewable
energy output of an economy, suggesting that education policymakers should promote
policies that enhance the literacy level.
Table 7 shows the diagnostic test results for data normality, serial correlation and
heteroskedasticity for the estimated ARDL model in this study. For normality, the null
hypothesis is that the residuals are normally distributed and are not rejected. Because the
extracted Jarque–Bera test’s corresponding p-value is greater than the 5% level of
significance. Hence, it is a confirmation that the residuals of the ARDL (1, 1, 0, 2, 0, 0)
model generated in this study are normally distributed. For testing serial correlation, the
Breusch–Godfrey LM test for serial correlation has been performed. The results indicated
that the p-value was higher than the 5% level of significance, confirming that the model is
free from serial correlation. Finally, to check for heteroskedasticity, the Breusch–Pagan–
Godfrey test of heteroskedasticity has been performed, which also indicated a p-value
that was higher than the 5% level of significance, confirming that the model is not
heteroskedastic.
Figures 4 and 5show the CUSUM and CUSUMSQ, which were used in this investigation
for stability diagnosis. In the case where the blue line lines lie inside the limits of two red
lines, the parameters used in the study are stable, and likewise, according to the CUSUM and
CUSUMSQ plots (Tanizaki, 2007). The CUSUM and CUSUMQ results show that the model
used in this study cleared the stability tests because the blue line in both tests fits inside the
red line borders.
Table 7.
Diagnostic test result
Test F-statistic p-value
Jarque Bera Test Statistics: Normality 1.3859 0.5100
Breusch–Godfrey LM Test: Serial Correlation 1.5834 0.2204
Breusch–Pagan–Godfrey Test: Heteroskedasticity 0.7637 0.6799
Source: Author calculation via Eviews 10 software
Figure 4.
CUSUM test for
model stability
IJESM

5. Conclusion and recommendations
The impact of macroeconomic variables on the share of non-renewable energy output was
examined in this study, which used annual time series data for South Asia from 1990 to 2021.
The relationship was investigated using the ARDL bounds testing technique. The study has
used the share of renewable electricity output out of total energy generation as a proxy for
renewable energy output. It has been found that remittance inflow might affect many factors of
an economy, but it does not impact the share of renewable energy generation in South Asia.
While urbanization, government expenditure and energy imports have a negative and
significant impact on renewable energy, apart from that, it has been found that the literacy
level has a positive and significant impact. This is an indicator that urbanization is causing an
increase in energy demand, which the government is fulfilling through its expenditure on
energy imports, but because it is negatively affecting the share of renewable energy output, it is
an indication that the import and local generation of energy are mostly happening in the form
of non-renewable sources. While the literacy level is increasing, it is making people more aware
of the sustainable environment and the importance of renewable energy.
Thus, it can be recommended that the governments and departments of power and energy
in the region’s respective countries must shift their focus to improving the literacy level of the
population while also investing funds in renewable energy sources. This will not only make
the region self-sufficient but will also improve the currency exchange rate and foreign
exchange reserves of the region’s countries as the region will not be required to import energy.
Furthermore, the related governments must encourage international cooperation on renewable
energy production and share information with the experts in the field, enhance investment
in the research and development of renewable energy sources to develop new and
cost-effective technologies. Promote local businesses that deal in renewable energy production
andgivethemataxincentivesothatmoreindustriescanworkinthesamefield.
Moreover, renewable energy needs both new infrastructure investments and widespread
societal acceptability to support and accelerate economic development. As this is the need of
the hour. This might be accomplished by reforming the energy business and replacing
existing energy infrastructure with new energy infrastructure. It is associated with the
closure of traditional industries, local environmental changes and the development of new
energy complexes. In addition, the government should think about the possibility of
introducing incentives for households to promote the installation of additional off-grid solar
Figure 5.
CUSUM of square
test for model
stability
South Asia’s
renewable
energy output

household systems. This would not only lower the costs for people to create solar home
systems, but it would also lower their dependency on non-renewable energy sources, which
are often not kind to the environment. For poor households that would otherwise be unable
to afford the use of contemporary renewable energy sources, the government ought to offer
financial assistance in the form of subsidies to encourage the usage of environment friendly
technologies. Furthermore, renewable energy should be included as a vital foundation for
national and regional development. This will assist in attaining SDG 7 (energy) while also
contributing to numerous other SDGs such as poverty reduction, health, water, business
development and climate change.
