
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12032-4
10.1016/j.heliyon.2024.e36001
e36001
Research Article
Estimating and forecasting suppressed electricity demand in Ghana under climate change, the informal economy and sector inefficiencies
Dramani John Bosco boscodramani@gmail.com
boscodramani@knust.edu.gh
ab⁎
Ofori-Mensah Kwame Ansere nanaansere@gmail.com
c
Otchere Nathaniel Oppong oppongotcherenathaniel@gmail.com
b
Frimpong Prince Boakye pbfrimpong@gmail.com
a
Adu-Poku Akwasi dukeadupoku@gmail.com
bd
Kemausuor Francis kemausuor@gmail.com
bd
Yazdanie Mashael Mashael.Yazdanie@empa.ch
e
a Department of Economics, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
b The Brew Hammond Energy Centre, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
c University of Insubria, Varese, Italy
d Department of Agricultural and Biosystems Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
e Urban Energy Systems Laboratory, Empa, Dübendorf, Switzerland
⁎ Corresponding author. Department of Economics, KNUST, Kumasi, Ghana. boscodramani@gmail.comboscodramani@knust.edu.gh
10 8 2024
30 8 2024
10 8 2024
10 16 e3600116 2 2024
23 7 2024
7 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Suppressed demand arises from inadequate energy access, resulting in unmet basic needs. Therefore, this study investigates the impact of the informal economy, rising temperatures, and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach. The study forecasts suppressed demand using Shared Socioeconomic Pathway (SSP) scenarios, offering insights for energy system planning. The results indicate that all the variables significantly affect suppressed demand in the mid-quantiles. Notably, transmission losses and growth of informal economy variables significantly impact suppressed demand within the 50th to 75th quantiles but have minimal impact before the 50th and after the 75th quantiles in the long run. Additionally, rising temperatures substantially increase suppressed demand by increasing electricity demand for cooling. All future scenarios project this growth trend will continue through 2050, albeit at varying rates. In the business-as-usual (BAU) case, suppressed demand is expected to steadily increase from 1782 MW in 2020 to 8636 MW in 2050. This trajectory aligns well with historical growth trends, which saw suppressed demand increase from 659 GWh to 1782 GWh between 2000 and 2020. SSP scenarios suggest that suppressed demand could grow substantially through 2050, driven by high losses and informal sector growth. Despite sustainable development narratives like SSP1, suppressed demand remains high without major grid and governance improvements. Comparing the results with past studies shows that our findings align with previous research but provide more nuanced insights by incorporating the effects of the informal economy and using advanced forecasting techniques. Practical policy implications include investing in green infrastructure, upgrading grid infrastructure, and formalising the informal economy to alleviate suppressed demand. These actions are critical for sustainable energy access and meeting future electricity needs effectively.

Keywords

Suppressed demand
Informal economy
Transmission losses
Climate change
Developing countries
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pmc1 Introduction

Suppressed energy demand arises when minimum service levels (MSL) of energy required to attain fundamental human needs, such as lighting, cooking, and heating, are either inadequate or unavailable to households and firms [1]. Households with no electricity and those who depend on dirty and inefficient energy sources such as biomass for cooking experience suppressed energy demand. In this case, prior energy consumption can be deficient in predicting future energy demand levels. Thus, suppressed energy demand represents the unsatisfied portion of energy demand and can serve as a barrier to reducing poverty. According to Ref. [2], suppressed demand can be grouped by scale and degree of shortfall in supply. It can be described as customers willing to pay for electricity but are unable to obtain it, as well as customers who demand a particular amount of power but are given less than that quantity.

Suppressed demand arises due to many factors, such as low income, dilapidated energy supply or demand infrastructure, and inadequate technology,1 among others. Households with low incomes tend to have low demand for energy. For instance, the energy demand for an activity such as lighting may be unmet until the income levels of the households rise to a certain threshold. In addition, energy demand may be suppressed due to a household's inability to purchase energy appliances and pay an installation fee. However, as incomes grow, energy demand begins to rise as the household allocates considerable resources to pay for electricity installation fees and purchase a number of appliances. The combination of low household income and high energy service costs prevents households from purchasing adequate energy to meet their basic needs. There is, therefore, a high probability that poor households will be compelled to purchase energy services at a low level that is insufficient to meet basic needs.

Similarly, poor electricity grid infrastructure accounts for significant suppressed demand. Generally, old and weak distribution and bulk supply infrastructure are less resilient to extreme temperatures and precipitation and are characterized by high unreliability and inefficiency in customer service delivery. The inefficacy accounts for substantial outages, which causes electricity rationing to consumers, reducing the reliability and number of hours available for energy services but not notional demand. For instance, between 2012 and 2016, Ghana experienced an excruciating and protracted power crisis that introduced power rationing. At the peak of rationing, customers were provided with an average of 12.5 h of electricity every three days [3,4]. This affected electricity demand as the suppressed portion widened and increased the cost of electricity consumption among households and firms since alternative sources of unclean and expensive energy surged. Suppressed demand can also arise because people designated to be connected to a national grid are not included in baseline demand estimates [5].

Additionally, suppressed demand will likely increase in the future depending on the extent to which different climate change and socio-economic development uncertainties change. Increasing ambient temperatures are anticipated to induce high cooling demand in the hot season but reduce demand in the cold season across numerous economic activities. Economic uncertainties such as population growth, structural change of the economy, technological development and climate change uncertainties such as global greenhouse emissions and radiative forcing, are expected to interact to influence future energy demand. The causes of the interactive effect of these multiple uncertainities are determined using the Shared Socioeconomic Pathways (SSPs) and climate scenarios. The SSPs are scenarios developed to help researchers analyse how future climate change impacts may vary under different socioeconomic development pathways [6]. They combine modelling with stakeholder storylines and empirical data to generate quantitative indicators across sectors and geographic areas to provide narratives for the plausible future evolution of society and natural systems, combining socioeconomic conditions and climate change scenarios [7,8]. The SSPs describe five broad narratives for potential socioeconomic trends over the 21st century [9]. SSP1 describes a sustainable development pathway with low challenges for climate change mitigation and adaptation. SSP2 describes a middle-of-the-road development pathway that follows historical patterns. SSP3 describes a pathway with high challenges for climate change mitigation and adaptation due to regional rivalry and resurgent nationalism. SSP4 describes a pathway with high inequality, where a small global elite dominates. Finally, SSP5 describes a fossil-fueled development pathway with high economic growth and rapid technological development. This study utilises the SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenario storylines, as characterised in the IPCC's 6th Assessment Report. The integrated scenario data is available through the IPCC Interactive Atlas [10].

This study aims to clarify these dynamics and offer recommendations for policy and infrastructure improvements to mitigate the effects of suppressed demand. [11] estimate the suppressed demand for Ghana to be 979 GWh in 2005, representing about 16.5 % of total consumption using blackout data from the World Bank. This high suppressed demand has implications for the Ghanaian economy. First, it suggests that low-income people consume less electricity than they would have if their income level were higher. Second, though the national electrification rate is very high (85 %), there are a large number of people waiting to be connected to the grid who are not considered in the estimation of market electricity demand.2 Thus, suppressed demand can affect the living standards of households and cause firms to produce below capacity. Third, the presence of such a significant suppressed demand can cause miscalculation of present demand, energy supply, and future demand forecasting. This will cause future energy supply and demand trends to deviate from current projections.

Inefficiencies in the electricity sub-sector, such as distribution losses, poor billing leading to low revenue collection, and overstaffing, can significantly weaken performance. Management of utilities with huge operating deficits is usually compelled to reduce future capital investment and skip maintenance, which adversely affects the power supply. According to Ref. [12], electrical utilities with 20 % system losses can increase electrification by only 0.8 % per annum since losses reduce profit levels and economically weaken their ability to undertake significant infrastructural investment. However, utilities that generate system losses below 20 % are financially capable of raising the electrification rate by 1.4 % per annum [5]. In addition, countries with utilities whose efficiency falls below average record 12 % suppressed demand, while those with above-average efficiency experience about 6 % suppressed demand [5]. This suggests that a high-efficiency power utility reduces losses and improves billing and revenue collection, which can drive high capital investment, maintenance, and electrification rates to reduce suppressed demand.

