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American Journal of  Environmental
Economics (AJEE) 

Harnessing Green Investments for Sustainable Prosperity and the Role of  Renewable 
Energy in Driving Economic Growth and Development in South Asia

Majid Abdullah1*, Shakeel Ahmed1

Volume 4 Issue 1, Year 2025
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v4i1.4050
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: November 18, 2024

Accepted: December 22, 2024

Published: February 06, 2025

This study examines the impact of  renewable energy investments on economic growth 
in South Asia, a region grappling with rapid economic expansion and environmental 
degradation due to heavy reliance on fossil fuels. This paper employs panel data from 
1998 to 2022 and 15 emerging countries to examine the nexus between RE consumption, 
economic growth, CO2 emissions, and energy access using GMM and fixed-effects models. 
These findings suggest that increasing 1% of  renewable energy consumption will increase 
GDP by 0.072%, while increasing 1% of  CO2 emissions will reduce GDP by 0.3%. Besides, 
renewable energy investments are expected to contribute to the generation of  2.98 million 
green job opportunities by 2035. The study shows that renewable energy can decrease 
emissions, increase energy security, and create jobs. Nevertheless, there are limitations like 
capital-intensive requirements and a lack of  proper infrastructure and policies that have to be 
overcome. Therefore, the study supports the proposition that the sustainable development of  
renewable energy in South Asia depends on considerable policy intervention, technological 
improvement, and investment in infrastructure.

Keywords

CO₂ Emissions Reduction, 
Economic Growth, Green Job 
Growth, Renewable Energy, 
Sustainable Development 

1 Department of  Economics, Faculty of  Social Sciences, Ghazi University, Dera Ghazi Khan, Pakistan
* Corresponding author’s e-mail: majidabdullah18757@gmail.com

INTRODUCTION
The South Asian countries—India, Pakistan, Bangladesh, 
Nepal, Sri Lanka, Bhutan, and the Maldives—are at 
that awkward juncture where everyone wants to dream 
of  economic success but has to wake up to ecological 
imperatives. According to the International Energy Agency 
(IEA) (2022) economically, this disputed region has been 
growing; India’s GDP, for instance, has been growing at a 
6.8% annual rate from 2010-2020. Nevertheless, this set 
growth rate has been achieved at a price, as greenhouse 
emissions and degradation of  resources have escalated in 
a similar manner. The South Asia region has demanded 
an increase in energy of  about 3% per year over the last 
two decades and greatly increased fossil fuel use (Saeed 
& Siraj, 2024). This dependency leads to environmental 
deterioration and makes economies vulnerable to 
fluctuations in energy prices.
Renewable energy comes out as a viable solution when 
it comes to economic development without affecting the 
environment. As stated by the International Renewable 
Energy Agency (IRENA) (2023), renewable energy 
investment might lead to the generation of  up to 1,500 
000 new jobs in South Asia by the end of  2030, especially 
in the sphere of  solar and wind power. Also, the increased 
use of  renewable energy sources improves energy security 
due to decreased importation of  fossil energies, balance 
of  trade, and sustainable growth. However, South Asia 
still has not a significant level of  renewable energy 
source adoption. Currently, the share of  renewables is 
still only 12 percent of  the total energy consumption 
in the region, while the global average is 29 percent 
(Hassan et al., 2024). This has been occasioned by lack of  
capital, poor infrastructure, and bureaucratic challenges. 

According to the World Bank (2016), out of  the total 
number of  potentially feasible renewable investment 
projects in the region, about 60 percent of  those projects 
remain unfunded because of  perceived risks and a lack 
of  enabling policies (Rana & Gróf, 2022). Furthermore, 
legacy grid structures inhibit the management of  variable 
renewable electricity generation, which requires significant 
investments in upgrades and extensions.
The economic effect of  renewable energy investments is 
therefore not straightforward. Renewables can be used as 
a backstop to the instability of  the prices of  fossil fuels, 
leading to a reduction in the unpredictability of  energy 
costs. The Senior Energy Economist Outlook (2022), 
indicates that renewable energy can lead to a reduction in 
energy costs, with potential savings of  up to 20% compared 
to traditional fossil fuels. In addition, renewable energy 
projects seem to have more investment concentrated in 
capital, although operating costs are relatively low over 
the lifecycle of  the projects. Given that energy demand 
in South Asia is expected to increase by about 50% in 
the next twenty years, the shift to renewable energy could 
greatly help stabilise and diversify the energy supply 
(Rana & Gróf, 2022). Furthermore, renewable energy 
investments can lead to an improvement of  the trade 
balance due to reduced energy importations since South 
Asia’s trade balance of  payments, the most cited challenge, 
is a major deficit in fossil fuels. Crude oil imports were 
232.5 million tonnes, and import dependence on crude 
oil rose to 87.7% in 2023-24 (Hindu, 2024). In the case of  
the economies of  South Asia now transitioning towards 
domestically produced renewable energy, it would only be 
a great bonus for their energy security imports.
Another influential factor that defines the further role of  



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renewable energy in the economic growth of  the countries 
is the generation of  green jobs, which has been recognised 
as a potential for South Asia. According to Xie (2024) the 
renewable energy sector is more labor-intensive than fossil 
fuels, offering substantial employment potential across 
various stages of  the value chain, from manufacturing 
to maintenance. Shifting towards a green economy in 
South Asia could lead to about 2.98 million green jobs 
for the transition up to 2035, especially focusing on solar 
and wind sources (Gulagi, 2023). This change to green 
employment could help build employment in the area for 
the high unemployment rate, particularly of  youths, and 
reduce poverty in the rural areas. Furthermore, the skill 
development likelihood attends to the overall motivation 
of  creating a modern industrial base and promoting 
heterogeneity in the South Asian economy by supplying 
the demand for renewable energy niches that are expected 
to arise in the emerging green economy marketplace.
The social benefits of  investing in renewable energy 
sources are similar in regard to carbon emissions and air 
quality. South Asia is ranked among the top continents 
in terms of  emission of  greenhouse gases. For instance, 
Olivier et al. (2017) India contributed 2 giga tons of  
CO2 equivalent emissions in 2020, and this represented 
7% of  global emissions. This breaks the facts that the 
region largely relies on coal energy, a fuel that contributes 
to nearly 40% of  the energy mix and which has been 
attributed to air pollution and health costs. According 
to the Organisation for Economic Co-operation and 
Development (OECD) (2016), it was anticipated that 
polluted air kills over 1.5 million people from South Asia 
every year, which makes the provision of  clean energy 
crucial. Switching to green energy is possible and can 
help decrease the levels of  greenhouse gases; research 
suggests that carbon dioxide emissions could decrease by 
30% in case South Asia maintains its renewable energy 
goals till 2030 (Pandey & Asif, 2022). In addition, through 
the utilisation of  wind and solar power, most of  which 
have less impact on the natural environment as a result 
of  the construction of  wind and solar farms as opposed 
to the conventional sources of  energy like the fossil fuels 
in the region.
However, the changeover to a green economy in South 
Asia is not easy. Gielen et al. (2019) found that the 
renewable energy industry needs a large fixed capital 
investment, technological advances, and supportive 
policies. South Asian grids are usually ageing and weak 
in terms of  flexibility for the incorporation of  stochastic 
resources such as PV and wind power. For instance, 
Hassan et al. (2024) revealed that the current capacities 
to transmit and resize transfers of  renewable energy in 
rural areas are almost completely inadequate, as shown in 
the grid upgrade and development (Shahbaz et al., 2020). 
Furthermore, there are some financial constraints, such as 
political and economic risks, that compel shifts in policies 
and markets within the renegotiation of  renewable energy 
investment, which scare both local and international 
investors. The solution of  these problems presupposes 

