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Energy Efficiency, Energy Pricing and Household Poverty: Implications
for Nigeria’s Energy Transition Strategy
Joseph Otsayi UDENYI,
1
Andrew NANDE,
2*
Adewale Emmanuel ADEGORIOLA,
3
Joseph PAUL,
4
1 2 3 4
Department of Economics, Federal University of Lafia
*Corresponding Author
DOI:
https://dx.doi.org/10.47772/IJRISS.2025.915EC00757
Received: 03 October 2025; Accepted: 18 October 2025; Published: 12 November 2025
ABSTRACT
Nigeria faces the dual challenge of widespread poverty and chronic energy constraints, making the design of a
sustainable and inclusive energy transition strategy both urgent and complex. While global evidence highlights
the role of energy efficiency in reducing household vulnerability and improving welfare, empirical research on
its poverty-reducing potential in Nigeria remains limited. This study investigates the impact of energy efficiency,
energy prices, and key socio-economic variables on household poverty, drawing on the Energy-Led Growth
Hypothesis (ELGH) and Welfare Economics Theory (WET) as its theoretical foundation. Using the
Autoregressive Distributed Lag (ARDL) technique, the analysis captures both short- and long-run dynamics.
The results show that energy efficiency significantly reduces poverty in both the short and long run, confirming
its potential as a cost-effective welfare-enhancing mechanism. Conversely, rising energy prices exert poverty-
increasing effects, while education (literacy) is found to be a significant driver of poverty reduction. Urbanization
also demonstrates strong short-run poverty-reducing effects, whereas variables such as government expenditure,
employment, household consumption, and inflation show weaker or insignificant impacts. The error correction
term indicates a stable long-run relationship, with about 31 percent of disequilibrium corrected annually. The
study concludes that energy efficiency should be prioritized within Nigeria’s energy transition strategy, but tariff
reforms must be carefully sequenced with social protection measures, targeted subsidies, and investments in
education and infrastructure. These findings highlight the importance of combining energy policy with welfare-
enhancing interventions to ensure that Nigeria’s transition to sustainable energy is inclusive and equitable.
Keywords: Energy efficiency; Household poverty; Energy transition; Welfare economics; Nigeria.
JEL Classification: Q43, I32, Q48, and D63
INTRODUCTION
Energy poverty has remained a persistent challenge across sub-Saharan Africa, undermining efforts to alleviate
income poverty, enhance health outcomes, and promote sustainable development. In Nigeria, which is Africa’s
most populous country, over 85 million people still lack access to electricity, while millions more experience
unreliable and unaffordable energy services (World Bank, 2021). This has entrenched multidimensional poverty
and deepened the socio-economic divide between rural and urban households. Historically, Nigeria’s energy
sector has been dominated by inefficient grid systems, dependence on fossil fuels (particularly diesel and petrol
generators), and minimal household-level investments in energy-efficient technologies (International Energy
Agency [IEA], 2022). These inefficiencies have contributed to high energy costs, frequent blackouts, and
reliance on harmful traditional fuels such as firewood and charcoal, especially among rural and low-income
populations.
Globally, energy efficiency has long been recognised as the “first fuel”, the cheapest and fastest strategy for
managing energy demand, reducing emissions, and enhancing welfare (IEA, 2018). For households, improved
energy efficiency translates into lower energy expenditures, better indoor air quality, and enhanced productivity,
particularly for women and small enterprises. Despite its transformative potential, energy efficiency has
historically been under-prioritised in Nigeria’s policy mix due to limited awareness, weak regulation, low
investment in efficient appliances and housing infrastructure, and poorly targeted subsidy systems.
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Against this backdrop, Nigeria launched its Energy Transition Plan (ETP) in 2022, becoming the first African
country to develop a comprehensive, data-driven roadmap to achieve net-zero emissions by 2060 (Federal
Government of Nigeria [FGN], 2022). A central pillar of this strategy is the mainstreaming of energy efficiency
across residential, industrial, and transport sectors. The rationale is clear: improving energy efficiency not only
reduces greenhouse gas emissions but also enhances affordability and energy access, which are the two
prerequisites for reducing household poverty. The ETP specifically targets interventions in clean cooking,
appliance standards, off-grid solutions, and energy-efficient buildings, aiming to deliver inclusive, low-carbon
growth and lift 100 million people out of poverty by 2030 (FGN, 2022).
Despite these ambitious goals, there remains a significant empirical gap in understanding how energy efficiency
interventions affect household poverty in practice. Most existing studies have focused on the macroeconomic or
environmental outcomes of energy efficiency, with little attention to its distributive impacts in developing
countries. This study addresses that gap by examining the relationship between energy efficiency and household
poverty in Nigeria, using data and policy insights that would be beneficial to the implementation of the ETP. It
seeks to answer a crucial policy question: Can energy efficiency be a tool for poverty reduction, or will the
energy transition reinforce existing inequalities?
Poverty in Nigeria has historically remained stubbornly high, with 40.7% of Nigerians projected to live below
the international poverty line of US$2.15/day (2017 PPP) by the end of 2024 (Frontier Markets, 2024). Inflation
averaging nearly 24.7% in 2023 has further eroded household welfare, driven by rising food and energy prices,
subsidy reforms, and currency depreciation (Frontier Markets, 2024). At the same time, over 71% of the
population lack access to modern energy services, exacerbating vulnerabilities and limiting welfare outcomes
(World Economic Forum, 2023).