Globally, the informal economy is vast and persistent. Sub-Saharan Africa (SSA) has the second-largest informal economy after Latin America, accounting for about 38 % of economic activity in SSA [13] According to Ref. [14], the informal economy does not comply with necessary government regulations on energy appliance standards and may be involved in illegal electricity connections. Thus, an increment in the informal economy is likely to cause a high level of unplanned energy demand since they operate in the shadows. Estimation of electricity demand does not usually account for the activity of the informal economy, leading to inadequate electricity supply to meet demand (i.e., suppressing demand).

The Electricity Company of Ghana has relied on suppressed demand estimates conducted by Acres International of Canada in 1995 to make investment decisions. At that time, the national electricity coverage was 32.45 % [15]. Since 1995, significant investments have been made in network improvement interventions as well as electricity extension and penetration, which might have influenced the size of the suppressed demand. For instance, the overall installed generation capacity of electricity and distributed generation grew by 9.3 % on average per annum from 2010 to 2020 [16]. In addition, investments in the power sector have caused a growth in long-term dependable capacity from 1940 MW in 2010 to 4842 MW in 2020, representing about 9.6 % growth on average [16]. Further, electrification coverage has grown astronomically to 85 % in 2020, positioning Ghana to achieve 100 % universal coverage by 2030 [16]. Based on this, the study by Acre International in 1995 is outdated and not an accurate representation of the current suppressed demand situation in Ghana. Thus, there is a need to estimate new suppressed demand figures that reflect the current network situation in Ghana.

This study has a crucial objective to estimate and forecast suppressed electricity demand in Ghana, considering the influence of climate change, the informal economy, and sector inefficiencies. This analysis is of paramount importance for energy system planning, as it provides a deep understanding of suppressed demand, thereby shedding light on unmet energy needs and aiding in the formulation of policies to enhance energy access and efficiency. By integrating SSP scenarios, this study offers a comprehensive approach to forecasting future suppressed demand, enabling policymakers to prepare for different development trajectories. The inefficiencies in the electricity sub-sector, such as transmission and distribution losses, poor billing, and overstaffing, significantly weaken performance and contribute to suppressed demand. These inefficiencies have critical implications for energy system planning, as they affect the availability and reliability of electricity supply. The informal economy, which often operates outside regulatory frameworks, exacerbates the issue by creating unplanned energy demand. Understanding these factors and their impacts on suppressed demand is essential for developing strategies to improve energy access and efficiency.

Though estimates of suppressed demand have grave importance for the planning and development of power sector infrastructure, there is a dearth of studies on it. One of the studies on suppressed demand includes [17], who finds that peak electricity demand has been significantly suppressed in Ghana's Greater Accra and Ashanti regions in Ghana by 3.1 % and 9.6 %, respectively. This study uses a transformer injection experiment to analyse suppressed demand and suggests that utilities should implement measures to prevent suppressed demand from becoming unmanageable. [11] also estimate suppressed demand using various techniques based on the sources of suppressed demand. First, they used customers on utility waiting lists and assumed that social demand is part of suppressed demand. Second, the authors apply the duration and frequency of blackouts from the World Enterprise Survey to estimate suppressed demand for SSA countries in 2005 with a minimum of 3 GWh (Equatorial Guinea) and a maximum of 10,803 GWh (Nigeria). Similarly [18], examines the energy consumption and suppressed demand in hot, arid climates and discloses that the mean yearly suppressed demand is 7846 kWh, representing about 77 % of the total annual electricity consumed among 210 households in Baghdad. In addition, the author finds suppressed demand to be very significant during summer, as it is about 3860 kWh higher than the mean demand in spring, autumn, and winter among the 210 households. [19] applies a heuristic algorithm to forecast suppressed demand in Iraq and reveals that electricity demand will likely be suppressed by about 15,283.5 MWh from 2017 to 2024.

However, the extant studies did not cover the effects of the informal economy and electricity sector inefficiency on suppressed demand. In addition, prior studies have not considered forecasting suppressed demand using the SSPs to understand the measures that should be implemented to moderate its negative impact on energy planning and rising average temperatures. We contribute to the extant literature in the following ways. First, we construct new estimates of electricity suppressed demand for Ghana from 2000 to 2021 since this data has not been built for Ghana in a time series form. Second, we estimate the effects of electricity sector inefficiencies and the size of the informal economy on suppressed demand by applying the Quintile Regression Technique. Finally, we forecast suppressed demand from 2022 to 2050 using accelerated economic growth and SSPs in Ghana.

The study is structured as follows: section 2, reviews prior studies of suppressed energy demand and the informal economy. Using the literature review, we obtain the methods suitable for constructing the data for the informal economy and suppressed demand and present them in section 3. Following this, the results and discussions are presented in section 4. Section 5 presents the conclusion and policy suggestions.

2 Literature review

Theoretically, household and residential demand for energy is based on a utility maximization framework. Households use energy to accomplish certain needs, such as heating and lighting. Thus, these households allocate their income among many competing demands, including energy to attain the highest level of fulfilment from their aggregate expenditure. Given that energy provides services that are needed by the household, an increase in income and a fall in price will raise the energy demand. The energy demand is also considered as a derived demand for the services it provides through the application of appliances. Therefore, the energy demand is based on a set of simultaneous decisions made by the consumer which involves the quantity and type of appliances to purchase and the rate of use of the appliances [20]. The type of appliances to purchase is determined by the technological features such as efficiency and the type of energy required.

The satisfaction gained from the energy demand can be maximized only if optimality conditions that link the marginal cost of supply and price are satisfied [21]. The quality of energy supply (capacity improvement, reducing outages, raising voltage level, etc.) influences energy demand. Thus, the optimality condition of quality supply of energy, requires the supply system be improved to the level where its marginal cost offsets the marginal satisfaction obtained by consumers. When the energy supply quality changes, the consumer's benefits are affected in both the short and long run. During the short run, if the supply quality reduces anticipated planned demand is affected and end-users experience no lighting for households and output loss for industries [21]. In the long run when consumers expect a reduction in the quality of supply, it will change their planned demand and behavioural patterns as they search for imperfect substitutes for a particular energy input. The effect of poor energy supply quality and the adjustments by consumers causes suppressed demand.

Fig. 1, depicts the conceptual framework of the study. The income and price of the energy demand are the principal drivers of suppressed energy demand. The availability and cost of appliances which causes the derived demand for energy equally affect the level of suppressed demand and vice versa. Improvement in the quality of energy supply, such as enhancing capacity, reducing shortages and expanding access will ultimatley reduce suppressed demand. Temperature and the size of informality can either affect suppressed demand directly or through the energy supply quality.Fig. 1 Conceptual framework of suppressed demand in Ghana.

Fig. 1

Empirically, the first theme on suppressed energy demand presents arguments on the effect of demand and supply-side determinants. The causes and determinants of suppressed energy demand are primarily divided into two groups: demand and supply-side causes. The major demand-side factor is viewed from the point of low income leading to customers' inability to demand a sufficient amount of energy to carry out their activities, as pointed out by Ref. [22]. The study points out that energy cost (high price) may also be a cause of suppressed demand. Energy cost is explained as the combination of low household incomes and high unit costs of energy, which does not allow households to afford sufficient energy for their basic needs. On the other hand, the supply-side factors identified by Ref. [17] includes the non-existence of energy supply facilities, inadequacy of the supply system (depressed voltages), under-capacity of the supply system (load shedding), and insecurity of the supply system (power outages).

