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 1. Home
 2. Social Sciences
 3. Geography, Planning and Development
 4. DOI:10.20944/preprints202403.1470.v1

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ENHANCING UNDERSTANDING THROUGH DATA VISUALIZATION: WHAT CAN AVAILABLE DATA
REVEAL ABOUT ACCESS TO ENERGY IN DISPLACEMENT CONTEXTS ON THE AFRICAN CONTINENT?

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A peer-reviewed article of this preprint also exists.


Tim Ronan Britton  *

,Philipp Baslik,

Lena Anna Schmid

,

Boris Heinz


Tim Ronan Britton  *

,Philipp Baslik,

Lena Anna Schmid

,

Boris Heinz

Show more

This version is not peer-reviewed

ABSTRACT

The extent of access to energy of displaced populations in settlements and camps
in Africa is largely unknown, given 94% of displaced persons without access to
electricity and 81% with a reliance on biomass for cooking. A multitude of
contextual factors, such as the location and the characteristics of housing, the
legal status, the socio-cultural background and the availability of humanitarian
and public services impact the living conditions and the needed energy services.
Limitations in accessing energy services have direct, multilayered, and
far-reaching implications, including impacts on health, nutrition, education,
protection, and livelihood. The objective of this article is to contribute to a
more comprehensive understanding of the current state of energy ac-cess in
displacement contexts on the African continent by identifying and utilizing
existing data. After a screening of the vast and various available information,
setting up of a database, consoli-dating the gathered data as well as assessing
the quality through a quality assessment method, the currently available
information is visualized and discussed. Remarkable differences in the access to
electricity for displaced persons across the countries are found. For both
electricity and clean cooking, the availability for displaced persons ranges
from nearly no access at all up to an access rate of 100%. More strikingly, the
results also show that besides South Africa, and the se-lected countries in the
Maghreb region, the access to both clean cooking and electricity for dis-placed
persons is remarkably low. At the same time, the poor data quality does not
allow to draw solid conclusions nor impactful implementation activities. Novel
conceptual frameworks and indicators are needed. Future research needs to focus
on a more comprehensive understand-ing of how energy is interwoven in the lives
of displaced persons, before a set of energy indicators can be derived. It is
essential that the concerned persons, the displaced persons themselves are
in-cluded in the research in a meaningful way.
Keywords: access to energy; displacement contexts; humanitarian settings; data
assessment; data visualization; electricity; cooking
Subject: Social Sciences  -   Geography, Planning and Development


1. INTRODUCTION

1.1. THE CONTEXT OF DISPLACEMENT AND ACCESS TO ENERGY

Global crises such as climate change, conflict and natural disasters have
resulted in a growing number of persons who were forced to leave their home.
UNHCR, the UN refugee agency, estimated that in 2024 there will be 130.8 million
displaced persons in the word [1]. Displaced persons are “persons or groups of
persons who have been forced or obliged to flee or to leave their homes or
places of habitual residence, either across an international border or within a
State […]” [2]. The term displaced persons includes but is not limited to
refugees, asylum seekers and internally displaced persons (IDPs), for which
relevant definitions can be found elsewhere [2,3,4]. Displacement has
implications not only for displaced persons themselves, but also for those
hosting them, which are often referred to as the host country and host
community. With 134 countries currently accommodating displaced persons [5],
active engagement with the displacement context is a global matter and it is a
matter that is growing in scale. As is shown in Figure 1, in the last decade the
number of displaced persons has continuously increased.
Figure 1. Evolution of the number of refugees, asylum-seekers, IDPs and other
persons of concern. Own compilation based on data from UNHCR [1].
While cumulative global statistics are relevant to highlight the increasing
relevance of the subject matter, it is essential to acknowledge that the lived
experiences of displaced persons vary greatly between different host countries
and contexts. A multitude of contextual factors, such as the location and the
characteristics of housing (settlement, camp-like, urban, rural, etc.) [6], the
legal status of displaced persons [7], the socio-cultural background and the
availability of humanitarian and public services have a major impact on the
conditions of living. In our study we focus on displaced persons residing in
settlements and camps. While in the humanitarian space there is no universally
agreed upon definition for the term settlement, we recognize that the terms
settlement and camp are in many cases used to describe settings with different
attributes (e.g., [4,6,8,9,10,11]) and we agree that the differentiation adds a
valuable dimension to the discourse.
One key factor determining the conditions of living is access to energy [12]. In
humanitarian settings different energy needs coexist. First, energy is needed to
enable the operation of humanitarian actors, such as electricity in the offices
of humanitarian organization and fuel for transportation [6,12]. Second,
displaced persons have a wide range of energy needs that are embedded in basic
areas of life [13], characterized by energy needs on a household level, such as
for electricity and cooking, energy needs for income generating activities or
businesses and energy needs on the community level, such as streetlights and the
operation of public facilities such as schools and hospitals [13]. In this
article, we focus on energy needs of displaced persons on an individual,
household and community level as opposed to generalized energy needs that are
associated with humanitarian operations.
The critical importance of the availability of clean, reliable, and affordable
energy services for a qualitive life is reflected in Target 7.1 of the
Sustainable Development Goals (SDGs) [14], which now explicitly includes
displaced persons [15]. Nevertheless, despite this critical importance, there
are fundamental shortcomings in the majority of displacement contexts. The
Global Platform for Action, a working group hosted by the United Nations
Institute for Training and Research (UNITAR), estimated that in 2022, 94 percent
of displaced persons living in settlements and camps did not have access to
electricity, and 81 percent of displaced persons living in settlements and camps
relied on biomass for cooking [16]. The limitations in accessing energy services
have direct, multilayered, and far-reaching implications, including impacts on
health, nutrition, education, protection, and livelihood [6,17,18,19]. For
example, food security is directly linked to a reliable fuel source [17], the
use of firewood for cooking is linked to health risks [20] - especially
respiratory and eye diseases [7] - and the lack of lighting in public spaces is
associated with conflicts, and sexual and gender-based violence [21,22].
Besides the direct impact on wellbeing of displaced persons, there is a
multitude of downstream impacts associated with the limitations in access to
energy. A significant challenge is the high dependency on biomass for cooking
leading to environmental degradation [6]. In some locations this has caused
camps being cleared of vegetation in a radius of several kilometers [23]. The
resulting scarcity of resources in turn can fuel conflicts, the consequences of
which are especially severe for vulnerable members of the respective communities
[23].
Therefore, international development organisations emphasize and recent
publications [16], [24] underline, that the affected African settlements have
the lowest levels of access to energy in displacement settings and this is,
where the most progress is needed to be made. While we acknowledge that research
of energy access in displacement settings is critical in all regions of the
globe, for pragmatic reasons, this study focuses on the African continent to
handle the various information sources and the vast array of data. Also, the
majority of the displacement settings in African countries are not connected to
an electricity grid whereas grid connection can frequently be found in other
parts of the world that have large numbers of displaced persons, specifically in
West and South Asia [16]. Furthermore, the African continent is by far hosting
the most displaced persons [1], which underpins the decision to focus our
research on this continent.
While global energy access indicators regarding electricity and clean cooking
are relevant to showcase the overall scale of the challenge, it is important to
note that access to energy is not a binary condition – grid connected vs. not,
cooking with firewood vs. not - but appears on a spectrum [25]. The Multi-Tier
Framework (MTF) of energy access introduced a multidimensional framework that
allocates energy access conditions to different tiers, in which Tier 0
constitutes no access at all and Tier 5 constitutes the highest level of access
[25]. In addition, it is significant that access to energy in itself is not a
value in isolation, but rather a precondition to satisfy multiple energy service
needs [26].
A further essential consideration regarding access to energy is the diversity of
energy demand [18]. The contextual diversity of displacement settings is
mirrored by the contextual diversity of the energy demands, the challenges that
are associated with limitations in energy conditions and preferences of
displaced persons. In the case of cooking, the associated challenges may be
influenced by “culture, geography, season, fuel type, local practices and
general awareness” [27]. Consequently, energy needs are not only determined by
the descriptive characteristics of a settlement, but also by individual
circumstances and individual preferences. The growing recognition and the
increased emphasis of the diversity of energy needs (e.g. [18,27,28,29]) is not
addressable by the predominantly technology-focused and top-down energy
interventions in settlements and camps.