Theoretically, this study is anchored on the Energy-Led Growth Hypothesis (ELGH) and the Welfare Economics
Theory (WET). ELGH posits that energy is a critical input in the growth process; improvements in efficiency
and affordability directly foster economic performance and social welfare (Apergis & Payne, 2012). WET
emphasises the role of redistributive policies, public investment, and inclusive access in ensuring that growth
translates into poverty reduction (Pigou, 1932; Sen, 1999). Together, these frameworks highlight energy
efficiency not just as a driver of growth, but also as a potential pathway for advancing welfare in resource-
constrained economies like Nigeria.
This study contributes to the literature in three ways. First, it introduces a normalised measure of energy
efficiency as a central explanatory variable in poverty analysis in Nigeria. Second, it integrates socio-economic
and macroeconomic determinants (household consumption, energy prices, government expenditure,
employment, education, inflation, and urbanization) within a unified theoretical framework grounded in ELGH
and WET. Third, it applies the Autoregressive Distributed Lag (ARDL) framework to capture both short- and
long-run dynamics, offering insights into the immediate and structural drivers of household poverty.
CONCEPTUAL AND THEORETICAL FRAMEWORK
Conceptual Framework
Household poverty is multidimensional, influenced not only by income but also by access to energy, healthcare,
education, employment, and macroeconomic stability. In Nigeria, persistent energy poverty constrains
household consumption, limits productivity, and exacerbates inequality (World Bank, 2023; IEA, 2022). This
study adopts a framework in which poverty (POV) is affected by a combination of energy-related variables and
socio-economic factors. Energy efficiency (ENEF) plays a central role, by reducing the amount of energy
required to generate a unit of output, higher efficiency lowers household energy expenditures, improves
affordability, and enhances welfare outcomes (IEA, 2018). Conversely, higher energy prices (ENERPr) increase
the cost of living, disproportionately affecting poor households. Household consumption growth (HHCEGr)
captures changes in household demand and welfare, while government spending growth (GOVEXPGr) reflects
public interventions that may mitigate poverty through subsidies, social protection, or investment in energy and
infrastructure (Oyadeyi et al., 2024).
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Other socio-economic variables include employment (EMP), which directly determines income-generating
capacity; education (EDUltr), which enhances human capital and access to better opportunities; inflation (INFL),
which erodes real purchasing power; and urbanization (URB), which influences access to infrastructure but can
also reinforce inequalities between rural and urban populations. Together, these variables provide a
comprehensive lens to assess how energy and macroeconomic conditions shape household poverty in Nigeria.
Theoretical Framework
This study is guided by two complementary theoretical perspectives: the Energy-Led Growth Hypothesis
(ELGH) and the Welfare Economics Theory (WET).
Energy-Led Growth Hypothesis (ELGH)
The Energy-Led Growth Hypothesis (ELGH) argues that energy is a fundamental driver of economic growth,
where improvements in energy access and efficiency directly enhance production, household welfare, and
overall economic performance (Apergis & Payne, 2012). Within this framework, energy efficiency plays a
crucial role by reducing costs and freeing resources for alternative consumption needs, thereby improving
welfare outcomes. However, higher energy prices may have the opposite effect, as they can reduce welfare and
hinder growth, particularly in contexts where households rely heavily on fossil fuels. Household consumption,
on the other hand, reflects welfare improvements that are often driven by affordable and efficient energy use,
while urbanization, with its relatively better energy infrastructure, can amplify the positive impact of energy
efficiency on welfare. Thus, ELGH suggests that an economy characterized by efficient energy use and
affordable energy prices is more likely to achieve sustained poverty reduction.
Welfare Economics Theory (WET)
The Welfare Economics Theory (WET) emphasizes fairness, redistribution, and the role of government in
ensuring that the benefits of economic growth are equitably shared across all segments of society (Pigou, 1932;
Sen, 1999). The theory highlights the limitations of market forces in addressing poverty and advocates state
intervention through public spending and inclusive policies. In this regard, government expenditure on energy
subsidies, education, and social protection is considered vital for enhancing welfare. Employment is equally
important, as it provides income and improves living standards, while education expands human capabilities and
supports long-term poverty alleviation. Inflation, however, undermines these welfare gains by eroding real
incomes and disproportionately affecting the poor. Poverty, therefore, emerges as the ultimate welfare indicator,
shaped by the combined influence of energy factors, macroeconomic dynamics, and social policies.
By combining ELGH and WET, this study recognizes that while energy efficiency can drive growth and welfare,
the distribution of benefits depends on complementary policies in education, employment, and social protection.
This integrated framework is particularly relevant for Nigeria, where high poverty coexists with chronic energy
and macroeconomic challenges.
Empirical Review
The empirical literature on the nexus between energy, poverty, and welfare has expanded in recent decades,
though findings remain mixed and context-specific. Several studies highlight the strong link between energy
access, affordability, and poverty reduction. For instance, Oyadeyi et al. (2024) and Adenikinju (2008) showed
that high energy costs in Nigeria disproportionately burden poor households, limiting welfare gains. Similarly,
Karekezi and Kimani (2002) noted that reliance on traditional biomass fuels in sub-Saharan Africa has reinforced
energy poverty and hindered household welfare. More recent evidence suggests that inadequate access to modern
energy services entrenches multidimensional poverty, particularly in rural areas (World Bank, 2023).