According to Ref. [11], suppressed demand is the contrast between national electricity demand and the available electricity at prevailing prices. It results from people waiting to get grid-connection when they are not captured in the electricity market demand forecast. Further, old and run-down infrastructure, as well as regular blackouts and brownouts, reduces consumption, but national demand remains unaltered. Low grid reliability, bureaucratic red tape, and household credit constraints suppress demand [23]. Also, poor infrastructure, and low technology levels were some of the causes of suppressed demand [22]. To tackle this [11], constructs a model to examine the costs of meeting power demand in SSA over 10 years. The model simulated an optimal approach for generating, transmitting, and distributing electricity in response to national demand increases. There has also been a substantial increase in grid-based electricity in recent years; however, this is insufficent to match growing demand [19]. realizes that unreliable electricity supply is a significant barrier to social and economic progress. The author builds a flexible and appropriate forecasting model based on a heuristic algorithm approach and asserts that the growth of the consumer price index is an important driver of suppressed demand in Iraq [19]. Similarly [18], estimates energy consumption and suppressed demand in a hot, arid climate and unearths that the mean annual suppressed demand is about 77 % of the total annual electricity consumed among 210 households in Baghdad. [24] compare the impact of diverse containment measures adopted by many countries in Europe in reaction to COVID-19 pandemic on electricity consumption patterns. The authors find that dissimilar COVID-19 containment measures provided different impact on electricity consumption. For example, countries with tougher restrictions, experienced severe reduction in electricity consumption while those with mild restrictions had a lesser reduction in electricity use. [25] apply the bond-driven prediction and ARDL techniques to estimate the impact of COVID-19 energy consumption in the USA and Germany. The findings reveal that electricity consumption was significantly depressed particularly during the period of intensive confinement due to the severity of the loss of human life.

Past studies have also rely on a number of different forecasting methods in the energy system in the last decades [26]. presents these in three main sections: the support vector regression (SVR) models, artificial intelligence models, and the traditional approaches such as the econometric models [27]. also elaborates on three forecasting techniques: traditional forecasting, modified traditional, and soft computing techniques. Forecasting demand models rely on the employed approach and the time scale used to forecast the short, medium and long-run demand. However, the accuracy of forecasting hinges on the quality of the analyzed data and the capacity to shock the model with important exogenous factors and not only the efficiency of numerical algorithms [28].

More so, GDP, which has been employed to measure the growth of an economy, has been indicated to be an integral facet of electricity demand [29]. Lin forecasts GDP to be inversely correlated with long-run electricity demand in China [30]. also finds that GDP per capita and population affect annual electricity demand by employing a forecast model from historical data and a linear logarithmic model. In Venezuela, a study by Ref. [31] carried out a cointegration and error correction analysis to predict electricity demand using electricity consumption by sector. Also [32], compares multiple regression and artificial neural networks (ANNs) approaches based on principal components with eleven input variables to predict electricity demand load projection in India in the long run, finding the ANNs method to be more efficacious.

Based on the literature review, it is worth mentioning that little attention has been given to other equally important determinants of suppressed demand. For instance, Ghana's informal economy is huge and possesses some characteristics that are likely to cause suppressed demand [14]. indicate that the informal economy is unregulated and thus does not comply with regulations on using standard electrical appliances. Again, the informal economy operates in the dark, so national energy estimates and forecasts do not account for it. Therefore, as the sector grows, it adds to the increasing energy demand. Since the existing energy supply does not account for the informal economy, it causes demand to exceed supply, leading to inadequate energy consumption. We try to find its effect on suppressed demand in Ghana. Again, inefficiencies in the electricity sector, such as distribution losses, poor billing leading to low revenue collection, and overstaffing, have significantly weakened power sector performance. Most power utilities in Ghana are currently operating with massive deficits, compelling them to reduce future capital investment and skip maintenance, which has adverse effects on the power supply. This suggests that high inefficiency within a power utility increases losses, worsens billing and revenue collection, and drives low capital investment, leading to increased suppressed demand.

3 Methodology

The methodology for estimating suppressed electricity demand involves three major sections: constructing data for suppressed demand, estimating the effects of the informal economy and inefficiencies using the QARDL model, and forecasting suppressed demand using SSP scenarios. To do this, we begin this section by estimating electricity-suppressed demand, which is the core of this analysis. We also argue that evaluating the pattern of suppressed demand is not direct, and it is better to consider the pattern in different scales and locations, that is allowing a richer understanding of the data using quantile regressions which an Autoregressive model cannot addressed. This method is semiparametric since it avoids assumptions of the parametric distribution of the regression errors, and this makes it a suitable model for correcting heteroskedasticity. We try to disentangle between the long-run and the short-run effects of this model. Third, we apply the SSPs to project suppressed demand in Ghana using estimates from the QARDL model.

3.1 Estimation of suppressed electricity demand

The estimation of suppressed energy demand does not follow established conventions. According to Ref. [19], the modelling approach for suppressed demand should be country-specific and supported by sufficient theoretical assumptions and economic intuitions. In other words, it is imperative for the researcher to possess a great understanding of the characteristics of electricity demand patterns and dynamics when estimating suppressed demand as a ratio of the actual amount of electricity generated and supplied. Based on this, we apply a heuristic technique to estimate the suppressed electricity demand for Ghana. Using the heuristic metthod we follow [18,19] to calculate the suppressed electricity demand as a percentage of the real extreme peak load that the generating system serves yearly. We calculate the real extreme peak load using a matrix of electricity used annually from 2000 to 2020 by industrial, residential, commercial and agriculture electricity consumers in Ghana. Our estimation is based on the following assumptions.

First, we assume that each class of consumers has an increasing electricity demand. The implication is that each of the consumers contributes significantly to suppressed demand. Second, none of the classes of consumers are discriminated against in the distribution of electricity by the distributing companies. Third, the estimate of suppressed demand for a particular class is different from all the estimates of suppressed demand for the remaining classes. This implies that the mean of the total estimate from the consumer classes can represent the suppressed demand for the country.

Following these assumptions, the first step is to calculate the electricity consumption by each class of consumers as a ratio of the total electricity consumed for the various years of the study. The second step entails the estimation of rolling moving averages using equation (1) [33]. This method is applied to ensure that we assign higher weights to the ratios of recent electricity consumption than to older electricity usage behaviours.(1) ∑i=1mγijm,…,∑i=1mγijm−1,…,∑i=1mγij1

Here, j=1 to 3 captures the class of consumers, γij represents the ratio of electricity consumption by a particular class of consumers to the aggregate consumption in year i, and m depicts the total number of years used for the estimation.

In the third step, we compute the mean of moving averages for all the sectors of energy usage. Fourth, and finally, we estimate the lowest consumption ratio expressed by the sectors emanating from the initial step and subtract it from the overall mean consumption ratios by the sectors. We then allocate higher weights to recent years of the series, emphasizing 2010 to 2020. This is because there was a decline in the demand for electricity for the industrial sector in 2010 as a result of VALCO reducing its demand in that year. Due to this, we identify weights between 1 and 3 and allocate them to data from 2010 to 2020. This method progresses with calculating the share of each sector relative to the total electricity demand. In the next step, we estimate the weighted share by multiplying the allocated weights by each electricity share in each sector for every year.

Finally, we add the suppressed demand evaluated in each sector to calculate the total suppressed demand. The suppressed demand in each sector is evaluated by multiplying the electricity demand in each sector by the suppressed demand share. The average of weighted shares is the weighted average of the shares of each sector. These steps are repeated for each sector.

3.2 Quantile regression

In this section, we estimate the effect of the informal economy and electricity sector inefficiency on suppressed demand together with other important determinants. Using quantile estimation presents a systematic process for investigating how regressors affect the shape, location and scale of the distribution of suppressed demand, that is providing a way to explain the heterogeneities in the suppressed demand evolution. The mathematical relationship between the dependent and independent variables can be expressed as Equation (2) [34].(2) lnSDt=β0+β1lnTlt+β2lnIEt+β3lnGDPKt+β4Tempt+εt

where SDt, Tlt,IEt, GDPKt,Tempt, and εt denote suppressed electricity demand, transmission losses as a percent of actual electricity supplied, the size of the informal economy as a percent of GDP per capita, mean temperature, and the error term, respectively. All these variables were logged except for temperature and transmission losses to stabilise their variances, normalize skewed data distributions or linearize the nonlinear relationships. Transmission losses and temperature were used in their level forms since we assume they have a linear relationship with suppressed demand and there are no outliers in their series. Inefficiencies in the electricity sector in terms of transmission losses reduce the amount of energy available for use; this can affect the cost of electricity and make it unaffordable, which will lead to an increase in suppressed demand. Since the informal economy operates in the dark, it is difficult for power utilities to capture its energy requirements and we expect a positive relationship between growth in the informal sector and suppressed demand. A high GDP per capita, on the other hand, is meant to improve income levels and make it possible for more people to afford electricity. Thus, growth in GDP per capita is expected to reduce suppressed demand. High temperatures have the potential to induce suppressed demand through an increase in cooling demand. As climate change-induced temperature rises, households and firms will increase demand for cooling devices such as air conditioners. This can raise suppressed demand since air conditioners are expensive and unaffordable to some consumers. Their acquisition can lead to additional electricity demand, which supply may not be able to meet.