1.2. THE LEVELS OF DATA AND THE CURRENT STATE OF AFFAIRS

In contrast to other sectors of the humanitarian response system, such as
waster, health and shelter, the provision of energy services is not
operationalized by established institutional actors [17,18,30] and energy has
historically not been a priority in humanitarian response [19]. Mirroring the
increased recognition of the central importance of access to energy on a global
level, efforts to further this challenge in displacement settings have increased
in the last decade. Thomas et al. [19] provided an overview of the most relevant
institutional initiatives, including the UNCHR’s commitment in 2019 to enable
Tier 2 electricity access by 2030 to all refugees as part of the so called Clean
Energy Challenge. The increased recognition of the essential importance of
energy in humanitarian settings is also reflected in the growing number of
scientific publications [24]. However, the overall scientific engagement with
the subject remains limited. Rosenberg-Jansen [24] identified a total of 115
relevant scientific documents in a literature review in 2022. The limitations in
scientific publications accompany limitations in available data. For a small
number of host countries case studies were conducted and published in scientific
journals that allow basic insights into the contextual conditions of energy
access in selected settlements and camps [17,31,32,33,34,35,36,37], while case
studies documented in grey literature provide additional selective insights
[24].
Both in the research and in the humanitarian response field, the critical role
of data has repeatedly been highlighted [31,38,39,40]. In a consolidating report
on the state of energy access in displacement settings, the GPA highlighted the
need for evidence and data to inform systemic change as one of their key
messages [16], which picks up on the Working Area Data, Evidence, Monitoring and
Reporting (Working Area V) that the GPA had outline in the Framework for Action
in 2018 [40]. Establishing a realistic and comprehensive overview of the energy
access situation is key to inform action on multiple levels. It is essential for
policy making to foster integration of displaced persons in national energy
access plans [41], for humanitarian assistance to inform planning and daily
operation and for research to further novel concepts and approaches [24] and for
donors to establish relevant funding programs. Therefore, accurate and detailed
data is significant to establish a comprehensive understanding of the diversity
of energy-related lived realities of displaced persons, which is a precondition
for relevant action and intervention on all levels.
Several initiatives have contributed to further the systematic collection and
sharing of data. One of the first initiative to systematically assess the
multifaceted role of energy in displacement settings was the Moving Energy
Initiative, introduced in 2015 by a consortium of organizations and hosted by
Chatham House [42]. In 2017 UNHCR initiated the Integrated Refugee and Forcibly
Displaced Energy Information System, which monitors the progress of improved
access to energy [43]. In 2018 the GPA was founded, which has taken up
coordination activities, highlighted pathways to efficiently improve energy
access and has published contextual insights as part of the READS program [44].
The Humanitarian Data Exchange platform, which is hosted by OCHA, includes data
sets on energy access in settlements and camps [45].
Despite these recent efforts to improve the information base on energy access in
displacement settings, the insights, and the data on energy access in
settlements and camps remains dispersed and fragmented. Numerous scientific
articles state the lack [16,39,46] and the poor quality [24,39,47] of available
data, which is rarely harmonized across the levels from individual over communal
to regional and international spheres and therefore insights differ, and
comparisons are barely justifiable. While one source might describe the
individual level in a country while another one might do so too but uses
different definitions of the type and the units of data, what hinders
integration and joint usage. Because of the general lack of data and the lack of
cohesion, the state of energy access in settlements and camps in large parts are
unknown. This is a fundamental limitation as a comprehensive information basis
constitutes a basic building block to further energy access in displacement
settings from a policy, research and implementation perspective.
The objective of this article is to contribute to a more comprehensive
understanding of the current state of energy access in displacement settings by
identifying and utilizing existing data. After a screening of the vast and
various available information, setting up of a database, consolidating the
gathered data as well as assessing the quality through a quality assessment
method, the currently available information is visualised and discussed. As far
as the overview allows, conclusions are drawn, as well as current content and
structure to the initial and pressing issue of improving access to energy for
displaced persons are mirrored. Two research questions are addressed:
(1) What insights can be gained from the available data on access to energy in
displacement contexts in several countries of the African continent?
(2) What are the limitations?
(3) What are the differences between multiple countries?
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. The claim of limited data
availability is tested. While available data from case studies are excluded
given their small percentage share of total available data as well as their more
extended differences in the type and composition of numbers, the identification
of relevant data for 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 data quality assessment procedure to the needs of our
study.
The number of displaced persons varies considerably by country [48] and so does
the available information about the related displacement setting. Countries with
a smaller population of displaced persons show little to no available data on
energy access [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 result, jointly accounting for 99.8% of displaced persons
across all African countries [48]. The countries of this study are listed in
Table 1.
Table 1. List of countries with more than 200,000 displaced persons in January
2024 [48], countries considered in this study.


