1. Introduction

2. Materials and Methods

2.1. Screening for Materials and Information

The character of the issue at hand and the fragmented state of the current affairs motivates to map and investigate differences between multiple countries and therefore include transnational data sets. In the screening for materials and information we exclude data from case studies given their small percentage share of total available data as well as their more extended differences in the type and composition of numbers.

In the initial step of selecting the countries to be covered in our study, we consider all countries on the African continent. The number of displaced persons hosted by the different countries on the African continent varies significantly [49]. The scope of the data and insights that are available for the characterization of the respective displacement settings reflects the number of displaced persons hosted. For countries hosting a small number of displaced persons significantly less data on energy access is available [24]. This fact directs this research to countries with available data and thereby to those with more than 20,000 displaced persons, resulting in 30 African countries, jointly accounting for 99.8% of displaced persons across all African countries [49]. The countries covered by our study are listed in alphabetical order in Table 1. A list of countries excluded from the data mapping exercise based on the information available regarding the number of displaced persons hosted by the country is provided in appendix (Table A1).

Table 1. List of countries with more than 20, 000 displaced persons in January 2024 [49], countries considered in this study.

Table 1. List of countries with more than 20, 000 displaced persons in January 2024 [49], countries considered in this study.

Countries	Number of displaced persons
Algeria	102,753
Angola	55,981
Burkina Faso	1,917,317
Burundi	99,251
Cameroon	1,473,294
Central African Republic	527,348
Chad	1,080,557
Congo	97,074
Cote d’Ivoire	937,027
Democratic Republic of the Congo	6,063,761
Djibouti	30,197
Egypt	358,523
Ethiopia	4,208,422
Kenya	1,078,815
Libya	206,330
Malawi	56,560
Mali	441,449
Mauritania	106,370
Mozambique	1,060,234
Niger	716,412
Nigeria	3,379,779
Rwanda	149,218
Somalia	3,002,276
South Africa	150,912
South Sudan	2,167,672
Sudan	4,685,356
Tanzania	247,196
Uganda	4,144,589
Zambia	81,090
Zimbabwe	23,063

The mapping of available data focuses on the general energy situation on two reference levels, namely the country level and at the settlement and camp level. The aim of the mapping exercise is to comprehend the state of energy access, however, in addition to indicators that explicitly characterize the state of access to energy, we also include contextual indicators in the mapping exercise. We use contextual indicators to describe what is commonly referred to as the enabling environment (e.g., [6]). The enabling environment examines how supportive the local regulatory and policy framework for energy standard improvements is, while the energy situation focuses on the local possibilities for electricity and cooking. Applicable sources were consulted, and available data was extracted in order to understand:

• − The share of renewables in the electricity mix
• − The extent of access to electricity
• − The number of displaced persons connected to the electricity grid
• − The national electricity prices and the price estimations in displacement settings
• − The number of displaced persons using biomass for cooking.
• − The possibilities for type of cooking and lighting as well as the dominant types
• − The level of maturity of the policy framework for access to cooking and electricity as well as for renewable energies

To investigate the two stated reference levels, the data acquisition and consolidation related to country-wide and settlement specificities where data was available and possible to distinguish accordingly.

2.2. Development and Application of a Data Quality Assessment

The description of the state of energy access in displacement settings inevitably raises the question of assessing the quality of the data that we identify. We therefore include a systematic quality assessment in our study and tailor a DQA to the needs of our study.

A DQA is a procedure to characterize data by its intention, view it in its composition and assess both intention and composition based on previously established quality criteria. The DQA differentiates the data amongst data sources and assesses through a scoring system. The resulting scores describe the quality rating of the concerned data and give an overall statement on its meaningfulness and significance.

The quality of data within a DQA is measured according to quality dimensions. The quality dimensions consist of a set of indicators with a scoring range for the evaluation. The data quality information, which is expressed by the set of indicators, constitutes additional information to guide decision making processes [50].

The quality of data within a DQA is measured on the basis of quality dimensions. The quality dimension consist of a set of indicators with a scoring range for the evaluation. The data quality information, which is expressed by the set of indicators, constitutes additional information to guide decision making processes [50].

