• *Rural electrification
  • *Association between rural electrification and agricultural output:
  • *Practical Evidence from Sub-Saharan Africa

Feasibility of rural electrification and connectivity—A methodology and case study - ScienceDirect

Feasibility of

  1. Agric and Farm
  2. Rural electrification and connectivity—A methodology and case study

Highlights

  • A comprehensive REVIEW of PROJECT integration & The different of Willingness To Pay (WTP), methods In rural electrification studies presented.
  • The Common Measurement Parameter, unit of service, that combines electricity and telecommunications in one service package to establish a link between customers’ preferred price and profitability for the service provider.
  • The Methodology for microgrid developers to adequately justify their investment plan and minimize the risks of non-payments.

Electricity Challenges: Despite the recent development, electrification of rural sub-Saharan Africa remains a major challenge because of remote locations and the low income of the population in those areas. There is a clear need for novel approaches for addressing this challenge. Proposal: New method for analyzing the feasibility of micro-grid projects based on consumers’ expressed willingness to pay for the service in rural sub-Saharan Africa and micro-grid project developers’ willingness to accept.

Introduction of a common measurement parameter, unit of service, that combines electricity and telecommunications in one service package to establish a link between customers’ preferred price and profitability for the service provider.

Further, the proposed methodology is used to analyze a location in rural Namibia. The results suggest that there is a price range for the service where a micro-grid project can be feasible while satisfying local consumers’ willingness to pay for the service. By using the proposed methodology, micro-grid developers can adequately justify their investment plan and minimize the risks of non-payments.

Keywords

Willingness to pay, Willingness to accept, Rural microgrids, Unit of service, Electrification

  1. Introduction

The number of people without access to electricity dropped from 1.2 billion in 2010 to 770 million in 2019

[1]. Topics – Analysis - IEA Many countries have achieved significant results in the electrification rate through comprehensive approaches in policies and planning. However, without stepped-up actions, it has been estimated that approximately 650 million people, or 8% of the global population, will still live without electricity in 2030

[2]. Statista. Projected population without access to electricity globally by region 2030. https://www.statista.com/statistics/561428/forecast-of-population-without-access-to-electricity-globally-by-region/ ; 2020 [accessed 2 November 2021].

Among those who will lack access to electricity, 90% will be living in sub-Saharan Africa (SSA), in areas where the national and regional girds may not always be economically viable for several reasons, including limited purchasing power, lower demand and consumption of energy, and geographical factors, such as people living in rural areas and being far away from the grid and also dispersed from each other

[3] Electricity access in Sub-saharan Africa: uptake

,[4]. Leveraging community-based organizations and fintech to improve small-scale renewable energy financing in sub-Saharan Africa

https://doi.org/10.1016/j.erss.2021.101949

View article,

Off-grid solutions, such as stand-alone systems and micro- and mini-grids, could be a solution not only for underserved people, but also for those who are near the low-voltage connection lines but are not connected for reasons like high connection costs (under-grid customers) and for those who are already connected but cannot afford appliances (idle-grid customers)

[5]. Furthermore, a continuous decrease in costs of the key components for power installations and improved load factors of micro- and mini-grids over the past decades have resulted in a reduction in the levelized cost of energy (LCOE), and it is expected to further decrease by two-thirds by 2030

[6]. Most of the recent generation micro-grid and mini-grid are equipped with technologies (mobile telecommunication services) that enable an increase in energy utilization and further facilitate business activities with real-time monitoring, demand forecasts, remote control, and prepaid payment options. Such features also enable income-generating equipment (productive-use appliances) to connect with off-grid systems, thus prompting local economic growth

[6].Currently, a large share of micro- and mini-grid development is largely carried out by national utility companies. However, private investment is expected to drive the sector in the coming years. The private sector is progressively engaged in specializing in the value chain of micro- and mini-grid projects, such as distribution, metering, solar PV, storage systems etc.

.[7] This will significantly increase the annual profit potential of the value chain of micro- and mini-grids. According to an estimate by the Energy Sector Management Assistance Program (ESMAP), the annual value chain profit has the potential to increase from $510 million in 2019 to $4.7 billion in 2030

[8].To enable economically viable and sustainable off-grid electrification projects, private investors and developers require economic modeling and planning tools. The economic viability of a project is measured in terms of securing initial capital funding for the project, and the operational and maintenance costs are covered by generating a satisfying amount of return on the initial investment. This requires a set of flexible operational plans that are also adaptable to future political, economic, and regulatory changes. Financial indicators, such as project and equity return expectation and the payback period, can be used to calculate the expected sales of electricity from off-grid projects and define the required revenue for making an investment decision[8]. Required revenue, in turn, defines a developer’s willingness to accept (WTA) the price for the service or goods. In the context of this paper, WTA is defined as an acceptable price level at which the sale of service will be suitable for the economic viability of a project.

Customers’ willingness to pay (WTP) for electricity services is another essential factor in the off-grid electrification project development. WTP is the range for the maximum monetary amount that a customer will pay for a unit of product or service. Understanding consumers’ WTP can help developers to introduce new products or services and pursue a pricing strategy that is suitable for the marketing environment while reducing the risk of losing valuable profit. Many studies have shown that the customer response evoked by price variation can have a significant effect on revenue and profit [5]

,[9].There are different determinants for the WTP for electricity in developing regions, viz. social, economic, demographic, cultural, and technical factors. These factors can determine or strongly affect the WTP of electricity consumers. For instance, according to a review [5]of different studies on the WTP of consumers in developing regions, variables such as household income, education status, substitution for a traditional energy source, the number of children of school-going ages, and home business (household entrepreneurial aspirations) were found to have a positive effect on consumers’ WTP. On the other hand, the study found that age, occupation, and household structures have a vaguer relationship to WTP in the literature. Moreover, cultural, and behavioral determinants, such as lack of trust, inattention, and information biases can be influencing factors. For instance, a lack of trust in the government in providing or improving electricity service will harm WTP; this lack of trust is mainly caused by past historical events that have shown politicians failing to deliver their promises. On the other hand, high trust in the government and a belief in political promises or a belief that higher prices will provide improved services can increase the WTP among consumers. Other cultural factors, such as acceptance of private electricity service providers and responsibilities of who should back up electricity provision can further complicate the WTP for electricity. From the technical perceptive, WTP can vary depending on the reliability of electricity supply. This can be measured based on consumers’ definition of reliability, frequency of brownouts, or duration of outages and provision of information about possible outages beforehand.

