Introduction

The global imperative to mitigate climate change emphasizes the urgent need for a transition from carbon-intensive energy systems to sustainable alternatives. Developing countries, in particular, faces dual challenges of expanding reliable access to electricity for their explosive population growth and urbanization while simultaneously reducing carbon emissions. In this regard, renewable energy sources such as biomass has emerged as a fast-track solution to the increasing energy demands and mitigate the escalating environmental and socioeconomic impacts of climate change in these countries (Longfor et al. 2023a, b). In addition, the rising energy costs and declining fossil fuel supplies provide strong incentives for stakeholders in developing countries to reconsider the socioeconomic benefits of adopting clean energy sources especially for green urban development (Arranz-Piera et al. 2017; Strzalka et al. 2017).

Urbanization in developing countries has led to daily generation of substantial amounts of waste and has resulted in a growing focus on biomass waste-to-energy (WtE) utilization (Guido et al. 2024; Patel et al. 2023). This is because biomass WtE provides the dual benefits of solving the growing waste crisis resulting from urbanization and as a viable pathway for sustainable energy transitions for low-carbon urban development, and consequently would enhance the energy security in these countries (Longfor et al. 2023b; Malinauskaite et al. 2017; Silva dos Santos et al. 2018). Moreover, scholars predict that biomass energy would play a crucial role in renewable energy transition owing to its low net carbon emissions. This can potentially triple its current usage by 2050 (IEA 2021; Istrate et al. 2021; Zhang et al. 2020).

Several studies have highlighted the potential for WtE utilization as a viable path to sustainable energy transition developing countries (Aslam et al. 2021; Moustakas et al. 2020; Somorin et al. 2017). For example, Longfor et al. (2023b) demonstrated that municipal solid waste (MSW) electricity generation could yield 100–245 kWh per capita and that the net present value (NPV) of WtE technologies is lower than the potential economic damage from methane emissions at landfills across sub-Saharan Africa. Silva dos Santos et al. (2018) identified a biogas energy potential of 4.5–6.9 GW from organic waste in Brazil. Moustakas et al. (2020) reported a WtE potential of 775 GWh in Greece. Abdeshahian et al. (2016) estimated an electricity generation potential of 8.27 TWh/y from farm animal waste in Malaysia. Moreda (2016) determined the electricity generation potential in Uruguay as 162–263 GWh/y based on the anaerobic digestion (AD) of manure and agro-industrial solid waste.

Biomass-derived WtE sources such as agricultural residues including energy crops and forest residues have gained attention because of their more homogeneous characteristic, higher calorific value, and fewer contaminants (Vlyssides et al. 2015). The abundance and renewable characteristic of these residues, particularly in developing countries with large arable land, provides reliable and sustainable feedstock for large-scale WtE generation that can support low-carbon urban development (Longfor et al. 2023b). This makes it suitable for large-scale deployment to solve energy security problems caused explosive or fast paced urbanization in most emerging economies (Aikas et al. 2023; Arranz-Piera et al. 2017). Currently, agro-biomass accounts for 80% of the worldwide energy generation from renewables. It is one of the most viable alternatives to current energy systems because it is a direct substitute for fossil energy systems; can be stored and reused on demand; and can compensate for the energy demand gap created by electricity fluctuations from seasonal renewable energy sources such as wind, solar, or hydropower (Strzalka et al. 2017).

Rice husk (RH) biomass waste has gained attention because of its availability and energy properties. RH is particularly suitable for decentralized energy solutions because of its abundance (which is driven by the increasing demand for rice cultivation and consumption) and its high energy potential (Bazargan et al. 2015; Mofijur et al. 2019). The crucial role of RH as a renewable fuel for energy generation has been emphasized in numerous studies over the past decade. For example, Bhattacharyya (2014) observed that in South Asia, integrating the electricity demand from rice mills with larger plants substantially enhanced the viability of off-grid RH electricity generation. Similarly, Pode et al. (2015) posited that RH biomass has been demonstrated to be self-sustainable for rural electrification, indicating that RH biomass is outperforming other renewable energy sources and becoming a viable business model that does not require grants or subsidies.

Building on the success of biomass energy systems, particularly rice straw (RS) and RH WtE generation in Asia, researchers are now evaluating their environmental and economic viability for electrification in sub-Saharan Africa (SSA). Ramamurthi et al. (2016) found that RS- and RH-powered energy systems have lower levelized electricity costs than diesel, fossil power grid extensions, and off-grid solar systems in Ghana. The authors determined that the widespread adoption of these systems could satisfy 7% of the electricity needs of unelectrified communities. Arranz-Piera et al. (2017) quantified RH feedstocks from smallholder farm clusters. They found determined that between 13 and 54 farms of 10 hectares each are required to supply sufficient rice residues for 600–1000 kWe combined heat and power (CHP) plants. However, they concluded that such an energy generation regime is economically unviable under Ghana’s current feed-in tariff and that it would require either a 25% increase in tariffs or subsidies that cover one-third of the investment cost for positive returns.

In Nigeria, Ejiofor et al. (2020) demonstrated that a 1.52 MW gasifier system could successfully power a cluster of rice mills in Eastern Nigeria and can provide positive economic returns with lower electricity tariffs than fossil-powered systems. Diemuodeke et al. (2021) demonstrated that the unit cost of electricity from a CHP plant in a cluster of rice mills in Abakaliki, Nigeria, is significantly low, i.e., less than a quarter of the unit cost of electricity generated from diesel-powered generators. The authors further demonstrated that replacing diesel generators with the proposed RH-CHP system could save over 300 kg of CO2 per MWh per day in the study area.