Based on equation (1) and following [35], we modelled a Quantile Autoregressive Distributed Lag (QARDL) to estimate the effects of the informal economy on suppressed demand in Ghana. This is to provide more intuition about this uneven distribution, by controlling for lagged regressors to investigate the heterogenous adjustments in the series. The QARDL model keeps the stationary process of the covariates whilst accounting for some explosive attributes that is some stationarity conditions are met in the whole distribution. This model is appropriate in several ways. The first is that this model considers the long-term association among the variables and the linked short term trajectory of quantiles of contributes as postulated by Ref. [36]. Also, this model is characterized by locational asymmetry [[37], [38], [39]]. That is pattern of the suppressed demand is affected by factors within its conditional distribution. More so, there has been the absence of co-integration observed in some studies while considering models like ARDL models and co-integration test [37]. However, this issue could be addressed by varying quantile co-integration measurements at short-term degrees, in the long run, factors remain moving persistently [40]. Shocks cause the co-integrating coefficient to vary across different quantiles in the QARDL model. This model outperforms nonlinear models which identify the intensity at zero. The QARDL model establishes nonlinearity through a data-generated process. These arguments make the QARDL model appropriate for analyzing the nonlinear and asymmetric relationship between transmission losses as a percent of actual electricity supplied, informal economy, GDP per capita, temperature and electricity-suppressed demand in Ghana. The QARDL model is given as shown in Equation (3).(3) SDt=α+∑i=1pθiSDt−i+∑i=1q1μiTlt−i+∑i=0q2ηiIEt−i+∑i=0q3γiGDPKt−i+∑i=0q4σiTempt−i+εt

where εt is the error term defined as SDt−E[SDt/Ft−1], with Ft−1 being the smallest σ−field generated by {Tlt,IEt,GDPKt,Tempt}, and p, q1,q2,q3 and q4 are lag orders selected by the Schwarz information criteria (SIC). Tlt,IEt,GDPKt,Tempt are the values of transmission losses, informal economy, gross domestic product per capita, mean temperature whiles SDt represents suppressed demand.

Cho et al. (2015) elaborated these arguments in the form of the QARDL (p, q) as below:(4) QSDt=α(τ)+∑i=1pθi(τ)SDt−i+∑i=1q1μi(τ)Tlt−i+∑i=0q2ηi(τ)IEt−i+∑i=0q3γi(τ)GDPKt−i+∑i=0q4σi(τ)Tempt−i+εt(τ)

where εt(τ)=SDt−QSDt (τ/ Ft−1) and τ quantile of SDt lies between 0 and 1 [41]. To avoid the serial correlation of ε, we generalize the QARDL, as shown in Equation (5).(5) Q△SDt=α+δTlTlt−1+δIEIEt−1+δGDPKGDPKt−1+δTempTempt−1+∑i=1q1−1μi(τ)ΔTlt−i+∑i=0q2−1ηi(τ)ΔIEt−i+∑i=0q3−1ρi(τ)ΔGDPKt−i+∑i=0q4−1γi(τ)ΔTempt−i+vt(τ)

Using the model in Equation (5), there remains a likelihood of contemporaneous correlation between vt and ΔTlt, ΔIEt, ΔGDPKt and ΔTempt. The previous correlations can be avoided by employing the projection of vt on ΔTlt, ΔIEt, ΔGDPKt and ΔTempt., with the form vt=φTlΔTlt+φIEΔIEt+φGDPKΔGDPKt+φTempΔTempt+εt. The resulting innovation εt is now uncorrelated with ΔTlt, ΔIEt, ΔGDPKt and ΔTempt. Incorporating the previous projection into Equation (5) and generalizing it to the quantile regression framework leads to the following QARDL-ECM model shown in Equation (6).(6) Q△SDt=α(τ)+∅(τ)(SDt−1−βTl(τ)Tlt−1−βIE(τ)IEt−1−βGDPK(τ)GDPKt−1−βTemp(τ)Tempt−1)+∑i=0q1−1ηi(τ)ΔTlt−i+∑i=0q2−1ρi(τ)ΔIEt−i+∑i=0q3−1γi(τ)ΔGDPKt−i+∑i=0q4−1θi(τ)ΔTempt−i+εt(τ)

The cumulative short-term impact of the previous suppressed demand on current suppressed demand is measured by δ=∑j=1P−1δi , while the cumulative short-term impact of current and past levels of transmission losses, informal economy, GDP per capita and temperature on current energy suppressed demand are measured by η*=∑j=1P−1ηi,ρ*=∑j=1P−1ρj, γ*=∑j=1P−1γj, θ*=∑j=1P−1θj, π*=∑j=1P−1πj and σ*=∑j=1P−1σj respectively. The long-term parameters and the long-term cointegrating parameters for informal economy, electricity sector inefficiency and gross domestic product are calculated as βIE*=−δIE∅, βTl*=−δTl∅, βIE*=−δIE∅,βGDPK*=−δGDPK∅, and βTemp*=−δTemp∅ respectively. The cumulative short-term parameters and the long-term cointegrating parameters are calculated using the delta method. It is worth noting that the ECM parameter ∅ should be significantly negative.

Specifically, we run the Wald test to investigate the nonlinearities on the speed of the adjustment parameter and the long-term integrating parameter. For each country and each parameter, we run four tests. For example, we test the following null hypothesis for the ρ* parameter. H0:ρ*(0.05)=ρ*(0.1)=ρ*(0.2)=…=ρ*(0.95) against an alternate one H1:∃i≠j/ρ(i)≠ρ(j) with i,j∈{0.05,0.10,0.20,Λ,0.80,0.90,0.95} and i ≠ j.

3.3 Data sources

Data is obtained from three primary sources: the annual reports of the Energy Commission of Ghana [42], the World Bank Development Indicators (WDI), and [43], spanning 2000–2020. The Energy Commission of Ghana is the technical regulator of electricity, natural gas, and renewable energy in Ghana. It collects and compiles data from energy utilities. Data readily available on the Commission's website includes residential, service, and industrial sector electricity consumption, electricity price, transmission losses number of customers, and reserve margin. We obtained the GDP time series data from the World Development Indicator [44]. Finally, the size of the informal economy of Ghana was obtained from Ref. [43]. We measure SD in thousands of GWh, transmission losses as a percent of actual electricity supplied, the size of the informal economy as a percent of GDP per capita, GDP per capita in thousands of US dollars and mean temperature in degrees Celsius.

3.4 Forecasting suppressed demand for energy planning

3.4.1 Long-term scenario development

Long-term scenarios were developed for Ghana based on the Shared Socioeconomic Pathway (SSP) projections and storylines [9]. The scenarios describe different socioeconomic growth and temperature changes until 2050 and beyond. Another scenario is an Accelerated Economic Growth (AEG) scenario adapted from Ref. [45] electricity demand scenarios, which assumes higher than historical economic growth rates driven by rising GDP per capita, reduced transmission losses, and a declining informal sector. Lastly, the Business-as-usual (BAU) scenario projects future demand based on historical economic, energy, land use, and emissions trends, which is at a slower rate than AEG.

3.4.2 Scenario definitions

Temperature projections for the SSPs are based on World Bank climate data, as presented in Fig. 2A [46]. The SSPs do not directly address the informal economy, losses, and GDP growth. However, suitable growth trajectories validated by curve fitting were assigned to each SSP storyline, as depicted in Fig. 2.Fig. 2 Scenario storyline data: historical and mean forecasted temperature changes (A); (B) historical and forecasted informal economy; (C) historical and forecasted transmission losses; and (D) historical and forecasted GDP per capita.