The purpose of this study is to propose a methodology for defining a feasibility area of an electrification and telecommunication project in the context of rural SSA based on an equilibrium between the WTA of electricity and connectivity providers and the WTP of the rural community for the service. Such an evaluation can help assess the projects based on the correlation of the customers’ WTP, with other relevant measurements, namely, ability to pay (ATP), that is, a customers’ financial ability measured based on his/her financial income. Hence, we propose a direct WTP survey to reveal the preference with respect to the customers’ energy and telecom needs. We also introduce a concept of a unit of service, which is designed to fit a quantified amount of bundled energy and telecom services as a benchmark. The aim is to help understand the true desires or preferences of customers. Oftentimes rural customers, in particular those who lacks access to electricity and telecom services, are faced difficulties in quantifying technical services. This can lead to under or overestimating their true demand and purchasing capability using power or energy units. The unit of service, therefore, serve as a measuring tool for developers based on customers willingness or ability. The relationship between the customers’ and developers’ price preference for a unit of service balances out and provides an equilibrium price. Application of unit of service can be found in sectors such as health service institutions, where, necessary patient care including transportation, medicine, lab tests, etc. are packaged together for billing or non-billing purposes 

[10].In this paper, we focus on answering the following questions: What is the preferable WTP survey method for first-time electricity users? How can a microgrid developer assess the financial feasibility of a project based on the customers’ willingness and ability? How a common instrument can be designed to bridge between the user and developer? How an acceptable price for can be reached based on customer and developer preferences?

The paper provides a study case analysis based on the proposed methodology and a survey from the rural village of Revon C in northern Namibia. The results suggest that using the direct WTP methodology can allow to define a feasibility area for the project developers and an affordability area for the customers to ensure long-term sustainability of the project. The main contributions to WTP studies in rural areas are 1) a measurement tool for quantifying demand based on preferred WTP is proposed, 2) unlike the already existing numerous economic-based and policy-oriented literatures, we provide a business feasibility approach founded on WTP of customers.

The paper is designed as follows: the second section provides definitions for the main concepts used in the study, such as WTP, ATP, and WTA, and discusses current WTP methods that are widely used to study rural areas. The third section reviews literature on rural electrification studies that apply the WTP approach. The fourth section describes a methodology for defining a feasibility area for an electrification and connectivity project based on WTP and WTA curves. The fifth section provides the results of a case study from the village of Revon C in Namibia and discusses the results, implications, and assumptions. Finally, the sixth section concludes the major findings of the research conducted in the paper.

  1. Definitions

Willingness to Pay (WTP) is a market research method used to understand the maximum price range at which a consumer is willing to buy a product or a service. Understanding consumers’ WTP can play a crucial role in new product development, pricing, and competition strategies. WTP measurements can involve three types of procedures: 1) a direct open format question, in which respondents are asked about their WTP for higher consumption of a particular good or a service, 2) a list model, in which a variety of values are presented for the participants, and they are asked about the amount they would pay for the listed values, and 3) a referendum model, where participants are asked whether they are willing to pay a certain amount of money for the good or not. 

[11].Based on the data collection method, WTP surveys can be divided into two major categories: revealed and expressed preferences. Revealed preference data are obtained from price responses. For instance, in the case of rural area customers, revealed preferences can be derived from the expenses of the local population for alternative sources of energy, such as kerosene for lamps or candles, and for charging phones. On the other hand, expressed preference data derived from surveys are frequently referred to as stated preferences or the contingent valuation method (CVM). In this case, consumers are asked to reveal the maximum amount they are willing to pay for a hypothetical service or product that is created by a market researcher. The methods produce a realistic scenario that offers a rational rate of electricity service that is also feasible for the electricity project

[12].Willingness to Accept (WTA) is a term that is used in economics to assess a monetary amount that a person is willing to accept to sell a service or a good. WTA is also used to evaluate a persons’ stated price for compensation to bear a loss or forgo an environmental service, such as pollution, noise, or property rights

[13]. However, in this research, we use WTA as the minimum monetary amount at which a service provider or seller is willing to sell without facing a financial loss or a positive net present value (NPV).

Another economic principle used in this paper is Ability to Pay (ATP), which states the amount of money that a person is able to pay relative to income and wealth. Although its application is mainly for taxation purposes

[14], in this paper we use the term to further assess the income effect that bounds people to pay more than their WTP. Income level plays an important role for the determination of WTP; [5] found a significant correlation between WTP and the income level of the respondents in developing countries. Thus, ATP should be considered in addition to WTP.

  1. Literature review on the contingent valuation method in electricity studies

Contingent valuation method (CVM) is a survey-based method for determining economic values of goods and services. The method is commonly used to estimate nonmarket values and non-use values in the areas of environmental economics and environmental impact assessment. The methods are applied by encouraging participants to consider trade-offs in the choices they make [15]. This method has been widely used to study individual preferences for basic infrastructure projects, such as water supply, electricity, and sanitation in developing countries.

Despite its extensive use, CVM has been criticized for its accuracy and reliability. For instance, the CVM survey method uses mainly a hypothetical market to study the trade-offs and preferences of participants. This can create a hypothetical bias that deviates from the market scenarios. Another challenge of the CVM studies can arise from strategic biases, where individuals understate their true WTP for public goods with the hope that others will pay enough for the service. This is also known as free riding behaviour. On the other hand, individuals can over plead their preference expecting that their WTP value would influence the provision of a good or a service. Furthermore, the information and scenarios provided for the survey respondents can influence the validity of the CV result both positively and negatively [11],[15].

CVM is the most widely used method for measuring the WTP of electricity development in developing areas. Some of the most common CVMs that are used in WTP studies for electricity are:

  • Single-Bounded Dichotomous Choice (SBDC): It is the most widely used method. It involves asking the respondents if they will pay a specified monetary amount to obtain a good or a service by giving them a YES or NO choice. This monetary amount (bid value) is varied across respondents, and the discrete choice format mimics a bargaining process [5]. 
  • Double-Bounded Dichotomous Choice (SBDC): In this method, respondents are given two consecutive YES or NO choices. If the first choice for the bid value is YES, the following choice bid value will be higher than the first, on the other hand, if the first choice is NO, the bid value in the second choice will be smaller than the first amount [19], [20], [21], [22].
  • Multi-Bounded Polychotomous Choice (MBPC): In this method, the respondents are presented with a panel of values and response categories that are arranged into a matrix. The respondents are then asked to mark the degree of confidence that they feel about paying or not paying for each amount that is listed in the matrix [5].
  • Payment Cards: This method lists possible values on a card and asks the customers to pick the value that best represents their WTP for the lower bound and the second choice as the upper bound [5].
  • Choice Experiments/comparative analysis: This method asks the respondents to choose between two programs (A and B), which comprise different attributes and costs to choose or to refuse. This allows marginal valuation of each attribute [5], [16].

There are a growing number of CVM-based WTP studies in developing countries that focus on the reliability and demand of electricity. For instance, Bhandari et al.