Globally, energy efficiency has been recognized as a cost-effective pathway for both environmental
sustainability and welfare improvement (IEA, 2018). Empirical studies have confirmed that efficiency gains
lower household expenditures and improve living conditions. For example, Ouedraogo (2013) found that energy
efficiency improvements in sub-Saharan Africa were positively associated with human development outcomes.
In Nigeria, however, empirical evidence on energy efficiency and household poverty is only beginning to
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emerge. Ibrahim R. Abubakar (2024) found that income, education, and urban–rural location significantly
influence households’ ability to switch from biomass to modern energy sources. The study emphasized that
targeted subsidies and appliance financing can drive efficiency and reduce energy poverty. This aligns with the
Federal Republic of Nigeria’s National Energy Efficiency Action Plan (2016), which highlights energy
efficiency as a key strategy to reduce energy costs and enhance welfare.
Empirical analyses further reveal the potential of efficiency measures in mitigating household expenditure
burdens. O. A. Lawal (2024) showed that energy audits and retrofitting could yield significant cost savings for
households, while D. E. Tietie, M. O. Afolabi, and A. O. Ajayi (2021) projected that demand-side energy
efficiency policies would substantially reduce household energy expenditures in Nigeria. E. N. Ogbeide-Osaretin
(2021) similarly found that energy consumption contributes to poverty reduction but stressed the importance of
addressing inequitable access to energy services.
The relationship between energy prices and welfare is complex. Rising fuel and electricity tariffs may encourage
efficiency but also impose welfare costs on low-income households. Yunusa et al. (2023) found that increases
in petroleum product prices in Nigeria had significant poverty-increasing effects, while Bobasu et al. (2025)
reported that energy price shocks in developing economies reduce household consumption and widen welfare
inequalities. Recent policy developments provide further evidence: reports by Reuters (2024), Dataphyte (2024),
and Energy for Growth Hub (2024) revealed that electricity tariff hikes and subsidy reforms have worsened
affordability concerns, forcing many households to revert to costly alternatives like diesel generators. N. T. M.
Tran, M. Karanja, and T. S. Adebayo (2025) confirmed this trend in a broader African context, noting that energy
cost poverty has intensified in Nigeria in recent years due to pricing reforms and inflationary pressures.
Beyond energy-specific factors, social spending, employment, and education also shape poverty outcomes.
Studies emphasize that public expenditure on subsidies, social protection, and infrastructure mitigates the
negative effects of energy costs (Ogunyemi & Oladapo, 2025; Oyadeyi et al., 2024; World Bank, 2023).
Employment has consistently been shown to reduce household poverty by enhancing income-generation
opportunities (Awotide et al., 2015), while education improves human capital, productivity, and resilience to
economic shocks (Barro & Lee, 2013).
Macroeconomic stability also plays a role. Persistent inflation erodes real incomes and disproportionately affects
the poor (Sulaiman, 2014). Urbanization, while offering better access to infrastructure and services, often leads
to new forms of inequality between urban and rural households, particularly in Nigeria, where informal
settlements face limited energy and welfare services (UN-Habitat, 2020).
Despite growing scholarship, three main gaps remain. First, most Nigerian studies emphasize energy access or
prices, with little focus on energy efficiency as a poverty-reducing mechanism. Second, the distributive effects
of energy efficiency within the context of broader socio-economic factors are underexplored. Third, few studies
have grounded empirical analysis within an integrated framework combining the Energy-Led Growth
Hypothesis (ELGH) and Welfare Economics Theory (WET). This study seeks to address these gaps by providing
an econometric analysis of the determinants of household poverty in Nigeria, with a focus on energy efficiency
and policy-relevant socio.
METHODOLOGY
Model Specification
The empirical model for this study is anchored on the Energy-Led Growth Hypothesis (ELGH) and the Welfare
Economics Theory (WET). The ELGH emphasizes the pivotal role of energy use and efficiency in stimulating
economic performance and welfare outcomes (Apergis & Payne, 2012). In this framework, energy efficiency
(ENEF) and energy prices (ENERPr) are key drivers of poverty dynamics. Efficient energy use reduces
production costs and improves household affordability, while higher energy prices increase the cost of living
and exacerbate poverty. The WET, on the other hand, underscores the importance of resource distribution,
government intervention, and macroeconomic stability in enhancing welfare (Pigou, 1932; Sen, 1999).
Accordingly, variables such as government expenditure (GOVEXPGr), employment (EMP), education
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(EDUltr), inflation (INFL), and urbanization (URB) are included as welfare-enhancing or welfare-reducing
factors that mediate the link between economic growth and poverty reduction.
Table 1: Description of Variables
Variable
Description
Measurement/Proxy
Expected
Sign
Justification
POV
Household
Poverty
Poverty gap at $2.15 a day (2017 PPP) (%)
—
Dependent
variable
measuring
poverty depth at
the household
level.
ENEF
Energy
Efficiency
Index
Then we normalize it (apply min-max
normalization) so that higher values reflect
better efficiency:
(−)
Higher
efficiency
(lower energy
per GDP)
reduces
household
energy cost and
poverty.
HHCEGr
Household
Consumption
Expenditure
Households and NPISHs Final Consumption
Expenditure (annual % growth)
(−)
Higher
consumption
indicates
improved
household
welfare,
lowering
poverty.