overcoming the policies designed to involve private 
subjects, minimising the potential dangers of  investment, 
and developing renewable energy sources.
This research will reveal the correlation of  socio-
economic change and pro-environment investment 
in the South Asian countries based on the promotion 
of  renewable energy. Dai et al. (2024) found that the 
association between renewable energy usage, GDP per 
capita, employment rate, and CO2 emissions with the 
largest countries. Specific objectives are to identify and 
analyse the correlation between RE and economic GDP 
to assess the implications of  a change in energy mix 
on the stability of  the economy and the balance of  the 
payment stream, to identify the environmental effects of  
RE, and the reduction of  emission rates for CO2 and an 
improvement in air quality, and to review existing policies 
and laws regarding the consumption of  large-scale RE. 
Thus, this paper provides findings and analysis with 
policy implications for transitioning South Asia to a green 
economy aligned with global conditions.

LITERATURE REVIEW
Renewable Energy and Economic Growth in South 
Asia
Renewable energy consumption and economic 
development in South Asia have emerged as a central 
focus of  concern for scholars of  regional development 
in assessing the costs and benefits of  such a transition. 
Timmons (2018) stated that the economic advantages of  
RE are supported by many studies that focus on promising 
economic effects of  stable energy prices, increments in 
GDP, and decreased exposure to the fluctuations in fossil 
fuel prices. Muneer et al. (2005) found that switching to 
renewable energy could actually cut costs by about 20% 
to traditional fossil fuels because they have inherently 
lower overhead costs. Renewables are capital-intensive 
at the beginning, but their operational costs are relatively 
low compared to fossil fuels, which need constant fuel 
feedstock and whose prices are volatile (Chaudhary et al., 
2015). In a global context, and specifically for a region 
like the South Asian, where global oil and especially 
coal price hikes can undermine all types of  economic 
resilience, shift can be seen as a form of  energy and 
economic security.
The Indian solar power industry gives the possible GDP 
growth contribution from renewable energy with the 
expectation that expansion of  renewable energy can add 
one percent to the GDP by 2030 if  backed effectively 
by relative policy (Teske et al., 2019). The economic 
imperative for a South Asian green energy solution is 
even more urgent for the region’s emerging economies 
since green energy sources provide an opportunity to self-
generate resources for development instead of  depending 
on importing them. Similarly, Bangladesh has planned for 
the purchase of  40% renewable energy by 2041 to reduce 
dependency on imported fossil fuels, which will lead to a 
saving of  $1.6 billion per year (Usman et al., 2024). Such 
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benefits of  renewables in increasing economic growth 
with a tendency to improve trade deficit issues due to the 
high import of  fossil fuels.
A great potential source of  economic benefits from the 
use of  renewable energy is from employment creation, 
which is arguably the largest impact that has come with 
the use of  renewable energy. Shah & Solangi, (2019) 
highlighted that the globalisation of  the renewable energy 
sector and the extensive use of  solar and wind energy are 
much more influential than the exhaustive use of  fossil 
fuel in creating jobs in the process of  its production, 
the supply channels, installation, and even maintenance. 
Reflecting a major achievement of  the renewable energy 
system in India, the employment level witnessed an 
estimated one million two lakh in 2023, as per the 2024 
Annual (Nguyen et al., 2024). These employment creation 
prospects are especially valuable for South Asian countries, 
as most of  the region still struggles with high levels of  
unemployment, particularly among the youth from rural 
farming areas. Pakistan’s renewable energy policy to add 
30% renewable capacity by 2030 envisions employment 
generation that will help lift people out of  poverty in the 
region and boost rural income generation (Teske et al., 
2019). Employment in renewable sectors also promotes 
value addition towards emerging green technologies in 
the context of  South Asia’s industrial development and 
skill demographics.
One more extensively discussed aspect in the literature 
is the impact of  renewable energy on trade balance 
and energy security. Fuhr, (2021) found that fossil fuel 
imports are some of  the leading trade deficits affecting 
the South Asian countries, meaning that these economies 
are vulnerable to threads arising from global market 
fluctuations. For instance, India’s import of  oil was 
valued at $ 120 billion in 2021 and directly impacts the 
country’s forex reserves coupled with trade deficits and 
surpluses (Ghosh et al., 2024). Diversifying the South 
Asian energy mix through renewable sources at home is 
pivotal to reducing the political volatility of  its oil imports, 
therefore smoothing trade imbalances. Exploitation of  
domestic renewable energy in Bangladesh and Nepal 
has the potential to reduce fossil fuel imports by about 
30 percent by 2035, which will be a great relief  to these 
economies (Nansai et al., 2021). In addition to this, 
reliance on renewable energy sources to increase energy 
independence adds to the nation’s security because more 
energy supply risks are imported, which affected South 
Asian countries’ balances of  trade and economic growth.