2. MATERIALS AND METHODS

A desk research exercise for data acquisition and consolidation was conducted to
map the energy situation in displacement contexts. Online databases, project
reports, scientific publications as well as web resources of implementing
organizations were reviewed for available data.

2.1. SCREENING FOR MATERIALS AND INFORMATION

The mapping focuses on the general energy situation at the settlement and at the
country level including indicators of the enabling environment. The part on 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 amount of displaced persons that is connected to the electricity grid
-
The national electricity prices and the estimations in displacement settings
-
The amount of displaced persons using biomass for cooking.
-
The possibilities for type of cooking and lighting and the dominant types
-
The level of maturity of the policy framework for access to cooking and
electricity as well as for renewable energies
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

A data quality assessment (DQA) is a procedure to grasp data by its intention,
view it in its composition and assess 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 stand
for the quality rating of the concerned data and give an overall statement on
its meaningfulness and significance.
The part within the DQA which considers the data composition corresponds to the
quality dimensions which consist of several indicators with a scoring range for
the evaluation. The indicators are used to determine the extent to which a
dataset is able to meet the related quality dimension.
Depending on the field of application and focus of the DQA, the quality
dimensions are variable. Various DQA frameworks were developed for different
matters [49]. For example, the World Health Organization (WHO) has published a
detailed approach on how to review and improve the quality of health-facility
data [50]. Other sources describe methodologies and guidelines for DQA
[51,52,52] or give an overview of existing frameworks [49]. While the existing
methodologies can be used as guideline for the data analysis, none of the
existing frameworks meets the needs of the current 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.
Country level
The analysis on the country level is subdivided into two categories. First, a
general overview of energy access of the considered countries by visualizing the
access to electricity and clean cooking in rural and urban areas and by
evaluating the current regulations and policies related to energy access. This
information is relevant to this study to examine how the technological progress
and the political environment of a country influence the access to energy for
displaced persons. Second, a specific overview by analyzing the access to
different types of energy forms for displaced populations. Looking at energy
access for displaced persons on a country level, it is possible to analyze
differences between host countries.
Camp and settlement level
For this analysis, the focus is on access to electricity and clean cooking in
refugee camps. Comparisons within a country but also transnational comparisons
will be included in the evaluation. This evaluation is particularly relevant as
it gives more detailed information on the different lived realities for
displaced persons from one camp to another and from one country to another.

2.2.1. QUALITY DIMENSIONS AND INDICATORS

The measurement tool to indicate the quality of data within a DQA are data
quality dimensions. Each of the dimension consists of several indicators, which
represent the evaluation criteria for the DQA. The indicators are used to
determine the extent to which a dataset is able to respond to the requirements
of a quality dimension.
For the purpose of this study, an own DQA framework that fits the requirements
for this study was created. The methodology behind the framework is based on a
work by Cameron [53]. After a thorough review of the existing quality
dimensions, the following five dimensions are considered as the most relevant
for data on energy access in displacements settings and included in the DQA:
A. Timeliness
Timeliness is defined as the extent to which the age of data is appropriate for
the task at hand [52] and obviously plays an essential role, as the situation
for displaced persons may change rapidly [39]. The indicator used to determine
the timeliness of each dataset is the number of years elapsed since the data was
collected. 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 as described
elsewhere [49] and is defined as the degree to which all relevant data is
included in a data collection [52]. Data on energy access in displacement
settings often show a lack of completeness since a lot of organizations only
collect information that is relevant for their own purpose and activities [39].
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
regards to the initial purpose for which it was initially collected for. 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
Another challenge depicts the accuracy of data. The limited sources of
quantitative data [39,46] often lead to simplified methodologies and undetailed
analysis of the access energy situation [54] causing a low level of accuracy.
Accuracy is the degree to which statistical data measures what it was intended
to measure [53]. In contrast to other DQA frameworks, where accuracy is mostly
evaluated through statistical analysis [49], 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 [53] coherence is defined as the degree to which data
can be combined with other information within an analytical framework. This work
includes the combination of datasets from different sources so that the
coherence of the used data becomes an essential parameter not only for the data
quality but also for the quality of our analysis. In order 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
Some data are published without additional information or explanations of the
methodology which causes a broad spectrum of possible interpretations. For a
detailed analysis, it is however crucial to be able to interpret any data
correctly. The quality dimension of interpretability is defined as the degree to
which additional information is necessary to interpret and utilize the data
correctly [53].
Here, the first indicator is the availability of additional information. In
particular, a detailed description of the methodology is necessary to interpret
data. The second indicator that is considered is the suitability of the sources
and used methods. This indicator allows to evaluate if the data is based on
appropriate sources and methods that have the capacity for the analysis at hand.