[16]conducted a survey in rural Niger to study the effect of a rural community’s WTP for renewable-based electricity supply. The study used comparative analysis to compare the WTP for electricity service with the collaborative consumption and cost of an off-grid system. The results showed that empowering communities to have a share of ownership in rural off-grid projects increased the WTP. In Ghana, Taale & Kyeremeh

[17 studied factors affecting households’ WTP for reliable power in the Cape Coast Metropolitan Area by using contingent valuation methods. The prohibit model was used to identify factors affecting the WTP for the reliability of electricity. Factors such as income, ownership of a separate meter, house size, and education were found to affect the WTP for reliability. The study showed that most of the households are willing to pay 44% more for improved reliability compared with their present electricity bill. Gunatilake et al

[18  investigated the benefit of reliable electricity in Madhya, India using a bidding game and the method of single-bounded closed dichotomous choice. In the study area, the existing grid supply was poor, and a better quality of power supply and service was the main priority among customers. In the study, the probability of consumers’ WTP for improved quality of electricity was studied by using a dichotomous choice approach. Further, Oseni et al. 2017

[19  studied the WTP for reliable electricity supply for self-generating (diesel backup) households in Nigeria. Double-bounded dichotomous choice was used as the method to study the WTP. The study found that having a backup generator increased the households’ WTP for reliability rather than decreased it. This was mainly due to the high cost of self-generation or outage cost that unreliable grid supply can cause. Similar electricity reliability- and service quality-based studies using dichotomous bidding methods can also be found in the literature for Senegal, Ethiopia, and Kenya

―――――

1. Introduction

The number of people without access to electricity dropped from 1.2 billion in 2010 to 770 million in 2019 [1]. Many countries have achieved significant results in the electrification rate through comprehensive approaches in policies and planning. However, without stepped-up actions, it has been estimated that approximately 650 million people, or 8% of the global population, will still live without electricity in 2030 [2]. Among those who will lack access to electricity, 90% will be living in sub-Saharan Africa (SSA), in areas where the national and regional girds may not always be economically viable for several reasons, including limited purchasing power, lower demand and consumption of energy, and geographical factors, such as people living in rural areas and being far away from the grid and also dispersed from each other [3], [4].

Off-grid solutions, such as stand-alone systems and micro- and mini-grids, could be a solution not only for underserved people, but also for those who are near the low-voltage connection lines but are not connected for reasons like high connection costs (under-grid customers) and for those who are already connected but cannot afford appliances (idle-grid customers) [5]. Furthermore, a continuous decrease in costs of the key components for power installations and improved load factors of micro- and mini-grids over the past decades have resulted in a reduction in the levelized cost of energy (LCOE), and it is expected to further decrease by two-thirds by 2030 [6]. Most of the recent generation micro-grid and mini-grid are equipped with technologies (mobile telecommunication services) that enable an increase in energy utilization and further facilitate business activities with real-time monitoring, demand forecasts, remote control, and prepaid payment options. Such features also enable income-generating equipment (productive-use appliances) to connect with off-grid systems, thus prompting local economic growth [6].

Currently, a large share of micro- and mini-grid development is largely carried out by national utility companies. However, private investment is expected to drive the sector in the coming years. The private sector is progressively engaged in specializing in the value chain of micro- and mini-grid projects, such as distribution, metering, solar PV, storage systems etc.[7] This will significantly increase the annual profit potential of the value chain of micro- and mini-grids. According to an estimate by the Energy Sector Management Assistance Program (ESMAP), the annual value chain profit has the potential to increase from $510 million in 2019 to $4.7 billion in 2030 [8].

To enable economically viable and sustainable off-grid electrification projects, private investors and developers require economic modelling and planning tools. The economic viability of a project is measured in terms of securing initial capital funding for the project, and the operational and maintenance costs are covered by generating a satisfying amount of return on the initial investment. This requires a set of flexible operational plans that are also adaptable to future political, economic, and regulatory changes. Financial indicators, such as project and equity return expectation and the payback period, can be used to calculate the expected sales of electricity from off-grid projects and define the required revenue for making an investment decision [8]. Required revenue, in turn, defines a developer’s willingness to accept (WTA) the price for the service or goods. In the context of this paper, WTA is defined as an acceptable price level at which the sale of service will be suitable for the economic viability of a project.

Customers’ willingness to pay (WTP) for electricity services is another essential factor in the off-grid electrification project development. WTP is the range for the maximum monetary amount that a customer will pay for a unit of product or service. Understanding consumers’ WTP can help developers to introduce new products or services and pursue a pricing strategy that is suitable for the marketing environment while reducing the risk of losing valuable profit. Many studies have shown that the customer response evoked by price variation can have a significant effect on revenue and profit [5], [9].

There are different determinants for the WTP for electricity in developing regions, viz. social, economic, demographic, cultural, and technical factors. These factors can determine or strongly affect the WTP of electricity consumers. For instance, according to a review [5] of different studies on the WTP of consumers in developing regions, variables such as household income, education status, substitution for a traditional energy source, the number of children of school-going ages, and home business (household entrepreneurial aspirations) were found to have a positive effect on consumers’ WTP. On the other hand, the study found that age, occupation, and household structures have a vaguer relationship to WTP in the literature. Moreover, cultural, and behavioural determinants, such as lack of trust, inattention, and information biases can be influencing factors. For instance, a lack of trust in the government in providing or improving electricity service will harm WTP; this lack of trust is mainly caused by past historical events that have shown politicians failing to deliver their promises. On the other hand, high trust in the government and a belief in political promises or a belief that higher prices will provide improved services can increase the WTP among consumers. Other cultural factors, such as acceptance of private electricity service providers and responsibilities of who should back up electricity provision can further complicate the WTP for electricity. From the technical perceptive, WTP can vary depending on the reliability of electricity supply. This can be measured based on consumers’ definition of reliability, frequency of brownouts, or duration of outages and provision of information about possible outages beforehand.

The purpose of this study is to propose a methodology for defining a feasibility area of an electrification and telecommunication project in the context of rural SSA based on an equilibrium between the WTA of electricity and connectivity providers and the WTP of the rural community for the service. Such an evaluation can help assess the projects based on the correlation of the customers’ WTP, with other relevant measurements, namely, ability to pay (ATP), that is, a customers’ financial ability measured based on his/her financial income. Hence, we propose a direct WTP survey to reveal the preference with respect to the customers’ energy and telecom needs. We also introduce a concept of a unit of service, which is designed to fit a quantified amount of bundled energy and telecom services as a benchmark. The aim is to help understand the true desires or preferences of customers. Oftentimes rural customers, in particular those who lacks access to electricity and telecom services, are faced difficulties in quantifying technical services. This can lead to under or overestimating their true demand and purchasing capability using power or energy units. The unit of service, therefore, serve as a measuring tool for developers based on customers willingness or ability. The relationship between the customers’ and developers’ price preference for a unit of service balances out and provides an equilibrium price. Application of unit of service can be found in sectors such as health service institutions, where, necessary patient care including transportation, medicine, lab tests, etc. are packaged together for billing or non-billing purposes [10].

In this paper, we focus on answering the following questions: What is the preferable WTP survey method for first-time electricity users? How can a microgrid developer assess the financial feasibility of a project based on the customers’ willingness and ability? How a common instrument can be designed to bridge between the user and developer? How an acceptable price for can be reached based on customer and developer preferences?

