vital element for the country's economic growth. Using an ex-post facto research design, the analysis employs a

chronological series of data from 1986 to 2022, sourced from the Central Bank of Nigeria's Statistical Bulletin,

various national agencies, and the World Development Indicators. The Autoregressive Distributed Lag (ARDL)

method is used to analyse both long-term and short-term relationships between electricity production and several

explanatory variables, including biomass energy funding, government spending on the power sector, energy

financing from commercial banks, energy-related foreign aid, and electricity consumption. The results reveal

that, over the long term, investment in biomass energy, government commitments to the electricity industry, and

renewable energy financing from commercial banks all significantly influence Nigeria’s electricity generation.

Conversely, foreign aid for renewable energy does not show a statistically significant effect. In the short term,

only electricity consumption has a strong and significant causal effect on generation capacity, while the other

Keywords: Biomass, Energy Finance, Electricity Generation, Autoregressive Distributed Lag, Nigeria.

The importance of energy in the development and growth of a nation cannot be overemphasized. Economic

development, growth and human prosperity are heavily dependent on adequate supply, security, and efficient use

of energy (Abdallah, et al. 2015). Lior (2012) suggested that energy resources and consumption are intimately

related to environmental quality and other vital resources, such as water and food. Lior (2012) proposed that

Africa’s energy deserves a close look and development to synergistically advance the quality of life of its

populace and sell global-capacity energy to the rest of the world.

The need to shift away from the long-term use of fossil fuels has been prompted by the global energy crisis and

growth, and reduction in carbon dioxide emission.

Nigeria is endowed with vast renewable energy resources and confronts a persistent challenge in satisfying its

escalating electricity demands amidst a rapidly expanding population and industrial sector (International Energy

Agency (IEA, 2021). In this context, the exploration of alternative energy sources emerges as a critical

imperative, with biomass energy standing out as a promising avenue. Biomass energy, derived from organic

materials such as agricultural residues, forest waste, and urban solid waste, offers a sustainable solution to

Nigeria's energy needs, while simultaneously mitigating environmental concerns associated with fossil fuel

dependency (Oladiran et al., 2020).

However, the widespread adoption of biomass energy for electricity generation in Nigeria encounters

multifaceted hurdles, foremost among them being the insufficient availability of financing mechanisms

Giartkasari and Nikensari (2022), Rashed, et al (2022), Onabote et al. (2021), Idoko (2021), Mesagan, et al

(2021), Onayemi, et al (2020). This incongruence necessitates a thorough examination of the efficacy of financial

investments, specifically in solar energy, to advance Nigeria's electricity generation landscape.

This article aims to delve into the intricate relationship between biomass energy finance and electricity

generation in Nigeria, examining the impact of financial mechanisms such as loans, grants, and subsidies on the

development and implementation of biomass energy projects (REN21, 2020). By analyzing the current state of

biomass energy finance in Nigeria, identifying existing challenges, and exploring opportunities for improvement,

this study seeks to elucidate the pivotal role that finance plays in shaping the trajectory of biomass energy

development in the country.

Through a comprehensive understanding of the dynamics of biomass energy finance, policymakers, investors,

electricity generation. In Section 3, we introduce the data utilized and detail our methodology for assessing the

influence of renewable energy financing on electricity generation in Nigeria. Section 4 delves into the

presentation and analysis of findings. Finally, Section 5 encompasses the conclusion and recommendations

drawn from the study.

Developing countries, including Nigeria, very often, have an abundant stock of untapped renewable energy

creation (Osiolo, 2016). In addition, using renewable energy increases the chances for energy self-sufficiency

whilst preventing environmental degradation (Tun, et al. 2019; Tun & Juchelková, 2019).

Mas’ud et al. (2015) also assessed the renewable energy readiness in Nigeria and Cameroon and found that there

is a high solar irradiation and excellent wind speed in the two countries. It was also stated that Africa has

environment and do no good. Despite this, biomass provides about 70% of the total energy consumption in some

developing countries Keles et al. (2017). Keles et al. (2017) anticipate that about 823 million people in Africa

will rely on biomass for cooking and heating in the developing country by 2030. Gujba et al. (2015) suggested

that the introduction of advanced stoves should be prioritized to reduce the health impact of indoor pollution and

also to reduce pressure on biomass resources.

Abolhosseini et al. (2014) identified that the two main solutions for reducing CO2 emissions and overcoming

the climate change problem are to replace fossil fuels with renewable energy as much as possible and enhance

energy efficiency. Keles et al. (2017) also noted that systematic data are still inadequate or unavailable for

biomass energy planning and for developing specific energy policies for supply and demand. It is required that

the biomass resources are appropriately managed and deployed for effective energy and power generation.

occurring but rather requires transformation from other energy forms, such as atomic, gasoline, or coal, solar,

biomass, etc, this theory aptly applies. It elucidates the process of converting raw energy into usable electricity,

aligning with the broader notion of production. This theory provides a theoretical framework for justifying the

study's focus on electricity generation, which mirrors the production process of transforming raw materials into

finished goods. Furthermore, like other economic activities, electricity production is subject to various

influences, including economic influences such as financing energy commodities, social, technological, and

political factors. Understanding these influences most especially the economic influence is pivotal for analyzing

electricity generation in Nigeria, making Production theory highly relevant to this study.