ENERPr
Energy Price
Average annual retail energy price (Premium
Motor Spirit, PMS)
(+)
Higher energy
prices reduce
access and
increase energy
poverty.
GOVEXPGr
Government
Spending
General government final consumption
expenditure (annual % growth)
(−)
Higher
spending
supports poor
households and
improves
access to
services.
EMP
Employment
Employment-to-population ratio, ages 15+,
total (%) (ILO estimate)
(−)
Higher
employment
reduces
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income-related
poverty.
EDUltr
Education
Literacy rate, adult total (% of population
ages 15 and above)
(−)
Education
enhances
earning
capacity,
reducing
poverty.
INFL
Inflation
Inflation, consumer prices (annual %)
(+)
High inflation
reduces
household
purchasing
power,
worsening
poverty.
URB
Urbanization
Population in urban agglomerations of >1
million (% of total population)
Ambiguous
Urbanization
may improve
access to energy
but increase
living costs.
Source: Author’s Compilation 2025
Together, these theories suggest that poverty is determined by both energy-related factors (ELGH) and
socioeconomic conditions (WET). Thus, the model can be expressed functionally as:
Econometric Form
The log-linear econometric specification is written as:
Where:
• α
0
= constant term
• α
1
-α
8
= slope coefficients measuring the effect of each explanatory variable on poverty
• μ
t
= error term
Estimation Strategy
Given the dynamic nature of poverty and the potential endogeneity among explanatory variables, the study
employs the Autoregressive Distributed Lag (ARDL) Model. This approach is suitable because: It allows for a
mixture of I(0) and I(1) variables, which is common in macroeconomic data, It provides both short-run dynamics
and long-run equilibrium relationships between poverty and its determinants and It also accommodates small
sample sizes while producing consistent estimates.
The ARDL error correction representation is specified as:
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Where X
t
represents the vector of explanatory variables, and ECT
t−1
is the error correction term capturing long-
run adjustments.
Presentation and Discussion of Results
Descriptive Statistics
Table 2: Descriptive Statistic Results
POV
ENEF
ENERPr
GOVEXGr
HHCEGr
INFL
EMP
EDUlrt
URB
Mean
17.695
732.93
97.764
19.374
4.9856
19.211
56.996
56.297
14.703
Median
18.400
740.18
57.500
2.0482
1.4533
13.007
57.982
55.447
14.887
Maximum
27.900
792.20
1030.0
565.54
59.388
72.836
58.497
70.198
17.244
Minimum
9.0000
671.61
0.3900
-34.031
-15.978
5.3880
53.225
51.078
11.610
Std. Dev.
6.9117
38.265
180.22
92.201
14.728
16.893
1.6720
4.8765
1.5346
Skewness
0.1125
-0.0365
4.0366
5.5384
1.5273
1.8411
-0.8990
1.2054
-0.2026
Kurtosis
1.6977
1.6320
20.346
33.272
6.1064
5.1614
2.1524
4.2806
2.1474
Jarque-Bera
2.8381
3.0498
594.84
1688.6
30.843
29.625
6.4213
12.110
1.4483
Probability
0.2419
0.2176
0.0000
0.0000
0.0000
0.0000
0.0403
0.0023
0.4847
Sum
690.10
28584
3812.8
755.57
194.44
749.25
2222.8
2195.6
573.42
Sum Sq. Dev.
1815.3
55639
12343
323041
8243.1
10844
106.23
903.66
89.496
Observations
39
39
39
39
39
39
39
39
39
Source: Authors Computation 2025
The descriptive statistics reveal important insights into the dynamics of poverty, energy efficiency, and
macroeconomic conditions in Nigeria between 1986 and 2024. Household poverty averaged 17.7 percent, with
a minimum of 9 percent and a maximum of 27.9 percent, suggesting that nearly one in five Nigerians lived below
the $2.15/day poverty line during the period. Energy efficiency, measured through a normalised index, exhibited
little variation with a mean value of 732.9, reflecting Nigeria’s persistently low but stable efficiency levels. In
contrast, energy prices were highly volatile, with values ranging from 0.39 to 1030, indicating the effects of
subsidy regimes, deregulation, and price shocks. Government spending growth also showed extreme fluctuations
(−34.0 to 565.5 percent), consistent with oil revenue cycles and fiscal instability. Household consumption growth
averaged 5 percent but was marked by sharp swings, while inflation remained persistently high at an average of
19.2 percent, peaking at 72.8 percent, thereby eroding household welfare. Employment ratios for ages 15+ were
relatively stable at around 57 percent, reflecting stagnant labour market opportunities, while adult literacy
gradually improved to an average of 56.3 percent. Urbanisation progressed steadily but remained modest,
averaging 14.7 percent of the population in large agglomerations. Tests of normality indicated that while poverty,
energy efficiency, and urbanization followed approximately normal distributions, variables such as energy
prices, government spending, household consumption, inflation, and literacy exhibited strong skewness and
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kurtosis, underscoring the presence of structural shocks and outliers. Overall, the data highlight a context of
persistent poverty amid volatile macroeconomic conditions, validating the need for Nigeria’s Energy Transition
Plan (ETP) as a pathway to greater stability and welfare improvement.
Unit Root Test
Table 3: Unit Root Test
Phillips-Perron (PP) at Level
Phillips-Perron (PP) at 1
st
Diff
Order of integration
Variables
T-Stats
Critical value.
