









































Pa
ge

 
1



Pa
ge

 
80

American Journal of  Applied 
Statistics and Economics (AJASE)

Estimating Rural Premium and Financial Crisis Effect on the Nexuses between Food, 
Energy, and Water Consumption on Urban-Rural Income Gap in South–Eastern Asian 

Countries Using Pooled Regression Analysis
Khambai Khamjalas1*

Volume 3 Issue 1, Year 2024
ISSN: 2992-927X (Online)

DOI: https://doi.org/10.54536/ajase.v3i1.2600
https://journals.e-palli.com/home/index.php/ajase

Article Information ABSTRACT

Received: February 29, 2024

Accepted: April 02, 2024

Published: April 06, 2024

Poverty and inequality reduction and access to affordable clean energy and clean water are 
among the global sustainable development goals. Yet, researchers have overlooked how 
food, energy, and water (FEW) resources can be instrumental in reducing the urban-rural 
income gap. In this article, ordinary least squares regression analysis was used to estimate 
rural premium and financial crisis effect on the nexuses between food, energy, and water 
consumption on urban-rural income gap using a sample of  data pooled from three Asian 
countries: China, India, and Indonesia over 2000 to 2019. No significant urban or crisis effect 
on the poverty rate was established. However, a significant crisis effect on the poverty gap 
was established, but not on the urban premium. A significant positive interaction between 
food insecurity and water was established. Water supply improves agricultural production, 
improves food security, and reduces poverty by raising income. However, modern clean 
energy is associated with rising income inequality. Modern energy technologies benefit a few 
wealthier individuals investing in the energy and agriculture sectors. Therefore, improved 
water access, particularly to support food production and affordability, and efficient utilization 
of  clean fuels and technologies among all individuals, regardless of  socio-economic class, are 
crucial to escaping poverty and achieving prosperity. 

Keywords

Energy, Food, Income Inequality, 
Poverty Gap, Poverty Rates, 
Water

1 School of  Economics and Trade, Hunan University, Yuelu District, Changsha 410006, P.R. China
* Corresponding author’s e-mail: baibay1980@gmail.com

INTRODUCTION
Ensuring equity in food, water, and energy resource 
distribution for a rapidly growing population remains 
a fundamental development challenge in South Asian 
countries. The population of  Asia is the largest among 
all the continents in the world. China (1.43 billion people) 
and India (1.37 billion) were the world’s most populous 
countries, with 19% and 18% of  the global population 
in 2019, respectively. The US and Indonesia are the third 
and fourth most populous countries, with 329 million and 
271 million people in 2019, respectively. The population 
of  Asia is expected to increase from 4.7 billion in 2023 
to 5.3 billion in 2055, still ranking first, followed by 
Africa at 2.7 billion, while other continents trail below a 
million. India is projected to surpass China as the world’s 
most populous country around 2027 (United Nations 
[UN], 2019). The UN (2019) anticipates that India could 
remain the world’s most populous country, with about 1.5 
billion people, followed by China with about 1.1 billion. 
The 2023 estimates, as of  October, indicate that the 
population of  Asia is 4.753 billion, equivalent to 59.1% 
of  the total world population (8.045). Southern Asia leads 
with about 2.027 billion, followed by Eastern Asia (1.662), 
South-Eastern Asia (0.686 billion), Western Asia (0.298), 
and Central Asia (0.078). The Asian urban population 
is estimated at 52.6 % (2.500 billion). By 2025, Asia will 
account for about 61.4 % of  the world’s population, with 
a 53.8 % (2.590 billion) urban population (Worldometer, 
2023). Given its rising population, the region faces 
mounting challenges in meeting the growing demand for 

food, water, and energy for a rapidly growing population. 
Consequently, Asia’s high population has contributed 
to income inequality between the rural households with 
adequate access to food, energy, and water, given their 
high incomes.
Food insecurity is a common problem worldwide, 
particularly in developing countries, often caused by 
rising population and natural disasters such as drought, 
flood, and epidemics. The 2021 Global Hunger Index 
(GHI) report indicated that despite the overall decline 
in GHI (score between 0: hunger and 100: worst) from 
series levels in 2006 (25.1) and 2012 (20.4) to moderate 
in level in 2021 (17.9) there is evident continental and 
intercountry disparity (Grebmer et al., 2021). Africa, 
South of  the Sahara regions, ranks highly (30.5 in 2012 
Vs. 27.1 in 2021), followed by South Asia (29.2 in 2012 Vs. 
26.1 in 2021), West Asia and North Africa (14.1 in 2012 
Vs. 12.7 in 2021), Latin America and the Caribbean (8.5 
in 2012 Vs. 8.7 in 2021), East and South-east Asia (11.0 
in 2012 Vs. 8.5 in 2021), and lastly Europe and Central 
Asia (7.5 in 2012 Vs. 6.5 in 2021). Despite the low GHI 
ranking (< 9.9) among South-east Asian Countries, there 
are countries with moderate (10.0 – 19.9) and serious 
(20.0 – 34.9) indices in 2021. Timor-Leste (32.4) leads 
in terms of  GHI, followed by Laos (19.5), Cambodia 
(17.0), Indonesia (18.0), the Philippines (16.8), and 
Vietnam (13.6). Malaysia (12.8), and lastly Thailand [11.7] 
(Grebmer et al., 2021; Our World in Data, n.d.). Drivers 
of  food insecurity include climate change through severe 
high drought sporadic rainfall patterns (Mbow et al. 2020). 