Fig. 2

SSP1-SSP5: these scenarios assume coordinated and cooperative efforts in order to stimulate local economic growth and development. Therefore, the informal economic sector is expected to grow but slowly; it will increase by 23 % relative to 2020 by 2050 (see Fig. 2B). Transmission system losses are projected to increase to 4.6 % by 2050, which is in the range of acceptable percentage transmission loss from the Public Utilities Regulatory Commission [47]. GDP per capita will grow by 140 % by 2050 relative to 2020 based on a linear curve fit. This scenario is the most optimistic scenario; hence the same attributes were assigned to the AEG scenario.

SSP2: This scenario assumes development trends in line with BAU. Therefore, the relative size of the informal sector is projected to grow by 23 % in 2050 relative to 2020 based on historical trends (based on a logarithmic curve fit). Transmission losses are expected to grow to 5.3 % in 2050 (see Fig. 2C), while GDP per capita is expected to grow by 78.2 % in 2050 relative to 2020.

SSP3: This scenario assumes poor local development, which exacerbates existing issues. It assumes a relative increase in the informal economy by 39 %, 5.9 % transmission losses (the worst recorded transmission in Ghana for the past 21 years) [48], and 28 % growth in GDP per capita in 2050 relative to 2020 (see Fig. 2D).

3.4.3 Forecast generation

Long-run QARDL estimates are multiplied by the annual projected values for each variable under each scenario. This gives the projected proportional change in suppressed demand due to each factor relative to 2020 levels. The effects are accumulated over time and applied to the 2020 suppressed demand baseline to generate forecasts from 2021 to 2050. The functional forms involving logarithms are interpreted and applied using Equations (7), (8) [49].

For a log-level model with dependent variable log(Y) and independent variable X, the interpretation of β1 is;(7) %ΔY=(100β1)ΔX

For a log-log model with dependent variable log(Y) and independent variable log(X), the interpretation of β1 is;(8) %ΔY=β1%ΔX

where; Y is the dependent variable (suppressed demand), X is the independent variable (informal economy, losses, GDP per capita, temperature), and β is the medium quintile level coefficient or elasticity.

4 Results and discussion

4.1 Historical suppressed demand

The summary statistics of the variables are presented in Table 1. Suppressed demand has a mean of 974.89 GWh and a standard deviation of 374.6 GWh, implying it is not stable. In our case, it rises throughout the period of study. The minimum and maximum values of suppressed demand are 544 GWh and 1782.37 GWh, respectively; suggesting significant deterioration in its determinants over the study period. Transmission losses (as a percentage of electricity transmitted) have a mean value of 3.99 % and a standard deviation of 0.73 %. The standard deviation suggests that losses, even though unstable, are less variable compared with the suppressed demand. The minimum and maximum estimates which are also relatively low, are 2.8 % and 5.9 %, respectively.Table 1 Descriptive statistics.

Table 1Variables	Obs	Mean	Std. Dev.	Min	Max	
 Suppressed demand (%)	21	974.89	374.6	544.37	1782.37	
 Transmission loss(%)	21	3.99	0.73	2.8	5.9	
 Informal economy (%)	21	24.02	11.55	3.04	47.29	
 GDP per capita ($)	21	1571.92	620.63	548.26	2394.14	
 Mean temperature (Degrees Celsius)	21	27.79	0.21	27.44	28.21	
Source: Authors' estimation

The average size of the informal economy is about 24 % with a standard deviation of 11.55, which is about 15 times more volatile than the volatility of transmission losses. The minimum and the maximum estimates are 3.04 % and 47.29 %, respectively. GDP per capita is found to have an average value of GH'1571.92 and a standard deviation of GH'620.63 for the period sampled. The temperature variable has a mean of 27° with the lowest standard deviation. The temperature has the lowest standard deviation since we used the mean temperature estimates and this does not make it an outlier. The distributions demonstrate that the variables are relatively stable since extremes rarely took place in the past. However, we addressed the high standard deviations of the variables by taking the logarithm to reduce their scales. Additionally, the post-estimation tests indicate the model does not suffer from non-normality of the residuals.

Table 2 presents the augmented Dickey-Fuller (ADF) unit root test results. It shows that none of the variables are stationary at their levels, indicating evidence of unit root. Therefore, the mean and variances of the variables vary with time. However, these variables attain stationarity at their first differences; that is, the test rejects the null of unit root at the 10 %, 5 % and 1 % significance levels. The absence of unit roots or stationarity suggests that the variables exhibit mean reversion along a constant long-run mean as they oscillate. This constitutes an important eligibility criterion for the QARDL model to be suitable for this study.Table 2 Augmented Dickey-Fuller (ADF) unit root test results.

Table 2Variables	Obs	Statistic	p-value	Statistic	p-value	
Panel A: Levels	
Suppressed demand	19	−3.491	0.040	−1.484	0.835	
Trans losses	19	−1.554	0.810	−1.574	0.802	
Informal sector	19	−2.894	0.164	−2.702	0.235	
GDP per capita	19	−1.817	0.697	−1.710	0.746	
Temperature	19	−4.132	0.006	−4.130	0.006	
Panel B: First Differences	
Suppressed demand	18	−3.565	0.033	−4.194	0.005	
Distribution loss	18	−3.279	0.070	−3.288	0.068	
Informal sector	18	−3.783	0.017	−3.279	0.070	
GDP per capita	18	−3.305	0.066	−3.446	0.046	
Temperature	18	−4.261	0.000	−4.256	0.004	
Note: ***p < 0.01, **p < 0.05, *p < 0.1. Statistics (p-value) are characteristics of the logarithmic form of the variables. This test controls for lags at 1 and a trend term.

Fig. 3 depicts a plot of the suppressed demand estimates. Generally, the graph shows a growing trajectory of suppressed demand with spikes in 2006, 2011, and 2016. The lowest estimate is about 500 GWh occurring between the years 2003 and 2007. This can be attributed to improved supply of electricity from 36.4 % in 2002 to 38.2 % in 2004.Fig. 3 Plot of suppressed demand estimates and quantile plot.

Fig. 3

4.2 QARDL cointegration results

The existence of cointegration among the variables is also necessary for the application of the QARDL estimation technique. To evaluate cointegration, an ADF unit root test was performed on the residuals from the long-run relationship model; the results are summarized in Table 3. The test rejects the null hypothesis of no long-run relationship among these variables at all conventional levels of significance, indicating evidence of cointegration since the F-statistic of 3.921 is greater than all the ADF critical values and thus indicates the presence of cointegration. Therefore, we advance with the QARDL model results as presented in Table 4. To check the robustness of our results, we compare them with ARDL estimates. Generally, a low quintile represents moderate suppressed demand while a high quintile reflects high suppressed demand. The results indicate that all the variables have significant effects on suppressed demand in the mid-quantiles. Notably, these variables significantly impact suppressed demand within the 50th to 75th quantiles but have no impact before the 50th and after the 75th quantiles in the long run. These results suggest electricity demand is significantly suppressed at higher consumption levels than at lower levels.Table 3 Cointegration results of QARDL specification.

Table 310 %	5 %	2.5 %	1 %	
I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	I(0)	I(1)	
 2.45	3.52	2.86	4.01	3.25	4.49	3.74	5.06	
 2.57	−3.66	−2.86	−3.99	−3.13	−4.26	−3.43	−5.37	
Note: I(0) and I(1) denotes the lower and upper critical values. The estimated F-statistic is 3.921 and the p-value is 0.059.

Table 4 Quantile autoregressive distributed lag bound long-run results.

Table 4Variables	(1)	(2)	(3)	(4)	(5)	(6)	
QR_10	QR_25	QR_50	QR_75	QR_90	ARDL	
Image 1	0.060	0.246	0.726***	0.733***	0.675*	−0.242	
(0.267)	(0.314)	(0.225)	(0.139)	(0.344)	(1.963)	
Image 2	0.107	0.153	0.212	0.251***	0.309*	−0.41	
(0.204)	(0.132)	(0.137)	(0.085)	(0.148)	(1.06)	
Image 3	0.147	0.211	0.448**	0.581***	0.332	1.21	
(0.232)	(0.231)	(0.166)	(0.136)	(0.192)	(1.442)	
Image 4	0.353	0.383	−0.036	−0.351*	−0.255	1.202	
(0.345)	(0.310)	(0.280)	(0.193)	(0.320)	(2.188)	
Image 5	1.479	−4.197	−15.257**	−13.622***	−12.086	–	
(7.519)	(8.412)	(6.268)	(3.769)	(8.936)		
Observations	21	21	21	21	21	20	
Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Dependent variables: log of suppressed demand.