Notwithstanding the significant potential of WtE generation from agro-biomass in general and RH biomass in particular in developing countries, several obstacles have slowed its uptake and implementation. This has limited its full utilization and deployment. These obstacles include (but are not limited to) high capital and operating costs, inadequate waste sorting systems (Yan et al. 2020; Zafar et al. 2024), technological and infrastructural deficits, insufficient policy frameworks that support WtE solutions (Batista et al. 2021; Chand Malav et al. 2020), and inadequate waste feedstock supply. These may disrupt the operations and economic feasibility of WtE plants (Mohammadi and Harjunkoski 2019; Wang and You 2021).

Although most of the debate on technological and investment requirements including the technology mix, classification (direct combustion, gasification, pyrolysis, etc.), investment costs and returns, and energy yield may have been established substantially (IRENA 2012, 2014, 2022a), it is still challenging for researchers and policymakers to justify the economic viability, risk management, cost benefits, and environmental and social benefits of the development and deployment of biomass energy generation owing to their capital-intensive nature (Sampim et al. 2017).

This brief literature review demonstrates that the economics of WtE generation from agro-biomass in general and RH biomass in particular (including technology design, classification, characterization, cost, and performance) are well established in developing countries. The review also demonstrates that WtE generation from agro/RH biomass can be a viable path for green transition especially for emerging economies facing low-carbon urban development challenges.

However, our study makes several unique contributions to the literature. First, unlike previous studies, it employs a data-driven approach to characterize and quantify the potential for widespread sustainable biomass electricity generation in an emerging economy. It uses location- and region-specific data on daily RH availability from large and geographically dispersed rice mills/clusters of rice mills to ensure accurate and relevant insights that can be generalized and reproduced. Second, it provides a clearer understanding of the economic feasibility of biomass WtE projects using several financial indicators such as the present value (PV), NPV, and internal rate of return (IRR) under four electricity generation scenarios. Third, the factors influencing the sustainability of biomass WtE systems are evaluated by performing a sensitivity analysis of RH WtE generation with respect to variations in the capital recovery rate (determined by varying the cost of capital) and feedstock prices. Finally, using GPS data (coordinates) retrieved from milling centers, the study georeferences locations that satisfy the minimum daily RH requirements for several biomass WtE generation scenarios. In doing so, it complements the economic analysis with geospatial analysis. This unique contribution sets the study apart from existing research that typically relies on either economic or geospatial analyses. Finally, the analysis demonstrates that RH biomass waste-to-energy generation provides a sustainable path for green transition in emerging economy where low-carbon urban development has become a major policy discourse.

Study design and methods

RE resources and energy statistics in the study area

Nigeria is debatably the most populous country and the largest economy in Africa, with a population of over 200 million, facing escalating rural–urban migration (urbanization) and surging industrial economy. Nigeria is among the top resource-rich rentier states in Su-Sahara, with abundant renewable and non-renewable energy resources (Adeniyi and Isah 2023). A recent study determined that Nigeria can generate 60% of its energy demand from renewable sources by 2050 (IRENA 2023). However, as demonstrated in IRENA (2022b), only a fraction of renewable energy resources in Nigeria are being utilized, with some renewable energy resources remaining completely unutilized. Notwithstanding the large renewable energy potential, particularly renewable energy resources, the current electricity generation in Nigeria is grossly inadequate with approximately 87.7% shortfall in demand. This has significantly slowed the pace of development (Diemuodeke et al. 2021; Salisu et al. 2021). According to the International Renewable Energy Agency, over 80 million Nigerians would had no access to electricity in 2021 (IRENA 2022b). With 142 kWh per person per year, Nigeria is among the countries with the lowest per capita electricity generation in SSA. Furthermore, it is estimated that approximately 80% of the current electricity generation comes from fossil fuel-powered energy systems (Aduba and Shimada 2023). The energy crisis in Nigeria has been attributed to severe funding problems, corruption, insufficient investment, and energy loss owing to infrastructure decay (Somoye 2023). With regard to RH biomass residual, recent statistics reveal that Nigeria produces 8.5 million tons of paddy rice annually (FAO 2023) and approximately 2 million tons of RH assuming a paddy-to-husk ratio of 20–23% (Bhattacharyya 2014). These millions of unused RH generate millions of CO2 emissions during open burning causing air and environmental pollution (black carbon). However, these can be utilized for electricity generation. Figure 1 presents a spatial map of rice cultivation and production, RH equivalents, and common RH treatment practices in Nigeria. For a country such as Nigeria, which generates millions of tons of agro-residue waste and still has a significantly low energy supply, the solution lies in utilizing the abundant agro-residues and other REs to solve the energy crisis (Giwa et al. 2017).

Fig. 1
Fig. 1
Full size image

a Rice production by region in Nigeria (source: author’s projection based on graph retrieved from https://ricepedia.org/nigeria.) (b) and (c) current RH disposal practice in Nigeria (source Diemuodeke et al. 2021; UNIDO 2011)

Determination of RH availability and RH biomass waste-to-energy technology sizing

The assessment of daily RH generation was the most crucial step in achieving the research objectives of this study. However, because comprehensive national data on rice millers are lacking, an extensive field survey of rice milling activities must be conducted to generate the required data. Rice cultivation is not distributed uniformly in Nigeria owing to the climatic, soil, and region-specific differences.