Pa
ge

 
81

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

There has been a remarkable rise in access to electricity 
globally over the past decades. The United Nations report 
2019 indicated that the global electricity access rate rose 
from 75% percent in 2000 to 90% in 2019 (United 
Nations Statistics Division [UN], 2021). The efforts 
align with the SDG7 7.1.1 target of  universal access 
to electricity by 2030. Yet, electrification progress has 
indicated regional discrimination. Central and Southern 
Asia and Sub-Saharan Africa registered the largest access 
deficit in 2019. In Central and Southern Asia, the access 
rate rose significantly from 59% in 2000 to 95% in 2019, 
whereas Sub-Saharan Africa rose from 24% to 46% over 
the same period. Besides, there is also evident urban-rural 
discrimination in access to electricity. The UN (2021) 
estimated that 97 million people in urban zones and 471 
million in rural remained unelectrified in Central and 
Southern Asia and Sub-Saharan Africa.
In addition, there is global inequality in access to clean 
fuels and technologies (liquid petroleum gas, natural 
gas, and biogas). In 2019, 66% of  the global population 
had access to clean cooking fuels and technologies, 
leaving about 2.6 billion households, mostly from Asia 
and Africa, reliant on inefficient and polluting cooking 
methods. While the global annual increase in the number 
of  people with clean cooking access from 2015 to 2019 
was remarkably high at 82.5%, regional inequality is 
evident. Central and Southern Asia registered a 23.6% 
increase, 12.8% in Eastern and South-Eastern Asia, 
27.2% in Sub-Saharan Africa, and 8.6% in Western and 
Northern Africa. Besides, urban-rural discrimination 
also exists. Generally, the urban population is the 
leading consumer of  clean energy. However, the annual 
growth rate of  access to clean energy in rural areas has 
registered an impressive growth since 2000 (about 0.1%), 
growing faster, reaching 2% in 2019. Contrarily, urban 
areas seem to be in a decelerating face since 2011, with 
improvements in clean cooking access stagnating in urban 
areas, registering a growth of  less than 0.5% since 2011 
(UN, 2021). The exponential growth in access to clean 
energy in rural areas could potentially help lower income 
inequality since energy is a resource that can be used in 
economic activities such as in the food and beverages, 
hotel, or hospitality sectors.
Generally, there is a remarkable electrification effort 
globally. The extent to which such a global agenda 
is welfare-oriented remained unexplored by existing 
empirical literature since the ultimate drive is to minimize 
climate change effects by minimizing carbon emissions. 
Since FEW are essential resources for socio-economic 
development, uneven distribution of  the resources could 
further widen income inequality between the rural and 
urban settings. Interestingly, between 2010-2019, the 
growth in access to clean fuels and technologies was 
dominated by the most populous countries, Brazil, China, 
India, Indonesia, and Pakistan, registering a combined 
growth rate of  2% (UN, 2021). Therefore, the current study 
examined how improved access to FEW resources influences 
the rural income gap in South-East Asian Countries.

LITERATURE REVIEW
Literature was reviewed based on the study’s objectives, 
investigating the relationship between food, energy, and 
water supply and household incomes.

Water Access, Food Availability, and Incomes
Increasing water productivity is vital in improving 
sustainable agriculture food security since water is useful 
in crop, tree, livestock, and fish production. Agriculture 
accounts for 72% of  global freshwater withdrawal from 
the world’s rivers, lakes, and groundwater aquifers yearly 
(Center for Strategic & International Studies, 2019). Most 
farming worldwide is carried out as mixed crop-livestock 
farming, covering about 2.5 billion hectares of  land (n.d.). 
As a result, Agriculture is a source of  farm income sales 
of  products like meat, milk and hides, livestock, and 
crop produce to a majority of  households, especially in 
the humid and sub-humid regions of  South-East and 
East Asia, which have registered the greatest increase in 
irrigated mixed farming systems (Food and Agriculture 
Organization [FAO], n.d.). Major constraints in livestock 
production, especially in arid and semi-arid areas, are feed 
shortage, animal diseases, and low productivity, especially 
during the dry season. Common coping strategies to feed 
scarcity among pastoralists in arid and semi-arid areas are 
conserving crop residues and hay, purchasing roughages, 
reducing herd size, and renting grazing land (Duguma & 
Janssens, 2021). Such remedies often lead to losses since 
dry roughages are not nutritious and may reduce animals’ 
live weight. Reducing herd size is often associated with a 
throwaway price since holding large herd in dry seasons 
could lead to huge losses from animal mortality (Roche 
et al., 2021).
Improved water access improves livestock production 
and health through increased fodder and legume feed 
production, livestock watering, and sustainable pasture 
grazing management, hence increasing earnings from 
livestock sales through increased live weight while 
reducing animal mortality (Mbow et al., 2020; Mayberry 
et al., 202; Ndlovu et al., 2020). Monjardino et al. (2020) 
established that maintaining a high-quality legume crop in 
the mixed crop-livestock system increases farm income 
and reduces financial risk from integrating more resilient 
livestock and higher crop revenue among traditional 
mixed smallholder farms in South-East Asia. Hashmi et 
al. (2021) established that while Pakistan has the world’s 
largest integrated irrigation system, water scarcity has 
constrained farmers to shift cultivation from water-
intensive crops like rice, wheat, cotton, and sugarcane 
to other crops and vegetables, which require less water, 
thus increasing pressure in the food market. Such a shift 
threatens the diversification in agriculture, reducing the 
incomes of  households with limited access to water for 
irrigation due to financial constraints in collecting water 
from a distant source by ferrying using vehicles. Thus, 
providing public or institutional water supply is essential 
in ensuring sustainability in agricultural production. 
Increased irrigation improved the productivity of  crops 



Pa
ge

 
82

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

by maintaining health and vigorously growing crops 
through optimized water, nutrients, and agronomic 
management (Descheemaeker et al., 2013).
Lastly, aquaculture is predominately reliant on water. 
According to the FAO (2021), in 2020, the total global 
aquaculture production comprised 122.6 million tonnes 
of  aquatic animals, with 87.5 million tonnes used for 
human consumption. The largest producing region is 
Asia, accounting for over 88.4% of  the total fisheries 
and aquaculture production of  aquatic animals in 2020, 
substantially higher than Africa (2.57), Americas (5.00), 
Europe (3.74), and Oceania (0.26). Due to its high 
population, China (mainland) has produced more farmed 
aquatic animals than the rest of  the world Since 1991. 
Its share in world aquaculture production was 64.13% 
for aquatic animals in 2020, followed by India (11.16), 
Indonesia (6.75), Vietnam (5.95), Bangladesh (3.34), 
and the rest of  Asia (8.67). Thus, increased water supply 
increases water usage in aquaculture, especially in Asian 
countries like Indonesia and China, which are leading 
world fish producers.
Domestic aquaculture increases the incomes of  
smallholder fish farmers supply while providing domestic 
consumption (Tran et al., 2017). Generally, improving 
agricultural water productivity creates synergies in crop 
and livestock production. It could consequently increase 
earning from sales of  the produce, raising the incomes of  
most households who would have trailed below poverty 
lines. Fitton et al. (2019) established that approximately 
11% and 10% of  current crops and grasslands could 
decline due to a reduction in water availability and may 
lose their productive capacity, particularly in Africa, the 
Middle East, China, Europe, and Asia. Thus, improving 
water access in these regions is critical in improving 
agricultural productivity and, hence, the incomes of  
households. Aquaculture’s benefits include food sources, 
livelihood improvement through sales of  aquatic life, and 
nutrition and health (Ahmed & Thompson, 2019; Mills et 
al., 2019; Dinesh, 2016).