2.2.2. EVALUATION AND THE SCORING SYSTEM

In order to evaluate and compare different datasets, a scoring system based on
previously described methodology [53] is used. Each dataset is analyzed by
assessing its ability to fulfil the conditions set by the indicators. This
allows to give a score ranging from zero to two for each dimension, with zero
being the lowest possible value and two the highest possible value to reach.
Table 2 shows the indicators of each dimension with the respective scoring
system. A maximum score of two points can be reached for each dimension
resulting in a total maximum score of ten points for the DQA.
Table 2. Evaluation matrix.

2.2.3. DATA SEARCH, VISUALIZATION AND INTERPRETATION

Energy access data on country level is included in the Energy Progress Report
[55]. It depicts the situation in displacement contexts in the datasets of the
Moving Energy Initiative (MEI) [56], the Humanitarian Energy Data Platform [54]
and the database on the Refugee Settlements Energy Access (RSEA) [57] published
by the European Commission.
Since displaced persons are rarely considered in the development of national
energy policies [58], the analysis of the regulations and policies on energy
access can only be done on the country level and in turn, this regulatory and
policy environment includes displacement contexts too. Accordingly, the
regulatory indicators for sustainable energy (RISE) [59] 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.
Table 3. Selected sources for the visualization of current data on energy access
in displacement settings.


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 [60] for displaced persons received
the highest score with 10 points, while the dataset of the Moving Energy
Initiative [56] and the Humanitarian Energy Data Platform [54] 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 given a score of seven or more points and this creates a
significant gap between the data for the host country contexts contrary to the
displacement context. The current analysis brings out that this is caused by the
scores for the dimensions “timeliness”, “completeness” and “accuracy”. The data
from the sources [54,56] scored 0 and 1 points respectively, and the sources
[55,57,59] reach 3 points for the aforementioned three dimensions. The
information is gathered in Table 4.
Table 4. Results of the DQA per source and dimension.
A detailed description of the results of the DQA can be found in the Appendix A.

3.2. VISUALISATIONS

3.2.1. DISPLACED POPULATIONS

The visualizations in this section are based on the data sets from [48] and [61]
they capture the 30 countries of 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 with the smallest displaced
populations are Zimbabwe with 23,063, Djibouti with 30,197 and Angola with
55,891 displaced persons. Figure 2 illustrates these facts by the total number
of displaced persons in each of the countries. Large regional differences can be
noted between East Africa, particularly in and around the horn of Africa, as the
region with the highest number of displaced persons, compared to Western, North
and South Africa in descending order. Djibouti is the single country hosting
less than one million displaced persons. The Maghreb region and Southern African
countries show smaller numbers, apart from Mozambique. In Western Africa,
considerable differences are notable with the two extremes of Mauritania and
Nigeria representing 106,000 and 3.4 million displaced persons respectively.
Figure 2. Number of displaced persons by country in third quarter of 2023, in
thousands. Own compilation based on data in [48], accessed in January 2024.
Figure 3 puts the data of Figure 2 in another perspective by showing the
percentage of displaced persons compared to the total population of the host
country. It can be observed that the 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. In this region, only Ethiopia
shows a value less than 5%. 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 South Sudan, where every fifth individual is a displaced person.
Figure 3. Share of displaced persons in the total population of the host
country. Own compilation based on data in [48], [61], accessed in January 2024.
Similar to the previous analyses, an examination of the country-specific shares
for the years from 2020 to 2022 reveal twelve countries experiencing a decrease
and nineteen witnessing an increase. DRC registered the most significant
reduction, as the number of displaced persons reduced by nearly half with a
decrease of 48.1%, followed by a reduction in South Africa of 39.7% and Libya by
36%. In contrast, Kenya nearly doubled its number of displaced persons with a
rise of 96%, followed by Burkina Faso and Mozambique who show a notable increase
of 75.1% and 52.5%. A closer look at the Sahel region reveals a substantial rise
in each country with the lowest percentage change noted in Mauritania of 11.3%.
Please refer to Figure 4 for insights across all 30 countries.
Figure 4. Changes in the share of displaced persons in the total population of
the host country and for the period of 2020 to 2022. Own compilation based on
data in [48], [61], accessed in January 2024.