6. Limitations of the study and future work
One of the limitations of the study is that it is based on time series data that has been taken
at a regional level, while for future studies panel data can be taken into account. Second, the
study has used a limited setof data from the years 1990 to 2021. To get more comprehensive
results, data for a longer period of time can be taken into consideration for future studies.
Third, the study has used only macroeconomic variables; a mix of macroeconomic and
microeconomic variables can be considered to get the combined effect.
References
Adams, R.H. and Cuecuecha, A. (2013), “The impact of remittances on investment and poverty in
Ghana”,World Development, Vol. 50, pp. 24-40.
Adewuyi, A.O. (2016), “Determinants of import demand for non-renewable energy (petroleum)
products: empirical evidence from Nigeria”,Energy Policy, Vol. 95, pp. 73-93.
Adom, P.K. (2013), “Time-varying analysis of aggregate electricity demand in Ghana: a rolling
analysis”,OPEC Energy Review, Vol. 37 No.1, pp. 63-80.
Akçay, S. and Demirtas, G. (2015), “Remittances and energy consumption: evidence from Morocco”,
International Migration, Vol. 53 No. 6, pp. 125-144.
Alawin, A.A., Rahmeh, T.A., Jaber, J.O., Loubani, S., Dalu, S.A., Awad, W. and Dalabih, A. (2016),
“Renewable energy education in engineering schools in Jordan: existing courses and level of
awareness of senior students”,Renewable and Sustainable Energy Reviews, Vol. 65, pp. 308-318,
doi: 10.1016/J.RSER.2016.07.003.
Amarawickrama, H. and Hunt, L. (2008), “Electricity demand for Sri Lanka: a time series analysis”,
Energy, Vol. 33 No.5, pp. 724-739.
An, J., Mikhaylov, A.Y., Ay,L. and Mb[aqkjd,A>. (2021), “Analysis of energy projects financial
efficiency and renewable energy generation in Russia”,Finance: Theory and Practice, Vol. 25
No. 5, pp. 79-91, doi: 10.26794/2587-5671-2020-25-5-79-91.
Asif, M. and Muneer, T. (2007), “Energy supply, its demand and security issues for developed and
emerging economies”,Renewable and Sustainable Energy Reviews, Vol. 11 No. 7, pp. 1388-1413,
doi: 10.1016/J.RSER.2005.12.004.
Atty Mardiana, D., Husin, Z., Hamzah, M.Z. and Kartoatmodjo, R.T. (2013), “Economy growth and oil
import requirement in Indonesia ”,Journal of Energy Technologies and Policy,Vol.3No.11.
Balitskiy, S., Bilan, Y. and Strielkowski, W. (2014), “Energy security and economic growth in the
European union”,Journal of Security and Sustainability Isues, Vol. 4 No. 2, pp. 123-130.
Baniya, B. and Giurco, D. (2021), “Resource-efficient and renewable energy transition in the five least
developed countries of Asia: a post-COVID-19 assessment”,Sustainability: Science, Practice and
Policy, Vol. 17 No. 1, pp. 404-413, doi: 10.1080/15487733.2021.2002025.
Baye, R.S., Ahenkan, A. and Darkwah, S. (2021), “Renewable energy output in Sub saharan Africa”,
Renewable Energy, Vol. 174, pp. 705-714, doi: 10.1016/J.RENENE.2021.01.144.
IJESM

Bulut, U. And Muratoglu, G. (2018), “Renewable energy in Turkey: great potential, low but increasing
utilization, and an empirical analysis on renewable energy-growth nexus”,Energy Policy,
Vol. 123, pp. 240-250, doi: 10.1016/J.ENPOL.2018.08.057.
Camacho-Guti
errez, P. (2010), “Dynamic OLS estimation of the U.S. import demand for Mexican crude
oil”,Munich Personal RePEc Archive.