4.3 Long and short run QARDL regression results

We present the QARDL estimation results in Table 4.

The results of the association between the outcome variable (suppressed demand) account for the model of the study. This function under quantile analysis follows a τ th conditional quantile function of suppressed demand. Monotone conditional quantile functions are necessary to implement constraints on coefficient forms. Therefore, we consider τ∈{0.1,0.25,0.5,0.75,0.9}; however, we rely on the 75th (0.75) and medium (0.50) quintile levels where the estimation successfully identifies a unique optimal solution for both long- and short-runs. The solution in τ∈{0.1,0.25,0.75,0.9} has an optimal solution though bandwidth is too large in the sandwich covariance estimation and hence errors in the sandwich covariance estimation. For the conditional quintile τ=0.5, the results of the distribution losses, informal economy, and suppressed demand for both the long- and short-run equilibrium relationships have been illustrated in Table 4.

First, our results reveal that temperature drives suppressed demand significantly. Increasing temperatures can cause high demand for cooling devices such as air conditioners, and since these devices are expensive, the majority of households are unable to afford them, leading to increasing suppressed demand. The results show that suppressed demand increases by 73 and 49.7 GWh when temperature increases by 1 °C in Ghana for both long- and short-run periods. This is in line with the findings of [14,15], who found a positive effect of temperature on suppressed demand. Second, we establish a positive and statistically significant relationship between transmission losses and suppressed demand in the long run. Transmission losses do not only reduce the amount of electricity available for use but may also raise electricity prices to prevent utilities from financial loss. Thus, the results suggest that suppressed demand increases by about 25 % and 15.4 % in the long- and short-runs, respectively, when transmission losses increase by 1 %. These results align with those of [50] who find that transmission and distribution losses generally reduce total electricity supply, creating a power shortage and suppressed demand.

In addition, informal economy has a significant positive impact on suppressed demand in both the long- and short-run, even though it is not statistically significant in the short-run, as shown in Table 5. The results indicate that a 1 % expansion in the informal economy increases suppressed demand by 0.581 % and 0.246 % in the long- and short–run, respectively. This indicates that when the informal economy (which is unregulated) grows, there is the potential for firms and households to connect to the grid illegally; this suppresses the availability of electricity to meet demand.Table 5 Short-run quantile estimation results for suppressed demand.

Table 5Variables	(1)	(2)	(3)	(4)	(5)	(6)	
QR_10	QR_25	QR_50	QR_75	QR_90	ARDL	
Image 6	−0.157	−0.080	0.497***	0.252	0.095	0.147	
(0.324)	(0.535)	(0.149)	(0.337)	(0.486)	(0.094)	
Image 7	−0.084	−0.069	−0.210*	−0.120	0.106	–	
(0.235)	(0.602)	(0.113)	(0.248)	(0.320)		
Image 8	0.067	0.054	0.154***	0.099	−0.015	0.059*	
(0.078)	(0.168)	(0.046)	(0.121)	(0.186)	(0.03)	
Image 9	−0.023	−0.035	−0.013	−0.057	−0.024	–	
(0.066)	(0.067)	(0.032)	(0.097)	(0.130)		
Image 10	0.169	0.126	0.246***	0.179	0.087	0.086	
(0.098)	(0.189)	(0.060)	(0.154)	(0.180)	(0.052)	
Image 11	0.022	−0.014	−0.130*	−0.045	0.026	–	
(0.137)	(0.156)	(0.063)	(0.159)	(0.066)		
Image 12	−0.321	−0.258	−0.560**	−0.260	0.394	–	
(0.522)	(0.802)	(0.208)	(0.712)	(1.181)		
Image 13	−0.103					–	
(0.304)						
Image 14		−0.101				–	
	(0.523)					
Image 15			−0.568**			–	
		(0.184)				
Image 16				−0.275		–	
			(0.498)			
Image 17					0.072	−0.061	
				(1.032)	(0.081)	
Image 18	0.018	0.036	0.034	0.065	0.099		
(0.106)	(0.123)	(0.024)	(0.097)	(0.199)		
Observations	19	19	19	19	19	20	
Note: Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1. Dependent variables: first difference of the log of suppressed demand.

Further, GDP per capita exhibits an inverse and significant effect on suppressed demand. A 1 % increase in the GDP per capita leads to a 0.351 % and 0.560 % decrease in suppressed demand in the long- and short-term, respectively. This finding contracdicts that of [14] who found that GDP raises suppressed demand. Our findings imply that a high per capita income provides households and firms with income which can help them purchase electrical appliances and pay for grid connections, thereby reducing suppressed demand. This negative effect of GDP per capita could be seen as an improvement of the entire economy, which has the potential to support better decision-making for power system planning and maintenance.

The error correction term (ectt−1) (Table 5) is statistically significant and demonstrates a prior expected negative sign. It indicates the short-run adjustment to the long-run equilibrium and displays that over 56.8 % deviations or shocks from the long-run equilibrium are corrected annually. This implies that the deviation from the long-term changes in suppressed demand is corrected by about 56 % in the subsequent year. This result indicates a relatively fast speed adjustment in the model.

4.4 Diagnostic tests

Fig. 4, Fig. 5 report the dynamics of the parameter estimates of the 50 % quintile. The grey area defines the 95 % confidence interval while the thick black line traces the movement of the variable. The parameter estimates exhibit different growth trajectories, indicating that these parameters behave differently in dissimilar quintiles. However, the parameter estimates are stable over time as they lie within the 95 % confidence interval. This shows that the estimates are relatively efficient and can be relied on for policy purposes.Fig. 4 Plot of long-run quantile regression coefficients (q = 0.50).

Fig. 4

Fig. 5 Plot of short-run quantile regression coefficients (q = 0.50).

Fig. 5

We also present results on test for normality of the residuals of these models based on skewness and kurtosis. The results indicate that we fail to reject the hypothesis that the residuals in all models except for the 90 % quantile model. This suggests that these models are normally distributed (see Table 6).Table 6 Results of some diagnostic tests.

Table 6	QR_10	QR_25	QR_50	QR_75	QR_90	
Skewness	0.5304	0.1456	0.9242	0.2397	0.0776	
Kurtosis	0.0027	0.9885	0.6417	0.8369	0.7656	

4.5 Forecasting results

The total suppressed demand projection under each long-term scenario is illustrated in Fig. 6. The historical data shows suppressed electricity demand in Ghana has been steadily increasing from 2000 to 2020, rising from 659 GWh to 1782 GWh. All future scenarios project this growth trend will continue through 2050, albeit at varying rates. The business-as-usual (BAU) case shows a steady increase in suppressed demand from 1782 MW in 2020 to 8636 MW in 2050. This trajectory aligns well with historical growth trends.Fig. 6 Total suppressed demand across scenarios.

Fig. 6

The SSPs show divergent trends. The SSP3 scenario has the highest suppressed demand by 2050 at 19,196 GWh, reflecting that the informal economy expands significantly while losses in electricity distribution increase. GDP growth is also projected to be slow. This trajectory highlights the critical need for supportive policies and investments to spur socioeconomic development and expand electricity access if such a future emerges. Lack of progress on these fronts could leave suppressed electricity demand extremely high. In contrast, the AEG2 scenario leads to the lowest suppressed demand of 2582 GWh in 2050, enabled by rapid economic growth, technology development, and declines in the informal sector and transmission losses.

The SSP1 and SSP5 scenarios combine elements of sustainability-focused (SSP1) versus fossil-fueled (SSP5) development and growth trajectories. It projects suppressed demand reaching 5758 and 8569 GWh in SSP1 and SSP5, respectively, by 2050 in Ghana; this is lower than SSP2 and SSP3 but still represents substantial growth compared to current levels. SSP1 and SSP5 both have high development, but SSP5 has the most climate change impacts and the lowest population growth. However, declines in the informal sector and losses are gradual.