Depending on the field of application and focus of the DQA, the quality dimensions differ. the most common quality dimensions are completeness, timeliness and accuracy. DQA frameworks were developed for different objectives [51]. For example, the World Health Organization (WHO) has published a detailed approach on how to review and improve the quality of health-facility data [52]. Other sources describe methodologies and guidelines for DQA [53,54,54] or give an overview of existing frameworks [51]. While the existing methodologies can be used as guideline for the data analysis, none of the existing frameworks meets the needs of the presented study. For this reason, a novel DQA framework based on existing methods and guidelines was developed. This DQA framework included the definition of a comprehensive set of quality indicators and the definition of the respective scoring system, quality dimensions and indicators. For the developing the DQA we followed the overall outline proposed by Cameron [55]. We utilized additional literature to specify relevant data quality dimensions and respective indicators.

2.2.1. Quality Dimensions and Indicators

The data quality dimensions are the basic building blocks for characterizing the quality of data within a DQA [50]. Each dimension consists of a set of indicators, which represent the evaluation criteria for the DQA. The indicators are used to determine the extent to which a dataset fulfills the requirements of a quality dimension.

For the purpose of this study, a DQA framework was tailored to fulfill the requirements of this study. We identified five relevant dimensions to comprehensively characterize the data quality for the purpose of our study. The dimensions are Timeliness, Completeness, Accuracy, Coherence, and Interpretability. For each dimensions relevant indicators were defined. It is important to note that because of the nature of the data mapped in this study, not all indicators constitute objective measurements. The development of the DQA framework was guided by recommendations in [55].

A.

Timeliness

Timeliness is defined as the extent to which the age of data is appropriate for the task at hand [54]. The situation for displaced persons can change rapidly [40] and outdated data poses a significant decline in the data quality. The indicator used to determine the timeliness of each dataset is the number of years elapsed since the data was collected. For the study we did not specify a specific timeframe and prioritize more recent data. If a dataset was based on several sources, the source with the oldest data was used as reference.

B.

Completeness

Completeness represents the most prominent dimension for DQA [51] and is defined as the degree to which all relevant data is included in a data collection exercise [54]. Data on energy access in displacement contexts often show a lack of completeness since the data collection is associated with a specific purpose of analysis [40]. The evaluation criterion for completeness is the number of countries considered in the specific dataset compared to the number of countries considered in this analysis. Therefore, the evaluation of completeness only refers to the specific purpose of this study and does not evaluate the completeness of the data with regard to the initial purpose for which it was initially collected. Some data sources do not have the same selection of countries for each indicator. For example, the data for electricity access is available for more countries than the data on clean cooking. In that case, the indicator with lowest number of countries is considered in this work as reference.

C.

Accuracy

Accuracy is the degree to which statistical data measures what it was intended to measure [55]. The limited sources of quantitative data [40,47] often lead to simplified methodologies, such as generalizations that are based on a small number of data pointy, and undetailed analysis of the access energy situation [56] causing a low level of accuracy. In contrast to other DQA frameworks, where accuracy is mostly evaluated through statistical analysis (see [51]), in the scope of this study, the accuracy of data is determined by the following indicators:

-

Availability of additional information: Additional information can give further explanation to the dataset and thus makes it possible to determine its accuracy.

-

Accuracy of the methodology: Depending on the used methods for the data collection and analysis, the accuracy will decrease or increase. For example, a detailed field survey will lead to a higher accuracy than a dataset based on a simplified model.

-

Real-world data or synthetic data: Real-world data is more accurate than synthetic data that is based on assumptions and approximative information, e.g., as the outcome of models and simulations.

D.

Coherence

In accordance with Cameron [55], for the purpose of our study, coherence is defined as the degree to which data can be combined with other information within an analytical framework. To evaluate the coherence of data, we use the following indicators:

-

Use of common methods: The utilization of known methods facilitates the analysis of data and reduces the risk of incoherencies.

-

Incoherence within the dataset or with other sources: If possible, the dataset will be cross-checked with other sources for the detection of incoherent data.

E.

Interpretability

Interpretability is defined as the degree to which additional information is necessary to interpret and utilize the data correctly [55]. Without a thorough description of the methodology the information value and the interpretability are limited.

The first indicator to describe the interpretability is the availability of additional information. The second indicator is the suitability of the sources and used methods. This indicator expresses whether the data is based on appropriate sources and relevant methods have been applied to obtain the data.