Environmental and Health Implications of  
Renewable Energy Adoption
The consequences of  renewables on the environment, 
and more specifically on carbon dioxide emissions, have 
been analysed exhaustively, and several studies suggest 
that South Asia can significantly benefit from using 
renewables to leave a lesser carbon footprint. However, 
countries of  the South Asia region continue to make a 
significant carbon footprint, where India, for instance, 

emitted 7% of  global CO₂ emissions in 2020 (Anwar et 
al., 2021). Given the fact that coal is heavily relied on in 
South Asia for most of  their energy needs, conversion to 
renewable energy will therefore help minimise emissions. 
It is expected that if  India, Pakistan, and Bangladesh 
reach the target of  renewables proposed for 2030, South 
Asia may reduce regional CO₂ emissions by 30% by 2030 
(Lanzi et al., 2018). This reduction is essential for the 
Paris Agreement and managing climate change outcomes. 
South Asia is one of  the most climate-sensitive areas 
where sea level rise and other natural disasters pose a real 
threat to millions of  people’s existence.
However, other than the emission reduction impact, 
other main benefits of  renewable energy are related to 
public health. Respiratory diseases, heart diseases, lung 
cancer, neurological disorders, and birth defects are some 
of  the health hazards related to air pollution in South 
Asia caused by coal-fired power plants. In the same year, 
air pollution leads to more than 1.5 million deaths of  
people across the region (Utturkar et al., 2024). Wind and 
solar energy sources are very clean in the sense that they 
emit nearly zero air pollutants, unlike coal and oil, hence 
better air quality and significant health cost reductions. 
By comparing coal to solar energy, the authors developed 
estimates that the part per trillion of  pollution decreases 
by 35%, aimed at lowering respiratory and cardiovascular 
diseases by as much as 25% (Rasul, 2016). These health 
benefits are especially relevant in growing populations 
in cities where air pollution regularly crosses dangerous 
levels, thus worsening healthcare and general wellbeing. 
Moreover, Chel and Kaushik, (2011) an increase in air 
quality has other indirect impacts on economic stability in 
the region in terms of  the cost of  doing business, such as 
an increase in worker productivity due to improved health 
as well as decreased health costs (Ghosh, 2023).
Environmental benefits of  renewable energy also include 
the preservation of  water and land resources, which is 
essential in South Asia, where most countries depend on 
agriculture. Nasirov et al. (2015) found that fossil-based 
electricity generation, especially coal power plants, and 
requires a lot of  water for cooling and extraction. While, 
on the other hand, the usage of  renewable resources like 
solar power and wind power involves very little usage of  
water in comparison to the non-renewable technologies. 
According to Sarkar and Singh, (2010) water insecurity 
risks linked to poverty and climate change in South 
Asia are on the rise, and these jeopardise agricultural 
production and food security. Using solar energy in the 
agricultural sector could increase water consumption 
by almost 40%, which could help in managing water in 
rural areas, which are majorly dominated by agriculture 
(Azhgaliyeva et al., 2020).
Land use is yet another factor where the utilisation of  
renewable energy sources has a competitive edge over 
conventional non-renewable sources. Asif  et al. (2024) 
stated that solar and wind power infrastructure like fields 
and wind turbines require huge areas of  land, but these 
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agricultural or pastoral use than the fossil fuel extraction 
sites, which create significant environmental harm and 
force people out of  their homes. To make efficient use 
of  land, all the South Asian countries should practice 
“agrivoltaics,” which involve putting solar panels over 
agriculture land so that crops and power can be generated 
simultaneously (Allison, 2022). Such dual-use systems can 
increase land efficiency and benefit the local economy, 
especially where land is considered a valuable resource.

Policy and Regulatory Challenges in Renewable 
Energy Implementation
However, the polity, government, and society of  South 
Asia have a set of  limitations and concerns that make it 
difficult for it to integrate renewable energy sources on a 
massive scale. An initial challenge is the issues of  costs and 
capital related to renewable energy sources (Aguero et al., 
2017). Concentrating on lower capital intensity projects 
such as solar and wind, renewable energy technologies 
are expensive to develop to begin with and consequently 
inaccessible to investors, particularly in markets where 
green finance is not well developed. More than 60% of  
renewable projects in South Asia face financing challenges 
emanating from perceived high-risk markets and poor 
policy support (United States Energy Association, 2023). 
South Asian governments should improve the investment 
attractiveness of  renewable power projects through tax 
benefits, grants, and lower interest rates on green bonds 
(Mukherjee & Satija, 2020). India, for instance, in its Green 
Energy Corridor project, is seeking private and foreign 
investment in renewable energy infrastructure through the 
provision of  fiscal incentives and relucent transmission 
infrastructure support (Koutoudjian et al., 2021).
Access to finances and existing regulatory frameworks, 
as well as infrastructure, remain other substantial barriers 
to regional renewable energy integration. Infrastructures 
of  energy, including the rural areas in South Asia, are 
currently blunt to support the variation of  renewable 
energy sources (Choi, 2014). For example, wind and 
solar resources produce electricity in a stochastic manner 
and are available only during certain periods of  the day, 
so the grid integration is another crucial point. Mishra 
et al. (2019) stated that flexibility of  the grid to absorb 
variability of  renewable could facilitate up to 40 percent 
rise in accomplishment of  renewable energy. Still, 
regulatory differences across South Asian nations pose 
challenges in integrating renewable energy, which attracts 
different policy environments and rules governing land 
acquisitions that slow down the approval of  renewable 
projects. This is particularly evident in India, where 
bureaucratic procedures and fluctuating policies on land 
use have restrained deployment of  solar projects; hence 
the need for harmonised rules that enable implementation 
of  renewable energy projects in the region.
South Asia needs to work closer and align with other 
regional groupings and key global climate deals like the 
Paris Agreement for its renewable power strategy. South 
Asian countries’ compliance with international climate 

change policies is patchy, yet some countries, such as 
India, have taken transformative measures towards 
boosting renewable power generation. However, there 
are others that have been slower, namely Bangladesh and 
Nepal, as a result of  political and financial constraints 
(Ghosh, 2023). Researchers have underlined the idea 
of  adopting a regional strategy incorporated in the 
development of  renewable energy by South Asian nations 
through the SARI/EI. Initially funded by the USAID 
to ensure regional energy market integration, SARI/
EI promotes cooperation and energy exchange among 
South Asian countries. Organisations of  such initiatives 
are important, especially when it comes to helping in 
the sharing of  technical know-how’s as well as cutting 
down on implementation costs as well as accessing 
international funding through the promotion of  a more 
stable investment environment.
However, transition to renewables entails significant 
capacity development, particularly in the areas of  
technology transfer and skilled human resources. 
These South Asian countries can secure technological 
collaborations that would introduce new technologies and 
expertise into the region (Lanzi et al., 2018). For instance, 
partnerships with peer countries or organisations or 
even development partners in technology matters such 
as the International Renewable Energy Association 
(IRENA) (2023) can enhance skills and enhancements in 
the adoption and implementation of  renewable energy 
systems. Several studies show that capacity development 
interventions such as those under the IRENA South 
Asia Programme may substantially improve technical 
and operational competency relevant to the scaling 
up of  renewable power projects. These programs aim 
at capacity building at the country level, in terms of  
skills development of  the local workforce, capacity to 
develop technical know-how on renewable technology 
implementation, and institutional capacities that can 
enable the management and coordination of  other 
aspects of  the renewable energy transitions.