Energy and Income Inequality
There is a lack of  consensus on the relationship 
between clean fuel and income inequality. Generally, the 
benefits of  access to clean energy sources are realized 
through employment opportunities, economic growth, 
education, industrialization, and improved healthcare 
outcomes (Acheampong, Dzator Shahbaz, 2021). Access 
to electricity improves the livelihoods of  households 
through incomes from investments and employment 
opportunities; improves food production through 
cultivation, harvesting, processing, preservation, and 
transportation; and improves water productivity through 
processes such as water desalination, filtering, treatment, 
distribution, harvesting, recycling (Biggs et al., 2015; 
Nilsson, Griggs, & Visbeck, 2016).
In China, Ma et al. (2021) established that increased per 
capita energy consumption leads to declining energy 
poverty alleviation and inequality reduction in rural 

households. Huang et al. (2020) outline that labour 
migration from rural to urban areas is a structural change 
and footpaths of  the negative effect of  energy supply on 
income inequality. According to the authors, low-carbon 
policies have the greatest impact on employment across 
all energy. Thus, labor will migrate from rural to urban 
areas for better jobs. The reduced rural population brings 
new opportunities for the modernization of  agriculture, 
increasing the income of  rural residents. The income gap 
among urban residents will widen in the short run due to 
the labor demand and education level differentials. In the 
end, equitable development of  resources between urban 
and rural areas is achieved.
Using panel data for 166 countries to investigate from 
1990 to 2017, Acheampong, Dzator, and Shahbaz (2021) 
established that access to electricity reduces global 
income inequality, while access to modern and clean 
energy increases global income inequality. Further, the 
authors revealed that rural and urban electrification 
reduced income inequality; however, the elasticity of  
urban electrification exceeds rural electrification. Thus, 
there could be heterogenous effects of  access to clean 
energy on income inequality in urban and rural settings 
– which was controlled for in this study to minimize the 
bias in the regression results.
Other studies have shown that access to clean energy 
increases income inequality. Using panel data from 46 
countries in Sub-Saharan Africa from 1990 to 2017, 
Sarkodie and Adams (2020) established that access to 
electricity widens income inequality. According to the 
authors, access to electricity widens the income gap 
between the rich and the poor since modern energy 
technologies tend to benefit the wealthier in terms of  
better investment opportunities, such as in the energy 
and agriculture sectors. Besides, there is no clear direction 
of  the causality effect between income inequality and 
renewable energy consumption. Uzar (2020) established 
that a declining income inequality will enhance 
renewable energy consumption. Xu and Zhong (2023) 
established that high-income inequality increases energy 
consumption. 
The lack of  consensus on whether income inequality 
implies that the findings could be contextual based on 
region, methodology, and variables used. In this study, 
data is pooled from three Asian countries to estimate how 
FEW nexuses influence can influence income inequality 
while controlling for the 2008/09 financial crisis and 
socio-economic class of  rural and urban settings.

METHODOLOGY
Study Design
The study adopts a panel study design. The study used 
a convenient sample of  three Asian countries, China, 
India, and Indonesia since the data on the rural and 
urban settings on all the study variables were missing. The 
two outcome measures were poverty rates and poverty 
gap. The predictors were FEW resource metrics: food 
insecurity, access to electricity, access to clean fuels and 



Pa
ge

 
83

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

technologies for cooking, and improved water access. 
A binary predictor was created to delimit 1 for if  urban 
and zero otherwise to establish the urban premium. 
Lastly, existing empirical evidence indicates that countries 
with high populations have been associated with greater 
income inequality (Krieger & Meierrieks, 2019). The 
data were collected from different sources, comprising 

the World Bank’s (WB) World Development Index 
Database (WDI), the Food and Agriculture Organisation 
(FAO), and Our World in Data (OWID) databases. The 
data was collected for three countries (China, India, and 
Indonesia) between 2000 and 2019. The study variables’ 
operationalization and data source are summarized in 
Table 1.

Table 1: Variables, Units, And Sources
Code Name Description Units Source
Pv_rate Poverty rate Poverty headcount ratio (% of  the population living 

below $2.15)
% World Bank. 

(n.d.-a)
Pv_gap poverty gap The ratio by which the mean income of  the poor 

falls below the poverty line ($2.15)
% World Bank. 

(n.d.-a)
Foodins prevalence of  severe 

food insecurity food 
insecurity

China and India: Food Insecurity Experience Scale 
(FIES). This indicator measures the proportion of  
people uncertain of  having or unable to acquire 
enough food because they have insufficient money 
or other resources. Indonesia; Prevalence of  
undernourishment (% of  population)

% FAO (2019)

Elcacc Access to electricity % of  the cohort (urban/rural) population who have 
success with electricity

% World Bank. 
(n.d.-b)

Cleanf Access to clean fuels and 
technologies for cooking

% of  the cohort population who have access to 
clean fuels and technologies for cooking

% World Bank. 
(n.d.-b)

Impwacc Improved water access People using at least a basic improved drinking water 
source includes piped water on premises (piped 
household water connection located inside the user’s 
dwelling, plot, or yard, public taps or standpipes, tube 
wells or boreholes, protected dug wells, protected 
springs, and rainwater collection).

% OWID, 
(n.d.-c).

Pop Population size Annual population size OWID
Notes. foodins by urban and rural areas were incomplete and were imputed using MA (3) and empirical studies depicting the rural-urban 
gap on food insecurity.