2.2.2. Evaluation and the Scoring System

In order to evaluate and compare different datasets, a scoring system was developed based on [55]. Each dataset is analyzed by assessing its ability to fulfil the conditions set by the indicators and for every indicator a score between zero and two is assigned.

Table 2 shows the indicators of each dimension with the respective scoring system. A data source can be assigned up to two points per dimension and ten points in total.

Table 2. Evaluation matrix which comprises the quality dimensions, indicators and evaluation criteria for the characterization of data quality for the purpose of this study.

Table 2. Evaluation matrix which comprises the quality dimensions, indicators and evaluation criteria for the characterization of data quality for the purpose of this study.

Dimension	Indicator			Points
Timeliness	Number of years elapsed since the data was collected/created.		More than four years or the number of years cannot be calculated	0
			Between two and four years	1
			Less than two years	2
Completeness	Does the data include countries considered in our analysis?	No		0
		Yes	Coverage error is greater than 20 %	0
			Coverage error is between 10 and 20 %	1
			Coverage error is less than 10 %	2
Accuracy	Does the reporting source give additional information (e.g., metadata, description of the collection and analysis of data) that help to determine the accuracy of the dataset?	No		0
		Yes	The data is based on a methodology that leads to inaccurate results (e.g., models with insufficient information, assumptions).	0
			The dataset is based on an appropriate methodology that leads to synthetic data (e.g., models with sufficient information).	1
			The dataset is based on an appropriate methodology that leads to real-world data (e.g., detailed surveys).	2
Coherence	Does the dataset use a common methodology that allows to compare and use the data with datasets from other sources?	No		0
		Yes	The dataset is incoherent.	1
			The dataset is coherent.	2
Interpretability	Is a detailed description of the methodology and relevant background information (objective of the analysis, used sources and contributors) available?	No		0
		Yes	The information used for the creation of the dataset are not suitable for the considered analysis.	1
			The information used for the creation of the dataset are suitable for the considered analysis.	2

2.2.3. Data Search, Visualization and Interpretation

The data search exercise focused on two reference levels, namely the country level, and the settlement and camp level. On the country level both data that is not specific to the displacement context is included to characterize the general context and the enabling environment. Energy access data on country level is included in the Energy Progress Report [57]. Data on the country level that explicitly characterizes displacement contexts includes the Moving Energy Initiative (MEI) [58], the Humanitarian Energy Data Platform [56] and the database on the Refugee Settlements Energy Access (RSEA) [59].

Since displaced persons are rarely considered in the development of national energy policies [60], the analysis of regulations and policies on energy access is limited to data that is non-specific to displacement contexts. Accordingly, the regulatory indicators for sustainable energy (RISE) [61] do not specifically account for the displacement context but nevertheless provide valuable guidance and insights into the enabling environment. Table 3 summarizes the available sources and distinguishes by the data categories of level, information and kind of indicator.

3. Results

3.1. Data Quality Assessment

The results of the DQA show a considerable range in terms of quality and composition of the data. The UNHCR database [62] for displaced persons received the highest score with 10 points, while the dataset of the Moving Energy Initiative [58] and the Humanitarian Energy Data Platform [56] share the lowest score with three points each. The latter two represent two sources with information specifically on access to energy in displacement contexts. The remaining sources were assigned a score of seven or more points. This creates significant discrepancies between the data characterizing the host country contexts to the data characterizing the displacement contexts. The discrepancies in the quality of the data are primarily a result of low scores for in the data quality dimensions timeliness, completeness and accuracy. The results of the DQA are summarized in Table 4. A comprehensive overview of the evaluation results, including a justification for the individual scores are in included in the appendix (Table A2).

Table 4. Results of the DQA per source and dimension

Table 4. Results of the DQA per source and dimension.

Table 4. Results of the DQA per source and dimension.

Source	Score per dimension					Total score
	Timeliness	Completeness	Accuracy	Coherence	Interpretability
ESMAP [57]	1	2	1	2	2	8
GPA [56]	0	0	1	1	1	3
EU [59]	1	0	2	2	2	7
UNHCR [49]	2	2	2	2	2	10
World Bank [61]	1	1	1	2	2	7
UN OCHA [58]	0	0	0	1	2	3

A detailed description of the results of the DQA can be found in the annex.