T-Stats
Critical value.
(d).
ENEF
-1.133442
-2.941145
-5.796772
-2.943427
I(1)
ENERPr
7.527468
-2.941145
-4.418616
-2.943427
I(1)
GOVEXgr
-6.388509
-2.941145
-
-
I(0)
HHCEgr
-8.159407
-2.941145
-
-
I(0)
INFL
-2.978553
-2.941145
-
-
I(0)
EMP
-1.454738
-2.941145
-4.288076
-2.943427
I(1)
EDUlrt
-2.896128
-2.941145
-6.277702
-2.945842
I(1)
URB
-2.153803
-2.941145
-6.720685
-2.945842
I(1)
POV
-1.230170
-2.941145
-4.964382
-2.945842
I(1)
Source; Authors Computation 2025.
Table 3 presents the results of the Phillips–Perron (PP) unit root test for the variables used in the study. The
results show that some variables are stationary at level, while others only become stationary after first
differencing. Specifically, government expenditure growth (GOVEXGr), household consumption expenditure
growth (HHCEGr), and inflation (INFL) are stationary at level, implying that they are integrated of order zero,
I(0). On the other hand, energy efficiency (ENEF), energy price (ENERPr), employment (EMP), literacy rate
(EDUlrt), urbanization (URB), and poverty (POV) are not stationary at level but achieve stationarity at first
difference, indicating that they are integrated of order one, I(1). This mix of I(0) and I(1) series justifies the use
of the Autoregressive Distributed Lag (ARDL) estimation framework, which is well-suited for handling
regressors with different integration orders, provided none is integrated of order two or higher. Thus, the
stationarity properties of the variables confirm the appropriateness of the ARDL estimation approach in
analyzing the long- and short-run dynamics between energy efficiency, energy price, and household poverty in
Nigeria.
Co-integration Test
Table 4: Bounds Co-integration Test
F-Bounds Test
Null Hypothesis: No levels relationship
Test Statistic
Value
Signif.
I(0)
I(1)
F-statistic
3.404104
10%
1.85
2.85
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K
8
5%
2.11
3.15
2.5%
2.33
3.42
1%
2.62
3.77
Source; Authors Computation 2025.
Table 4 reports the results of the ARDL bounds co-integration test. At the 5% significance level, the lower bound
critical value (I(0)) is 2.11, while the upper bound critical value (I(1)) is 3.15. The calculated F-statistic of
3.404104 is greater than the upper bound critical value of 3.15. This implies that the null hypothesis of no long-
run relationship is rejected at the 5% significance level. Therefore, there is evidence of a stable long-run
equilibrium relationship among household poverty, energy efficiency, energy price, government spending,
household consumption, inflation, employment, education, and urbanization in Nigeria over the study period.
This finding justifies the estimation of both long-run and short-run ARDL models to capture the dynamic
interactions among the variables.
Autoregressive Distributed Lag (ARDL) Estimation
Table 5: ARDL Estimation Results
Dependent Variable: POV
Variable
Coefficient
Std. Error
t-Statistic
Prob.
Long-Run Estimates
ENEF
-0.395803
0.134302
-2.947106
0.0075
ENERPr
0.023931
0.011103
2.155311
0.0423
GOVEXgr
0.021820
0.013846
1.575901
0.1293
HHCEgr
0.074531
0.078078
0.954570
0.3502
INFL
0.086895
0.095461
0.910270
0.3725
EMP
-3.704332
2.229252
-1.661692
0.1108
EDUlrt
-0.886388
0.412896
2.146758
0.0431
URB
-0.418154
3.328204
-0.125640
0.9012
C
480.0890
183.9012
2.610581
0.0160
Short-Run Estimates
D(ENEF)
-0.084505
0.020730
-4.076523
0.0005
D(ENEF(-1))
0.138896
0.024291
5.717973
0.0000
D(ENERPr)
0.007318
0.002490
2.939091
0.0076
D(GOVEXgr)
0.006672
0.003851
1.732342
0.0972
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D(HHCEgr)
0.022790
0.023252
0.980138
0.3377
D(INFL)
0.026571
0.026344
1.008608
0.3241
D(EMP)
-0.114673
0.446280
-0.256952
0.7996
D(EDUltr)
-0.143470
0.065945
2.175609
0.0406
D(URB)
-2.198297
0.374061
-5.876833
0.0000
CointEq(-1)*
-0.305780
0.044151
-6.925815
0.0000
R-squared
0.671363
Mean dependent var
0.005405
Adjusted R-squared
0.618357
S.D. dependent var
2.640280
S.E. of regression
1.631093
Akaike info criterion
3.963771
Sum squared resid
82.47435
Schwarz criterion
4.225001
Log likelihood
-67.32976
Hannan-Quinn criterion
4.055867
Durbin-Watson Stat
2.221633
Source; Authors Computation 2025.
Table 5 presents the ARDL long-run and short-run estimates of the determinants of household poverty in Nigeria.
In the long run, Energy efficiency (ENEF) exerts a statistically significant negative effect on poverty, with a
coefficient of –0.3958 (p < 0.01). This implies that improvements in energy efficiency reduce household poverty
in the long run, consistent with the argument that efficient energy use lowers energy costs and enhances welfare.