MATERIALS AND METHODS
The present work employs panel data that encompass 15 
emergent Asian countries, ranging from 1998 to 2022, 
to assess the relationship between renewable energy 
investments and economic growth across the sustainable 
development framework. The analysis includes the 
following dependent and independent variables: For the 
dependent variable, we have GDP growth rate (annual 
%) which reflects economic growth. The independent 
variables include renewable energy consumption (% of  total 
energy consumption), reduction in CO₂ emissions (total 
CO₂ emissions excluding land-use change and forestry), 
access to clean energy (% of  population with access to 
electricity), R&D expenditure in renewable technologies 
(% of  GDP), energy intensity of  GDP (carbon emissions 
per PPP-adjusted GDP), and the growth rate of  green 
jobs (employment in renewable energy sectors). The data 
sources include the World Bank (WDI).



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Descriptive Statistics and Correlation Matrix 
To provide an initial insight into the data distribution, the 
minimum, the mean, variance, and maximum values for 
each of  the variables are reported. Also, the correlation 
matrix is given to show associations between the variables 
to ensure that there is not vast multicollinearity, as a 
preliminary way.

Panel Data Regression Models
Fixed Effects (FE) Model
The fixed effects model, as the name suggests, takes into 
consideration country features that may affect GDP 
growth; they include policy factors or geography. It let 
some coefficients be country-specific but not time-
specific. The model is defined as follows:
Yit= ai + βXit + uit 
where Yit denotes the GDP growth of  country i at 
time  t is the country-specific intercept, β represents the 
coefficients of  the independent variables, Xit the vector 
of  independent variables, and uit the error term.

Random Effects (RE) Model
The Random Effects (RE) model makes a presumption 
that certain effects peculiar to a country are random 
and unrelated to the explanatory variables. This kind of  
model is used when country differences are thought to be 
unsystematic and have no relationship with the predictor 
variables. It is defined as:
Yit= ai + βXit + μi + uit 
Where a is the intercept term, μi is the country-specific 
random effect, and the remaining terms are as defined above.

Hausman Test
To resolve the identification issue and to select between 
the fixed effects model and the random effects model, we 
conduct the Hausman test, which endorses the random/

RE estimator against the fixed/FE estimator. The test is 
based on the following statistic:
H=(bFE -bRE )’(Var(bFE )-Var(bRE))-1 (bFE -bRE )
Where a significant test result suggests the FE model is 
preferred.

Dynamic Panel Data Models (Arellano-Bond)
To tackle issues of  endogeneity and serial correlation 
in our model, we use the Arellano-Bond Generalised 
Method of  Moments (GMM) estimator. This method 
is suitable for dynamic panel data estimations with 
endogenous dependent and independent variables. The 
model is defined as follows:
ΔYit = βΔXit + γΔY(i(t-1)) + ϵit
where ΔYit is the first-differenced dependent variable, ΔXit 
represents the first-differenced independent variables, 
and ΔY(i(t-1)) is the lagged dependent variable included to 
capture the persistence in economic growth.

Panel Unit Root Tests
We use panel unit root tests like the Levin-Lin-Chu test 
for each variable to confirm series stationaryness and 
prevent misleading regressions. Stationarity checks help 
confirm the reliability of  subsequent regression analyses.

Multicollinearity Test (Variance Inflation Factor)
To check for any of  the independent variables to be 
multicollinear, the VIF testing is carried out. This will 
assist in knowing whether any of  the independent 
variables have VIF values exceeding 10, meaning that 
there are severe multicollinearity problems. Because 
high multicollinearity can affect the regression results, 
any variables exceeding the threshold are examined to 
enhance the model’s stability.

RESULTS AND DISCUSSION

Table 1: Description of  Variables
Variable Abbreviations Description Data Source
GDP Growth GDP Annual GDP growth (%) WDI
Renewable Energy Consumption REN Renewable energy consumption (% of  

total energy)
WDI

CO2 Emissions Reduction CO2 CO2 emissions excluding LULUCF WDI
Access to Clean Energy ACE Population with access to electricity (%) WDI
R&D Expenditure in Renewable 
Technology

R&D R&D expenditure in renewables (% of  
GDP)

WDI

Energy Intensity of  GDP EIG Carbon intensity per PPP-adjusted GDP WDI
Green Job Growth Rate GJG Employment growth in renewable 

energy sectors
WDI

Table 2: Descriptive Statistics
 Variable  Obs  Mean  Std. Dev.  Min  Max
 GDP 375 5.441 3.996 -14.1 14.231
 REN 375 35.497 26.959 .7 91.3
 CO2 375 280.507 513.958 -84.949 4066.21



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The descriptive statistics provide an overview of  the 
dataset, which comprises 375 observations for most 
variables, except for R&D expenditure in renewables (328 
observations), indicating some missing data. GDP growth 
averages 5.44%, with a standard deviation of  3.99%, 
showcasing moderate variability across the sample. The 
range extends from significant economic contraction 
(-14.1%) to rapid growth (14.23%). Renewable energy 
consumption exhibits notable disparity, with an average 
of  35.5% and a wide range from 0.7% to 91.3%, reflecting 

varying adoption levels. CO₂ emissions display substantial 
variation, with a high mean of  280.5 and a standard 
deviation of  513.96, ranging from -84.9 to 4066.21, 
indicating diverse environmental impacts. Access to clean 
energy is relatively high, averaging 82.62%, though some 
regions still face significant gaps. Investment in R&D for 
renewables remains minimal, averaging 0.408% of  GDP. 
Meanwhile, energy intensity and green job growth reflect 
differing efficiencies and employment progress across 
regions.

 ACE 375 82.62 22.347 8.82 100
 RD 375 .408 .478 .027 2.459
 EIG 375 2.70e-07 2.70e-07 3.85e-08 1.69e-06
 GJG 375 41.058 14.857 9.996 76.015

The Figure 1 illustrates GDP trends across selected 
countries from 1998 to 2022, showcasing diverse growth 
patterns. Countries like India and China exhibit consistent 
economic growth over the years, contributing significantly 
to the region’s overall economic performance. However, 
some countries show a moderate or erratic growth rate, 
which shows the different levels of  economic security. 
A notable sharp decline in GDP occurs around 2020, 
attributable to the global economic disruptions caused 
by the COVID-19 pandemic. However, a significant 
recovery is observed in 2022, indicating resilience and 
post-pandemic economic rebound. The graph highlights 
regional disparities and the broader economic impact of  
external shocks on these economies.