Fig. 7 breaks down the net change in suppressed demand by each contributing factor (informal economy, losses, GDP per capita, and temperature) and scenario in the year 2050. Different factors contribute variably to suppressed electricity demand across future scenarios for Ghana. Transmission losses have the largest impact on suppressed electricity demand across most scenarios, except AEG, highlighting the critical importance of improving grid efficiency to help curb suppressed demand growth in the future. Informal sector growth also drives suppressed demand substantially across scenarios, indicating the pressing need for policies and regulations to promote the formalisation of electricity access and curb unchecked informal expansion. At the same time, rising temperatures consistently add to suppressed electricity demand across all scenarios, reflecting growing cooling needs as climate change accelerates. While higher GDP per capita is projected to help moderately lower suppressed demand across the scenarios through its income effect, the reductions remain quite limited in magnitude compared to the significant contributions from transmission losses, informal sector growth, and rising cooling demand.Fig. 7 Contribution of each factor to suppressed demand across scenarios between 2020 and 2050.

Fig. 7

At the same time, there should be targeted reforms to formalise and regulate the informal economy, which will facilitate more efficient electricity distribution and governance, thereby helping reduce suppressed demand. Finally, demand-side management policies like smart metering, energy-efficient appliance standards, financing mechanisms for decentralised renewable energy technologies, and educational campaigns can optimise electricity use and reduce waste. Enforcing and adhering to these suggested policies can indirectly alleviate the drivers of suppressed demand and promote sustainable and universal access to electricity.

4.6 Broader contributions and limitations of the study

This study makes several important contributions that enhance the literature on suppressed electricity demand in developing countries. Methodologically, the use of quantile autoregressive distributed lag (QARDL) modelling to estimate asymmetric impacts of various factors on suppressed demand across different quantiles and the adoption of multi-scenario techniques linking socioeconomic projections to forecast suppressed demand trajectories is a novel approach in this field of literature. Given the flexibility of the methods used, they can potentially be adopted by other developing countries seeking to analyse drivers of suppressed demand and generate plausible national forecasts to inform policy.

Another key contribution is the emphasis this analysis places on the governance of the informal sector. The findings highlight how a lack of regulation and accountability mechanisms in the informal economy can substantially escalate suppressed electricity demand over time by enabling illegal connections and energy use practices. This aspect of the informal sector driving suppressed demand has received inadequate attention in the literature. Yet, it is highly policy-relevant for developing countries with sizable informal sectors. Similarly, by identifying rising cooling needs due to higher temperatures as a consistent contributor to hike suppressed demand across future scenarios, the study draws attention to an underappreciated dimension, i.e. the interlinkages of climate change with energy access and suppressed electricity demand.

However, some limitations should be acknowledged when applying the study's findings more broadly. The scenario design and analysis do not incorporate key policy aspects like subsidy policies, pricing, tariff regimes, and demand-side incentives. So, the generalizability of future suppressed demand projections across other developing countries may vary considerably depending on country-specific development contexts and policy priorities.

In addition, the timeframe selected for the analysis (2000–2020) is relatively short for time series analysis. Although this period was chosen due to the availability of consistent and reliable data, a longer timeframe would better capture long-term dynamics and trends. The Energy Commission of Ghana, primary source of the data began the data collection in 2000 on the country-specific data that we used in the study. There are no other sources of these data except from the Energy Commission.

The QARDL model, while offering nuanced insights, also comes with complexities that increase the risk of specification errors. Ensuring the robustness of the results is challenging, and errors from data construction can be compounded at this stage. Furthermore, forecasting using SSP scenarios involves assumptions and estimations that introduce uncertainties. Errors from previous stages can accumulate, impacting the reliability of long-term projections.

5 Conclusion and policy recommendations

This study highlights the critical role of strategic planning in addressing suppressed electricity demand in Ghana. It examines the impact of the informal economy and electricity transmission losses on suppressed electricity demand in Ghana from 2000 to 2020 using a QARDL model. The results of the QARDL estimation show that transmission losses have a significant and positive effect on suppressed demand. Similarly, the informal economy directly and negatively affects suppressed demand. This suggests that the unregulated sector of the economy increases suppressed demand due to non-compliance with energy appliance standards and involvement in illegal electricity connections, which limit supply, reduce revenue collection, and exacerbate total suppressed demand. Increasing temperatures are expected to significantly raise electricity demand for cooling, which will disproportionately affect marginalized and low-income groups reliant on climate-sensitive livelihoods such as agriculture, leading to additional suppressed demand. These findings underscore the scientific value added of our study. In addition, these findings can be generalized to cover countries in SSA since our development patterns are similar with high degree of informality.

The analysis also highlights the crucial role of reducing transmission losses in mitigating suppressed electricity demand in Ghana across all scenarios. Inefficient grid infrastructure with high losses is projected to be the leading driver of higher suppressed demand in various scenarios. Even under development-oriented narratives, losses substantially contribute to suppressed demand, underscoring the need for significant grid upgrades and efficiency improvements. The AEG scenario demonstrates the potential impact of infrastructure upgrades and loss reduction efforts, reducing the contribution of losses.

The study's findings emphasize the need for targeted policy interventions and infrastructural investments to mitigate the impact of rising temperatures and improve energy access and efficiency. These actions are essential to ensure sustainable energy access and to meet the growing demand effectively. The findings of this study also offer a framework for developing comprehensive energy policies that can adapt to various future socioeconomic and environmental conditions, providing actionable insights for energy system planning and policy development. Future research should aim to extend the analysis over longer timeframes and incorporate additional factors, such as rural electrification, subsidy policies and demand-side incentives, to further enhance the robustness of the findings.

CRediT authorship contribution statement

John Bosco Dramani: Writing – review & editing, Conceptualization. Kwame Ansere Ofori-Mensah: Data curation. Nathaniel Oppong Otchere: Data curation. Prince Boakye Frimpong: Writing – review & editing. Akwasi Adu-Poku: Writing – original draft, Validation. Francis Kemausuor: Supervision. Mashael Yazdanie: Supervision, Methodology.

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.

Acknowledgements

This work was supported by the 10.13039/501100001711 Swiss National Science Foundation, Switzerland (SNF) (grant number IZSTZ0_193649).

1 Technology barriers to suppressed demand emanate from the high initial cost of utilizing an energy technology such as compact fluorescent light (CFL) bulbs. Though CFL bulbs consume relatively low amounts of electricity compared to incandescent bulbs, they are expensive and most consumers are unable to purchase them. Thus, the demand for CFL bulbs is suppressed because of the high initial cost leading to low penetration of the technology.

2 Market demand represents the demand for electricity that induces structural transformation and economic growth of a country.
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References