3.2. Visualisations

3.2.1. Displaced Populations

The visualizations in this section are based on the data sets from [49] and [63]. The visualization capture all 30 countries covered by this study

The Democratic Republic of Congo (DRC) with 6.1 million displaced persons emerges as the country with the highest number followed by Sudan with 4.7 million, while Ethiopia and Uganda host 4.2 million each. Together, these four countries host nearly half of the total number of displaced persons of all selected countries (49.4%). The countries hosting the smallest numbers of displaced persons are Zimbabwe (23,063 persons), Djibouti (30,197 persons) and Angola (55,891 persons). Figure 2 shows the total number of displaced persons for all countries covered in this study. Large regional differences exist between the different regions with most displaced persons hosted by countries in East Africa, particularly in and around the horn of Africa.

To characterize the scale of displacement it is relevant to look at both the total and the relative number o displaced persons hosted by a country. Figure 3 shows the number of displaced persons hosted by a country in relation to the total population of the country. The percentage share of displaced persons is highest in South Sudan with 19,9%, followed by Somalia with 17,1%, and Sudan with 10,0%. A particularly high share of displaced persons compared to the total population of the host country is discernible in the countries of the Eastern Sahel region as well as their bordering countries to the south. Looking at the Western, Northern and Southern region of Africa, a different picture emerges as the percentages are relatively low with only Cameroon and Burkina Faso having a share of more than 5%. Overall, the shares in the selected countries differ considerably and they range from 0,1% in Zimbabwe to 19,9% in South Sudan.

To provide an indication of trends, Figure 4 shows the percentage change of the total number of displaced persons hosted per country between 2020 and 2022. In a total of twelve countries there was a decrease in the total number of displaced persons hosted between 2020 and 2022, and in eighteen witnessing there was an increase. In the DRC there was the most significant decrease, as the number of displaced persons almost halved with a decrease of 48,1%. In South Africa and Libya, the number of displaced persons hosted also declined significantly with a decrease of39,7% and 36% respectively. In contrast, in Kenya the number of displaced persons almost doubled with an increase of 96%. The second and third highest increases were recorded in Burkina Faso and Mozambique with an increase of 75.1% and 52.5% respectively.

3.2.2. Regulations and Policies

In this subsection relies on data from the Regulatory Indicators for Sustainable Energy (RISE) assessment [61]. The RISE score encompasses the multi-dimensional aspects of policies and regulations and allows to compare national policy and regulatory frameworks for sustainable energy implementations on a global scale covering 140 countries. The RISE scores allow for basic insights into the local enabling environment. It assesses countries’ policy and regulatory support based on four pillars , namely (1) access to electricity, (2) access to clean cooking , (3) energy efficiency, and (4) renewable energy [61]. The RISE assessment provides a country score on a scale between 0 (lowest possible score) and 100 (highest possible score) for each pillar. The scores for each pillar are derived from a number of tailored indicators. An overview of all indicators, as well as a description of the applied methodology is provided in [61]. In addition to the scores for each pillar, an overall score is calculated by averaging the score of the four pillars. RISE dataset does not cover all countries that are included in our study. The RISE data set includes no data on Libya and Djibouti and only partially includes data on Algeria and Egypt, for which no data on electricity access and clean cooking is available.

Figure 5 shows the overall RISE score of the countries. The overall RISE score covers all pillars and therefore encompasses multi-dimensional aspects of policies and regulations supporting access to energy activities.. There is a large spread in the overall scores between the countries considered in our study. With a score of eight, South Sudan has the lowest score and with a score of 78 Rwanda has the highest score. The data showcases regional discrepancies with predominantly high scores in the Maghreb region, Southern Africa, and East Africa. Conversely, the Sahel region showcases predominantly low overall scores, with only Nigeria surpassing a score of 50.

The RISE overall country scores are derived from the four RISE pillars. The scores for the individual pillars provide more granular insights into regulatory conditions for the four thematic fields. Three out of the four pillars are directly linked to access to clean energy and are included as visualization in this study (Figure 6-8). We consider the indicators related to energy efficiency of secondary relevance to this study and therefore do not include the respective visualization.