Dickey, D.A. and Fuller, W.A. (1979), “Distribution of the estimators for autoregressive time series with
a unit root”,Journal of the American Statistical Association, Vol. 74 No.366, pp. 427-431.
Energy Agency, I. (2021), “Review 2021 assessing the effects of economic recoveries on global energy
demand and CO 2 emissions in 2021 global energy”, available at: www.iea.org/t&c/
Fatima, N., Li, Y., Ahmad, M., Jabeen, G. and Li, X. (2019), “Analyzing long-term empirical interactions
between renewable energy generation, energy use, human capital, and economic performance in
Pakistan”,Energy, Sustainability and Society, Vol. 9 No. 1, doi: 10.1186/S13705-019-0228-X.
Filippini, M. and Pachauri, S. (2004), “Elasticities of electricity demand in urban Indian households”,
Energy Policy, Vol. 32 No. 3,pp. 429-436.
Ghosh, S. (2009), “Import demand of crude oil and economic growth: evidence from India”,Energy
Policy, Vol. 37 No. 2, pp. 699-702.
Gielen, D., Boshell, F., Saygin, D., Bazilian, M.D., Wagner, N. and Gorini, R. (2019), “The role of
renewable energy in the global energy transformation”,Energy Strategy Reviews, Vol. 24,
pp. 38-50, doi: 10.1016/J.ESR.2019.01.006.
Gong, Y. (2011), “Does labour out-migration reduce firewood consumption?”,International Seminar on
Internal Migration and Urbanization and Their Socioeconomic Impacts in Developing Countries:
Challenges and Policy Responses,Fuzhou, China.
Gozgor, G., Lau, C.K.M. and Lu, Z. (2018), “Energy consumption and economic growth: new evidence
from the OECD countries”,Energy, Vol. 153, pp. 27-34.
Gujarati, D.N. (2003), Basic Econometrics, 4th ed. McGraw Hill.
Halicioglu, F. (2007), “Residential electricity demand dynamics in Turkey”,Energy Economics, Vol. 29
No. 2, pp. 199-210.
Harris, R.I.D. and Sollis, R. (2003), “Applied timeseries modelling and forecasting”, J. Wiley, available at:
www.wiley.com/en-s/AppliedþTimeþSeriesþModellingþandþForecasting-p-9780470844434
Hasan Kaya, V. and Elster, D. (2018), “Comparison of the main determinants affecting environmental
literacy in Singapore, Estonia and Germany”,International Journal of Environmental and
Science Education, Vol. 13 No. 4, pp. 373-389.
Haque, N. and Rashid, S. (2022), “Effects of cumulative energy aid projects on renewable energy
generation capacity”,Environmental Development, Vol. 42, p. 100692, doi: 10.1016/J.
ENVDEV.2021.100692.
IEA (2020a), “Global status of clean energy innovation in 2020 –clean energy innovation –Analysis –
IEA”, available at: www.iea.org/reports/clean-energy-innovation/global-status-of-clean-energy-
innovation-in-2020
IEA (2020b), “Tracking SDG 7”, The Energy Progress Report 2020. www.trackingsdg7.esmap.org/
data/files/download-documents/tracking_sdg_7_2020-full_report_-_web_0.pdf
IEA (2022), “Government energy spending tracker”,Iea.Org, available at: www.iea.org/news/clean-
energy-spending-in-governments-economic-recovery-packages-has-surged-by-50-since-the-end-
of-october-reaching-unprecedented-levels
Iwayemi, A., Adenikinju, A. and Babatunde, M.A. (2010), “Estimating petroleum products demand
elasticities in Nigeria: a multivariate cointegration approach”,Energy Economics, Vol. 32 No. 1,
pp. 73-85.
Jamil, F. and Ahmad, E. (2011), “Income and price elasticities of electricity demand: aggregate and
sector-wise analyses”,Energy Policy, Vol. 39 No. 9, pp. 5519-5527.
South Asia’s
renewable
energy output

Jeffrey, M.W. (2012), “Introductory econometrics: a modern approach”, South-Western, Cengage
Learning, available at: www.economics.ut.ac.ir/documents/3030266/14100645/Jeffrey_M.