The paper provides a study case analysis based on the proposed methodology and a survey from the rural village of Revon C in northern Namibia. The results suggest that using the direct WTP methodology can allow to define a feasibility area for the project developers and an affordability area for the customers to ensure long-term sustainability of the project. The main contributions to WTP studies in rural areas are 1) a measurement tool for quantifying demand based on preferred WTP is proposed, 2) unlike the already existing numerous economic-based and policy-oriented literatures, we provide a business feasibility approach founded on WTP of customers.

The paper is designed as follows: the second section provides definitions for the main concepts used in the study, such as WTP, ATP, and WTA, and discusses current WTP methods that are widely used to study rural areas. The third section reviews literature on rural electrification studies that apply the WTP approach. The fourth section describes a methodology for defining a feasibility area for an electrification and connectivity project based on WTP and WTA curves. The fifth section provides the results of a case study from the village of Revon C in Namibia and discusses the results, implications, and assumptions. Finally, the sixth section concludes the major findings of the research conducted in the paper.

2. Definitions

Willingness to Pay (WTP) is a market research method used to understand the maximum price range at which a consumer is willing to buy a product or a service. Understanding consumers’ WTP can play a crucial role in new product development, pricing, and competition strategies. WTP measurements can involve three types of procedures: 1) a direct open format question, in which respondents are asked about their WTP for higher consumption of a particular good or a service, 2) a list model, in which a variety of values are presented for the participants, and they are asked about the amount they would pay for the listed values, and 3) a referendum model, where participants are asked whether they are willing to pay a certain amount of money for the good or not [11].

Based on the data collection method, WTP surveys can be divided into two major categories: revealed and expressed preferences. Revealed preference data are obtained from price responses. For instance, in the case of rural area customers, revealed preferences can be derived from the expenses of the local population for alternative sources of energy, such as kerosene for lamps or candles, and for charging phones. On the other hand, expressed preference data derived from surveys are frequently referred to as stated preferences or the contingent valuation method (CVM). In this case, consumers are asked to reveal the maximum amount they are willing to pay for a hypothetical service or product that is created by a market researcher. The methods produce a realistic scenario that offers a rational rate of electricity service that is also feasible for the electricity project [12].

Willingness to Accept (WTA) is a term that is used in economics to assess a monetary amount that a person is willing to accept to sell a service or a good. WTA is also used to evaluate a persons’ stated price for compensation to bear a loss or forgo an environmental service, such as pollution, noise, or property rights [13]. However, in this research, we use WTA as the minimum monetary amount at which a service provider or seller is willing to sell without facing a financial loss or a positive net present value (NPV).

Another economic principle used in this paper is Ability to Pay (ATP), which states the amount of money that a person is able to pay relative to income and wealth. Although its application is mainly for taxation purposes [14], in this paper we use the term to further assess the income effect that bounds people to pay more than their WTP. Income level plays an important role for the determination of WTP; [5] found a significant correlation between WTP and the income level of the respondents in developing countries. Thus, ATP should be considered in addition to WTP.

3. Literature review on the contingent valuation method in electricity studies

Contingent valuation method (CVM) is a survey-based method for determining economic values of goods and services. The method is commonly used to estimate nonmarket values and non-use values in the areas of environmental economics and environmental impact assessment. The methods are applied by encouraging participants to consider trade-offs in the choices they make [15]. This method has been widely used to study individual preferences for basic infrastructure projects, such as water supply, electricity, and sanitation in developing countries.

Despite its extensive use, CVM has been criticized for its accuracy and reliability. For instance, the CVM survey method uses mainly a hypothetical market to study the trade-offs and preferences of participants. This can create a hypothetical bias that deviates from the market scenarios. Another challenge of the CVM studies can arise from strategic biases, where individuals understate their true WTP for public goods with the hope that others will pay enough for the service. This is also known as free riding behavior. On the other hand, individuals can over plead their preference expecting that their WTP value would influence the provision of a good or a service. Furthermore, the information and scenarios provided for the survey respondents can influence the validity of the CV result both positively and negatively [11], [15].

CVM is the most widely used method for measuring the WTP of electricity development in developing areas. Some of the most common CVMs that are used in WTP studies for electricity are:

  • Single-Bounded Dichotomous Choice (SBDC): It is the most widely used method. It involves asking the respondents if they will pay a specified monetary amount to obtain a good or a service by giving them a YES or NO choice. This monetary amount (bid value) is varied across respondents, and the discrete choice format mimics a bargaining process [5].
  • Double-Bounded Dichotomous Choice (SBDC): In this method, respondents are given two consecutive YES or NO choices. If the first choice for the bid value is YES, the following choice bid value will be higher than the first, on the other hand, if the first choice is NO, the bid value in the second choice will be smaller than the first amount [19], [20], [21], [22].
  • Multi-Bounded Polychotomous Choice (MBPC): In this method, the respondents are presented with a panel of values and response categories that are arranged into a matrix. The respondents are then asked to mark the degree of confidence that they feel about paying or not paying for each amount that is listed in the matrix [5].
  • Payment Cards: This method lists possible values on a card and asks the customers to pick the value that best represents their WTP for the lower bound and the second choice as the upper bound [5].
  • Choice Experiments/comparative analysis: This method asks the respondents to choose between two programs (A and B), which comprise different attributes and costs to choose or to refuse. This allows marginal valuation of each attribute [5], [16].

There are a growing number of CVM-based WTP studies in developing countries that focus on the reliability and demand of electricity. For instance, Bhandari et al. [16] conducted a survey in rural Niger to study the effect of a rural community’s WTP for renewable-based electricity supply. The study used comparative analysis to compare the WTP for electricity service with the collaborative consumption and cost of an off-grid system. The results showed that empowering communities to have a share of ownership in rural off-grid projects increased the WTP. In Ghana, Taale & Kyeremeh [17] studied factors affecting households’ WTP for reliable power in the Cape Coast Metropolitan Area by using contingent valuation methods. The prohibit model was used to identify factors affecting the WTP for the reliability of electricity. Factors such as income, ownership of a separate meter, house size, and education were found to affect the WTP for reliability. The study showed that most of the households are willing to pay 44% more for improved reliability compared with their present electricity bill. Gunatilake et al. [18] investigated the benefit of reliable electricity in Madhya, India using a bidding game and the method of single-bounded closed dichotomous choice. In the study area, the existing grid supply was poor, and a better quality of power supply and service was the main priority among customers. In the study, the probability of consumers’ WTP for improved quality of electricity was studied by using a dichotomous choice approach. Further, Oseni et al. 2017 [19] studied the WTP for reliable electricity supply for self-generating (diesel backup) households in Nigeria. Double-bounded dichotomous choice was used as the method to study the WTP. The study found that having a backup generator increased the households’ WTP for reliability rather than decreased it. This was mainly due to the high cost of self-generation or outage cost that unreliable grid supply can cause. Similar electricity reliability- and service quality-based studies using dichotomous bidding methods can also be found in the literature for Senegal, Ethiopia, and Kenya [20], [21], [22]. A su mmary of sample cases is given in Table 1.