Figure 6 depicts the score for the pillar renewable energy in which Rwanda emerges as the country with the highest score of 91, followed by Egypt and South Africa with a score of 85 each. With a score of eight South Sudan showcases the lowest score, followed by. Burundi and Congo, which both have scores below 20. Regional discrepancies are visible, with countries in Southern Africa showcasing comparatively high scores, with the exception of Angola (42). In Western Africa, comparatively low scores prevail. With a score of 65 Nigeria is the only country in Western Africa with a score above 50

Figure 7 depicts the RISE scores for the pillar electricity access. Rwanda has the highest score (90), and South Sudan has the lowest score (8).

The scores for clean cooking scores in Figure 8 are visibly lower, with an average score of 37, compared to the average score of 48 for renewable energy in Figure 6 and of 56 for electricity access in Figure 7. Nevertheless, several countries in East Africa exhibit comparatively high scores. Kenya showcases the highest score among the countries considered with a score of 83. Within East Africa Somalia stands out with a comparatively low score of 10. Conversely, the Eastern Sahel region and their bordering countries to the south showcase low values, including Chad (17), Sudan (20), the Central African Republic (23= and South Sudan (8). Southern Africa also reveals lower clean cooking scores, inducing South Africa (27), Zimbabwe (26), and Mozambique (29). The low score of South Africa is a stark contrast to the high scores for the other pillars.

3.2.3. Access to Electricity

Host country population

For the characterization of the electricity access on the country level, we rely on data from the Energy Progress Report [57]. The Energy Progress Report allows for a differentiation between the overall electricity access rate (Figure 9), the electricity access rate of the rural population (Figure 10) and the electricity rate for the urban population (Figure 11). We consider all three indicators relevant for addressing the stated research matter. The Energy Progress Report contains data on the total energy access rate and the electricity rate of the urban population for all countries that were selected for this study (see Subsection 2.1). The data on energy access rates of the rural population in the Energy Progress Report does not cover Angola, Burkina Faso, Libya and Mauritania.

Figure 9 shows the share of the total population with access to electricity. Access rates vary greatly among the countries considered in this study and range from eight percent in South Sudan to 100 percent in both Algeria and Egypt. The majority of countries with low access rates are in Central Africa. The spectrum of electricity access rates of the rural populations, as depicted in Figure 10, showcases a strong polarization. Among the countries included in this study, only three countries have a rural electrification rate above 90%, namely Algeria, Egypt and South Africa. With only one percent of the rural population having access to electricity, Chad and DRC showcase the lowest electricity access rates among the countries considered in this study.

In contrast to the low electricity access rates in rural areas, the urban electrification in most countries is well advanced as 13 out of the 30 countries have access rates higher than 90%. Overall only four countries have an urban electrification rate below 50%, namely Central African Republic (CAR), the DRC, Chad and South Sudan.

Displaced persons

The data for the visualization in Figure 12 stems from the Moving Energy Initiative[58] and covers all concerned countries of this study. The highest shares of displaced persons with electricity grid access are in Egypt (100%), Libya (98%) and South Africa (85%). Sudan (2%), Tanzania (2%) and Malawi (0%) exhibit very low electricity access rates amongst the displaced persons. The data reveals a prevailing trend of relatively low access in most countries, with 14 nations recording less than 30% grid connectivity for displaced persons. Comparing the share of the total population of the host country with access to electricity with the share of displaced persons with access to electricity), suggests that there is a correlation. The percentage shares of the host population with access to eectrcity and the percentage share of displaced persons with access to electricity are of the same magnitude in the majority of countries (e.g., Egypt, South Africa, Côte d’Ivoire, Nigeria, and ). However, this tendency is not true for all countries. In Algeria and Kenya significant discrepancies exist between the two rates of electricity access.