Data Analysis
The study used pooled ordinary least square (OLS) 
regression analysis to examine the urban premium 
and financial crisis effect on poverty rate and poverty 
gap and the nexuses between food, energy, and water 
consumption on urban-rural income gap in south-eastern 
Asia. The data was pooled across rural and urban settings 
in three Asian countries: China, India, and Indonesia 
from 2000 to 2019. Therefore, a rural-urban setting 
premium is captured by using an urban dummy. A rural 

dummy variable, labeled rural which takes the value “0” 
for each ith observation in a rural setting and takes the 
value “1” for the urban setting. A crisis dummy variable, 
labeled crisis will be created that takes the value “0” for 
each ith observation for the years between 2000 and 2010 
and takes the value “1” for each observation ith from the 
year 2010 to 2019. Let Yi be the welfare measure i, the 
study seeks to fit two OLS linear regression models in the 
form represented in Equation 1.
Where Yi are predict income inequality and poverty 

index, X(i,k) is a vector of  FEW resources’ measures; ∆ is 
the differential operator whose order is dependent on the 
stationarity of  the data; α is the interaction effect between 
treatment and time, βs are the regression coefficients of  
each of  the four FEW metrics, namely, the prevalence 
of  severe food insecurity, access to electricity, clean fuels 
and technologies for cooking, and water; τ denotes the 

interaction effect of  the FEW resources that help identify 
the nexus between FEW in influencing in urban and rural 
income gap; φ is the crisis effect (crs), ω is the urban 
(urbn) premium/loss; θ is the interaction effect crisis and 
urban setting (crs*urbn); ∆lnpop is  natural of  population 
size, πi is the population effect; i is the observation index, 
s is the observation index, c is the country index, and t 



Pa
ge

 
84

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

is the time index; and εi is the error term capturing the 
variation in outcome measures not accounted for by the 
model.

RESULTS
The OLS regression analysis was done to estimate 
rural premium and financial crisis effect on the nexuses 
between food, energy, and water consumption on the 
urban-rural income gap using a sample of  data pooled 
from three Asian countries: China, India, and Indonesia. 
To ensure the robustness and validity of  the findings, the 
diagnostic tests are first reported as follows.

Diagnostics Tests
Four diagnostic tests, stationary, multicollinearity, 
heteroscedasticity, and normality tests, were examined to 
ensure that the reported regression results do not bias the 

regression estimates.

Stationary Test
The stationarity test was done using a Fisher-type 
Augmented Dickey-Fuller unit-root test since it does 
not require strongly balanced data (Choi, 2001)). It tests 
the null hypothesis that all panels contain a unit root 
against an alternative hypothesis that at least one panel 
is stationary.
The stationary test was done separately since the data 
was panel data stacked by urban-rural setting. All the 
variables provide sufficient evidence that the urban first 
differenced data is stationary at a 10% significance level. 
Besides, all the variables provide sufficient evidence that 
the rural first differenced data is stationary at a 10% 
significance level. Therefore, the first differenced series of  
continuous data was used in regression analysis (Table 2).

Table 2: Stationarity Test for Urban and Rural Data
Variable Urban Rural

Level First difference Level First difference
Inverse 
Chi-squared

p-
value

Inverse 
Chi-
squared

p-
value

Inverse 
Chi-
squared

p-
value

Inverse 
Chi-
squared

p-
value

Poverty rate 5.17 0.522 26.11*** 0.000 0.791 0.992 16.66* 0.011
poverty gap 6.82 0.338 23.78*** 0.001 2.69 0.846 24.04*** 0.001
food insecurity 21.88*** 0.001 10.98* 0.089 21.82*** 0.001 10.64*** 0.100
Access to electricity 12.67** 0.049 43.93*** 0.000 6.85 0.335 29.86*** 0.000
Access to clean fuels and 
technologies for cooking

42.11*** 0.000 23.08*** 0.001 37.67*** 0.000 11.59*** 0.072

Improved water access 38.88 0.000 16.63** 0.011 73.67*** 0.000 11.27* 0.080
Population growth 8.8 0.185 11.35* 0.078 9.92 0.128 16.93** 0.010

Notes: The reported statistics are based on the inverse-normal transformations. The drift option was specified since all the series’ means 
are nonzero. Two lags in the ADF regressions and cross-sectional means were removed using demean; degrees of  freedom = 6. Significant 
codes: *** p < .001; ** p < .01; * p < .05.

Multicollinearity
The multicollinearity of  the predictors (excluding the 
interaction terms) was examined using the variance 

inflation factor and Tolerance factor. Since the average 
VIF is less than 5, multicollinearity is not a severe problem 
in the regression results. See Table 3.

Table 3: Variance Inflation Factors of  The Predictor Variables
Variable Variance inflation factor Tolerance factor
lnimpwacc_d1 10.73 0.093
lnpop_d1 9.52 0.105
urban 7.52 0.133
urbanpostcrisis 3.17 0.315
postcrisis 2.47 0.404
lncleanf_d1 2.32 0.431
lnfoodins_d1 1.39 0.719
lnelcacc_d1 1.08 0.927
Mean VIF 4.78



Pa
ge

 
85

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

Heteroskedasticity Test
The results provide strong evidence of  violation constant 
variance at a 5% significance level in the poverty rates, 
based on the Breusch-Pagan test for heteroskedasticity,  

χ2(1) = 32.3, p = .001 and the poverty gap model, χ2(1) = 
20.14, p < .001 (Table 4).
Thus, clustered standard errors by country are used to 
correct for heteroscedasticity.

Table 4: Breusch-Pagan / Cook-Weisberg Test for Heteroskedasticity
Model Degrees of  freedom Chi-square Statistic p-value
Poverty rate model 1 32.30 0.000
Poverty gap model 1 20.14 0.000

Normality Test
The residuals for the poverty rates and poverty gap models 
(without interaction terms) are approximately normally 

distributed, indicating that the models substantially satisfy 
the linearity assumption (Figure 1).

Figure 1: Histogram of  the Residuals for The Poverty Rates and Poverty Gap Models

Correlation Analysis
The correlation analysis of  the study variables based on 
the first differenced variables was done by setting (rural 
and urban) and aggregately using the pooled data. The 
correlation between poverty rate vs. food insecurity and 
electricity access is not statistically significant in all three 
panels at a 10% significance level (p > .1). Besides, the 
correlation between the poverty gap vs. improved water 

access and clean fuel access is not statistically significant 
in all the three panels at a 10% significance level (p > 
.1) However, poverty rates seem to be positive and 
statistically significantly correlated with improved water 
access (r = .305, p < .1) and clean fuel access (r = 0.299, p 
< .1) in urban areas only at a 10% significance level (Table 
5). (See Figure 2– 5) (Table 5).