3.2.2. REGULATORIES AND POLICIES – THE ENABLING ENVIRONMENT OF THE COUNTRY

This subchapter deals with the data of the Regulatory Indicators for Sustainable
Energy (RISE) evaluation [59] and points out the state of regulations and
policies regarding access to energy in the countries. 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. It assesses countries’ policy and regulatory support for each
of the four pillars of sustainable energy - access to electricity, access to
clean cooking (for 54 access-deficit countries), energy efficiency, and
renewable energy. The RISE dataset does not include the countries of Libya and
Djibouti in general and it does not provide for data on electricity access and
clean cooking for Algeria and Egypt.
Figure 5 presents these scores which encompass multi-dimensional aspects of
policies and regulations supporting access to energy activities with 0 being the
lowest and 100 the highest performance. RISE scores reflect a snapshot of a
country’s policies and regulations in the energy sector, organized by the three
pillars of sustainable energy: Energy Access, Energy Efficiency, and Renewable
Energy. The overall scores are spread widely with South Sudan recording the
lowest score of 8 whereas Rwanda emerges as the country with the highest score
of 78. Regional patterns unveil relatively high values in the Maghreb region
pointing out Algeria with 68 and Egypt with 76, South Africa with 64 and East
Africa with countries such as Ethiopia, Kenya, Uganda, Rwanda, and Malawi with
overall scores above 50. Conversely, the Sahel region reports mainly low overall
scores, with only Nigeria surpassing a score of 50. It can further be noted that
a limited number of countries report very low or very high scores as there is
only one country with a score below 10 and three countries which achieved scores
above 70.
Figure 5. RISE overall score on the country regulatory and policy environment in
2021. Own compilation based on data in [59], accessed in January 2024.
Figure 5 shows the RISE overall scores while the following figures provide more
detailed insights by looking into the various thematic pillars and associated
scores of the RISE evaluation. Figure 6 illustrates the scoring in the pillar
renewable energy in which Rwanda emerges as the country with the highest score
of 91, followed by Egypt and South Africa with 85. Identically to the order of
countries in the overall scores in Figure 5, South Sudan reports the lowest
score of 8 while Burundi and Congo are the two other countries with scores below
20 in Figure 6. Regional patterns become apparent, with Southern Africa
showcasing predominantly high scores, apart from Angola with 42. In Western
Africa, low scores prevail since no country in the region surpasses a score of
50, except for Nigeria with a score of 65. Some countries show considerable
differences compared to the overall score such as South Africa with 85 whereas
its overall score is 64.
Figure 6. RISE score for the pillar renewable energy in 2021. Own compilation
based on data in [59], accessed in January 2024.
The pillars electricity access and clean cooking are presented in Figure 7 and
Figure 8, and as mentioned previously they do not provide for scores for the
countries of northern Africa, specifically Egypt and Algeria, which were however
considered for the renewable energy pillar.
Figure 8. RISE score for the pillar clean cooking in 2021. Own compilation based
on data in [59], accessed in January 2024.
Figure 7. RISE score for the pillar electricity access in 2021. Own compilation
based on data in [59], accessed in January 2024.
The analysis of electricity access regulations and policies results in the
highest average score among all considered RISE pillars as average score of the
selected countries is 56. Figure 7 reveals that Rwanda stands out with the
highest score of 90, whereas South Sudan reports the lowest score at 8. Most
countries in East Africa demonstrate high scores with values of at least 68 for
Kenya, Ethiopia, Tanzania and Uganda. However, discrepancies are also visible in
this region as Somalia with 31 and Burundi with 39 received comparably low
scores. Noteworthy variations emerge within Western Africa, where Nigeria and
Côte d’Ivoire exhibit scores above 60, whereas Mauritania and Mali show lower
scores of 25 and 45, respectively.
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 high numbers with a minimum of 50 in Tanzania and Kenya
showcasing the highest score at 83. As shown in Figure 8, only Somalia stands
out as an exception with a notably lower score of 10. Conversely, the Eastern
Sahel region and their bordering countries to the south report low values with
Chad of 17, Sudan of 20, the Central African Republic of 23 and South Sudan of
8. Southern Africa also reveals lower clean cooking scores, with South Africa at
27, Zimbabwe at 26, and Mozambique at 29. Particularly in the case of South
Africa, this low score is surprising as the country exhibited high scores for
the other pillars.

3.2.3. ACCESS TO ELECTRICITY

Host country population
For the visualizations of the electricity access on the country level, the data
of the Energy Progress Report [55] was used. While this dataset covers all
selected countries in its evaluation of the electricity access for the total
population and the urban population, it does not include data for Angola,
Burkina Faso, Libya and Mauritania when analyzing access to energy in rural
areas.
Assessing the share of the total population with access to electricity reveals
substantial disparities among the selected countries. Figure 9 illustrates these
differences regarding electricity access and ranging from 8% in South Sudan to
100% in Algeria and Egypt. In contrast to the Maghreb region and South Africa
where the access rate is at least 70%, the countries in Central Africa represent
the lowest percentages that do not surpass 20%. These differences are even more
visible when we look at the access to electricity in rural areas. As illustrated
in Figure 11, only three countries have a rural electrification rate above 90%,
namely Algeria, Egypt and South Africa. Furthermore, the previously mentioned
disparity between the selected countries is now even further stretched as only
1% of the rural population of the Chad and the DRC have access to electricity.
Another notable pattern emerges as eight countries exhibit rural electrification
rates below 10%.
Figure 9. Share of the total population in the country with access to
electricity in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.
Figure 11. Share of population in the country with access to electricity in the
urban area in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.
Figure 8. RISE score for the pillar clean cooking in 2021. Own compilation based
on data in [59], accessed in January 2024.

Figure 9. Share of the total population in the country with access to
electricity in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.

Figure 10. Share of the rural population in the country with access to
electricity in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.
Figure 10. Share of the rural population in the country with access to
electricity in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.

Figure 11. Share of population in the country with access to electricity in the
urban area in 2021, in percent. Own compilation based on data in [55], accessed
in January 2024.