_Wooldridge_Introductory_Econometrics_A_Modern_Approach__2012.pdf
Jennings, P. (2009), “New directions in renewable energy education”,Renewable Energy, Vol. 34 No. 2,
pp. 435-439, doi: 10.1016/J.RENENE.2008.05.005.
Fotio, H.K., Nchofoung, T.N. and Asongu, S.A. (2022), “Financing renewable energy generation in SSA:
does financial integration matter?”,Renewable Energy, Vol. 201, pp. 47-59, doi: 10.1016/J.
RENENE.2022.11.047.
Kandpal, T.C. and Broman, L. (2014), “Renewable energy education: a global status review”,Renewable
and Sustainable Energy Reviews, Vol. 34, pp. 300-324, doi: 10.1016/J.RSER.2014.02.039.
Katherine, T. (2016), “IEA: $44 trillion in energy investment won’t limit climate change to 2 degrees
Greentech media”, available at: www.greentechmedia.com/articles/read/iea-44-trillion-in-energy-
investment-wont-limit-climate-change-to-2-degrees
Kim, C. (2021), “A review of the deployment programs, impact, and barriers of renewable energy
policies in Korea”,Renewable and Sustainable Energy Reviews, Vol. 144, p. 110870, doi: 10.1016/J.
RSER.2021.110870.
Kim, H.S. and Baek, J. (2013), “Assessing dynamics of crude oil import demand in Korea”,Economic
Modelling, Vol. 35, pp. 260-263.
Liu, Z., Xu, J., Wei, Y., Hatab, A.A. and Lan, J. (2021), “Nexus between green financing, renewable
energy generation, and energy efficiency: empirical insights through DEA technique”,
Environmental Science and Pollution Research, Vol. 1, pp. 1-14, doi: 10.1007/S11356-021-17092-3/
FIGURES/4.
Luchansky, M.S. and Monks, J. (2009), “Supply and demand elasticities in the U.S. ethanolfuel market”,
Energy Economics, Vol. 31 No. 3, pp. 403-410.
Lusardi, A. (2019), “Financial literacy and the need for financial education: evidence and implications”,
Swiss Journal of Economics and Statistics, Vol. 155 No. 1, pp. 1-8, doi: 10.1186/S41937-019-0027-5/
FIGURES/2.
Ma, Y. and Wang, F. (2023), “Dutch disease via remittances and natural resources: a perspective of
global economy”,Resources Policy, Vol. 80,p. 103248, doi: 10.1016/J.RESOURPOL.2022.103248.
Mazur, A. (2011), “Does increasing energy or electricity consumption improve quality of life in
industrial nations? ”,Energy Policy, Vol. 39 No. 5, pp.2568-2572.
Moore, A. (2011), “Demand elasticity of oil in Barbados”,Energy Policy, Vol. 39 No. 6, pp. 3515-3519.
Muhammad, S. and Faridul, I. (2011), “Financial development and income inequality in Pakistan: an
application of ARDL approach”,Journal of Economic Development, Vol. 36 No. 1, pp. 35-58, doi:
10.35866/CAUJED.2011.36.1.003.
Murshed, M., Abbass, K. and Rashid, S. (2021), “Modelling renewable energy adoption across South Asian
economies: empirical evidence from Bangladesh, India, Pakistan and Sri Lanka”,International
Journal of Finance and Economics, Vol. 26 No. 4, pp. 5425-5450, doi: 10.1002/IJFE.2073.
Nag, A.K. and Sarkar, S. (2018), “Modeling of hybrid energy system for futuristic energy demand of an
Indian rural area and their optimal and sensitivity analysis”,Renewable Energy, Vol. 118,
pp. 477-488, doi: 10.1016/J.RENENE.2017.11.047.
Niamir,L.,Ivanova,O.,Filatova,T.,Voinov,A.andBressers,H.(2020),“Demand-side solutions for climate
mitigation: bottom-up drivers of household energy behavior change in The Netherlands and
Spain”,Energy Research and Social Science, Vol. 62, p. 101356, doi: 10.1016/J.ERSS.2019.101356.
Ojapinwa, V.T. and Lawani, D. (2022), “Diaspora remittances, renewable energy and enterprise growth
in Nigeria”,The Romanian Economic Journal, No. 83, doi: 10.24818/REJ/2022/83/04.