Table 5: Correlation Analysis Between First Differenced Poverty Gap and Rate Vs. Food Insecurity
Correlation pairs Aggregate Rural Urban
Food insecurity Poverty rate 0.058 0.101 0.006

Poverty gap 0.075 0.031 0.111
Water access Poverty rate -0.001 -0.1335 0.306*

Poverty gap -0.048   -0.138 0.194
Clean fuel access Poverty rate 0.186* 0.171 0.299*

Poverty gap 0.141 0.149 0.241
Electricity Access Poverty rate 0.143 0.169 0.174

Poverty gap 0.148 0.187 0.152
Notes. *** p<0.01, ** p < 0.05, * p < 0.1



Pa
ge

 
86

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

Figure 2: Correlation Analysis Between First Differenced Poverty Rate and Gap Vs Food Insecurity

Figure 3: Correlation Analysis Between First Differenced Poverty Rate and Gap vs. Improved Water Access

Figure 4: Correlation Analysis Between First Differenced Poverty Rate and Gap vs. Clean Fuel



Pa
ge

 
87

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

Figure 5: Correlation Analysis Between First Differenced Poverty Rate and Gap vs. Electricity Access

REGRESSION RESULTS
The regression results indicate no significant urban 
premium effect on poverty rates (β= 0.006, p > .05). 
Access to clean fuels and technologies for cooking is the 
only resource that contributes to increasing poverty rates 
at a 10% significance level (β=1.508,p<.1) and poverty 
gap (β=1.741,p<.05) at a 5% significance level.  Thus, a 
1% increase in clean fuels and technologies increases the 
poverty rate and gap by 1.508% and 1.741%, respectively. 

The results show no significant urban (-0.160) and crisis 
(-0.156) effect on poverty rate. However, a significant 
negative crisis effect at a 10% significance level (β= 
-0.245, p<.1) on poverty gap was established but not 
urban premium (β= -0.226, p >.1). Further, a significant 
interaction between food insecurity and water (β= -176.1, 
p < .05) and between clean fuel and cooking technologies 
and electricity (β= 12.59, p > .05) on poverty gap was 
established (See Table 6).

Table 6: The OLS Regression Results Predicting the Urban Premium and Crisis Effect on the Nexuses between 
FEW Resources and Poverty Rates and Poverty Gaps
Variables (1) (2) (3) (4) (5) (6)

Ln Poverty 
rates_D1

Ln Poverty 
rates_D1

Ln Poverty 
rates_D1

Ln Poverty 
Gap_D1

Ln Poverty 
Gap_D1

Ln Poverty 
Gap_D1

lnfoodins_d1 0.0455 0.660 0.0627 0.408
(0.411) (0.850) (0.430) (1.159)

lncleanf_d1 0.199 1.508* 0.277 1.741**
(0.242) (0.509) (0.215) (0.365)

lnelecacc_d1 0.537 1.203 0.705 0.535
(0.299) (1.077) (0.491) (1.114)

lnimpwacc_d1 10.14 11.67 4.833 4.640
(8.140) (9.168) (8.893) (12.27)

foodins_fuel 2.761 8.982
(6.991) (7.804)

foodins_electricity -1.582 1.248
(13.04) (13.66)

foodins_water -111.6 -176.1**
(40.43) (32.48)

fuel_electricity 9.261 12.59*
(4.046) (3.210)

fuel_water -111.4 -114.9
(53.56) (51.61)



Pa
ge

 
88

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

electricity_water -220.8 -177.7
(184.2) (177.4)

urban 0.006 -0.160 -0.212 0.022 -0.165 -0.226
(0.005) (0.082) (0.139) (0.014) (0.069) (0.132)

postcrisis -0.156 -0.212 -0.176 -0.245*
(0.101) (0.0892) (0.0920) (0.0801)

urbanpostcrisis 0.134 0.229 0.135 0.235
(0.107) (0.106) (0.0864) (0.0904)

lnpop_d1 6.656* 5.989 5.895* 5.092
(1.619) (2.659) (1.847) (3.069)

Constant -0.160 -0.199* -0.187** -0.188 -0.168* -0.144
(0.064) (0.052) (0.043) (0.066) (0.057) (0.071)

Observations 114 114 114 114 114 114
R-squared 0.000 0.217 0.251 0.002 0.183 0.217

Notes. Robust standard errors are in parentheses. The clustered standard errors were done in urban and rural settings. All the series were 
first differenced (D1) since all the series were first stationary at first difference.; Significant codes: *** p < 0.01, ** p < 0.05, * p < 0.1.

DISCUSSION
The study used pooled OLS regression analysis to 
examine the urban premium and financial crisis effect 
on poverty rate and poverty gap, the nexuses between 
food, energy, and water consumption on the urban-rural 
income gap in South-eastern Asia. Using sample data 
pooled across a rural and urban setting in three Asian 
countries, China, India, and Indonesia, from 2000 to 
2019, the regression results indicate that access to clean 
fuels and technologies for cooking is the only resource 
that contributes to increasing poverty rates and gaps. 
The correlation analysis indicates that poverty rates are 
positive and statistically significantly correlated with 
improved clean fuel access in urban areas but not rural 
areas. While clean energy has been rising globally, its 
benefits and impacts may vary across rural and urban 
areas due to economic barriers to accessing clean energy 
services. Clean fuels and cooking technologies, including 
solar, electric, biogas, natural gas, liquefied petroleum gas 
(LPG), and alcohol fuels, including ethanol, are more 
abundant in urban areas (UN, 2021). Comparatively, 
while rural areas have seen a substantial increase in 
cleaner gaseous fuels, biomass fuels, such as charcoal, 
are the dominant form of  cooking energy. Such trends 
could be influenced by the fact that clean energy could 
be expensive to rural households, where most have lower 
incomes and larger families than those in urban areas. 
Thus, rural households might otherwise opt for firewood 
collected for free in the neighborhood; poor households 
are also likely to have low access to energy. As a result, 
clean energy supply creates a negative feedback loop in 
urban areas.
The high abundance in urban areas and a greater social 
stratification means that the elasticity of  poverty rates is 
highly responsive to clean energy supply. Ma and Liao 
(2018) established that the income effect is positive 
for cleaner fuels (LPG and electricity) but negative 
for biomass fuels like coal. Adopting cleaner fuels as 