In contrast to the low access to electricity 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%. This can be further confirmed by the fact
that only the four countries of the Central African Republic (CAR), the DRC,
Chad and South Sudan have an urban electrification rate below 50%.
Displacement population
The data for the visualization in Figure 12 stems from the moving energy
initiative [56] and covers all concerned countries of this study. The analysis
of the access to the electricity grid for displaced persons reflects both
similarities and divergences among the selected countries. While the highest
shares of displaced persons with electricity grid access are observed in Egypt
with 100%, Libya with 98% and South Africa with 85%, Sudan, Tanzania with both
2% and Malawi with 0% exhibit very low rates. 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. When we compare the results to
Figure 9 on the share of the total population with access to electricity, we see
a correlation that countries with a high share of the population with access to
electricity also demonstrate a high share of displaced persons with grid access.
This correlation is notably evident in Egypt with 100% in both cases, South
Africa with 85% and 89%, Côte d’Ivoire with 67% and 71%, Nigeria with 65% and
60%, and Ethiopia with 50% and 54%. However, some countries, e.g. Algeria with
10% and 100% or Kenya with 10% and 77%, challenge this correlation. A look at
the DQA further emphasizes the need for validation by more accurate and detailed
analyses.
Figure 12. Share of displaced persons with access to the national grid in 2018,
in percent. Own compilation based on data in [56], accessed in January 2024.
Camp and settlement level
The Humanitarian Energy Data Platform [54] is referred to for the electricity
access on a displacement camp or settlement level. This source encompasses a
total of 74 camps, situated across 16 out of the 30 selected countries. As
illustrated in Figure 13 and Figure 14, there is a significant disparity in
electricity access with a substantial majority of the camps and settlements of
70% that lack any form of access to electricity. In turn, 26% of the camps and
settlements report partial access to electricity and a mere 4% have full access
to electricity.
Figure 13. Percentage of camps and settlements in which residents have access to
electricity in 2022, in percent. Own compilation based on data in [54], accessed
in January 2024.
Figure 14. Number of camps and access to electricity across countries. Own
compilation based on data in [54], accessed in January 2024.
Looking at the same dataset but from a country perspective, the previous
findings of considerable disparity can be further emphasized. Merely three camps
that are located in Algeria, Congo and Sudan report full access to electricity
whereas a complete lack of electricity access is revealed in all considered
camps situated in Djibouti, Democratic Republic of Congo (DRC), Ethiopia,
Rwanda, South Sudan and Tanzania.
It is crucial to point out that there is data available for the camps and
settlements included into this study, however for various camps and settlements
no data is available. A review of the data in [56,57] provides evidence on the
fact that there exist approximately 219 camps and settlements and for two thirds
of those no data is available. This share varies from country to country. For
instance, in Sudan the combined sources indicate 62 different camps while data
on the camp situation is only available for five camps.
Figure 12. Share of displaced persons with access to the national grid in 2018,
in percent. Own compilation based on data in [56], accessed in January 2024.

Figure 13. Percentage of camps and settlements in which residents have access to
electricity in 2022, in percent. Own compilation based on data in [54], accessed
in January 2024.

Figure 14. Number of camps and access to electricity across countries. Own
compilation based on data in [54], accessed in January 2024.


3.2.4. ACCESS TO COOKING

Country population
Data in the Energy Progress Report [55] cover all considered countries, except
for Libya, and we make us of those to provide visual insights into the cooking
situations. Figure 15 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 percentages of clean cooking access, with Algeria and Egypt
achieving full coverage at 100%. Additionally, Angola, Sudan and Mauritania show
a notable alignment between percentages of access to clean cooking and
electricity. However, a considerable change in this pattern is visible in all
the other regions, where the access to clean cooking lags significantly behind
electricity access rates. This discrepancy is most prominent in East Africa,
where electricity access levels were reaching at least 40%, all countries,
except for Kenya and Sudan, report clean cooking access percentages of less than
10%.
Figure 15. Clean cooking access rate of the total population in the countries in
2021, in percent. Own compilation based on data in [55], accessed in January
2024.
The investigations into access to clean cooking in rural areas underscore the
low development stage as Figure 16 showcases universally low values for the
selected countries. Among the sampled countries, only the four countries
Algeria, Egypt, Sudan, and South Africa report rural clean cooking access rates
exceeding 50%. In contrast, 24 out of the 29 selected countries exhibit rural
clean cooking access rates below 10%, signifying a widespread deficiency in
rural areas. Moreover, 10 countries within the dataset report an absence of any
clean cooking access in rural regions.
Figure 16. Clean cooking access rate of the population in rural areas, in
percent. Own compilation based on data in [55], accessed in January 2024.
Similar to the total and rural population rates to clean cooking access, the
examination of clean cooking access in urban areas reveals distinct patterns,
with notable differences across regions. Urban areas in the Maghreb and South
Africa again showcase high rates of access to clean cooking with at least 96%.
The countries in the Sahel region display comparatively much higherrates for
clean cooking access in urban environments. Specifically, only Mali reports a
clean cooking access rate below 10% in this region. In contrast, five out of the
seven Sahel countries considered in this analysis Mauritania, Burkina Faso,
Nigeria, Chad, and Sudan exhibit clean cooking access rates above 30%, with
Mauritania and Sudan surpassing 70%.
Displacement population
As illustrated in Figure 18, the use of biomass exhibits differences across the
considered countries with Algeria showing the highest share of non-biomass usage
with 99%, whereas South Sudan and Malawi do not have any displaced persons using
a cooking fuel that differs from biomass. Overall, significant differences
between countries persist, with the Sahel region notably presenting lower
values, as all countries except Nigeria have a non-biomass usage rate below 25%
for displaced populations. Furthermore, it can be noted that Maghreb and South
Africa stand out with high shares of displaced persons not using biomass, with
percentages above 90% and hence mirroring the host countries clean cooking
access rates. Countries such as South Sudan, Uganda, the Central African
Republic (CAR), Chad, and Mozambique exhibit relatively small shares of
displaced persons not using biomass, also aligning with tendencies observed in
the clean cooking access rate of their respective host populations.
Figure 18. Share of displaced persons in the countries that are not using
biomass for cooking, in percent. Own compilation based on data in [56], accessed
in January 2024.
Camp and settlement level
The data sources [54,56] were used for the analysis of the access to cooking in
displacement contexts. The two sources cover 146 camps in 20 out of the 30
selected countries. According to Figure 19, firewood emerges as the predominant
source of cooking fuel in refugee camps, constituting the majority at 61%.
Approximately a third of the considered camps adopt a combination of firewood
and fossil fuels leading to 22% for firewood or LPG, 13% for firewood, charcoal,
gas or fuel. However, only 4% do not depend on firewood and employ alternative
cooking fuels such as gas, biomass or briquettes.
Figure 19. Type of cooking fuel in displacement camps and settlements for
considered countries, in percent. Own compilation based on data in [54,56],
accessed in January 2024.
A country-level examination of the type of cooking fuel used within refugee
camps reveals that firewood emerges as an exclusive cooking fuel in several
countries. Notably, the refugee camps 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
refugee camps. The entire array of information can be seen in Figure 21.
Figure 15. Clean cooking access rate of the total population in the countries in
2021, in percent. Own compilation based on data in [55], accessed in January
2024.