Osei, B., Kunawotor, M.E. and Kulu, E. (2022), “Renewable energy production and employment:
comparative analysis on European and Asian countries”,International Journal of Energy Sector
Management, doi: 10.1108/IJESM-04-2022-0015/FULL/XML.
IJESM

Osiolo,H.H.(2021),“Impact of cost, returns and investments: towards renewable energy generation
in Sub-Saharan Africa”,Renewable Energy, Vol. 180, pp. 756-772, doi: 10.1016/J.RENENE.
2021.08.082.
Ozturk, I. and Arisoy, I. (2016), “An estimation of crude oil import demand in Turkey: evidence from
time-varying parameters approach”,Energy Policy, Vol. 99, pp. 174-179.
Pastor, D.J. (2020), “The effects of renewables portfolio standards on renewable energy generation”,
Economics Bulletin, Vol. 40 No. 3, pp. 2121-2133.
Pesaran, M.H., Shin, Y. and Smith, R.J. (2001), “Bounds testing approaches to the analysis of level
relationships”,Journal of Applied Econometrics, Vol. 16 No. 3,pp. 289-326.
Phillips, P.C.B. and Perron, P. (1988), “Testing for a unit root in time series regression”,Biometrika,
Vol. 75 No. 2, p. 335.
Pourazarm, E. and Cooray, A. (2013), “Estimating and forecasting residential electricity demand in
Iran”,Economic Modelling, Vol. 35, pp. 546-558.
Razmi, S.F., Moghadam, M.H. and Behname, M. (2021), “Time-varying effects of monetary policy on
Iranian renewable energy generation”,Renewable Energy, Vol. 177, pp. 1161-1169, doi: 10.1016/J.
RENENE.2021.06.020.
Reichardt, K. and Rogge, K. (2014), “How the policy mix and its consistency impact innovation: findings
from company case studies on offshore wind in Germany”, Working Paper Sustainability and
Innovation No. S7/2014.
Roberts, I. and Rush, A. (2012), “Understanding China’s demand for resource imports”,China Economic
Review, Vol. 23 No. 3, pp. 566-579.
Sa’ad, S. (2009), “An empirical analysis of petroleum demand for Indonesia: an application of the
cointegration approach”,Energy Policy, Vol. 37 No. 11, pp. 4391-4396.
Sanchez, D.L. and Sivaram, V. (2017), “Saving innovative climate and energy research: four
recommendations for mission innovation”,Energy Research and Social Science, Vol. 29,
pp. 123-126.
Shahzad, U., Radulescu, M., Rahim, S., Isik, C., Yousaf, Z. and Ionescu, S.A. (2021), “Do Environment-
related policy instruments and technologies facilitate renewable energy generation? Exploring
the contextual evidence from developed economies”,Energies, Vol. 14 No. 3, p. 690, doi: 10.3390/
EN14030690.
Sharma, R., Sinha, A. and Kautish, P. (2021), “Does renewable energy consumption reduce ecological
footprint? Evidence from eight developing countries of Asia”,Journal of Cleaner Production,
Vol. 285, p. 124867, doi: 10.1016/J.JCLEPRO.2020.124867.
Sinha, A., Gupta, M., Shahbaz, M. and Sengupta, T. (2019), “Impact of corruption in public sector on
environmental quality: implications for sustainability in BRICS and next 11 countries”,Journal
of Cleaner Production, Vol. 232, pp. 1379-1393.
Sun, H., Awan, R.U., Nawaz, M.A., Mohsin, M., Rasheed, A.K. and Iqbal, N. (2021), “Assessing the socio-
economic viability of solar commercialization and electrification in South Asian countries”,
Environment, Development and Sustainability, Vol. 23 No. 7, pp. 9875-9897, doi: 10.1007/S10668-
020-01038-9/METRICS.
Sun, C., Khan, A., Liu, Y. and Lei, N. (2022), “An analysis of the impact of fiscal and monetary policy
fluctuations on the disaggregated level renewable energy generation in the G7 countries”,
Renewable Energy, Vol. 189, pp. 1154-1165, doi: 10.1016/J.RENENE.2022.03.027.