Table 1. Sample literature on WTP for electrification services.

  • WTP study method
  • Country
  • Description
  • Power supply
  • Source
  • Choice experiment/ comparative analysis
  • Niger
  • The effect of rural community energy sharing and ownership in the improvement of WTP
  • Off-grid
  • Bhandari et al., 2020 [16]
  • Stated preference/contingent behaviours
  • Ghana
  • Factors affecting the WTP for reliable power supply
  • Grid
  • Taale & Kyeremeh, 2016 [17]
  • Bidding game and dichotomous choice
  • India
  • Estimating the benefits of an improved electricity supply to rural households
  • Gunatilake et al.,2012 [18]
  • Double-bounded dichotomous choice
  • Nigeria
  • The effect of self-generation on WTP for reliable power supply
  • Grid and own generation
  • Oseni et al. 2017[19]
  • Ethiopia
  • The walfare gain of renewable energy and WTP attributes for green energy transition
  • Entele, 2020 [20]
  • Household preference for a renwable-based off-gird system in addition to the already connected grid suppply
  • Arega & Tadesse, 2016 [21]
  • Kenya
  • Estimating WTP for grid and PV generation and the relation of the household’s entrepreneurial aspiration and reliability
  • Abdullah & Jeantyc, 2011 [22]
  • Open-ended questions, dichotomous choice
  • Senegal
  • WTP for high-quality electricity service
  • Gird & undergird

Deutschmann, Postepska, & Sarr, 2021 [23]

Nonetheless, most of the above-mentioned WTP studies are widely used to provide insight for policymakers from the economic standpoint rather than for market price research or compilation of a business strategy, which call for further studies that apply the results of WTP studies for novel price research and business feasibility.

4. Methodology

4.1. Introducing a concept of a unit of service

The rural population in SSA often has very limited purchasing power. From the consumers’ perspective, it is very important to evaluate how much they would be willing to spend for a service or a product, while from the service provider’s perspective, it is imperative to ensure that customers can pay for the service to secure a required revenue flow. The local population in rural communities has certain energy needs; however, in many cases, they lack understanding of concepts like a measure of energy unit, cost per energy unit, and data transfer unit. Therefore, we introduce a concept of a unit of service based on the Fusion Grid project [23], [24], [25], [26], which considers the provision of a bundled service for electricity and connectivity to rural locations. One unit of service consists of a power and data package of 500W nominal capacity and 512 Mb of internet data per month to cover basic needs. Those basic needs can include the use of television (80W) for 4 h a day, three phone charges per day, laptop charging, lighting (5 × 5 W LEDs) for 6 h of daily usage, and a refrigerator (50–100W). Customers with higher power or data needs may require more units of service depending on their demand and purchasing power, and therefore, the package can be scaled up to cover the increase in demand. The concept of a unit of service goes in line with home solar systems, which are often marketed with electric appliances that they can power (Fenix International, Azuri, BBOX). Representation of power solutions in such a way removes barriers of communication to potential customers who lack knowledge of measures of energy or data units.

4.2. Defining WTP for combined service

As it has been discussed above, assessment of WTP is usually based on the value that customers place on a good or a service considering their purchasing power, which often is the main challenge in rural areas. To determine the WTP for the combined service of electricity and connectivity (WTP𝐸&𝐶), we propose a survey where customers should indicate how much they would be willing to pay monthly for electricity and connectivity. The algorithm below shows how the WTP curves are created using the survey data and running price index. The running price indexes are price values (€/unit) that are artificially created for reference purpose.

Algorithm 1: Algorithm for WTP/unit of service analysis

Input: DATA_WTP_survey_matrix A; Reference running price index matrix B
Output: PRINT: N_m
Initialization: initialize matrix N_m
1: Fist statement  LOOP Process
2:for j = 1: until length (DATA) do
3: initialize matrix N
4: for I = 1: until length (DATA) do
5: if matrix A(i) ≥ matrix B(j) then
6: matrix N(i) = matrix A(i)
7: end-if
8: matrix N_m(j) = sum (matrix N)/matrix B(j)
9: end-for
10: end-for

The algorithm first builds matrix N and matrix N_m to store new values. Then it executes a nested loop: in the inner iteration, we extract and store values that are greater than or equal to the reference running price index (matrix B), and in the outer iteration, we extract matrix N_m by dividing the sum of matrix N with matrix B. The data points extracted from matrix N_m create the WTP curve. Let’s assume that the WTP data (Matrix_A) = [10,20,30,40,50] and the reference running price index (Matrix_B) =[5,10,15,20,25]. The step-by-step execution of Algorithm 1 can be as follow:

  • Step 1: Read the WTP and the reference running index values from file
  • Step 2: Create matrix_N and matrix N_m to store new values.
  • Step 3: Checks of each raw element if matrix_A(I ≥ matrix_B(j) and store in matrix_N(i)

       for example, matrix_A (0) = 10 is greater than matrix_B (0) = 5, thus matrix_N (0) = 10.

  • Step 4: Repeat the same steps for each raw.
  • Step 5: Divide the sum the matrix_N values with each of the raw elements of matrix_B to find

matrix N_m elements. For example, the sum of the matrix_N = 150 and matrix_B (0) = 5, thus matrix N_m (0) = 30.

  • Step 5: Repeat for same steps each raw. 
  • Step 6: Exit the loop.

The result table for the example will be presented in TABULAR FORM Ref table 2.

Table 2. Example table for Algorithm 1.


000000000000000000000000000000000000000000000000000000000000000000000 00000000000000000000

Based on the resulting values, it is now easy to create the WTP curve or “demand-curve”, that illustrate the relationship between changing price (matrix_N) and quantity demanded (matrixN_m) in a given period of time. (See Fig. 1).

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Fig. 1. WTP curve for example table.

As can be seen from the above figure, the price change has an effect on the and quantity of unity of service quantity demanded, where lowering the price value create more demand.

The main assumption in this methodology is that the customer with a higher WTP than the price for the unit would buy more units of service, proportionally to the WTP and the price for the unit difference. This assumption goes in line with the notion that customers with a higher purchasing power have a higher power demand, thus, would need more units of service to cover that demand. However, the relation between the increase in power demand and the number of units needed is not necessarily proportional.