primary cooking fuel was income elastic among rural 
households but inelastic for urban households, whereas 
the substitution of  dirty fuels is all income inelastic. 
Thus, rural households may not be responsive since most 
rely on biomass. Other studies have suggested reverse 
causality, from income inequality to access to clean 
fuels. Using data from 14 Latin American and Caribbean 
countries, Murshed (2023) established that increasing 
income inequality aggravates the urban-rural inequality 
in clean cooking fuel accessibility by improving and 
reducing urban and rural clean cooking fuel access rates, 
respectively. In another study, Acheampong, Dzator, and 
Shahbaz (2021) revealed that rural and urban electrification 
reduced income inequality. However, the elasticity of  
urban electrification exceeds rural electrification. Thus, 
the greater social stratification means that the elasticity of  
poverty rates is highly responsive to clean energy supply.
In addition, most high-income earners rely on clean 
energy; hence, their consumption and utilization levels 
in income-generating activities such as in the hotel or 
hospitability and industry sector can go up. However, 
most low-income earners in urban areas rely on clean 
fuels for household cooking (UN, 2021). The same also 
applies to rural households who might heavily rely on 
clean fuels for home usage and not for income generation. 
Thus, raising clean energy could increase the proportion 
of  the population living below the poverty line, usually 
taken as half  the median household income of  the total 
population, as a few households earn relatively more than 
the majority if  the households. The assertion is consistent 
with the stronger evidence that a 1% increase in clean 
fuels and technologies increases the poverty gap by 
1.741%. To mitigate such tradeoff, affordability of  clean 
fuels and cooking technologies and enhancing income-
generating activities that utilize clean energy in rural and 
urban low-income households should be a top priority in 
achieving SDG 7.
The results also indicated a significant positive interaction 



Pa
ge

 
89

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

between food insecurity and water. The finding implies 
that the positive impact of  food insecurity on poverty 
gaps is lessened by increasing water access. The finding 
is consistent with the expectation that agricultural 
production relies heavily on water. Water supply is 
essential for agriculture, as it affects the productivity and 
sustainability of  crop and livestock systems. For instance, 
Fitton et al. (2019) established that approximately 11% and 
10% of  current crops and grasslands could decline due to 
reduced water availability and may lose their productive 
capacity, particularly in Africa, the Middle East, China, 
Europe, and Asia.
Water supply also minimizes annual crop and livestock 
losses, reducing poverty gaps and improving food security 
(Ndlovu, Prinsloo & Le Roux, 2020). Irrigated cropping 
systems can benefit from improved water productivity by 
maintaining healthy, vigorously growing crops through 
optimized water, nutrient, and agronomic management 
(Descheemaeker et al., 2013). Irrigation also reduces 
the risk of  crop failure due to droughts and allows for 
multiple cropping seasons (Organisation for Economic 
Co-operation and Development, n.d.).
Likewise, water supply supports livestock production, 
an important income source and nutrition for many 
poor households. Water availability and quality affect 
animals’ health, growth, and reproduction. Livestock 
water productivity can be increased through sustainable 
grazing or feeding management from planted crops and 
pasture and livestock watering availability, leading to 
reduced animal mortality, which is a common problem 
among pastoralists who live in arid and semi-arid areas 
(Descheemaeker et al., 2013; Gusha, 2019; Otte et al., 
2019). Livestock also provide manure, which can fertilize 
crops and improve soil quality. Conversely, crop residues 
as animal feed, animal traction for land preparation, and 
crop-livestock rotations for pest and disease control 
create synergies between crops and animals, improving 
agriculture’s efficiency and sustainability (Baiyeri et al., 
2019). Thus, water scarcity negatively impacts poverty 
gaps propagated through reduced crop and livestock 
production.
The finding is also consistent with empirical evidence. 
For instance, Monjardino et al. (2020) established that 
maintaining a high-quality legume crop in the mixed 
crop-livestock system increases farm income and reduces 
financial risk from integrating a more resilient livestock 
and higher crop revenue among traditional mixed 
smallholder farms in South East Asia. Besides, aquaculture 
improves livelihoods through selling aquatic life (Ahmed 
& Thompson, 2019; Mills et al., 2019; Dinesh, 2016). 
Thus, increasing population access to basic improved 
drinking water sources includes piped water, public taps, 
wells or boreholes, and springs, increasing water available 
for crop production, livestock keeping, and aquaculture. 
The increasing incomes, in turn, lessen poverty gaps as 
low incoming earners’ incomes rise.
The results also indicated a significant positive interaction 
between clean fuel and cooking technologies and 

electricity on the poverty gap. The finding implies that 
clean energy widens poverty gaps. The study finding is 
consistent with existing empirical evidence of  Sarkodie 
and Adams (2020) that established that rising access to 
clean energy has increased income inequality in Sub-
Saharan Africa. According to Sarkodie and Adams (2020), 
access to electricity increases the income gap between the 
rich and the poor. Modern energy technologies benefit 
the wealthier in terms of  better investment opportunities, 
such as in the energy and agriculture sectors. Besides, Xu 
and Zhong (2023) established that high-income inequality 
increases energy consumption, implying a tentative 
positive correlation between access to clean energy. While 
Huang et al. (2020) argue that clean energy supplies reduce 
income inequality through structural unemployment, 
where labour migrates from rural to urban areas until 
an equilibrium is attained, it might occur in the long run 
since existing rural households might not get the requisite 
skills to take up new jobs created the energy sector. Thus, 
a rising energy supply can keep increasing the incomes of  
a few individuals, whereas the majority who live in urban 
areas remain unemployed or unemployed.
Lastly, the results indicated that poverty gaps were generally 
lower in the post-global financial crisis of  2008-2009. 
The finding can be associated with structural changes 
in employment and investments that the financial crisis 
might have shaped due to the housing market bubbles in 
the real estate sector (Bartmann, 2017). The shock might 
have shifted investments to more resilient sectors like 
energy and food supplies. Besides, lending institutions’ 
moral hazard might have made banks minimize market 
risks by financing more resilient sectors dealing with 
FEW resources since they are the basic life-supporting 
needs. Other confounders include rising education skills 
(Lee & Lee, 2018), declining unemployment rates, and 
labour unions that push for equity in wage distribution 
through their collective bargaining power (Dosi et al., 
2018), and welfare policies such as progressive taxation 
(Oishi, Kushlev, & Schimmack, 2018) that have been 
established to lower income inequality.

CONCLUSIONS
In this study, OLS regression analysis was done to 
estimate rural premium and financial crisis effect on the 
nexuses between food, energy, and water consumption 
on urban-rural income gap using a sample of  data pooled 
from three Asian countries: China, India, and Indonesia. 
The results revealed increased clean energy (fuel and 
electricity) has contributed to rising poverty gaps. To 
mitigate the tradeoff  between clean energy supply and 
poverty supply, especially in rural areas, a holistic and 
inclusive approach is needed to ensure that clean fuels 
and technologies for cooking contribute to sustainable 
development for all. Besides, supplying affordable clean 
fuels and technologies and enhancing income-generating 
activities that utilize clean energy in rural and urban low-
income households should be a top priority in achieving 
SDG 7.