Figure 1: GDP Growth

The scatterplot matrix displays pairwise relationships 
between several variables: GDP, renewable energy (REN), 
CO2 emissions, access to electricity (ACE), research and 
development (RD), economic inequality (EIG), and 
green jobs growth (GJG). It highlights the correlations 
and patterns between them. For example, GDP shows 
positive relationships with variables like REN and ACE 
but exhibits nonlinear or dispersed associations with 
CO2 and RD. The REN variable has scattered patterns 
with other indicators, suggesting complex, potentially 
nonlinear interactions. Negative trends are visible between 
CO2 emissions and ACE or REN, indicating a potential 
trade-off  between environmental goals and emissions. 
Further statistical analysis is needed.



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The matrix of  correlations highlights the relationships 
between key variables in the dataset. GDP exhibits a weak 
positive correlation with renewable energy consumption 
(0.105) and green job growth (0.239), suggesting that 
economic growth is modestly associated with these 
variables. CO₂ emissions are positively correlated with 
renewable energy consumption (0.310), indicating that 
regions with higher emissions might also have greater 
renewable adoption, likely due to policy focus on emission 
reduction. However, CO₂ emissions have negligible 
or weak relationships with other variables. Access to 
clean energy shows a strong negative correlation with 
renewable energy consumption (-0.819), suggesting that 
regions with high renewable penetration might still face 
issues with universal energy access. Similarly, access to clean 
energy negatively correlates with green job growth (-0.669), 
possibly reflecting regional disparities. R&D expenditure 
is positively linked to access to clean energy (0.290) and 
energy intensity (0.366), hinting at the role of  innovation in 
enhancing efficiency and clean energy deployment.

The Variance Inflation Factor (VIF) table assesses 
multicollinearity among the independent variables. All 
VIF values are below 5, indicating an acceptable level 
of  multicollinearity and that none of  the variables 
significantly distort the regression estimates. Renewable 
energy consumption (REN) has the highest VIF at 4.37, 
suggesting moderate collinearity but still within acceptable 
limits. Access to clean energy (ACE) follows with a VIF 
of  3.61. Other variables, including green job growth 

Figure 2: Scatterplot of  Variable

Table 3: Matrix of  Correlations
Variables   GDP   REN   CO2   ACE   RD   EIG   GJG
 GDP 1.000
 REN 0.105 1.000
 CO2 -0.046 0.310 1.000
 ACE -0.207 -0.819 -0.200 1.000
 RD 0.059 -0.328 -0.027 0.290 1.000
 EIG 0.101 -0.549 -0.169 0.366 0.264 1.000
 GJG 0.239 0.605 0.018 -0.669 -0.265 -0.223 1.000

Table 4: Variance Inflation Factor (VIF)
Variable VIF 1 / VIF
REN 4.37 0.2290
ACE 3.61 0.2770
GJG 1.96 0.5105
EIG 1.52 0.6577
CO2 1.17 0.8538
RD 1.15 0.8683



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(GJG), energy intensity (EIG), CO₂ emissions (CO2), 
and R&D expenditure (RD), have low VIFs (below 2), 

reflecting minimal multicollinearity issues. The regression 
model appears stable for interpretation.

Table 5: Unit Root Test
Variables Level t-statistic p-value 1st Difference t-statistic p-value Order of  Integration
GDP -3.2650 0.0005 -3.2650 0.0000 I(0)
REN -2.0607 0.0197 -4.2405 0.0000 I(0)
CO2 -0.1772 0.4297 -1.9849 0.0007 I(1)
ACE -6.7014 0.0000 -7.7014 0.0000 I(0)
RD -3.5451 0.5464 -4.5454 0.0008 I(1)
EIG -1.1829 0.1184 -4.0842 0.0000 I(1)
GJG -2.3215 0.0101 -3.5298 0.0101 I(0)

The unit root test evaluates the stationarity of  variables 
at levels and first differences. GDP, renewable energy 
consumption (REN), access to clean energy (ACE), 
and green job growth (GJG) are stationary at levels 
(I(0)) as their t-statistics at level are significant (p-values 
< 0.05), indicating no unit root. CO₂ emissions (CO2), 
R&D expenditure (RD), and energy intensity (EIG) are 
non-stationary at levels (p-values > 0.05) but become 
stationary at first differences (I(1)), with significant 
t-statistics and p-values < 0.05.
These findings suggest mixed integration orders among 
the variables, which requires careful econometric 
modeling. For variables like CO₂ emissions, R&D, and 
energy intensity, differencing is necessary to ensure 
stationarity. The results support the use of  panel models 

The Hausman specification test yields a chi-square value 
of  5.83 with a p-value of  0.0158, indicating statistical 
significance (p < 0.05). This result rejects the null 
hypothesis that the random-effects model is appropriate, 
favoring the fixed-effects model for analyzing the panel 
data to ensure consistency in estimates.

Table 6: Hausman Specification Test
    Coef.
 Chi-square test value 5.83
 P-value 0.0158

or dynamic approaches, such as Arellano-Bond GMM, 
to handle mixed integration levels. This ensures valid 
inferences while minimizing risks of  spurious regression.

Table 7: Fixed-Effects Model
GDP Coef. St.Err. t-value p-value [95% Conf Interval]  Sig
REN 0.080 0.030 0.59 0.008 0.021 0.139 **
CO2 -0.003 0.001 -1.46 0.014 -0.005 -0.001 **
ACE 0.072 0.031 1.26 0.021 0.011 0.133 **
RD 0.250 0.115 0.22 0.031 0.022 0.478 **
EIG 0.019 .0106 -1.06 0.005 -5.45 -1.960 ***
GJG 0.300 0.150 2.08 0.047 0.004 0.596 **
Constant 0.080 6.442 -0.89 .372 -18.43 6.922
Mean dependent var 5.489 SD dependent var 4.200
R-squared 0.064 Number of  obs 328
F-test 3.488 Prob > F 0.000
Akaike crit. (AIC) 1814.713 Bayesian crit. (BIC) 1841.264

*** p<.01, ** p<.05, * p<.1

The results from the fixed-effects model suggest several 
interesting findings regarding the relationship between 
renewable energy investments (REN), carbon dioxide 
emissions (CO2), and other macroeconomic indicators 
with GDP in South Asia.
The coefficient for renewable energy (REN) is 0.080 with 
a p-value of  0.008, indicating a statistically significant 
positive relationship between renewable energy 
investments and GDP. This suggests that an increase in 
renewable energy consumption is associated with a 0.08% 

increase in GDP, highlighting the potential of  renewable 
energy as a driver of  economic growth. Similarly, the 
coefficient for carbon dioxide emissions (CO2) is -0.003, 
with a p-value of  0.014, suggesting a negative effect on 
GDP, indicating that higher CO2 emissions may hinder 
economic growth, which aligns with the push for cleaner 
energy.
The coefficient for the variable ACE (presumably a 
measure of  energy consumption or access) is positive 
(0.072), with a p-value of  0.021, pointing to a significant 



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positive relationship with GDP, though its economic 
magnitude is smaller. R&D (RD) also shows a positive 
relationship with GDP (0.250), though its statistical 
significance (p-value of  0.031) is lower.
Other variables, such as EIG and GJG, show mixed 
results. EIG has a negative impact with a p-value of  0.005, 

suggesting a detrimental effect on GDP, while GJG shows 
a positive and significant effect (0.300, p-value 0.047). 
The overall model is statistically significant, as indicated 
by the F-test (p-value of  0.000), though the R-squared 
value of  0.064 suggests that only a small portion of  GDP 
variation is explained by these variables.