4.3. Defining WTA of developer

FG (Fusion Grid) is a smart grid with an embedded connection to the Internet, and thus, all processes can be automated, monitored, and managed remotely. The main cost component of such a grid is the capital cost, which includes all the equipment costs for the production of electricity and the provision of Internet access. The variable cost of production of electricity is close to zero because it is based on renewable energy resources, and only the storage operation causes some variable costs, being also mainly a fixed cost. The variable cost of the Internet traffic is also zero, and it is mainly a subject of the capital costs of a base station and connection to the backhaul. The marginal cost of the services provided with access to the Internet, for instance, educational programs and digital platforms for jobs, is also very low. Therefore, the main cost component for the FG concept is capital expenses (CAPEX). The CAPEX of the off-grid system consists of the price of PV panels, batteries, inverter, LTE base station, cables, and other supporting equipment and their installations. The operating expenditure (OPEX) includes expenses for running and maintain the off-grid system. The key factor in the formation of WTA is the power system size, which determines the initial investment, see function (1).(1)WTA=𝑓(𝐶𝐴𝑃𝐸𝑋,𝑂𝑃𝐸𝑋)

The required revenue on an annual basis for the developer can be defined using the function for net present value (NPV). A project is financially feasible, if the NPV is at least zero:(2)NPV=∑𝑡=1𝑇Revenue𝑡(1+𝑟)𝑡+CAPEX𝑛≥0WhereRevenue𝑡=𝑚𝑜𝑛𝑡ℎ𝑙𝑦𝑓𝑒𝑒𝑝𝑒𝑟𝑠𝑢𝑏𝑠𝑐𝑟𝑖𝑝𝑡𝑖𝑜𝑛∗𝑛𝑢𝑚𝑏𝑒𝑟𝑜𝑓𝑠𝑢𝑏𝑠𝑐𝑟𝑖𝑏𝑒𝑟𝑠𝑡

Where T is the lifetime of the project, r is the discount rate, Revenue𝑡 is the required revenue to make the project neutral, and CAPEX𝑛 is the capital investments for n units of service. Further, the required revenue should be divided by the number of units of service that the off-grid system can serve:(3)WTA𝑡=Revenue𝑡𝑛

An off-grid system with a specific size can provide a limited number of units of service, because power and energy are limited by equipment restrictions and solar conditions that affect the generated power and energy. The system can be scaled with an increment of consumer needs and the number of units of service needed. Therefore, the cost of the concept increases with the size thereby affecting the WTA of the supplier, and thus, with an increase in the size of the FG concept installation the WTA will also increase. However, the relationship between CAPEX and WTA is not linear because of the nonlinear relationship between the size of a unit of service and the size of equipment needed to satisfy the needs for electricity and connectivity. An additional solar panel or battery storage can impact the CAPEX considerably while serving only one additional unit of service. Besides, yearly revenue variation can also affect the WTA. The revenue variation is due to fluctuations in monthly fees or the number of subscribers.

4.4. Equilibrium between WTA and WTP

A desirable market price for the service consisting of electricity and connectivity could be represented as an equilibrium price between WTA and WTP, see the equation below and Fig. 2, where the horizontal axis shows the number of units of service, and the vertical axis displays the price per unit.(4)WTA=WTP𝐸&𝐶

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Fig. 2. WTP and WTA curves.

The area above the blue WTA line is the acceptance area for the project developer; any price above this line would result in a positive project NPV and thus, investments in the off-grid system and base station would be justified. The area below the red WTP line represents the customers’ willingness to pay, and thus, at the unit price below the red line customers will be willingly paying for the service.

  • 5. Results: Case study analysis

5.1. Study case area: Revon C

The study case area is located in Revon C, Okarevona, a rural village in northern Namibia. The area is a densely populated informal settlement with over 700 residents in the Oshikoto region, see Fig. 3. Most of the residents have a low income and are dependent on small businesses in the informal sector. Electricity supply is one of the main infrastructural challenges in the area, and only a few residents have access to electricity because of the high cost of connecting to the local grid.

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Fig. 3. Okarevona village, Oniipa, Namibia.

A survey on electricity and connectivity needs was conducted between December 2018 and April 2019 in the village of Revon C as part of the Fusion Grid (FG) project to define the demand for these services as well as the WTP of the households within the community [24]. The survey includes both qualitative and quantitative questions which encompass different social and behavioural aspects. The questionnaire included a direct question about the willingness to pay for electricity and connectivity (Internet) monthly and the income level of the household. For selecting participants for the interview, a purposive sampling method (non-probability method) was used. Purposeful sampling – which is also known as judgemental, subjective, or selective sampling– is oftentimes used in qualitative research to identify and select information by focusing on participants which can enable to effectively answer the research questions.[28]. There were 107 respondents to the survey, each respondent representing his or her household. With an average household size of more than five people, the survey covered more than 76% of the community.

According to the survey, 37% of the community had access to electricity at the end of 2018. The regional electricity distributor NORED is the main supplier for the area. More than 90% of the community’s households are not happy with the service, either because of not having access to electricity or because of the quality of the service. The survey shows, based on the exchange rate of 2018, that the average monthly income is 4 040 Namibian dollars (N$), or 221 €, which is lower than the average wage of 6 626 N$, or 363€, in the country.

From all responses, only 69 were qualified for the analysis, as some respondents did not answer the question on WTP for electricity or the Internet (or both), and some indicated a WTP higher than their monthly income, which we treated as an unreliable response. Based on the survey, the WTP range was from 20 N$ per month to 4 400 N$ per month (1.15 €/month to 253 €/month). The average WTP was 655 N$ per month, or 38.25 €/month, which was 21.6% of the average income. The WTP curve was defined using the data from 69 respondents, who answered all the required questions and whose WTP did not exceed their income. Most of the respondents, 66%, indicated income levels not exceeding 5000 N$ per month per household, which is also reflected in a limited purchasing power and WTP, see Fig. 4 (a). Combined WTP for electricity and telecom services was from 200 N$ to 800 N$ for 75% of responses, see Fig. 3 (b).

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Fig. 4. Income level and WTP for combined service.

The electrification and digitalization pilot of the Fusion Grid (FG) research project is installed and currently operating in the village since 2019 [24]. The FG project aims to provide electricity, Internet connectivity, and digital tools to sparsely populated areas that lack access to both electricity and telecom services. The project is designed with a notion that providing electricity and telecom services can improve the quality of life in rural communities while enabling small businesses to flourish with the aid of digital tools. The FG pilot microgrid includes solar PV, lithium-ion battery systems, and a 4G LTE base station, currently providing electricity and connectivity for five households. The microgrid control system is equipped with a power demand and weather forecast algorithm that enables to optimize and ensure adequate supply throughout the day [24], [25], [26], [27].

5.2. Willingness to pay and willingness to accept of Revon C

The WTA curve is based on the capital cost data from the FG project, and it is defined in the methodology discussed in section 4.3. Table 3 presents the NPV-based cost calculation result for different units of services that can be provided by the FG microgrid system. In our analysis, we assume the WTA is constant. However, we acknowledge that can be a variation caused by yearly revenue imbalance.

Table 3. Cost calculation for a unit of services of the FG microgrid system.

Unit of Service

Capital cost [€]

Required revenue per year [€/Year]

Required revenue per unit of service per year [€/unit/year]

Required revenue per unit of service per month [€/unit/month]



In the analysis, the lifetime assumed for the microgrid system is 20 years and the interest rate 5%. Further, one unit of service comprises a power and data package of 500 W nominal capacity and 512 Mb of internet data per month.