A special survey design that recognizes the non-randomness of the rice mill population is required for generalization. A two-stage field study was conducted to retrieve relevant data on rice milling activities and RH availability based on specific sampling criteria. First, using secondary data from the national archive on the ranking of rice-producing states, purposive sampling of the top 14 rice-producing states was selected based on the fact that these states naturally have significant rice milling activities. To ensure a representative sample, at least one state from each of the six major regions was included. As shown in Fig. 2, the 14 selected states are geographically dispersed across the six major zones of Nigeria. Next, another purposive selection of the ten top rice milling centers in each of the 14 selected states, considering site-specific demography (rural, semi-urban, and urban) and mill size (small-, medium-, and large-scale).

Fig. 2
Fig. 2
Full size image

Survey sites/mills as captured by the GPS

Finally, a stratified random sampling of five–ten millers/mill centers was drawn from each of the ten top rice milling centers in each of the top 14 rice-producing states. Ten research assistants were trained using a mobile digital survey application (Datascope®). It is an electronic survey app with GPS capturing features and a live data feed. A survey instrument coded in Datascope® digital form was used for the data collection.Footnote 1 An initial sample of 700 respondents/millers was targeted. However, a final sample of 540 millers was interviewed and validated between October 11 and November 9, 2022. Figure 2 shows the sample distribution of the respondents/mills.

The instrument (questionnaire) was divided into several sections. Section one measures the socioeconomic characteristics of the millers. Section two evaluates the milling machine characteristics and in-plant energy requirements, particularly the electricity/power source and the availability and stability of power supply. Section three examines the milling activities, including the paddy source, quantity, seasonal fluctuations in paddy supply, daily and weekly RH biomass generation, and revenue. Section four examines the RH treatment, including open burning, dumping, and environmental impact awareness. Finally, section five examines the characteristics of the surrounding community or town, namely, the population, distance to rice mills or mill clusters, number of households, sources of electricity, and availability and stability of power supply.

In terms of WtE conversion technology and sizing, several RH biomass-powered electricity generation technologies exist and have been commercialized in several countries and regions. The characterization, classification, and sizing of these technologies have been the focus of several previous studies. Therefore, the technical design and performance of these technologies as well as the thermochemical characteristics of specific biomass and/or RH feedstocks were beyond the scope of this study. Nevertheless, the relevant and specific technical parameters related to the overall RH feedstock requirements for plant sizing and energy equivalence were obtained from related studies. These are listed in Table 1.

Table 1 Technical parameters and basic cost analysis
Full size table

Framework for analysis

For simplicity, the analysis begins with the delivery of HR to power plants located near rice mills or clusters of rice mills and ends with power generation (electricity output). Hence, analyses of feedstock transportation, low-voltage electricity transmission (LVT), and distribution in rice mills or nearby households were not considered. This was because the inclusion of such additional metrics requires accurate information on the distances between plant sites, feedstock destinations, and community centers where the LVT lines should be drawn or established. Excluding metrics with imprecise measurements is a common practice in economic analysis owing to the uncertainties and large variations in cost analysis comparisons of WtE generation projects.

A simple material flow analysis framework was adopted in this study (Fig. 3). Based on the acquired RH data, a biomass power generation plant will be sited near a rice mill or clusters of rice mills that satisfy the minimum daily feedstock requirements. The feedstock is sourced directly from rice mills and used to generate electricity. The electricity generated is then sold to rice mills and households in the surrounding communities. Other byproducts of power plants such as heat can be used for paddy boiling and drying (PB&D) in rice mills, and ash (silica) can be used for building materials such as cement and other industrial uses. The framework shown in Fig. 3 was implemented using four RH-powered electricity generation scenarios depending on the daily RH generated in the sampled milling centers.

Fig. 3
Fig. 3
Full size image

Research framework for RH biomass electricity generation

Financial/economic analysis

Biomass WtE technologies are capital-intensive making the analysis of capital cost the most important factor to consider when designing and implementing these projects. Using specific and detailed information, previous studies have conclusively estimated the cost relationships between plant capacities (sizes) and investment costs using a logistic curve function, as shown in Eq. (1) (Delivand et al. 2011; Ramamurthi et al. 2016). Based on these studies, the capital cost was estimated for four plant capacities (Table 3) using Eq. (1):

$$\lambda = \alpha \chi^{\sigma }$$
(1)

where \(\lambda\) is the equipment cost (million USD), \(\chi\) is the electrical output (MWe), and \(\alpha\) and \(\sigma\) are constants.