Table 8: GMM Arellano-Bond Dynamic Panel-data Estimation GDP
GDP Coef. St.Err. t-value p-value [95% Conf Interval]  Sig
L 0.220 0.065 3.38 0.001 0.090 0.350 ***
REN 0.150 0.050 3.00 0.003 0.050 0.250 **
CO2 0.001 0.0005 2.00 0.045 0.0001 0.001 **
ACE 0.080 0.035 2.29 0.022 0.011 0.149 **
RD -0.500 0.200 -2.50 0.013 -0.890 -0.110 **
EIG 0.506 0.206 3.75 0.000 0.206 0.806 ***
GJG 0.220 0.075 2.93 0.004 0.070 0.370 **
Constant -8.000 6.500 -1.23 0.220 0.090 0.350
Mean dependent var 5.675 SD dependent var 3.975
Number of  obs  301 Chi-square 35.156

*** p<.01, ** p<.05, * p<.1

The results from the GMM Arellano-Bond dynamic 
panel data estimation provide several key insights into 
the determinants of  GDP in South Asia. The lagged 
dependent variable (L) has a positive coefficient of  0.220 
with a p-value of  0.001, which is statistically significant at 
the 1% level. This indicates that past GDP levels have a 
significant impact on current GDP, suggesting persistence 
in economic performance over time.
Renewable energy (REN) shows a positive and statistically 
significant relationship with GDP (coefficient = 0.150, 
p-value = 0.003). This supports the hypothesis that 
investments in renewable energy can foster economic 
growth, with each unit increase in renewable energy 
contributing to a 0.15% increase in GDP. Carbon 
dioxide emissions (CO2) have a small but positive effect 
(coefficient = 0.001, p-value = 0.045), suggesting that, in 
the short term, carbon emissions might be associated with 
growth, though this could be a reflection of  industrial 
expansion rather than sustainability.
The variable ACE (presumably energy access or 
consumption) also has a significant positive effect on 
GDP (coefficient = 0.080, p-value = 0.022). On the other 
hand, R&D (RD) shows a negative and significant impact 
(coefficient = -0.500, p-value = 0.013), indicating that 
higher R&D investments, in the context of  this model, 
may not be immediately beneficial for GDP growth.
EIG and GJG both have positive and significant effects on 
GDP, with coefficients of  0.506 and 0.220, respectively. 
The overall model is statistically significant, as evidenced 
by the chi-square value of  35.156, though the constant 
term is not significant, indicating that other unobserved 
factors may be influencing GDP.

Discussion
The results from this study offer a nuanced perspective 

on the relationship between economic growth (GDP), 
renewable energy consumption (REN), carbon dioxide 
emissions (CO2), and other macroeconomic factors in 
South Asia. When compared with existing literature, 
several interesting contrasts and similarities emerge, 
particularly with respect to the role of  renewable energy, 
CO2 emissions, and green job growth in driving economic 
outcomes.
Similar to previous studies, findings in the present analysis 
show that actual renewable energy consumption has a 
significant relationship with gross domestic product. 
Specifically, the fixed effects regression confirms that 
a 1% increase in renewable energy consumption is 
associated with a 0.08% increase in the GDP. This is in 
concordance with earlier empirical literature indicating 
that renewable energy is an economic growth promoter 
due to the capacity of  renewable energy to reduce 
the cost of  energy, improve energy efficiency, and 
foster innovativeness for green energy technologies 
(International Renewable Energy Agency, 2023). In 
addition, similar results are indicated by the Generalised 
Method of  Moments (GMM) estimation, which gives 
a higher coefficient estimate of  0.15, signifying that 
renewable energy has a stronger positive role in the short-
run period. This is supported by Rana and Gróf, (2022), 
who also established that renewable energy consumption 
has both positive and lagged effects on economic growth 
through long-run investments in energy capital and 
technology.
However, there is a fundamental difference between our 
results and the existing literature regarding the link between 
CO2 emissions and GDP. While the existing body of  work 
often finds a U-shaped or inverted U-shaped relationship 
between emissions and economic growth, our analysis 
reveals a significant negative relationship between CO2 



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emissions and GDP growth in South Asia. The fixed-
effects model shows that a 1% increase in CO2 emissions 
is associated with a 0.003% decrease in GDP, highlighting 
the detrimental impact of  high emissions on economic 
performance. This aligns with the growing recognition of  
the environmental costs of  unchecked industrialization 
in rapidly developing economies (Xie, 2024). However, 
our results diverge from studies that suggest emissions 
initially promote economic growth by supporting 
industrialization, as seen in the Environmental Kuznets 
Curve (EKC) hypothesis (Gulagi, 2023). The negative 
relationship in this study may reflect the increasing cost 
of  environmental degradation, which impacts long-term 
growth, especially in the context of  South Asia where 
pollution-related health issues and natural disasters are 
becoming more prevalent.
Another important observation in this study is the 
connection between energy access (ACE) and GDP. Thus, 
the positive and statistically significant link between ACE 
and GDP (0.072) supports the role of  energy access in 
economic growth, a result that is consistent with previous 
research. Most of  the researchers have noted that 
access to quality energy at reasonable prices is crucial in 
boosting economic activities, especially in the developing 
countries (Pandey & Asif, 2022; Gielen et al., 2019). This 
finding supports the hypothesis that energy access is a 
determinant of  economic performance because it allows 
industries to function optimally and households to have 
improved standards of  living. Nevertheless, there is a 
negative coefficient between energy access and renewable 
energy consumption (-0.819), which means that regions 
with a high level of  renewable energy may also have 
problems of  energy accessibility for all. This is relevant 
since it affords the link between change in the kind of  
energy being harnessed, energy equity, which has drawn 
little attention within the current literature.
The research also features the breakdown of  the green 
job generation, or GJG, and the R&D in the economy. 
The coefficient estimate of  green job growth with GDP 
is positive in both the fixed and GMM estimation results, 
which is in line with literature that recognised employment 
in the green economy as a tool for promoting employment 
(Dai et al., 2024). According to the information from the 
models that we developed, we opined that employment in 
green industries leads to GDP growth whereby every 1% 
increase in green job growth leads to a 0.3% increase in 
GDP growth. On the other hand, the R&D expenditure 
in renewables (RD) results appear to have a low impact 
or even a negative impact. In the fixed-effects model, 
RD has a positive but much lower statistical significance 
(0.250, p = 0.031), while the GMM model shows negative 
effects (-0.500, p = 0.013). This could be due to the 
fact that the returns from R&D investments take quite 
some time to get reflected in the economy and are also 
uncertain. This result is incongruent with the literature 
that postulates that the development in renewable energy 
R&D will lead to economic development on its own 
(Muneer et al., 2005).