Settlement and camp level

The description of electricity access on a displacement camp or settlement level as depicted in Figure 13 and Figure 14 is based on data from the Humanitarian Energy Data Platform [56]. The data details in which camps and settlements access to electricity is the residents have in principle access to electricity. . It is important to note that characterizing electricity access is more complex than what the visualizations suggests. First, electricity access is not a binary condition. Describing energy access in relation to the tier of access is more relevant. A low tier of electricity access means that only basic appliances can be operated for a limited time per day. Second, high electricity access rates of settlements do not automatically translate to all displaced persons having access to electricity. A number of barriers, such as financial barriers may prevent persons accessing electricity, even though in principle electricity is available in a settlement or camp. This source encompasses a total of 74 camps, situated across 16 out of the 30 selected countries It is important to note that the camps and settlement covered by the data base only represent a minority of overall camps and settlements within the studied countries. A review of the data in [58,59] suggests that 219 camps and settlements exist in the countries selected for this study., According to the available data, the majority of the camps and settlements (70%) lack any form of access to electricity. In turn, 26% of the camps and settlements report partial access to electricity and only4% have access to electricity (see Figure 13).

Figure 14 provides country-specific insights and shows significant disparities between the countries. Only in three camps and settlements among the locations for which data is available access to electricity is available (locations in Algeria, Congo, and Sudan). A lack of electricity access is revealed in all considered camps and settlements situated in Djibouti, Democratic Republic of Congo (DRC), Ethiopia, Rwanda, South Sudan and Tanzania.

3.2.4. Access to Clean Cooking and Lighting

Country population

For the characterization of the state of energy for cooking on a country level we rely on data from the Energy Progress Report [57]. The available data cover all considered countries, except for Libya. The available data is visualized in Figure 15, which reveals considerable differences regarding the access to clean cooking across the selected countries. Similar to the trends observed for the electricity access in Figure 9, countries within the Maghreb and South Africa exhibit high percentage shared of access to clean cooking, with Algeria and Egypt achieving full coverage (100%). There is no clear correlation between the data insights on access to electricity (Subsection 3.2.3) and access to clean cooking.

The available data allows to further distinguish between the rural population (Figure 16), and the urban population (Figure 17). When considering the clean cooking access rate of the rural population, is can be noted that among the selected countries, only four countries (Algeria, Egypt, Sudan, and South Africa) showcase clean cooking access rates exceeding 50%. In contrast, 24 out of the 29 selected countries exhibit clean cooking access rates of the rural population below 10%. Moreover, 10 countries within the dataset report an absence of any clean cooking access in rural regions.

With regard to access to clean cooking in urban environments, the Maghreb region and South Africa showcase high rates with rates of at least 96%. The countries in the Sahel region showcase a significant discrepancy between urban and rural environments, with considerably higher rates of clean cooking access rates among the urban population

Displaced persons

The state of access to clean cooking of displaced persons is visualized in Figure 17, and is based on data from [58]. The available data differs from the data that is used to characterization of the state of clean cooking in the host countries (Figure 15-17), which is why a different indicator is used, namely the usage of cooking fuels other than biomass. The usage of non-biomass fuels for cooking is therefore used as an alternative description of clean cooking. The dependency on biomass among displaced persons differs significantly between the host countries included in this study. Algeria has the highest share of non-biomass usage with 99%, whereas South Sudan and Malawi do not have any displaced persons using cooking fuels other than biomass. Overall, significant differences between exist between the countries included in this study. In the Sahel region displaced persons are predominantly dependent on biomass. The Maghreb region and South Africa stand out with high shares of displaced persons with access to clean cooking. The high shares of access to clean cooking is mirroring the percentage share of the host population with access to clean cooking. Other countries, such as such as South Sudan, Uganda, the Central African Republic (CAR), Chad, and Mozambique, also show similar access rates amongst the host population and the displaced persons hosted.

Settlement and camp level

The characterization of the state of access to clean cooking relies on data from [56,58] The available data cover 146 camps in 20 out of the 30 selected countries, which poses a significant limitation in the coverage of the data. As shown in Figure 19, firewood is the primary source of cooking fuel in camps and settlements (61 %).In total 35 percent of the camps and settlements, for which data is available, are characterized as using a combination of firewood and other higher-tier fuels. Only 4% of setlltments and camps do not depend on firewood and employ exclusively alternative cooking fuels such as gas, biomass or briquettes.

In addition to the overall statistic, the available data allows for a country-specific characterization of the array of cooking fuels used per camp. A country-specfic examination of the type of cooking fuel used within camps and settlements reveals that firewood emerges as an exclusive cooking fuel in several countries. Notably, the camps and settlements in Cameroon, the Central African Republic (CAR), Djibouti, the Democratic Republic of Congo (DRC), and Somalia depend solely on firewood for their cooking needs. There are however examples of countries with a relatively diverse use of cooking fuel. For example, Chad, Ethiopia, and Rwanda have at least four different types of cooking fuels used in camps and settlements.