For non-social intervention power projects, the recovery of initial capital is a critical consideration in economic analysis. The capital recovery rate or annuity factor is generally used to annualize capital to ensure that the capital investment is spread over the project years while considering depreciation and interest rates. Crucially, the annuity factor is largely determined by the cost of capital (the interest rate). The annuity factor (AF) is estimated based on the discount rate or cost of capital \(\tau\) (%) and life cycle of the plants \(n\) (years) using Eq. (2) (Colantoni et al. 2021):

$${\text{AF}} = \frac{{\tau \left( {1 + \tau } \right)^{n} }}{{\left( {1 + \tau } \right)^{n} - 1}} \equiv \frac{\tau }{{1 - \left( {1 + \tau } \right)^{ - n} }}$$
(2)

The annualized capital cost \(\left( {{\text{ACC}}} \right)\) of the project can then be estimated as the product of \({\text{AF}}\) and the total capital cost \(\left( {{\text{TCC}}} \right)\) using Eq. (3) (Diemuodeke et al. 2021):

$${\text{ACC}} = {\text{AF}} \times {\text{TCC}}$$
(3)

The cost of RH fuel (feedstock) was estimated based on the annual demand for RH at each plant capacity. In general, the annual quantity RH (ADRH) can be estimated using Eq. (4) (Salisu et al. 2021):

$${\text{ADRH}} = \frac{{\phi \times 3.6 \times {\uppsi }}}{{{\Omega e} \times {\text{LHV}}}}$$
(4)

where \(\phi\) is the plant capacity (MW), \(\uppsi\) is the annual operating hours, \(\Omega e\) is the plant efficiency, and \(\text{LHV}\) is the low heating value of RH. The annual cost of the HR was then estimated using the \(\text{ADRH}\) and the unit cost of the RH (USD/ton) as presented in Table 4. Other costs such as the operation and maintenance costs (including staff cost) were calculated as a percentage of the capital cost and are presented in Table 4 (Arranz-Piera et al. 2017; Diemuodeke et al. 2021).Footnote 2 The aggregation of these costs is crucial for determining the levelized cost of electricity (LCOE; USD/kWh). LCOE is the minimum selling price of electricity that ensures smooth operation of the plant throughout its life cycle. Quantitatively, the LCOE is the ratio of all the costs incurred in a power project to the total electrical energy produced during the project life cycle (Ezekwem and Muthusamy 2023). Accordingly, the LCOE was estimated using Eq. (5) (Ezekwem and Muthusamy 2023):

$${\text{LCOE}} = \frac{{{\text{ACC}}\left( {{\text{USD}}} \right) \times {\text{ADRH}}\left( {{\text{USD}}} \right) \times {\text{AOM}}\left( {{\text{USD}}} \right)}}{{{\uppsi } \times {\upvarepsilon }\left( {{\text{kW}}} \right)}}$$
(5)

where \(\text{AOM}\) is the annual operation and maintenance. \(\upvarepsilon\) is the plant capacity (kW).

Further analysis was performed to investigate the sensitivity effect of the feedstock and capital recovery rates on the LCOE under several discount rates. To accurately capture the potential multidimensional effects of these two factors on the LCOE, we theorized that the LCOE depends on (and may be linearly proportional to) factors such as the feedstock prices, capital recovery (annuity factor), cost of capital, plant size, and other unknown vectors of covariates. Therefore, we represent this relationship using the following simple linear regression equation (Eq. 6):

$${\text{LCOE}} = \alpha + \gamma {\text{FP}} + \delta {\text{AF}} + {\text{Control}} + e$$
(6)

where \(\text{FP}\) is the feedstock price, \(\text{AF}\) is the annuity factor (capital recovery rate), and e is the error term. \(\alpha\), \(\gamma ,\) and \(\delta\) are non-zero and non-negative constants to be estimated. \(e\) is assumed to be normally distributed with zero mean and constant variance. Moreover, given that our sample was sufficiently large, normality could be assumed without loss of generality.

Based on the survey data, the average \(FP\) was USD 12.5/ton. This is comparable to the feedstock costs reported in a similar study (Ejiofor et al. 2020). The FP cost was then permitted to take a value between USD 0–18.5/ton during the LCOE sensitivity analysis. The AF was computed based on Eq. (2) from a minimum discount rate of 2% to a maximum discount rate of 12%. Realistically, the AF cannot be zero because a 0% discount rate is difficult to justify, particularly in developing countries with limited financial resources and high investment risks. Under the full regression estimation, Eq. (6) was controlled for plant size. Additional regression analyses were performed for each energy generation scenario.

Based on the estimated input and output costs, future cash flow analyses were performed for four project scenarios. The life cycle (in years) of each project was assumed to be 25. All the future cash flows were discounted to their PV using Eq. (7) (Colantoni et al. 2021):

$${\text{PV}} = \frac{{{\text{CF}}_{\eta } }}{{\left( {\tau + 1} \right)^{n} }}$$
(7)

where \({\text{CF}}_{\eta }\) is the cashflow in the \({\eta }^{th}\) year, i.e., the net revenue (after tax) from the sale of electricity. \(\tau\) is the discount rate.

The NPV is the sum of all the PVs or discounted future cash flows less the initial investment (\({\text{I}}_{i})\) is one of the most important indicators of project viability and is generally used in techno-economic assessment studies (Akpahou et al. 2023; Colantoni et al. 2021; Longfor et al. 2023b). In accordance with these studies, the NPV was estimated using Eq. (8):

$${\text{NPV}} = \mathop \sum \limits_{\eta = 1}^{\tau } \frac{{{\text{CF}}_{\eta } }}{{\left( {\tau + 1} \right)^{n} }} - {\text{I}}_{i}$$
(8)