Pa
ge

 
90

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

Improved water access is crucial in increasing crop and 
livestock production and lowering poverty gaps. Water 
supply improves agricultural production, improving 
food security and reducing poverty by raising income 
from crops, livestock, and fish farming, especially among 
low-income households heavily reliant on agriculture. 
Thus, equitable access to water resources, regardless of  
the socio-economic class in the society, particularly to 
support food production, is instrumental in reducing 
poverty and hunger.

REFERENCES
Acheampong, A. O., Dzator, J., & Shahbaz, M. 

(2021). Empowering the powerless: does access 
to energy improve income inequality?. Energy 
Economics, 99, 105288. https://doi.org/10.1016/j.
eneco.2021.105288 

Ahmed, N., & Thompson, S. (2019). The blue dimensions 
of  aquaculture: A global synthesis. Science of  the Total 
Environment, 652, 851-861. https://doi.org/10.1016/j.
scitotenv.2018.10.163 

Akhtar, S., Ahmed, R., Begum, K., Das, A., & Banu, 
S. (2023). CRISPR/Cas in Improvement of  Food 
Crops for Feeding the World into the Future. In 
Advanced Crop Improvement, Volume 2: Case Studies of  
Economically Important Crops (pp. 529-566). Cham: 
Springer International Publishing. https://doi.
org/10.1007/978-3-031-26669-0_19 

Baiyeri, P. K., Foleng, H. N., Machebe, N. S., & Nwobodo, 
C. E. (2019). Crop-livestock interaction for sustainable 
agriculture. Innovations in Sustainable Agriculture, 557-
582. https://doi.org/10.1007/978-3-030-23169-9_18 

Bartmann, R. (2017). Causes and effects of  2008 financial 
crisis. Furtwangen, Germany: HFU Business School, 
Internationale Betriebswirtschaft.

Biggs, E. M., Bruce, E., Boruff, B., Duncan, J. M., Horsley, 
J., Pauli, N., ... & Imanari, Y. (2015). Sustainable 
development and the water–energy–food nexus: 
A perspective on livelihoods. Environmental Science 
& Policy, 54, 389-397.  https://doi.org/10.1016/j.
envsci.2015.08.002 

Descheemaeker, K., Bunting, S. W., Bindraban, P., 
Muthuri, C., Molden, D., Beveridge, M., ... & Jarvis, D. 
I. (2013). Increasing water productivity in agriculture. 
Managing water and agroecosystems for food 
security. Wallingford (UK): CABI Publishing, 104-123. 
Dimension_WDI_Time. 

Dinesh, D. (2016). Agricultural practices and technologies 
to enhance food security, resilience and productivity in a 
sustainable manner: Messages to the SBSTA 44 agriculture 
workshops. http://hdl.handle.net/10568/71051 

Dosi, G., Pereira, M. C., Roventini, A., & Virgillito, M. E. 
(2018). The effects of  labour market reforms upon 
unemployment and income inequalities: an agent-
based model. Socio-Economic Review, 16(4), 687-720. 
https://doi.org/10.1093/ser/mwx054 

Duguma, B., & Janssens, G. P. (2021). Assessment of  
livestock feed resources and coping strategies with dry 

season feed scarcity in mixed crop–livestock farming 
systems around the gilgel gibe catchment, Southwest 
Ethiopia. Sustainability, 13(19), 10713. https://doi.
org/10.3390/su131910713 

FAO (Food and Agriculture Organization), (2021). 
Aquaculture production. [online; cited October 
2023.] Available from URL: https://www.fao.
org/3/cc0461en/online/sofia/2022/aquaculture-
production.html 

FAO (Food and Agriculture Organization), n.d., 
Livestock in crop land. [online; cited October 2023.] 
Available from URL: https://www.fao.org/3/
x5304e/x5304e04.htm 

Fitton, N., Alexander, P., Arnell, N., Bajzelj, B., Calvin, K., 
Doelman, J., ... & Smith, P. (2019). The vulnerabilities 
of  agricultural land and food production to future 
water scarcity. Global Environmental Change, 58, 101944. 
https://doi.org/10.1016/j.gloenvcha.2019.101944 

Gusha, B. (2019). Livestock Water Productivity: Towards 
Improving Rural Livelihoods from Livestock in Semi-
Arid Rangelands (Doctoral dissertation, Rhodes 
University). https://commons.ru.ac.za/vital/access/
services/Download/vital:34084/SOURCE1 

Hashmi, H. A., Belgacem, A. O., Behnassi, M., Javed, K., 
& Baig, M. B. (2021). Impacts of  Climate Change on 
Livestock and Related Food Security Implications—
Overview of  the Situation in Pakistan and Policy 
Recommendations. Emerging Challenges to Food 
Production and Security in Asia, Middle East, and 
Africa: Climate Risks and Resource Scarcity, 197-239. 
https://doi.org/10.1007/978-3-030-72987-5_8 

Huang, H., Roland-Holst, D., Wang, C., & Cai, W. (2020). 
China’s income gap and inequality under clean 
energy transformation: A CGE model assessment. 
Journal of  Cleaner Production, 251, 119626. https://doi.
org/10.1016/j.jclepro.2019.119626 

Krieger, T., & Meierrieks, D. (2019). Income inequality, 
redistribution and domestic terrorism. World 
Development, 116, 125-136. https://doi.org/10.1016/j.
worlddev.2018.12.008 

Lee, J. W., & Lee, H. (2018). Human capital and income 
inequality. Journal of  the Asia Pacific Economy, 23(4), 
554-583. https://doi.org/10.1080/13547860.2018.1
515002 

Ma, C., & Liao, H. (2018). Income elasticity of  cooking fuel 
substitution in rural China: Evidence from population 
census data. Journal of  cleaner production, 199, 1083-
1091. https://doi.org/10.1016/j.jclepro.2018.06.215 

Ma, S., Xu, X., Li, C., Zhang, L., & Sun, M. (2021). 
Energy consumption inequality decrease with energy 
consumption increase: Evidence from rural China at 
micro scale. Energy Policy, 159, 112638. https://doi.
org/10.1016/j.enpol.2021.112638 

Mayberry, D., Hau, D. K., Dida, P. R., Bria, D., Praing, J., Mata, 
A. D., ... & Nulik, J. (2021). Herbaceous legumes provide 
several options for increasing beef  cattle productivity in 
eastern Indonesia. Animal Production Science, 61(7), 698-
707. https://doi.org/10.1071/AN20545 



Pa
ge

 
91

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

Am. J. Appl. Stat. Econ. 3(1) 80-91, 2024

Mbow, C., Rosenzweig, C. E., Barioni, L. G., Benton, T. 
G., Herrero, M., Krishnapillai, M., & Diouf, A. A. 
(2020). Food Security (No. GSFC-EDAA-TN78913).