Based on the results from the WTP survey calculation and the above WTA calculation, the WTP and WTA graphs are created (see Fig. 5). In addition, the ATP of customers has been added. We use two tiers, 20% and the percent of customers reported monthly income, to evaluate the ATP against the unit of service quantity, which is a theoretical expected share of expenditure for electricity and telecom services. This can be used as an indication that the participants did not over- or understate their WTP.

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Fig. 5. WTA and WTP of the study case.

According to the results, the customers’ WTP for unit service increases steadily when the price drops. Similar trends can also be seen for the ATP values. On the other hand, when the monthly prices increase, the WTA for the FG increases. The equilibrium price where WTP and WTA are equal is around N$898 or €50 per unit of service per month. At this price, the developer will install a microgrid sized at 20 units of service and will be able to cover the capital costs. The feasibility area for the project in the study case is found for a microgrid sized between 5 and 20 units of service, which in our case corresponds to a 2.5–10 kW microgrid installation with a base station providing 2.5–10.24 Gb of monthly data. The larger the spread between WTP and WTA, the higher the IRR of the project, and thus, the profitability of the project.

The equilibrium prices can change based on the customers’ future WTP and cost factors affecting the WTA of the microgrid system. For instance, if the customers’ WTP increases from the current position, the WTP curve will move to the right and a new equilibrium price will be created. In contrast, if the customers’ WTP decreases, for instance, because of a low income or finding an alternative option, the WTP curve will change in the lower part of the graph, creating a new price point. Similarly, the WTA curve of the supplier can shift to the right or left of the graph because of different cost factors. For instance, if the capital or operational cost of the microgrid system is subsidized, the unit of service price will fall, moving the WTA curve to the right. This will create a new decreased equilibrium price. Other cost factors, such as a lower equipment and installation price, can also attribute to the movement of the WTA curve. The prices of key components, such as PV panels and battery systems, are expected to further decrease in the coming years. This will affect the WTA of suppliers when scaling up microgrid systems. By analysing the relation of WTP and WTA, microgrid developers can strategize their business in relation to the behaviour of their customers and changes in their customer base.

6. Discussion

Access to electricity and telecom services remains a major challenge for many rural communities. Nevertheless, there is an untapped market potential, which requires novel ideas and approaches for the rollout of these services in rural settings. Understanding the market, potential consumers’ preferences, and their willingness to pay for the services is the key for providing solutions that fit the local environment, as many studies reviewed in this paper suggest. Customers’ willingness to pay (WTP) is an important factor in studies on off-grid rural electrification projects. So far, most of the WTP studies in rural areas largely focused on improvements in the quality and reliability of the electricity services, and the evaluation results mainly concentrating on the economic cost–benefit analysis for policymakers rather than on business strategies (Gunatilake et al.,2012, Arega & Tadesse, 2016, Abdullah & Jeantyc, 2011, and Deutschmann, Postepska, & Sarr, 2021). In electrification projects, which rely on revenue from electricity sales, the project developers’ understanding of consumer behavior has a significant impact on the revenue and profit of the project. Hence, accurate estimation of customers’ WTP is important for compilation of business strategies.

Bundling dissimilar products or services as a single combined unit is used by many service providers as a marketing and price strategy. The bundled packages typically offer different price and services options. We argue that packaging services as a unit of service can help developers to design the quantity needed and the packaging options. The proposed methodology for evaluating a microgrid and connectivity project based on customers’ WTP and the cost of the microgrid system, or WTA for the project developer, addresses the uncertainties related to the feasibility of such projects. In addition, introduction of a common measurement parameter as a unit of service helps to better integrate the new power and telecom solutions, especially in communities lacking understanding of the measures of power and Internet data units. While WTA can be easily assessed based on the project CAPEX, consumers’ WTP is based on surveys on potential customers. In our research WTP survey is constructed of direct questions on how much potential consumers would like to pay for the service on a monthly basis, which are then converted into the WTP for the unit of service. Establishing such an evaluation mechanism helps avoid hypothetical bias, which is common in WTP survey methods (L. Venkatachalam,2004). We argue that rural people may be unable or find it difficult to quantify their demand and consumption for electricity or telecom services. Hence, the WTP survey should mainly focus on the amount of money these people are willing to spend on those services rather than on questions that might be difficult to understand or force them to imagine unquantifiable values. Furthermore, ability to pay (ATP) is compared with WTP to avoid implications of strategic bias. Even if the responses to a survey are influenced by strategic bias, a higher ATP than WTP indicates that the consumers are not over pleading their WTP, which is the most relevant factor when ensuring future revenue flow. In the case of a higher WTP than ATP, over pleading of WTP may take place, and further research must be conducted. An ATP curve can be used as a control measure for strategic bias.

The feasibility range is further defined by constructing WTP and WTA curves, determining a feasible service pricing at the range where the WTP is higher than the WTA. The results of this study show that both the customers and developers would be satisfied with the pricing below the equilibrium price of €50 per unit of service per month. Because of the nonlinear nature of WTA and WTP, it is hard to estimate the optimal pricing for the service; nonetheless, the methodology provides a clear understanding of the current potential of the market and a viable pricing strategy that ensures a sustainable revenue flow for the developers and keeps prices at an affordable level for the consumers.

However, even if poor rural customers are willing to pay a substantial part of their income for electricity, subsidies should also be considered as part of the financial scheme by the government bodies. The subsidies can be on initial investment or on reducing the energy cost for customers.

7. Conclusion

Overall, this paper aimed to cover the research gap found in the reviewed literature, which often focuses on economic analysis for policy implications rather than on business analysis. Further, the paper proposed a novel methodology to analyse the economic feasibility of electrification and connectivity, which can serve as a pricing tool for sustainable project and capacity building.

The main assumption of the methodology is that an individual consumer’s demand for units of service is directly related to their WTP, which might not be the case in a real situation. In our study, we kept the size of the unit of service small to limit the impact of the assumptions on the results. Also, the study does not cover the developers’ risk associated with such economic and price evaluation methods. For better accuracy, the size of the unit of service can be further reduced. In addition, the economic benefit from household appliances, demand, and consumption of energy should be included, and thus, WTP data based on revealed preferences can be produced for future studies. By combining both methods it is possible to further improve the quality of work and reduce the biases associated with WTP study methods. In future, it would be interesting to compare such finding with the other WTP and pricing methods, to make the findings more wide-ranging. Besides, future research could focus on studying a user-centric experience of a unit of service actual off-grid customers.

The future impact of this study can be the possibility of using a unit of service in assessing bundling public goods such as water, electricity, telecom, digital services, health services, etc. in rural areas’ context. In addition, policymakers can incorporate such methods for appropriate incentives and subsides design in rural electrification programs.