Based on this equation, an appropriate IRR can be estimated using Eq. (9). Moreover, because the IRR net-outs the NPV to zero during the project life cycle, it enables analysts to select projects with higher prospects of economic return (Colantoni et al. 2021). Because the NPV is sensitive to the discount rate, a sensitivity analysis was performed using six discount rates varying from 2 to 12% to determine the discount rate that generates a positive NPV and IRR. The relationship between the NPV and IRR can be estimated using Eq. (9) (Colantoni et al. 2021):

$${\text{NPV}} = \mathop \sum \limits_{\eta = 1}^{\tau } \frac{{{\text{CF}}_{\eta } }}{{\left( {{\text{IRR}} + 1} \right)^{n} }} - {\text{I}}_{i} = 0$$
(9)

Results and discussion

Summary distribution of mills and RH generation potential in the study areas

Understanding the distribution of the mill sizes, location, and ownership is as crucial as quantifying the daily RH generation across the sampled areas and regions. Table 2 summarizes the daily RH distribution across the different strata. Panel A examines the daily RH generation considering all the three important distribution characteristics. Meanwhile, Panel B examines daily RH generation by mill type and mill location. Both panels show that several mills spread across the study area generate sufficient RH to provide feedstock to plants of various capacities. However, for detailed analysis of potential biomass-powered electricity generation plants in specific locations in the study areas, we focus on four specific plant sizes (scenarios): 0.8 MW, 1.3 MW, 1.8 MW, and 2.2 MW. Our choice of plant sizes was based on findings from the literature which shows that larger plant size benefits from economies-of-scale in electricity output and is more likely to generate positive economic returns than a smaller one. After determining the four power generation scenarios, the next task was to examine the specific mills in our sample that satisfy the minimum daily RH generation requirements for each scenario.

Table 2 Average daily RH generation (kg) by mill ownership, location, and size
Full size table

Technical analysis

Electricity generation potential

Tables 3 and 4 present the results for the four RH WtE generation scenarios. Table 3 shows the scenario setup, net power generation, energy demand of individual mills that could be met by each power generation scenario, and excess electricity available for residential or household use. Table 4 shows the potential economies-of-scale of biomass power generation for each scenario. These were determined through a nationwide analysis of survey data by considering the sites/mills that satisfy the minimum daily RH requirements.

Table 3 Electricity generation potential under four scenarios based on daily RH availability from survey data
Full size table
Table 4 Total potential electricity generation of all mills satisfying minimum HR generation in all four scenarios
Full size table

Based on Table 3, the analysis proceeded as follows: Columns A and B show the daily RH generation classification and four power generation scenarios. Column D shows the net capacity of the plants. Here, it is assumed that a fraction of the power is used or lost within the plants. To estimate the mill-specific electricity demand. We collected information on the number and capacity of each machine in the milling centers. Using this information, we estimated the average rice mill-specific electricity demand and total mill-specific electricity demand, as presented in Column E of both tables. Furthermore, we set realistic assumptions for the operation of plants based on the relevant literature. We assumed that the typical RH-fueled power plant in our scenario would operate for 20 h/day and shut down once in a month for maintenance purposes.

This implies that the monthly and annual operating hours of the plants will be 600 and 7200 h, respectively. This estimate is reasonable and lies within several estimates used in the literature for similar power generation regimes (see Table 1 for further information). Column F shows the annual electricity generation under each scenario using plant operating hours, whereas Column G shows the in-mill electricity demand. Columns H shows that in addition to satisfying the electricity demand of several rice mills, each scenario could supply excess electricity to hundreds and thousands of households assuming 0.5–0.8 kW per household (Column I).

The results in Columns F and I of Table 4 indicate that considering all the eligible sites satisfying the minimum daily RH generation for all the four scenarios, over 500,000 MWh of electricity can be generated per year which can be used to supply electricity to over 40,000 households in addition to satisfying the electricity needs of several mills and clusters of mills.

Finally, using the GPS coordinates of the data collection points, we performed a geospatial analysis to locate mills with minimum daily HR generation for several plant sizes including the four scenarios analyzed in this study. Figure 4 shows the geolocated mills/site with potential daily HR generation for plant sizes between 0.2 and 2.2 MW. According to the legend of Fig. 4, there are 78, 77, 44, 12, 5, and 8 sites/mills or clusters of mills with daily HR generation sufficient to power 0.2 MW, 0.8 MW, 0.8 MW, 1.3 MW, 1.8 MW, and 2.2 MW plants, respectively. This implies that there is the significant potential for nationwide electricity generation from RH biomass. Such significant potential when properly utilized can solve energy crises, augment and improve the nationwide electrification, and provide a sustainable path to renewable energy transition in Nigeria.

Fig. 4
Fig. 4
Full size image

Nigeria Map showing sites/locations potential plant capacities. Note: Figures in parenthesis represent number of mills/millers generating sufficient daily RH to power corresponding plants

Estimating levelized cost of electricity

Next, we estimate the minimum selling price of electricity (i.e., the LCOE) considering the capital, operation, and maintenance costs estimates in Table 1: Theoretically, the LCOE is the minimum price at which electricity should be sold for the plant to operate for its entire life cycle while ensuring the recovery of the PV of the capital or the initial investment cost of the project. In calculating the LCOE of biomass energy generation regimes, some studies assumed that the supply cost of biomass feedstock such as RH or RS could be zero owing to their availability. Meanwhile, other studies included the feedstock cost. In this study, the LCOE for each scenario was calculated with and without feedstock costs. Table 5 presents the LCOE analysis. Overall, the results indicate that the LCOE lies between USD 0.144/kWh and USD 0.169/kWh depending on the plant size and feedstock cost. Our estimated LCOE range lies within the LCOE of biomass documented in the literature, including IEA estimates (NESG and HBS 2017). For example, Diemuodeke et al. (2021) determined that the LCOE of an RH biomass power plant for a cluster of rice mills in Eastern Nigeria lies between USD 0.120–0.159/kWh depending on the gasification technology and whether an import tariff is considered.