Energy prices (ENERPr) have a positive and significant impact (0.0239, p < 0.05), suggesting that rising energy
prices increase household poverty, possibly through higher living costs and reduced disposable income.
Government expenditure growth (GOVEXgr) and household consumption growth (HHCEgr) both show positive
but statistically insignificant coefficients, implying that although they expand over time, their impact on poverty
reduction is limited, likely due to inefficiencies and leakages in fiscal and consumption channels. Inflation
(INFL) also has an insignificant positive effect, reflecting Nigeria’s structural inflationary pressures that may
not always translate directly into welfare outcomes. Employment (EMP) reduces poverty (–3.7043), but the
effect is insignificant at the 5% level, indicating weak job creation or low-quality employment. Education,
measured by literacy rate (EDUltr), has a negative and significant effect (-0.8864, p < 0.05) underlining the
importance of education as a structural driver of poverty alleviation.. Urbanisation (URB) is negative but
insignificant, suggesting that urban growth has not effectively reduced poverty, possibly due to the prevalence
of slums and informal sector vulnerabilities.
In the short run, changes in energy efficiency (D(ENEF)) reduce poverty significantly (–0.0845, p < 0.01), while
its lagged effect (D(ENEF(–1))) shows a negative and highly significant relationship (0.1389, p < 0.01). This
indicates that while immediate gains from efficiency improvements reduce poverty, adjustment dynamics in the
subsequent period may temporarily increase poverty before stabilising. Energy prices (D(ENERPr)) exert a
positive and significant effect (0.0073, p < 0.01), again confirming that short-run spikes in energy costs worsen
poverty. Government expenditure growth (D(GOVEXgr)) is positive and marginally significant at the 10% level,
suggesting a short-term poverty-reducing potential that may depend on expenditure composition. Household
consumption growth (D(HHCEgr)), inflation (D(INFL)), and employment (D(EMP)) remain insignificant,
pointing to weak short-term linkages with poverty outcomes. Education (D(EDUltr)) has a negative and
significant effect (-0.1435, p < 0.05), further reinforcing the long-run finding of a literacy–poverty paradox.
Urbanisation (D(URB)) has a large negative and highly significant effect (–2.19830, p < 0.01), showing that
rapid urban growth in the short run tends to exacerbate poverty, likely due to urban congestion, unemployment,
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and infrastructure deficits.
The error-correction coefficient (CointEq(–1)) is negative and highly significant (–0.3058, p < 0.01), confirming
the presence of a stable long-run equilibrium. The coefficient suggests that approximately 30.6% of the deviation
from long-run poverty equilibrium is corrected each year, indicating a moderate speed of adjustment toward
stability after short-run shocks. The R-squared (0.6714) and adjusted R-squared (0.6184) values indicate that the
explanatory variables account for about 62% of the variations in household poverty. The Durbin-Watson statistic
(2.22) suggests the absence of autocorrelation, while the information criteria (AIC, SIC, HQ) confirm the
model’s goodness of fit. Overall, the findings highlight the crucial role of energy efficiency, energy prices,
education, and urbanisation in explaining poverty dynamics in both the short and long run.
Post Estimation Test
Table 6: Post Estimation Diagnostic Test Results
Test
Test Statistic
Prob.
Value
Decision
CONCLUSION
Serial Correlation LM
Test
F-stat =
1.3489
0.2821
Not Significant
No serial correlation
Heteroskedasticity
Test
F-stat =
0.6666
0.7812
Not Significant
No heteroskedasticity (constant
variance)
Normality Test
Jarque-Bera =
1.3771
0.5023
Not Significant
Residuals are normally distributed
RAMSEY Reset Test
F-stat =
0.8951
0.3809
Not Significant
Model is correctly specified (no
omitted variable bias)
Model Stability Test
CUSUM and
CUSUMSQ
—
Within 5%
critical bounds
Model is stable and free from
misspecification
Source: Author’s computation (2025)
Post estimation Chart and Graphs
Figure 1: Normality Test Result
0
1
2
3
4
5
6
7
8
9
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Series: Residuals
Sample 1988 2024
Observations 37
Mean 7.92e-14
Median 0.242067
Maximum 4.163983
Minimum -5.645977
Std. Dev. 2.604640
Skewness -0.387730
Kurtosis 2.459720
Jarque-Bera 1.377081
Probability 0.502309
Figure 2: CUSUM Graph Figure 3: CUSUM Square Graph
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-15
-10
-5
0
5
10
15
04 06 08 10 12 14 16 18 20 22 24
CUSUM
5% Significance
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
04 06 08 10 12 14 16 18 20 22 24
CUSUM of Squares
5% Significance
Source: Authors (2025)
The diagnostic checks confirm that the estimated model is statistically reliable and robust. The Serial Correlation
LM test (F-stat = 1.3489; p = 0.2821) indicates the absence of autocorrelation in the residuals, suggesting that
the model errors are independent over time. Similarly, the Heteroskedasticity test (F-stat = 0.6666; p = 0.7812)
fails to reject the null hypothesis of homoskedasticity, implying constant error variance across observations. The
Jarque-Bera normality test (JB = 1.3771; p = 0.5023) confirms that the residuals are normally distributed, a key
assumption for valid statistical inference. The Ramsey RESET test (F-stat = 0.8951; p = 0.3809) shows that the
model is correctly specified with no evidence of omitted variable bias. Furthermore, the CUSUM and
CUSUMSQ stability tests demonstrate that the model’s parameters remain stable within the 5% critical bounds
throughout the sample period. These results, reinforced by the accompanying diagnostic charts (Figures 2–4),
suggest that the estimated model is well-specified, free from major econometric problems, and suitable for policy
interpretation.