CRediT authorship contribution statement

Henock Dibaba: Conceptualization, Methodology, Writing – original draft. Iurii Demidov: Visualization, Data curation. Evgenia Vanadzina: Supervision, Conceptualization, Methodology, Writing – review & editing. Samuli Honkapuro: Supervision, Writing – review & editing. Antti Pinomaa: Writing – review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] IEA. Access to electricity – SDG7: Data and Projections. https://www.iea.org/reports/sdg7-data-and-projections/access-to-electricity; 2020 [accessed December 2, 2020].   Google Scholar

[2] Statista. Projected population without access to electricity globally by region 2030. https://www.statista.com/statistics/561428/forecast-of-population-without-access-to-electricity-globally-by-region/; 2020 [accessed 2 November 2021].   Google Scholar

[3] P. Blimpo, M. Cosgrove-Davies

  • Electricity access in Sub-saharan Africa: uptake
  • Reliability and Complementary Factors for Economic Impact. The World Bank, Washington, DC (2019)
  • Google Scholar

[4] H. Butu, B. Nasafon, S. Park, J. Huh

[5] R. Rahnama

[6] Irena

  • Electricity storage and renewable: costs and markets to 2030
  • Abu Dhabi, UAE (2017)   Google Scholar

[7] M. Pedersen, W. Wehrmeyer, I. Nygaard

[8] World Bank

  • Energy Sector Management Assistance Program
  • Mini Grids for Half a Billion People. Washington, DC (2019) Google Scholar

[9]  Frankfurt: Frankfurt School – UNEP Collaborating Centre for Climate and Sustainable Energy Finance. Renewable Energy in Hybrid Mini-Grids and Isolated Grids: Economic Benefits and Business Cases; 2015.  Google Scholar

[10] Texas Children’s Hospital. Using a Healthcare Enterprise Data Warehouse and Analytics to Reduce Labour Costs; 2013.  Google Scholar

[11]  C. Breidert, M. Hahsler, T. Reutterer

[12] USIAID, & NRECA International, Ltd. Guides for Electric Cooperative Development and Rural Electrification. Cooperative Development, Energy Sector Management Assistance Program. Virginia, USA; 2019. https://www.nrecainternational.coop/what-we-do/cooperative-development/.  Google Scholar

[13] OECD Glossary of Statistical Terms. Willingness to accept Definition. https://stats.oecd.org/glossary/detail.asp?ID=6612 ; 2003. [accessed 22 January 2021].  Google Scholar

[14] Koritnik B, Podlipnik J. The Ability-to-Pay Principle as a Primarily Constitutional Basis for Tax Norms of a Financial Nature; 2017. https://www.researchgate.net/publication/319955126_The_Ability-to-Pay_Principle_as_a_Primarily_Constitutional_Basis_for_Tax_Norms_of_a_Financial_Nature.  Google Scholar

[15] L. Venkatachalam

[16] R. Bhandari, V. Sessa, R. Adamou

[17] F. Taale, C. Kyeremeh

[18] Gunatilake H., et al. Willingness to Pay for Good Quality, Uninterrupted Power Supply in Madhya Pradesh, India Asian Development Bank; 2012. Google Scholar

[19] M. Oseni

[20] T. Arega, T. Tadesse

[21] B.R. Entele

  • Analysis of households’ willingness to pay for a renewable source of electricity service connection: evidence from a double-bounded dichotomous choice survey in rural Ethiopia
  • Heliyon, 6 (2) (2020), p. e03332  View PDFView articleView in ScopusGoogle Scholar

[22] S. Abdullah, W. Jeanty

[23] W. Deutschmann, A. Postepska, L. Sarr

[24] Dibaba H, Vanadzina E, Demidov I, Honkapuro S, Pinomaa A. Improving the Affordability of Rural Microgrids using Demand Response Programs: A Case Study in Rural Namibia. 4th SEE SDEWES conference; 2020 June 28 –July 2; Sarajevo, Bosnia & Herzegovina. Google Scholar

[25] E. Vanadzina, A. Pinomaa, S. Honkapuro, G. Mendes

[26] Lana A, Demidov I, Pinomaa A, Carrillo D, Pyrhönen O. Energy Management Methodology for Fusion Grid, IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), 2019; Sep 29-Oct 2, Romania. Google Scholar

[27] Lana A, Demidov I, Pinomaa A, Carrillo D, Pyrhönen O. Techno-economic analysis of network configuration of PV-based off-grid distribution system. 25th International Conference on Electricity Distribution, 2019; June 3-6, Spain. Google Scholar

[28] K. Fröhlich

  • Development of a citizen-centric e-government model for effective service delivery in Namibia
  • Namibia University of Science and Technology, Windhoek (2019)  [Master’s thesis]

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  • KEYWORDS
  • Climate change adaptation
  • Sustainable communities
  • Community Based Organizations
  • Energy poverty
  • Renewable energy financing
  1. Introduction

Climate change is recognized as the most critical threat facing humanity and disproportionately affecting the rural poor in communities where adaptive capacity is significantly low [1]. For approximately 100 million of these individuals, increasing risk from climate change implies that they could be plunged even deeper into extreme poverty from 2030 [2]. This propensity of climate change to cause and indeed perpetuate poverty is a huge concern for global leaders and nations seeking to pursue green growth.

For several regions where poverty already exists, persisting energy dearth only accelerates the rate of impoverishment. Given that the majority of these communities constitute smallholder farmers, continuing climate change and variability negatively impacts food security, livelihoods and health, creating a vicious cycle of poverty [1], [3]. Developing energy systems for such regions can be a herculean task requiring multi-criteria considerations of varying yet equally important trade-offs in order to create situation-specific solutions [4]. Installing large on-grid energy systems in such communities is obviously ill advised; because individuals in these regions are neither high-income earners nor large-scale energy consumers.

The number of people without electricity in Sub-Saharan Africa (SSA) is estimated to increase by 11% in 2030, reaching 665 million [5]. Yet the availability of energy is strongly linked to poverty and development [6]. Extant research posits that under current policies, the 2030 target of universal energy access appears unattainable in SSA [7]. To overturn this projection, the generation capacity for electric energy must grow at 13% per annum [8].

Despite the recognized role of clean and renewable energy in, adoption has not been on par with the resources accrued to the SSA region over decades. Since 1951, the World Bank has spent over $62.5 billion ($78.5 billion if regional projects spanning more than one country are included) on several facets of the energy sector in the region albeit with insignificant improvements especially in rural communities. Even in places where access has improved, only 57% of homes are connected and concerns of high cost, low consumption, as well as low reliability linger [9].

This perspective examines the administration of energy financing through Multilateral Financial Institutions (MFIs) in Sub-Saharan Africa over several decades. It also highlights the unique opportunity in integrating fintech with already existing local CBOs and proposes a framework for improving energy access in rural agrarian communities. We relied on content analysis, and selective reviews of scientific and grey literature to arrive at our conclusions. Data for all projects from sectors that covered “energy” and “power” in SSA were collected from the World Bank’s official website [10]. These included metrics on total project cost, financiers, approval and closing dates, project scope and scale.

 

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