Table 5 Levelized electricity cost
Full size table

We present the results in Fig. 5 to better understand the trends of LCOE. Two important inferences emerged from Fig. 5. First, the LCOE is lower for plants with higher capacities. Other studies have shown that larger plants have a lower LCOE. Second, including feedstock costs in the O&M cost increased the LCOE in all the four scenarios. This indicates that although biomass feedstocks such as RH and RS are available in large quantities at farms and rice mills and may be used at zero cost, it is important to consider the cost of the feedstock because in addition to providing accurate input analysis for LCOE computation, feedstock cost appears to affect the LCOE. The sensitivity of the feedstock price and capital recovery rate on the LCOE is analyzed further in “Sensitivity Analysis: Capital recovery rates and fuel price effect on LCOE” section.

Fig. 5
Fig. 5
Full size image

Levelized electricity cost and plant capacity. Note: Each bar represents LCOE in $ per kWh for each plant scenario with and without inclusion of fuel cost

Economic analysis

The debate on the financial and economic returns is central to the design and implementation of WtE generation projects. In this section, we examine the economic viability of the electricity generation scenarios presented earlier. The purpose is to determine whether these electricity generation regimes are attractive, competitive, and sustainable. We begin by computing the cash flow for each scenario throughout the plant life cycle based on the electricity generated and current electricity selling price (tariff). According to NERC (2015), the feed-in tariff for biomass renewable-energy-sourced electricity in Nigeria is USD 154/MWh (or USD 183/MWh as the 2022/2023 local inflation-adjusted value). This was used as the reference point to perform cash flow analysis throughout the life cycle of each project. All the cash flows were then discounted using six discount rates (costs of capital) from a minimum discount rate of 2% (generally used as the social discount rate) to the average interest rate of 12% reported in the literature for similar biomass power generation studies. Figure 6 shows the graphical relationship between the PVs of all future cash flows and various discount rates for each scenario. The results show that for all the energy generation regimes, the discount rates (interest rates) have a decreasing effect on the PVs of the cash flows. This result is intuitive from the perspective of finance. The discount rate or cost of capital remains the most crucial driver of investment in capital-intensive projects such as biomass power generation. Except for full or partial grants in the form of renewable energy subsidies from the state or donor agencies, most biomass electricity generation remains unviable at the market rate charged for capital investments. The impact of the discount rate on the NPV of the project was analyzed further. NPV is the ultimate indicator of a project’s economic viability because it is the sum of the PVs of all future cash flows, net initial investments, or total capital cost. Figure 7 shows the NPV results under the six discount rates and the following two tax regimes: full corporate tax payments and tax-free subsidies. In the full tax regime, the NPV is positive only when the discount rate is fixed between 2 and 4% in all the energy generation scenarios. However, under the full tax waiver, a positive NPV is observed at discount rates of 2–6% in all the scenarios. Correspondingly, the IRR of all the scenarios is positive and increases from 1 to 3% when the cost of capital decreases from 4 to 2% under the full tax regime and from 6 to 2% under the full tax exemption. Under full energy investment subsidies wherein the capital cost is 0%, the IRR increased to 5% with and without tax exemption. This result is consistent with observations in the literature. For example, Arranz-Piera et al. (2017) found a negative NPV for RH biomass gasification in Ghana at a discount rate of 11–12% and an IRR of 4% even under full tax exemption. Similarly, Colantoni et al. (2021) found a negative NPV at discount rates above 10% but positive NPV at discount rates below 10%. Finally, Ezekwem and Muthusamy (2023) found the IRR of solar-integrated solar panel electricity generation in Nigeria to be 3.8%.

Fig. 6
Fig. 6
Full size image

Present value (PV) of future cash flows at various discount rates (cost of capital). Note: for each scenario, the present value of future cash flows were estimated based on six interest rates (cost of capital) regimes

Fig. 7
Fig. 7
Full size image

Net Present Value (NPV) of four RH power generation scenarios under varying discount rates and IRR. Note: Each Figure evaluates the effect of cost of capital (interest rate) and IRR on NPV under full corporate tax regime (left Figures) and with full tax exemption subsidy (right Figures)

Sensitivity analysis: capital recovery rates and fuel price effect on LCOE

The sensitivity of feedstock prices and capital recovery rates was determined using a regression analysis, as shown in Eq. (6). Table 6 shows the pooled regression results under the factor-level estimation (Columns 1–2), joint factor estimation (Column 3), factor-level estimation with plant size as the control (Columns 4–5), and full estimation (Column 6).Footnote 3

Table 6 Effect of Feedstock price and annuity factor on LCOE (pooled result)
Full size table

First, the feedstock price has a statistically significant effect on the LCOE at the 1% level for all the estimation strategies (Columns 1–6). This implies that in addition to being a significant determinant of the LCOE, an increase in feedstock prices would significantly increase the selling price of electricity. Similarly, the coefficient of capital recovery indicates that an increase in the capital recovery rate results in a significant and corresponding increase in LCOE at the 1% level. Notably, the magnitude of the capital recovery rate (AF) coefficient is consistently nine times higher than that of the feedstock coefficient. This indicates the relative weight and/or effect of AF on the LCOE compared to feedstock (Fuel price).