1 Gavaldão M. Battye W. Grapeloup M. François Y. Suppressed Demand and the Carbon Markets: does development have to become dirty before it qualifies to become clean? Field Actions Sci. Rep. 2012 Special Issue 7
2 Godfrey M.M. Robiou M.P. SUPPRESSED DEMAND -- CAUSES 1996 MEASUREMEDm AND CURES
3 Abeberese A.B. Ackah C. Asuming P. Productivity Losses and Firm Responses to Electricity Shortages: Evidence from Ghana 2021
4 Abeberese A.B. Online Appendix for Electricity Cost and Firm Performance: Evidence from India 2017
5 Eberhard A. Shkaratan M. Powering Africa: meeting the financing and reform challenges Energy Pol. 42 Mar. 2012 9 18 10.1016/j.enpol.2011.10.033
6 Merkle M. Creating quantitative scenario projections for the UK shared socioeconomic pathways Climate Risk Management 40 Jan. 2023 100506 10.1016/j.crm.2023.100506
7 Lund M.T. Myhre G. Samset B.H. Anthropogenic aerosol forcing under the shared socioeconomic pathways Atmos. Chem. Phys. 19 22 Nov. 2019 13827 13839 10.5194/acp-19-13827-2019
8 Estoque R.C. Ooba M. Tukuya T. Hijioka Y. Projected land-use changes in the shared socioeconomic pathways: insights and implications [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/32378037/
9 Calvin K. IPCC, 2023: climate change 2023: synthesis report Lee H. Romero J. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team IPCC Jul. 2023 Intergovernmental Panel on Climate Change (IPCC) Geneva, Switzerland 10.59327/IPCC/AR6-9789291691647
10 IPCC IPCC AR6-WGI Atlas [Online]. Available: https://interactive-atlas.ipcc.ch/atlas
11 Rosnes O. Vennemo H. Powering up: Costing Power Infrastructure Spending Needs in Sub-saharan Africa 2009 World Bank
12 P. V. S. N. Tallapragada, “Nigeria's electricity sector- electricity and gas pricing barriers,” International Association for Energy Economics, no. First Quarter, pp. 29–34.
13 “Understanding the Informal Economy: Concepts and Trends | The Long Shadow of Informality: Challenges and Policies.” Accessed: December. 18, 2023. [Online]. Available: https://elibrary.worldbank.org/doi/abs/10.1596/978-1-4648-1753-3_ch2.
14 Basbay M.M. Elgin C. Torul O. Energy consumption and the size of the informal economy Economics 10 1 Dec. 2016 20160014 10.5018/economics-ejournal.ja.2016-14
15 Kumi E.N. The Electricity Situation in Ghana: Challenges and Opportunities 2017
16 Energy commission Energy Outlook for Ghana 2021
17 Godfrey M. Quarshie E. Robiou M.P. Suppressed demand-causes, measurement and cures Proceedings of IEEE. AFRICON ’96 2 Sep. 1996 962 967 10.1109/AFRCON.1996.563025
18 Shallal B. Domestic energy use and suppressed energy demand in hot-arid climate phd 2019 Cardiff University https://orca.cardiff.ac.uk/id/eprint/130128/
19 Mohammed N.A. Modelling of unsuppressed electrical demand forecasting in Iraq for long term Energy 162 Nov. 2018 354 363 10.1016/j.energy.2018.08.030
20 Hasanov F.J. Mikayilov J.I. Revisiting energy demand relationship: theory and empirical application Sustainability 12 7 Jan. 2020 10.3390/su12072919
21 Dias-Bandaranaike R. Munasinghe M. The demand for electricity services and the quality of supply Energy J. 4 2 1983 49 71
22 Spalding-Fecher R. Suppressed demand in the clean development mechanism: conceptual and practical issues J. Energy South Afr. 26 2 Apr. 2017 2 10.17159/2413-3051/2015/v26i2a2190
23 Lee K. Miguel E. Wolfram C. Experimental Evidence on the Demand for and Costs of Rural Electrification 2016 National Bureau of Economic Research Cambridge, MA 10.3386/w22292
24 Bahmanyar A. Estebsari A. Ernst D. The impact of different COVID-19 containment measures on electricity consumption in Europe Energy Res. Social Sci. 68 Oct. 2020 101683 10.1016/j.erss.2020.101683
25 Li Z. Ye H. Liao N. Wang R. Qiu Y. Wang Y. Impact of COVID-19 on electricity energy consumption: a quantitative analysis on electricity Int. J. Electr. Power Energy Syst. 140 2022
26 W. C. Hong, Intelligent Energy Demand Forecasting, vol. vol. 10. London: Springer.
27 Singh A.K. Ibraheem S.K. Muazzam M. Chaturvedi D.K. An overview of electricity demand forecasting techniques Netw. Complex Syst. 3 3 2013 38 48
28 Warren K. Managing uncertainty in electricity generation and demand forecasting IBM J. Res. Dev. 60 1 Jan. 2016 8:1 8:13 10.1147/JRD.2015.2496822
29 Lin B.Q. Electricity Demand in the People's Republic of China: Investment Requirement and Environmental Impact 2003 Asian Development Bank
30 Bianco V. Manca O. Nardini S. Electricity consumption forecasting in Italy using linear regression models [Online]. Available: https://www.researchgate.net/publication/222699945_Electricity_consumption_forecasting_in_Italy_using_linear_regression_models
31 Franco M. Blanco D. Blequett W. Guglia M. Alvarado E. Cointegration methodology and error correction model used to forecast the electricity demand of the Venezuelan electric system - period 2004-2024 2006 IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, Caracas, Venezuela: IEEE 2006 1 8 10.1109/TDCLA.2006.311608
32 Saravanan S. S K. T C. India's electricity demand forecast using regression analysis and artificial neural networks based on principal components IJSC 2 4 Jul. 2012 365 370 10.21917/ijsc.2012.0057
33 Mohammed N.A. Modelling of unsuppressed electrical demand forecasting in Iraq for long term Energy 162 Nov. 2018 354 363 10.1016/j.energy.2018.08.030
34 Dramani J.B. Frimpong P.B. Ofori-Mensah K.A. Modelling the informal sector and energy consumption in Ghana Social Sciences & Humanities Open 6 1 2022 100354 10.1016/j.ssaho.2022.100354
35 Cho J.S. Kim T. Shin Y. Quantile cointegration in the autoregressive distributed-lag modeling framework J. Econom. 188 1 Sep. 2015 281 300 10.1016/j.jeconom.2015.05.003
36 Godil D.I. Sharif A. Rafique S. Jermsittiparsert K. The asymmetric effect of tourism, financial development, and globalization on ecological footprint in Turkey Environ. Sci. Pollut. Res. 27 32 Nov. 2020 40109 40120 10.1007/s11356-020-09937-0
37 Godil D.I. Sharif A. Ali M.I. Ozturk I. Usman R. The role of financial development, R&D expenditure, globalization and institutional quality in energy consumption in India: new evidence from the QARDL approach J. Environ. Manag. 285 May 2021 112208 10.1016/j.jenvman.2021.112208
38 Lahiani A. Revisiting the growth-carbon dioxide emissions nexus in Pakistan Environ. Sci. Pollut. Res. 25 35 2018 35637 35645 Revisiting the growth-carbon dioxide emissions nexus in Pakistan | Environ. Sci. Pollut. Control Ser..” Accessed: June. 8, 2024. [Online]. Available: https://link.springer.com/article/10.1007/s11356-018-3524-7
39 Shahbaz M. Lahiani A. Abosedra S. Hammoudeh S. The role of globalization in energy consumption: a quantile cointegrating regression approach Energy Econ. 71 2018 161 170 10.1016/j.eneco.2018.02.009
40 Xiao Z. Functional-coefficient cointegration models J. Econom. 152 2 Oct. 2009 81 92 10.1016/j.jeconom.2009.01.008
41 Kim T.-H. White H. “ESTIMATION, inference, and specification testing for possibly misspecified quantile regression,” in maximum likelihood estimation of misspecified models: twenty years later, vol. 17 Fomby T.B. Carter Hill R. Advances in Econometrics vol. 17 2003 Emerald Group Publishing Limited 107 132 10.1016/S0731-9053(03)17005-3
42 Energy commission Energy Outlook for Ghana 2021
43 Dramani J.B. Frimpong P.B. Ofori-Mensah K.A. Modelling the informal sector and energy consumption in Ghana Social Sciences & Humanities Open 6 1 2022 100354 10.1016/j.ssaho.2022.100354
44 World Bank World development indicators | DataBank [Online]. Available: https://databank.worldbank.org/source/world-development-indicators
45 Essandoh-Yeddu J. Addo S. Attieku S. Aboagye E. Electricity demand scenarios for Ghana’s long term development plans 2017 IEEE PES PowerAfrica 2017 IEEE 291 294 Accessed: December. 18, 2023. [Online]. Available: https://www.researchgate.net/publication/318737594_Electricity_demand_scenarios_for_Ghana's_long_term_development_plans
46 World Bank Group World Bank climate change knowledge portal [Online]. Available: https://climateknowledgeportal.worldbank.org/
47 PURC, “Public Utilities Regulatory Commission, Ghana. Electricity Major Tariff Review Decision.” Accessed: April. 11, 2023. [Online]. Available: https://www.purc.com.gh/attachment/454880-20210309110313.pdf.
48 Energy Commission “National Energy Statistics.” 2022 [Online]. Available:
49 Wooldridge J.M. Introductory Econometrics: A Modern Approach 2013 South-Western Cengage Learning [Online]. Available: https://books.google.com.gh/books?id=4TZnpwAACAAJ
50 Depuru S.S.S.R. Wang L. Devabhaktuni V. Electricity theft: overview, issues, prevention and a smart meter based approach to control theft Energy Pol. 39 2 Feb. 2011 1007 1015 10.1016/j.enpol.2010.11.037