Empirical research by Zhe (2024) investigated the relationship between renewable resources and electricity

The results demonstrate that increases in financing from the Rural Electrification Agency, government spending,

commercial bank investments, and foreign aid positively impact electricity generation in both the short and long

generation. These findings emphasize the importance of enhancing funding for solar energy projects,

diversifying government allocations to the electricity sector, and fostering a supportive environment for foreign

aid and investments. Beyond enriching existing literature and theoretical frameworks, this research offers

practical insights for policymakers, industry stakeholders, and researchers aiming to promote renewable energy

investments for sustainable electricity generation in Nigeria.

Similarly, in the Nigerian context, Mansur (2023) analyzed factors influencing electricity generation from 1981

to 2021. Applying Vector Autoregressive (VAR) model techniques like Impulse Response Functions (IRFs) and

Variance Decomposition, the study revealed that electricity generation responds negatively to shocks from power

loss and demand. Conversely, it showed a significant positive response to shocks in government funding,

challenges associated with the coexistence of I(0) and I(1) regressors in a Johansen-type framework. In line with

the theoretical disposition supported by Bernard and Adenuga (2016) who opined that production process is not

only driven by current energy resources, but by the changes in the previous output of the industry sector, this

study therefore modified equation 4 and specified an ARDL model of the form:

ELG

To determine the long-run relationship and the short-run dynamics of the ARDL model In equation 5, the longrun

and the short-run form of equation is specified thus:

+ µ 7

Having estimated the ARDL model in equation 5, the ARDL estimation technique simultaneously estimated

equation 6 and 7 that is both long-run and short-run ARDL model. Where in the short-run estimation; the error

correction term (ect) was automatically generated and estimated. The estimated results of the ect measure the

speed of adjustment needed to converge back to long-run equilibrium after a short-term shock to the model.

10MKw in 1985, increased to about 20MKw by the early 1990s, remained stable until around 2007, and then

declined to below 15MKw by 2022. Biomass Energy Finance (BEF): Started at 0, rose to around N10 billion by

the early 1990s, surged past N20 billion around 2000, and stayed around N20 billion with minor fluctuations

until 2022. Federal Government Electricity Sector Finance (GEFN): Gradually increased, peaking slightly above

N5 billion in 2005 and reaching about N7 billion by 2022. Commercial Banks' Energy Financing (CEFN):

Remained flat until the mid-1990s, then gradually rose to around N5 billion by 2006, and stabilized between N3

billion and N5 billion until 2022. Energy Finance from Foreign Aid (FEFN): Stayed nearly flat, slightly below

N5 billion throughout the period. Electricity Consumption: Remained constant from 1985 to 2022. All series

show an upward trend, indicating potential non-stationarity. Thus, a unit root test is needed to determine the

order of stationarity of the series.

Table 2 shows different criteria from which a lag length of our model variables was selected. All four criteria:

FPE, AIC, HQIC, and SBIC indicate the selection of a maximum of four (1) lags in our model as shown by the

asterisk (* ) along the fourth lag. The AIC lag length was selected to estimate the ARDL model.

The ARDL Bounds test in Table 3 shows an F-statistic of 15.93, exceeding the upper bound, indicating

cointegration and a long-run relationship among the variables. Consequently, it is essential to estimate and

interpret the long-run model and the relationships between the independent and dependent variables.

The long-run results reveal a critical narrative about the drivers of electricity generation in Nigeria, where the

electricity consumption is associated with a 0.63% increase in generation, reflecting a demand-driven expansion

of the power sector. Among the financial variables, foreign aid (LNFEF) shows a significant and positive longrun

impact with a coefficient of 0.030 (p-value = 0.0024), suggesting that sustained aid flows do contribute to

capacity building over time. Similarly, commercial banks' energy finance (LNCEF, coefficient = 0.037, p-value

= 0.0283) and biofuel finance (LNBEF, coefficient = 0.011, p-value = 0.0251) also demonstrate significant,

though smaller, positive long-run effects.

However, the stark exception is government expenditure on the electricity sector (LNGEF), which, despite a

positive coefficient of 0.002, is statistically insignificant (p-value = 0.8367). This insignificance, especially when

contrasted with the effectiveness of commercial finance and foreign aid, strongly suggests that the problem is

not a lack of funding but rather how funds are managed. The ineffectiveness of government expenditure can be

structures. Commercial finance (LNCEF) is subject to market discipline, profitability analysis, and stricter

oversight, which forces more efficient allocation and use of capital. Foreign aid (LNFEF), while sometimes

hampered by donor-driven agendas, often comes with its own project management frameworks and oversight

mechanisms that can, to some extent, bypass the most corrupt layers of domestic bureaucracy. This allows it to

have a significant long-run impact where purely government-managed funds fail. In conclusion, the coefficients

tell a story where the source and governance of financing are paramount. For Nigeria, policy must focus not only

on mobilizing capital but, more critically, on implementing institutional reforms—such as enhancing

transparency, strengthening public financial management, and reducing political interference to ensure that all

financial investments, especially public expenditure, can effectively catalyze long-term growth in electricity

generation.