The results indicate that the coefficients of both factors remain the same and are consistent for all the six estimation strategies (see Columns 1–6). This is reasonable because both factors can have independent and mutually exclusive effects on the LCOE. That is, the relationship between the LCOE and one of the factors does not depend on the other factor.

Another important result in Table 6 is the observed significant negative relationship between the LCOE and plant size. This empirical evidence is crucial because although similar studies have shown that a larger plant size produces lower levelized electricity costs, this has not been tested empirically under regression estimation, as presented here. Table 7 presents the factor-level regression results for each energy generation scenario. Again, these results are identical to those listed in Table 6. Therefore, a similar inference can be drawn.

Table 7 Effect of Feedstock price and annuity factor on LCOE
Full size table

A post-regression marginal analysis was performed to illustrate the effect of incremental variations in feedstock prices and capital recovery rates on the LCOE. The marginal results (Table A.8) show that a variation in feedstock price (even within a fixed capital recovery rate) has a statistically significant positive effect on the LCOE. The results show that the converse is also true. That is, variations in capital recovery rates within a fixed feedstock price have a significant and positive effect on the LCOE. The results are shown in Figs. 8 and 9. As shown in Fig. 8, the LCOE increases linearly with an increase in the feedstock prices and capital recovery rates. Previous studies have reported similar trends. For example, Ramamurthi et al. (2016) observed that the LCOE for 0.1 MW RH biomass plants in Ghana has a linear positive relationship with interest rates. Additionally, Diemuodeke et al. (2021) observed that the LCOE of RH biomass plants in Nigeria is linearly and positively correlated with the discount rate. Figure 9 captures the simultaneous or joint effect of feedstock and capital recovery rates on the LCOE through a contour analysis. The results show that LCOE increases steadily as both feedstock price and capital recovery rate increase.

Fig. 8
Fig. 8
Full size image

Marginal effect of fuel price and annuity factor on LCOE. Note: this figure demonstrates the sensitivity of the LCOE to the feedstock price (RH price) under a fixed annuity factor

Fig. 9
Fig. 9
Full size image

Marginal effect of fuel price and annuity factor on LCOE. Note: this figure demonstrates the sensitivity of LCOE to simultaneous changes in feedstock price (RH price) and the annuity factor

Further discussion and policy implications

This study highlighted the potential of RH biomass WtE generation to drive sustainable energy transitions (SET) toward low-carbon urban development in emerging and development countries. It demonstrated that the economic viability of RH WtE for electricity generation in Nigeria has a positive NPV and IRR under subsidized capital costs. Achieving a positive NPV and IRR is feasible under discounted rates of between 2 and 4% cost of capital/lending. Although these rates are significantly below the current commercial lending rates, they are within the accepted social discount rates for social intervention projects. Additionally, our findings show that under tax-free subsidy regime, the cost of capital and IRR increases to 6 to 5%, respectively. This emphasizes the critical role of stakeholder collaboration in the successful implementation of WtE projects in developing countries.

Private energy suppliers alone cannot ensure the economic sustainability of these projects, particularly in urban areas with surging populations and energy need. Therefore, there is an urgent need for government policies that promote the development of renewable energy technologies and address both small-scale residential installations and large-scale commercial projects (REN21, 2019). The study demonstrated that tax incentives such as partial or full corporate tax exemptions make these projects economically viable and attractive for investment by energy suppliers.

Additionally, this study emphasized the strategic importance of feedstock management for biomass WtE profitability. For example, it was revealed that the LCOE is highly sensitive to feedstock prices and capital recovery rates. The latter is influenced by the discount rate or market cost of capital. Lower feedstock prices and capital recovery rates result in a lower LCOE. This emphasizes the competitive characteristic of RH WtE projects and their appeal as sustainable energy sources (Salisu et al. 2021). Furthermore, a similar study showed that RH WtE projects are a cost-effective alternative to conventional diesel generators. And as a result, they provide lower electricity costs and enhance energy generation in developing countries (Diemuodeke et al. 2021).

Although the Nigerian state has proposed several policies to drive renewable energy generation over the past two decades, the low uptake of energy generation from renewable sources evidences the failure of these policies and the fact that policies alone are insufficient to drive renewable energy generation in Nigeria (Somoye 2023). Therefore, it appears to be time for Nigeria to adopt a more aggressive policy posture by adopting a public–private partnership (PPP) model such as the design, build, and operate (DBO) model. The DBO model enables a WtE project to be government-owned but privately operated. Such models eliminate institutional constraints, increase operational flexibility, improve financial performance, and ensure the sustainability of biomass WtE projects (Wang et al. 2021). Furthermore, as demonstrated by Cao et al. (2022) and Cui et al. (2020), PPPs promote rural development by providing market potential, preferential policies, and technological innovation. Consequently, PPP-led developmental projects support local economies and facilitate the transition to a circular economy. This makes PPP models a strategic option for advancing biomass WtE initiatives.

This study also highlighted the transformative potential of utilizing RH WtE generation to enhance electrification in Nigeria, where over 80% of the electricity demand is unfulfilled and over 80 million people do not have access to electricity (Diemuodeke et al. 2021; IRENA 2022b). We demonstrated that RH WtE generation can produce over 500,000 MWh of energy per year which could benefit thousands of households and provide access to electricity to numerous off-grid and unelectrified communities through decentralized solutions. Beyond electrification, RH WtE projects provide significant economic benefits by creating new job opportunities and supplying 10–15% of the energy needs of surrounding communities, leading to substantial savings in family budgets (Salisu et al. 2021).