Lighting access at the settlement and level

In addition to data on clean cooking on a settlement and camo level, we present data on lighting in settlements and camps. The characterization of dominant types of lighting in settlements and camps is based on data from the Moving Energy Initiative [58]. The data is visualized in Figure 20 and cover a total of 115 camps from 18 out of the 30 considered countries. Figure 20 shows that overall, in the majority of settlements and camps the displaced persons primarily dependent on torches for lighting. Only in 23 percent of settlements and camps are the majority of displaced persons use liquid fuel as source for lighting and only in10 % of the settlements and camps the majority of displaced persons used solar lighting.

The comparison of dominant types of lighting in the camps and settlements between the different countries for which data is available reveals differences. . Out of the 18 countries for which data is available, in 13 countries in the camps and settlements displaced persons are primarily dependent on torches for lighting. Sudan is the only country covered by the dataset, in which displaced persons in all settlements and camps are primarily dependent on liquified fuels. Ethiopia stands out as the only country among the considered nations with settlements and camps in which the displace persons primarily utilize solar energy for lighting.

3.2.5. Livelihood Possibility - Right to Work and to Move in and out of the Camp/Settlement

The right to work as well as the possibility to move in and out of the camp or settlement are for the overall legal status of displaced persons in a country and are prerequisites for most livelihood generating activities. We include visualization of the data on the right to work and the right move freely from Humanitarian Energy Data Platform [58]. Figure 23 shows the percentage of settlements and camps in which the residents have the right to work and distinguishes between countries that have signed the Comprehensive Refugee Response Framework (CRRF) and countries that have not signed the CRRF. The CRRF is a United Nations framework designed to make the response to displacement more just and sustainable [64].

Mirroring the structure of Figure 23, Figure 24 shows the percentage of settlements and camps in which the residents have the right to move freely in and out of the settlement or camp and distinguishes between countries that have signed the and countries that have not signed the framework. The right to work exists in 55% to 57% of camps and settlements of the considered countries with only a slight variation depending on the country being a signatory to the CRRF. The freedom to move varies between 53% and 62% for countries having signed the CRRF versus not.

Figure 22. Type of lighting in displacement camps and settlements by country. Own compilation based on data in [58], accessed in January 2024.

Figure 22. Type of lighting in displacement camps and settlements by country. Own compilation based on data in [58], accessed in January 2024.

Figure 23. Percentage camps and settlements in which residents have right to work and percentage of countries that are signatories of the Comprehensive Refugee Response Framework (CRRF). Own compilation based on data in [58], accessed in January 2024.

Figure 23. Percentage camps and settlements in which residents have right to work and percentage of countries that are signatories of the Comprehensive Refugee Response Framework (CRRF). Own compilation based on data in [58], accessed in January 2024.

Figure 24. Percentage camps and settlements in which residents have right to move in and out of camp and percentage of countries that are signatories of the Comprehensive Refugee Response Framework (CRRF). Own compilation based on data in [58], accessed in January 2024.

Figure 24. Percentage camps and settlements in which residents have right to move in and out of camp and percentage of countries that are signatories of the Comprehensive Refugee Response Framework (CRRF). Own compilation based on data in [58], accessed in January 2024.

3.2.6. Project Activities on Access to Energy in Displacement Contexts

Figure 25 visualizes data from the Humanitarian Energy Data Platform which covers 27 out of the 30 countries selected. Algeria, Egypt and the CAR and not included in the data set. The number of projects on energy access in displacement contexts differs considerably across countries. East Sahel and East Africa, notably Ethiopia and Kenya with 136 projects each, represent the regions with the highest number of energy-related projects. Ethiopia, Kenya, Chad, and Sudan collectively account for nearly half of all the projects with 49% of the 27 countries. Consequently, there are several countries which exhibit a very limited number with South Africa, Congo and Mali representing the lowest values at only one project each. Overall, it can be noted that the number of projects per country remains quite modest, with nine countries having fewer than 10 projects.

4. Discussion

5. Conclusions

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