DISCUSSION OF FINDINGS
The results provide policy-relevant insights into how Nigeria’s energy transition can be pro-poor. Consistent
with the Energy-Led Growth Hypothesis, energy efficiency significantly reduces household poverty in both the
short and long run. This aligns with evidence that efficiency gains lower energy expenditures and free resources
for welfare-enhancing uses (IEA, 2018; Ouedraogo, 2013). Conversely, rising energy prices increase poverty,
corroborating studies linking fuel and electricity tariff hikes to welfare losses among low-income households
(Ogunyemi, & Oladapo, 2025; Yunusa, et al., 2023). These findings highlight that efficiency policies can be
inclusive, but tariff reforms must be sequenced with protective measures to avoid regressive impacts.
Education (literacy) also exerts a strong poverty-reducing effect, consistent with welfare economics theory and
prior studies linking human capital to resilience and income growth (Barro & Lee, 2013; Awotide et al., 2015).
Urbanization reduces poverty in the short run, likely due to better infrastructure and labour opportunities,
echoing UN-Habitat (2020) and World Bank (2023) insights. Together, energy efficiency, education, and urban
infrastructure emerge as complementary drivers of welfare gains. Other variables government expenditure,
employment, household consumption, and inflation were weaker or insignificant. This suggests that the
composition and targeting of fiscal policy matter more than aggregate growth (World Bank, 2023), and that job
creation without quality and energy access linkages may not yield durable poverty reductions. The short- versus
long-run dynamics reflect transitional complexities: efficiency yields strong welfare benefits, while energy price
reforms impose immediate costs (Bobasu, et al., 2025). The significant error-correction term confirms a stable
long-run relationship, implying that short-run interventions aligned with structural reforms can produce
sustained poverty reduction.
Policy implications are clear. First, energy efficiency policies should be central to the transition. Second, tariff
reforms must be accompanied by targeted transfers, lifeline tariffs, or subsidies to protect vulnerable households
(Sen, 1999). Third, investments in education and skills, coupled with urban and rural energy infrastructure, can
spread the welfare gains more equitably. Finally, fiscal policy should prioritize targeted spending on social
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protection, education, and energy access programs (Ogunyemi, & Oladapo, 2025; Oyadeyi et al., 2024; World
Bank, 2023). Overall, the study fills an important gap by demonstrating that energy efficiency directly reduces
poverty in Nigeria, but also underscores the need for complementary policies to ensure that the benefits of the
energy transition are inclusive and equitable.
CONCLUSION AND POLICY RECOMMENDATIONS
This study investigated the nexus between energy efficiency, energy prices, and household poverty in Nigeria
within the framework of the Energy-Led Growth Hypothesis (ELGH) and Welfare Economics Theory (WET).
Using the ARDL estimation technique, the results revealed that energy efficiency significantly reduces poverty
in both the short and long run, underscoring its potential as a pro-poor driver of welfare within Nigeria’s energy
transition strategy. Conversely, energy prices were found to exert poverty-increasing effects, highlighting the
welfare risks of unbuffered tariff adjustments. Education (literacy) emerged as another significant poverty-
reducing factor, reinforcing the central role of human capital development in breaking the cycle of poverty.
Urbanization also showed strong short-run poverty-reducing effects, suggesting that access to infrastructure and
economic opportunities in urban areas can alleviate welfare deprivation. Other variables such as government
expenditure, household consumption, employment, and inflation produced weaker or statistically insignificant
effects, suggesting that their impact depends heavily on policy design, targeting, and institutional efficiency.
The findings carry important policy implications for Nigeria’s energy transition agenda. First, prioritizing energy
efficiency should be a cornerstone of policy, as efficiency measures not only reduce household energy costs but
also deliver long-term poverty reduction. This requires investment in efficient appliances, building standards,
and renewable-based off-grid systems, particularly in rural and peri-urban areas. Second, given the poverty-
increasing effect of energy prices, a gradual and socially sensitive approach to tariff reforms is necessary. Cost-
reflective pricing should be accompanied by targeted subsidies, lifeline tariffs, or direct cash transfers to shield
vulnerable households from welfare losses during the transition. Third, the significant role of education calls for
stronger investments in human capital development, including literacy programs, vocational training, and skills
upgrading to improve household resilience and income-generating capacity. Fourth, the positive short-run role
of urbanization implies that urban infrastructure expansion such as electrification, housing, and transport, can
complement energy efficiency efforts, but this must be balanced with policies to address rural energy poverty to
avoid deepening regional inequality.
Finally, the study emphasizes that energy transition policies should be embedded within a broader framework of
inclusive welfare economics, where fiscal expenditure is deliberately targeted at reducing vulnerability and
expanding opportunities. Public spending should prioritize investments in renewable energy infrastructure,
education, and social protection programs that directly reach poor households. By combining efficiency-oriented
reforms with redistributive policies, Nigeria can design an energy transition strategy that not only delivers
environmental benefits but also advances inclusive development and poverty reduction.