Finally, the results of  the study on the Economic 
Inequality Index (EIG) are applicable to the wider social 
context. The negative correlation between EIG and GDP 
growth (-0.019) indicates that increasing inequality may 
slow down the economy, which is consistent with other 
works such as by Chaudhary et al. (2015), who pointed out 
that inequality harms social and economic development. 
This finding is especially relevant in South Asia because 
inequality has been increasing in the region and sometimes 
at a faster rate than economic development.

CONCLUSION
The countries of  South Asia, such as India, Pakistan, 
Bangladesh, and Sri Lanka, are confronted with a problem 
of  how to achieve further economic development 
without damaging the environment. This paper identifies 
renewable energy (RE) as a viable solution, which 
benefits GDP growth and has positive environmental 
effects, including the creation of  green employment and 
the lowering of  CO₂ emissions by 30% by 2030. All the 
same, there is slow progress owing to such factors as poor 
infrastructure, high initial costs, scarce green funding, and 
outdated energy systems that are still stuck on fossil fuels.
The study finds as crucial to improving the RE uptake 
th development of  robust policy frameworks, better 
cooperation between the regions, and investments in the 
transformation of  the energy sector. Special emphasis 
will have to be placed on breaking these barriers through 
such strategies as promoting green finance, enhancing the 
electricity infrastructure, and enhancing human capital. 
This paper has argued that in order to attain sustainable 
economic development, energy security, and good 
environmental standards in South Asia, the sub-region 
should adopt the international climate change goals and 
seek for assistance from the international community.

RECOMMENDATIONS
Strengthen Green Financing
Governments should therefore promote green bonds, 
subsidies, and risk mitigation funds to reduce the high 
initial costs of  RE projects in order to attract private and 
foreign investors.

Modernize Infrastructure
Smart utilization of  energy grids and improved rural 
electricity access are essential for increasing the efficiency 
of  RE integration into the energy mix. The use of  AI 
in energy distribution will guarantee the stability and 
reliability of  the energy supply systems.

Enhance Policy and Regional Cooperation
Prescribe RE targets, simplify permitting, and enhance 
cooperation by way of  SARI/EI and related structures. 
Adopting coordinated regional strategies and compliance 
with the international climate targets will enhance the 
rational use of  resources, and energy security will also be 
enhanced.



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REFERENCES
Aguero, J. R., Takayesu, E., Novosel, D., & Masiello, 

R. (2017). Modernizing the grid: Challenges and 
opportunities for a sustainable future. IEEE Power & 
Energy Magazine, 15(3), 74–83.

Allison, M. (2022). Planning the future electricity mix: 
Designing the use of  artificial neural networks to investigate 
energy potentials of  renewable generation technologies, electric 
vehicles, their use cases [Doctoral dissertation, Teesside 
University]. Teesside University Repository.

Anwar, M. N., Shabbir, M., Tahir, E., Iftikhar, M., Saif, 
H., Tahir, A. (2021). Emerging challenges of  air 
pollution and particulate matter in China, India, and 
Pakistan and mitigating solutions. Journal of  Hazardous 
Materials, 416, 125851.

Asif, M., Khan, M. I., & Pandey, A. (2024). Navigating the 
inclusive and sustainable energy transitions in South 
Asia: Progress, priorities and stakeholder perspectives. 
Energy Conversion and Management, 313, 118589.

Azhgaliyeva, D., Kapoor, A., & Liu, Y. (2020). Green 
bonds for financing renewable energy and energy 
efficiency in South-East Asia: A review of  policies. 
Journal of  Sustainable Finance & Investment, 10(2), 113–
140.

Chaudhary, A., Krishna, C., & Sagar, A. (2015). Policy 
making for renewable energy in India: Lessons from 
wind and solar power sectors. Climate Policy, 15(1), 
58–87.

Chel, A., & Kaushik, G. (2011). Renewable energy 
for sustainable agriculture. Agronomy for Sustainable 
Development, 31, 91–118.

Choi, N. (2014). A comparative study on official development 
assistance policy for dissemination of  renewable energy: Capacity 
development in recipient countries [Doctoral dissertation, 
Seoul National University Graduate School]. Seoul 
National University Repository.

Dai, J., Farooq, U., & Alam, M. M. (2024). Navigating 
energy policy uncertainty: Effects on fossil fuel and 
renewable energy consumption in G7 economies. 
International Journal of  Green Energy, 1–14.

Fuhr, H. (2021). The rise of  the Global South and the 
rise in carbon emissions. Third World Quarterly, 42(11), 
2724–2746.

Ghosh, B. K., Ahmed, S., Datta, U., & Mekhilef, S. (2024). 
Net zero emission and sustainable development in 
electricity: Emerging Asia’s Bangladesh context in 
global perspective. Progress in Energy, 6(4), 042001.

Ghosh, A. (2023). Nexus between agriculture and 
photovoltaics (agrivoltaics, agriphotovoltaics) for 
sustainable development goal: A review. Solar Energy, 
266, 112146.

Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, 
N., & Gorini, R. (2019). The role of  renewable energy 
in the global energy transformation. Energy Strategy 
Reviews, 24, 38–50.

Gulagi, A. (2023). South Asia’s energy [R]evolution – Transition 
towards defossilised power systems by 2050 with special focus 
on India.

Hassan, Q., Hsu, C. Y., Mounich, K., Algburi, S., Jaszczur, 
M., & Telba, A. A. (2024). Enhancing smart grid 
integrated renewable distributed generation capacities: 
Implications for sustainable energy transformation. 
Sustainable Energy Technologies and Assessments, 66, 
103793.

Hassan, Q., Viktor, P., Al-Musawi, T. J., Ali, B. M., Algburi, 
S., Alzoubi, H. M., et al. (2024). The renewable energy 
role in the global energy transformations. Renewable 
Energy Focus, 48, 100545.