Mills, D., Tilley-WorldFish, A., Pereira, M., & Fleming, 
A. (2019). project Exploring options to improve 
livelihoods and resource management in Timor-
Leste’s coastal communities. https://www.aciar.gov.
au/sites/default/files/project-page-docs/final_
report_fis.2010.097.pdf  

Monjardino, M., Philp, J. N. M., Kuehne, G., 
Phimphachanhvongsod, V., Sihathep, V., & Denton, 
M. D. (2020). Quantifying the value of  adopting a 
post-rice legume crop to intensify mixed smallholder 
farms in Southeast Asia. Agricultural Systems, 177, 
102690. https://doi.org/10.1016/j.agsy.2019.102690 

Murshed, M. (2023). The relevance of  reducing income 
inequality for eliminating urban-rural divide in clean 
cooking fuel accessibility: Evidence from Latin 
America and the Caribbean. Energy, 278, 127718. 
https://doi.org/10.1016/j.energy.2023.127718 

Ndlovu, E., Prinsloo, B., & Le Roux, T. (2020). Impact 
of  climate change and variability on traditional 
farming systems: Farmers’ perceptions from south-
west, semi-arid Zimbabwe. Jàmbá: Journal of  Disaster 
Risk Studies, 12(1), 1-19. http://dx.doi.org/10.4102/
jamba.v12i1.742 

Nilsson, M., Griggs, D., & Visbeck, M. (2016). Policy: map 
the interactions between Sustainable Development 
Goals. Nature, 534(7607), 320-322. https://doi.
org/10.1038/534320a 

OECD (Organisation for Economic Co-operation and 
Development), n.d., Water and agriculture. [online; 
cited October 2023.] Available from URL: https://
www.oecd.org/agriculture/topics/water-and-
agriculture/ 

Oishi, S., Kushlev, K., & Schimmack, U. (2018). Progressive 
taxation, income inequality, and happiness. American 
psychologist, 73(2), 157. https://psycnet.apa.org/
doi/10.1037/amp0000166 

Otte, J., Pica-Ciamarra, U., & Morzaria, S. (2019). A 
comparative overview of  the livestock-environment 
interactions in Asia and Sub-Saharan Africa. Frontiers 
in Veterinary Science, 6, 37. https://doi.org/10.3389/
fvets.2019.00037 

Our World in Data, n.d. -a, “Global hunger index”. Our 
World in Data. [online; cited October 2023.] Available 
from URL: https://ourworldindata.org/grapher/
global-hunger-index 

Our World in Data., n.d. -b, “Installed solar energy 
capacity [Chart]. [online; cited October 2023.] 
Available from URL: https://ourworldindata.org/
grapher/installed-solar-pv-capacity. 

Pacific, A., East, M., America, S., & Content, S. (2024). 
Water, Energy, Food, and Terrorism: Towards a 
Nexus Approach. Energy & Environment. https://

saisreview.sais.jhu.edu/water-energy-food-and-
terrorism-towards-a-nexus-approach/ 

Roche, X., Rozstalnyy, A., TagoPacheco, D., Pittiglio, C., 
Kamata, A., Beltran Alcrudo, D., ... & Sumption, K. 
(2021). Introduction and spread of  lumpy skin disease 
in South, East and Southeast Asia: Qualitative risk 
assessment and management. Food & Agriculture 
Org.. 

Sarkodie, S. A., & Adams, S. (2020). Electricity access, 
human development index, governance and income 
inequality in Sub-Saharan Africa. Energy Reports, 6, 
455-466. https://doi.org/10.1016/j.egyr.2020.02.009 

Tran, N., Rodriguez, U. P., Chan, C. Y., Phillips, M. 
J., Mohan, C. V., Henriksson, P. J. G., ... & Hall, S. 
(2017). Indonesian aquaculture futures: An analysis 
of  fish supply and demand in Indonesia to 2030 
and role of  aquaculture using the AsiaFish model. 
Marine Policy, 79, 25-32. https://doi.org/10.1016/j.
marpol.2017.02.002 

UN, D., 2019, World population prospects 2019: 
Highlights. United Nations Department for Economic 
and Social Affairs. [online; cited October 2023.] 
Available from URL: https://population.un.org/
wpp/Publications/Files/WPP2019_Highlights.pdf  

United Nations Statistics Division, (2021). The Sustainable 
Development Goals Report 2021 - Extended Report: 
Goal 7. Indicator 7.1.2: Proportion of  population 
with primary reliance on clean fuels and technology, 
p. 4. [online; cited October 2023.] Available from 
URL:  https://unstats.un.org/sdgs/report/2021/
extended-report/Goal%20%287%29_final.pdf  

Uzar, U. (2020). Is income inequality a driver for 
renewable energy consumption?. Journal of  cleaner 
production, 255, 120287. https://doi.org/10.1016/j.
jclepro.2020.120287 

World Bank, n.d.-a, Poverty and equity data portal. [online; 
cited October 2023.] Available from URL: https://
pip.worldbank.org/poverty-calculator?src=BGD. 

World Bank, n.d.-b., World development indicators. 
[online; cited October 2023.] Available from URL: 
https://databank.worldbank.org /source/world-
development-indicators#selected  

World Bank, n.d.-c., Prevalence of  moderate or severe 
food insecurity in the population (%). [online; cited 
October 2023.] Available from URL: https://data.
worldbank.org/indicator/SN.ITK.MSFI.ZS 

Worldometer, 2023, Population of  Asia (2023). 
Worldometer. [online; cited October 2023.] Available 
from URL:  https://www.worldometers.info/world-
population/asia-population/ 

Xu, Q., & Zhong, M. (2023). The impact of  income 
inequity on energy consumption: The moderating 
role of  digitalization. Journal of  Environmental 
Management, 325, 116464. https://doi.org/10.1016/j.
jenvman.2022.116464