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APPENDIX
Data
Year GDPPCGR EDULRT EMP
TPEC/GDP
GOVEXGR HHCEGR INFL POV URB ENENPR ENEF
1986 -2.52667 56.12032 58.497 671.6111 2.182242 -9.35083 5.717151 18.4 11.60998 0.39 1
1987 0.574529 56.12032 58.489 677.2977 2.135637 -5.5938 11.29032 18.4 11.87072 0.39 0.952843
1988 4.593067 56.12032 58.404 679.5984 2.090982 6.979885 54.51122 18.4 12.14178 0.42 0.933765
1989 -0.70992 56.12032 58.343 685.1884 2.048155 -7.60591 50.46669 18.4 12.41899 0.6 0.887409
1990 8.877 56.12032 58.28 697.6002 2.007048 23.81864 7.3644 18.4 12.70656 0.6 0.784483
1991 -2.18037 55.44675 58.283 712.5468 1.967558 5.90801 13.00697 18.4 12.96306 0.7 0.660536
1992 2.023629 55.44675 58.275 722.2551 1.059521 16.92783 44.58884 24.4 13.14057 0.7 0.580029
1993 -4.50715 55.44675 58.19 715.3422 2.876441 -8.24414 57.16525 24.4 13.25282 5 0.637355
1994 -4.31075 55.44675 58.129 680.0624 2.894572 -9.85231 57.03171 24.4 13.36715 15 0.929917
1995 -2.5956 55.44675 58.066 681.1587 1.427395 4.958746 72.8355 24.4 13.48801 11 0.920825
1996 1.596033 55.44675 58.069 692.3822 -0.05851 19.98309 29.26829 27.9 13.61801 11 0.827754
1997 0.37252 55.44675 58.028 698 2.894012 -3.2421 8.529874 27.9 13.75185 11 0.781168
1998 0.032474 55.44675 57.982 685.3233 1.729373 -0.33343 9.996378 27.9 13.89214 25 0.88629
1999 -1.94111 55.44675 57.926 692.0342 1.699974 -8.53888 6.618373 27.9 14.03376 20 0.830639
2000 2.317775 55.44675 57.905 700.3827 1.671558 1.768542 6.933292 27.9 14.17276 30 0.761409
2001 3.146425 55.44675 57.927 716.1132 1.644076 59.38751 18.87365 27.9 14.30863 30 0.630962
2002 12.27614 55.44675 57.985 719.4873 -12.0786 15.22115 12.87658 27.9 14.44601 26 0.602982
2003 4.495156 54.77318 57.951 740.1777 5.779098 10.76781 14.03178 18.8 14.58746 42 0.431405
2004 6.345041 54.77318 57.966 740.8816 -23.9262 0.226824 14.99803 18.8 14.73501 50 0.425567
2005 3.609661 54.77318 57.956 749.5896 565.5388 12.75268 17.86349 18.8 14.88733 57.5 0.353356
2006 3.238343 70.19835 58.001 735.6947 10.46888 -13.7169 8.225222 18.8 15.02407 65 0.468581
2007 3.741687 70.19835 58.039 741.2802 35.75064 34.58342 5.388008 18.8 15.10529 65 0.422262
2008 3.899943 51.07766 58.067 742.8051 90.75034 -15.9781 11.58108 18.8 15.18834 75 0.409617
2009 5.130162 51.07766 58.095 711.3473 4.426677 22.28252 12.53783 18.8 15.27156 65 0.670484
2010 5.081875 51.07766 58.111 744.8358 -8.07509 1.739826 13.74005 10.3 15.35563 65 0.392777
2011 2.437007 51.07766 58.133 766.3292 17.84247 -3.05619 10.82614 10.3 15.43947 65 0.214541
2012 1.403509 51.07766 55.691 785.2607 4.573578 0.00566 12.22424 9.9 15.52917 141 0.05755
2013 3.832366 51.07766 53.225 766.9222 -1.98196 21.06499 8.495518 9.9 15.62846 97 0.209623
2014 3.552162 51.07766 53.671 750.9709 -10.2574 0.613701 8.047411 9.9 15.7425 97 0.341901
2015 0.076962 51.07766 54.094 764.1229 -7.01454 1.453278 9.009435 9.4 15.87368 87 0.232837
2016 -4.05271 51.07766 54.506 767.2427 -11.8973 -5.72998 15.69681 9.4 16.01398 145 0.206966
2017 -1.70987 51.07766 54.951 770.3624 -15.116 -0.95243 16.50227 9.4 16.15486 145 0.181095
2018 -0.59039 62.01601 55.383 773.4821 -7.98878 5.220878 12.09511 9 16.30465 145 0.155225
2019 -0.26346 62.01601 55.805 776.6019 33.1644 -0.99314 11.39642 9 16.47127 145 0.129354
2020 -4.16206 62.01601 54.812 779.7216 8.78314 -0.98381 13.24602 9.1 16.65154 147.5 0.103483
2021 1.182828 62.01601 54.886 782.8413 61.57662 25.62268 16.95285 9.1 16.85185 161 0.077613
2022 0.823296 62.01601 55.476 785.9611 -34.0309 -7.68239 18.84719 9.2 17.07071 195 0.051742
2023 1.585152 62.01601 54.685 789.0808 15.85602 2.533398 15.09017 12.7 17.11039 540 0.025871
2024 1.597317 62.01601 54.549 792.2006 3.154494 2.470803 15.37642 18.6 17.24412 1030 3.98E-07