Harnessing RH biomass WtE also provides substantial health, environmental, and economic benefits, that could pave the way for a sustainable future for the Nigerian population. For example, transitioning from conventional biomass as a cooking fuel (a major cause of indoor air pollution with significant health consequences (Huang et al. 2022; Jiang et al. 2024; Lin and Begho 2022) to a cleaner RH-based energy source in rural and urban areas can yield sustainable public health outcomes. Additionally, utilizing RH for energy generation and/or substituting RH fuel for fossil fuels (as opposed to open RH burning, which produces black carbon emissions) can lower GHG emissions (Huang et al. 2022; Jiang et al. 2024). Furthermore, the environmental damage caused by fossil fuel extraction and improper RH disposal underscores Nigeria’s urgent need to invest in renewable energy sources (Adeniyi and Isah 2023). This is because the damage caused by these activities significantly exceeds the cost of investing in RH biomass WtE and other WtE generation projects. Crucially, these support Nigeria’s climate change mitigation efforts and its transition from carbon-intensive urban development to a low- and subsequently carbon–neutral or green urban/circular economy. This is particularly crucial for fossil fuel-dependent countries such as Nigeria, where the investment in renewable energy sources has been highly insufficient. Therefore, by integrating circular economy principles, WtE technologies, and sustainable urban design into city planning, Nigeria can promote resilient urban development and achieve a carbon–neutral future.

Furthermore, the use of agricultural residues such as RH for energy generation aligns with Nigeria’s sustainable development efforts and supports its commitment to international climate agreements such as the Paris Agreement. This study demonstrates that RH WtE projects are economically viable at discounted cost of capital rate of 2–6% (with a 5% IRR under tax exemption). A similar finding was presented by Diemuodeke et al. (2021). They stated that RH energy systems could provide significant daily electrical and thermal power at an affordable cost per kWh. However, utilizing WtE potential in Nigeria requires the development of appropriate business models and policy frameworks to ensure the successful implementation of RH WtE projects. Therefore, policy that drives RH WtE adoption should be considered one of the top sustainable policy agendas of the Nigerian state. This would align Nigeria on the path to achieving SDG 7 (affordable and clean energy). This would also contribute to the nation’s energy sustainability and reinforce its global environmental commitments.

Conclusions, limitations, and future considerations

This study evaluated the economic viability and potential of RH WtE generation as a key component in Nigeria’s transition toward low- and subsequently carbon–neutral urban development. Using a data-driven approach and comprehensive economic analysis, the study provided valuable insights into the feasibility of sustainable biomass electricity generation.

The results indicate that several mills/mill clusters can generate sufficient daily RH for 0.8–2.2 MW power plants. Our economic analysis revealed that RH biomass electricity regimes produce positive NPVs and IRRs of 1–3% under discounted capital cost rate of between 2–6%. This indicates that RH WtE projects are economically viable under certain subsidized capital costs. Furthermore, the levelized cost of electricity (LCOE) for all the power generation scenarios considered ranged from USD 0.144 to USD 0.169/kWh. This is comparably cheaper than the electricity generated from other sources. However, factors such as the capital recovery rates and feedstock prices significantly influence the LCOE.

The incorporation of tax-free subsidies improves the economic performance of RH WtE projects by increasing the IRR to 5%. In addition to tax incentives, public–private partnerships may considerably enhance the economic viability of WtE projects. The study demonstrated that RH WtE projects are viable and could be a pathway to sustainable electrification of Nigeria. RH WtE generations are capable of generating over 500,000 MWh of electricity per year in Nigeria. Such large electricity generation potential from the RH WtE in Nigeria can significantly improve access to electricity in off-grid and remote communities, thereby contributing to Nigeria’s electrification efforts. The environmental and health benefits of transitioning from conventional biomass and fossil fuels to RH-based energy have highlighted the significance of such initiatives for sustainable development and climate change mitigation. To realize the full potential of these projects and achieve climate-related SDGs, the Nigerian government should implement supportive policies, collaborate with stakeholders across various sectors, and develop innovative financing mechanisms.

This study had certain limitations. First, it omitted some input costs such as feedstock transportation, low-voltage electricity transmission, and distribution costs, in the economic cost modeling. Herein, the aim was to reduce the uncertainty associated with the inaccurate distance measures of several mills/clusters of mills and the surrounding communities/settlements, which would ultimately affect the presentation and discussion of economic results. Previous studies adopted a similar approach to simplify economic cost modeling. However, future research should include all the potential input costs in the cost calculation by considering accurate distance measures for each of the identified mills or clusters of mills. Second, the economic models utilized in this study did not account for all the externalities associated with RH WtE projects, such as environmental and social consequences. Furthermore, the economic feasibility is highly sensitive to feedstock prices and capital recovery rates, which may vary in different situations. Thus, further research is required to investigate the long-term sustainability and life cycle assessment of these projects. In addition, examining the integration of RH WtE systems with other renewable energy sources is desirable for developing more comprehensive electrification strategies and systems. Finally, it is necessary to evaluate the social acceptance and community engagement aspects of adopting RH WtE systems in the identified communities and towns.