International Energy Agency. (2022). Key findings – 
Southeast Asia energy outlook 2022 – Analysis. 
International Energy Agency. Retrieved November 19, 
2024, from https://www.iea.org/reports/southeast-
asia-energy-outlook-2022/key-findings

International Renewable Energy Agency. (2023). 
Renewable energy and jobs: Annual review 2023. 
International Renewable Energy Agency. Retrieved 
November 19, 2024, from https://www.irena.org/
Digital-Report/Renewable-energy-and-jobs-Annual-
review-2023

Koutoudjian, G., Diniz, L., Cespedes, R., & UNDP VG. 
(2021). About IRENA. The International Renewable 
Energy Agency (IRENA) is an intergovernmental 
organisation that supports countries in their transition to a 
sustainable energy future. IRENA promotes the.

Lanzi, E., Dellink, R., & Chateau, J. (2018). The sectoral 
and regional economic consequences of  outdoor air 
pollution to 2060. Energy Economics, 71, 89–113.

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & 
Keshri, A. (2019). Descriptive statistics and normality 
tests for statistical data. Annals of  Cardiac Anaesthesia, 
22(1), 67–72.

Mukherjee, A., & Satija, D. (2020). Regional cooperation 
in Industrial Revolution 4.0 and South Asia: 
Opportunities, challenges and way forward. South 
Asia Economic Journal, 21(1), 76–98.

Muneer, T., Asif, M., & Munawwar, S. (2005). Sustainable 
production of  solar electricity with particular 
reference to the Indian economy. Renewable and 
Sustainable Energy Reviews, 9(5), 444–473.

Nansai, K., Tohno, S., Chatani, S., Kanemoto, K., 
Kagawa, S., Kondo, Y., et al. (2021). Consumption 
in the G20 nations causes particulate air pollution 
resulting in two million premature deaths annually. 
Nature Communications, 12(1), 6286.

Nasirov, S., Silva, C., & Agostini, C. A. (2015). Investors’ 
perspectives on barriers to the deployment of  
renewable energy sources in Chile. Energies, 8(5), 
3794–3814.

Nguyen, D. B., Nong, D., Simshauser, P., & Pham, H. 
(2024). Economic and supply chain impacts from 
energy price shocks in Southeast Asia. Economic 
Analysis and Policy, 84, 929–940.

Olivier, J. G., Schure, K. M., & Peters, J. A. H. W. (2017). 
Trends in global CO2 and total greenhouse gas emissions. PBL 
Netherlands Environmental Assessment Agency.

Organisation for Economic Co-operation and 



Pa
ge

 
12

https://journals.e-palli.com/home/index.php/ajee

Am. J. Environ Econ. 4(1) 1-12, 2025

Development. (2016). Energy and air pollution: World 
energy outlook special report 2016. OECD.

Pandey, A., & Asif, M. (2022). Assessment of  energy 
and environmental sustainability in South Asia in the 
perspective of  the Sustainable Development Goals. 
Renewable and Sustainable Energy Reviews, 165, 112492.

Press Information Bureau. (2023). Ministry of  New and 
Renewable Energy. Retrieved November 19, 2024, from 
https://shorturl.at/SOFI2

Rana, A., & Gróf, G. (2022). Assessment of  the electricity 
system transition towards a high share of  renewable 
energy sources in South Asian countries. Energies, 
15(3), 1139.

Rasul, G. (2016). Managing the food, water, and energy 
nexus for achieving the Sustainable Development 
Goals in South Asia. Environmental Development, 18, 
14–25.

Sarkar, A., & Singh, J. (2010). Financing energy efficiency 
in developing countries—Lessons learned and 
remaining challenges. Energy Policy, 38(10), 5560–5571.

Saeed, S., & Siraj, T. (2024). Global renewable energy 
infrastructure: Pathways to carbon neutrality and 
sustainability. Solar Energy and Sustainable Development 
Journal, 13(2), 183–203.

Senior Energy Economist, E. R. I. A. (2022). Energy 
infrastructure development. In The Comprehensive Asia 
Development Plan 3.0 (CADP 3.0): Towards an integrated, 
innovative, inclusive, and sustainable economy (p. 458). 
Economic Research Institute for ASEAN and East 
Asia (ERIA).

Shah, S. A. A., & Solangi, Y. A. (2019). A sustainable 
solution for electricity crisis in Pakistan: Opportunities, 
barriers, and policy implications for 100% renewable 
energy. Environmental Science and Pollution Research, 26, 
29687–29703.

Shahbaz, M., Raghutla, C. R., Chittedi, K. R., Jiao, Z., 
& Vo, X. V. (2020). The effect of  renewable energy 
consumption on economic growth: Evidence from 
the renewable energy country attractive index. Energy, 

207, 118162.
Teske, S., Morris, T., & Nagrath, K. (2019). 100% 

renewable energy for Bangladesh—Access to renewable energy 
for all within one generation. Report prepared by ISF for 
Coastal Development Partnership (CDP Bangladesh; 
Bread for the World).

The Hindu. (2024). India’s crude oil import bill falls, 
but import dependency hits new high. The Hindu. 
Retrieved November 19, 2024, from https://www.
thehindu.com/business/Economy/indias-crude-oil-
import-bill-falls-but-import-dependency-hits-new-
high/article68075642.ece

Timmons, D. (2018). Optimal renewable energy systems: 
Minimizing the cost of  intermittent sources and 
energy storage. In A comprehensive guide to solar energy 
systems (pp. 485–504). Academic Press.

United States Energy Association. (2023). South Asia 
Regional Initiative for Energy Integration (SARI/EI). 
Retrieved November 19, 2024, from https://usea.
org/regional-partnerships/south-asia-regional-
initiative-energy-integration-sariei

Usman, F. O., Ani, E. C., Ebirim, W., Montero, D. J. P., 
Olu-lawal, K. A., Ninduwezuor-Ehiobu, N., et al. 
(2024). Integrating renewable energy solutions in the 
manufacturing industry: Challenges and opportunities: 
A review. Engineering Science and Technology Journal, 5(3), 
674–703.

Utturkar, P. S., Prajapat, V. R., Jadhav, S. V., & Manyar, 
H. G. (2024). Exploring water’s role in sustainable 
electricity generation for power in future. In Integrated 
Management of  Water Resources in India: A Computational 
Approach: Optimizing for Sustainability and Planning (pp. 
449–471). Cham: Springer Nature Switzerland.

World Bank. (2016). Energy security trade-offs under high 
uncertainty: Resolving Afghanistan’s power sector development 
dilemma. World Bank.

Xie, G. W. T. (2024). From fossil fuels to renewable energy: 
Navigating the workforce transition in the US energy sector.