Figure 16. Clean cooking access rate of the population in rural areas, in
percent. Own compilation based on data in [55], accessed in January 2024.

Figure 17. Clean cooking access rate of the population in urban areas, in
percent. Own compilation based on data in [55], accessed in January 2024.
Figure 17. Clean cooking access rate of the population in urban areas, in
percent. Own compilation based on data in [55], accessed in January 2024.

Figure 18. Share of displaced persons in the countries that are not using
biomass for cooking, in percent. Own compilation based on data in [56], accessed
in January 2024.

Figure 19. Type of cooking fuel in displacement camps and settlements for
considered countries, in percent. Own compilation based on data in [54,56],
accessed in January 2024.

Figure 20. Type of lighting in displacement camps and settlements for considered
countries, in percent. Own compilation based on data in [54,56], accessed in
January 2024.

Figure 21. Type of cooking fuel in displacement camps and settlements by
country. Own compilation based on data in [54,56], accessed in January 2024.
Figure 21. Type of cooking fuel in displacement camps and settlements by
country. Own compilation based on data in [54,56], accessed in January 2024.

Lighting access at the camp and settlement level
This paragraph describes the status quo of the access to lighting in the camp
and settlement levels since it is part of the above shown cooking access data.
Figure 20 is based on data from the moving energy initiative [56] which covers a
total of 115 camps from 18 out of the 30 considered countries. Figure 20
illustrates the dominant case of torch usage for lighting purposes across camps
and settlements. More than two thirds are torch dependent whereas only 23% use
liquid fuel as source for lighting. Looking towards the use of renewable energy,
we note that less than 10 % of the considered camps and settlements rely on
solar dependent lighting.
Figure 20. Type of lighting in displacement camps and settlements for considered
countries, in percent. Own compilation based on data in [54,56], accessed in
January 2024.
An examination of the data from country-level perspective further indicates
substantial disparities across the countries. 13 out of 18 countries only have
torches as source of lighting in their refugee camps. The graph also reveals
that Sudan is the only case within the dataset, which has camps that purely
depend on liquid fuel and Ethiopia stands out as the only country among the
considered nations with camps utilizing solar energy as a lighting source.

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 prerequisites and indicators for livelihood generating
activities. Accordingly, and as it was available in the data sources, we include
Figure 23 which points out the percentages across countries for having the right
to work and freedom of movement as well as linked to the country having signed
the Comprehensive Refugee Response Framework (CRRF). The objective of the CRRF
is to coordinate programming between refugees and host communities, to share
best practices and develop shared analyses, to discuss policy issues with a view
to agreeing on common positions as a basis for dialogue with the respective
government and further stakeholders, among others. 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 joined the CRRF
versus not.
Figure 23. Right to work and right to move in/out of camps and settlements, as
well as percentage of countries that are signatories of the Comprehensive
Refugee Response Framework (CRRF) across countries, in percent. Own compilation
based on data in [56], accessed in January 2024.

3.2.6. PROJECT ACTIVITIES ON ACCESS TO ENERGY IN DISPLACEMENT CONTEXTS

Figure 24 visualizes data from the Humanitarian Energy Data Platform which
covers 27 out of the 30 countries selected, not considering Algeria, Egypt and
the CAR. 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, whereas all the other considered
countries show numbers below 20 except for Burkina Faso, Niger and Zimbabwe.
This difference becomes even more visible when we look at the four countries
with the highest number of 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.
Figure 24. Number of energy-related projects in the displacement context per
country in 2021. Own compilations based on data in [54], accessed in January
2024.
Figure 22. Type of lighting in displacement camps and settlements by country.
Own compilation based on data in [56], accessed in January 2024.
Figure 22. Type of lighting in displacement camps and settlements by country.
Own compilation based on data in [56], accessed in January 2024.

Figure 23. Right to work and right to move in/out of camps and settlements, as
well as percentage of countries that are signatories of the Comprehensive
Refugee Response Framework (CRRF) across countries, in percent. Own compilation
based on data in [56], accessed in January 2024.

Figure 24. Number of energy-related projects in the displacement context per
country in 2021. Own compilations based on data in [54], accessed in January
2024.