Tamsila, N.Z.I.B.S.Z.S.A. (2021), “The discursive ethnocentrism in Morrison’s Sula: a study of racial
speech”,Jahan-e-Tahqeeq, Vol. 4 No. 1, pp. 31-42.
Tanizaki, H. (2007), “Asymptotically exact confidence intervals of cusum and cusumsq tests: a
numerical derivation using simulation technique”,Communications in Statistics –Simulation
and Computation, Vol. 24 No. 4, pp. 1019-1036, doi: 10.1080/03610919508813291.
South Asia’s
renewable
energy output

Tao, W., Guang-Shun, H., Jing, G., Yue, Y. and Lin-Lin, L. (2020), “Energy consumption and economic
growth in China’s marine economic zones-an estimation based on partial linear model”,Energy,
Vol. 205, p. 118028.
Vidyarthi, H. (2015), “Energy consumption and growth in South Asia: evidence from a panel error
correction model”,International Journal of Energy Sector Management, Vol. 9 No. 3, pp. 295-310.
Wadud, Z., Noland, R.B. and Graham, D.J. (2010), “A semiparametric model of household gasoline
demand”,Energy Economics, Vol. 32 No. 1, pp. 93-101.
Walter, R. (2010), “European system of accounts –ESA 2010”, available at: www.ec.europa.eu/eurostat/
documents/3859598/5925693/KS-02-13-269-EN.PDF/44cd9d01-bc64-40e5-bd40-d17df0c69334
Wang, S., Wang, J., Li, S., Fang, C. and Feng, K. (2019), “Socioeconomic driving forces and scenario
simulation of CO
2
emissions for a fast-developing region in China”,Journal of Cleaner
Production, Vol.216, pp. 217-229.
Wang, J., Zhang, S. and Zhang, Q. (2021), “The relationship of renewable energy consumption to
financial development and economic growth in China”,Renewable Energy, Vol. 170, pp. 897-904,
doi: 10.1016/J.RENENE.2021.02.038.
World Bank (2018), “Urban population growth (annual %) –South Asia jdata. World bank”, available
at: www.data.worldbank.org/indicator/SP.URB.GROW?locations=8S
World Bank (2021), “World development indicators jDataBank”, available at: www.databank.
worldbank.org/source/world-development-indicators#
World bank (2022), “Literacy rate, adult total (% of people ages 15 and above) –South Asia jdata”,
available at: www.data.worldbank.org/indicator/SE.ADT.LITR.ZS?locations=8S
Zaman, Q.U., Wang, Z., Zaman, S. and Rasool, S.F. (2021), “Investigating the nexus between education
expenditure, female employers, renewable energy consumption and CO
2
emission: evidence
from China”,Journal of Cleaner Production, Vol.312, p. 127824.
Xuan Ao, H., Eftekhari, M., Aungkulanon, P., Almutairi, K. and Mostafaeipour, A. (2022), “Impact of
economic and government investment in residential solar power plant on energy system
sustainability”,Sustainable Energy Technologies and Assessments, Vol. 52, p. 102050, doi:
10.1016/J.SETA.2022.102050.
Xue (2015), “Effectiveness of federal and state government policies on renewable energy generation
undergraduate research opportunities program, 2015”.
Ziramba, E. (2010), “Price and income elasticities of crude oil import demand in South Africa: a
cointegration analysis”,Energy Policy, Vol. 38 No. 12, pp. 7844-7849.
About the authors
Imran Khan is a PMP certified Operations and Project Management Professional having 14þyears of
industry experience across local and multinational companies with an area of expertise in global
remittances, foreign exchange market, banking operations and financial services. He has a keen
interest in research work, and his research interest includes sustainable development, migration
studies, remittance behavior and financial economics. Imran Khan is the corresponding author and
can be contacted at: i.khan1984@gmail.com
Darshita Fulara Gunwant is an International Relations and Marketing Expert having 12þyears of
industry experience across local and multinational companies with an area of expertise in global
commodity price, remittances, macroeconomics and marketing techniques. She has a keen interest in
research work, and her research interest includes sustainable development, price volatility,
remittance behavior and financial economics.
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