4. DISCUSSION

4.1. AVAILABLE DATA ON ENERGY ACCESS IN DISPLACEMENT CONTEXTS

Research on and practical implementations of energy access in displacement
contexts is still in its early stages. While a number of researchers and
practicians have tried to gain more clarity on the situation of energy access
and contribute to the understanding of local, regional and country
circumstances, there is the need for conceptual studies, novel approaches,
progressive policy making and financing to systematically advance this field and
improve the lived realities of large population on different levels [16,24].
Data and evidence are essential as a foundation for efficient and meaningful
decision taking and progress that then is possible to be applied practically.
The data search of this study revealed that while being scattered, relevant data
exists that allows for the formulation of a generalized information base on the
state of art. Overall, the number of transnational data sets that comply with
minimum quality standards and that were categorized as relevant for this study
is small. Six relevant sources were identified that allowed to formulate eleven
indicators with six of them on the country, and five on the camp and settlement
level. All presented data is quantitative data, which was prepared, visualized,
and explained. Further, a DQA was tailored and applied to assess the quality of
the information achieved. The DQA revealed significant shortcomings on energy
access in displacement contexts, a finding that coincides with statements from
multiple publications from recent years [13,16,24]. The overall low-quality
scores in the DQA are related to several reasons.
First, the data is often incomplete. For example, the data on clean cooking
access published by [54] includes 53% of the countries of this study. The
shortcomings of data becomes also visible when looking at the number of camps
and settlements that were considered in the dataset of [54]. For the overview on
access to electricity, the source provided information on 74 camps and
settlements across 16 countries. Looking at the camps and settlements found in
[56] and [57], it can be seen that there are at least 216 camps in the 16
countries mentioned. This means, that at least 66% of the existing camps are not
included in the dataset of [54]. On the one hand, this allowed to research,
visualize and discuss more details about the situation in parts, but it leaft
out a larger number of locations and it raises the question as to whether and
how this can be applied to estimate the overall picture of the situation in
these countries.
The second reason for the low-quality score depends on the category of
timeliness. Some of the data considered in this study is 3 to 10 years old and
potentially does not take into account latest changes. The energy infrastructure
and the environment change over a longer period of time slowly and the data
seems to still have high relevance for these purposes, however the number of
displaced persons fluctuates in shorter time intervals. The MEI data originate
from 2010 [32] and provide useful indication, but it is essential to generate
up-to-date knowledge to estimate the current situation.
The third reason explaining the limitations in the quality of the data is its
accuracy. There is a considerable lack of field data at the individual and at
the community level in the form of survey and interviews with displaced persons.
The models employed to produce the information are often oversimplified leading
to inaccurate results. An example showing this issues is the study performed by
MEI [56]. Although some of the data is derived from interviews, which is for
example the case in [54] , an extensive survey in order to improve data
accuracy, as it was sought in the development of the Energy Progress Report
[55], is yet to be realized in a similar manner.
A fourth problem uncovered by the DQA concerns the category coherence. The
analysis of the data of MEI [56] and the Humanitarian Energy Data Platform [54]
showed inconsistencies, i.e. among the data on clean cooking. The two sources
published data on clean cooking in several camps and settlements while 45 of
those appear in both datasets. Nevertheless, only in five out of 45 cases do
both agree on the type of fuel used for cooking. This could be due to the
different years of publication, although it would suggest only minor
discrepancies. The inconsistency in 40 cases is difficult to reconstruct.
The limited data quality poses challenges since reliable and informed decision
making requires consistent and needs-based data. The currently available
information on energy access in displacement contexts provide valuable insights
but at the same time offers merely estimates and indications that do not
suffices as guidance for further development and expansion of the sector, but
rather serve as a broad overview. Further research is needed that is based on
overarching criteria to enable comparability across settings and the evaluation
of individual contextual situations. In addition, existing data collection
designs, which are mainly top-down and high-level rather than needs-based,
individual and community-oriented, should be complemented to include the
influences of energy solutions on the perceived life realities of displaced
persons.

4.2. DATA INSIGHTS FROM A COUNTRY PERSPECTIVE

Policies and regulations
The evaluation of RISE has shown that there are considerable differences in
terms of current regulation and policy environment for the integration of
electricity access and clean cooking between the selected countries. For
electricity access, the scores range from as low as 8 in the case of South Sudan
to 90 in Rwanda.. The data of RISE further revealed that most of the selected
countries had relatively low scores, particularly for the pillar clean cooking,
raising the question of how these results might translate into access to clean
cooking for displaced persons. As the objective of RISE is to demonstrate the
extent to which a countries regulatory framework contributes to achieving
Sustainable Development Goal 7 [59], the assumption could be made that displaced
persons who live in countries with a relatively high score might have better
chances to obtain electricity and clean cooking access than displaced persons
hosted by countries with low scores. With the currently available data, it is
however impossible to prove this assumption. In fact, a high correlation between
the RISE scores and the electrification rate or clean cooking access for
displaced persons was not found.
Electricity
Similarly, there were many disparities found in electricity access for the
generall population of the countries. Particularly in rural areas, access to
electricity varied widely, ranging from full access (e.g. Egypt) to nearly no
access to electricity (e.g. 1 % in Chad), raising the question of how those
percentages may also have an influence on the electricity access for displaced
persons in the respective countries. A look at the results of the study
performed by MEI [56] reveals some insights. On the one hand, there are
countries that show similar values regarding electricity access for both, the
total population and displaced persons (e.g. Chad for low access or South Africa
for high access). However, there are also countries such as Algeria, where the
electrification rate is generally at 100% but only 10% of the displaced persons
have electricity access. With the considered data in this study, a significant
correlation between the general electrification rate of a country and the
electricity access for displaced persons could not be made. Nevertheless, even
though the data cannot be regarded as the result from a thorough data design and
generation exercise, there are indications the situation in the host country has
a considerable impact on the availability of electricity for displaced persons.
Clean cooking
While both, the Energy Progress Report [55] and MEI data [56] show a similar
picture for the availability of clean cooking options, also no correlation could
be established between clean cooking access for the total population and for
displaced persons. For example, the amount of the population of Sudan that has
clean cooking solutions at hand is at 61% but only 1% of the displaced persons
in Sudan benefit from access to clean cooking devices. However, the shockingly
low shares in clean cooking access for the majority of the countries, in
particular in rural areas, emphasizes that the access to clean cooking
represents not only a challenge for displaced persons but also for the general
population.
Compared to the electrification rate, the results of the MEI study on access to
clean cooking demonstrate an even more pronounced disparity between the selected
countries. The proportion of population in the Maghreb region and South Africa
that have access to clean cooking is very high - at least 90%, while 12 of the
remaining 26 countries have an access rate of less than 10%. The data therefore
clearly indicates that the differences in the living conditions of displaced
people become even more apparent when access to clean cooking is analyzed.

4.3. DATA INSIGHTS FROM A CAMP PERSPECTIVE

Electricity
The analysis from the camp perspective emphasizes the conclusions drawn in the
previous section. Particularly, the low electrification rate becomes visible
with the data from [54] as more than two thirds of the 74 considered camps do
not have any access to electricity. This finding underpins the statements found
in other scientific articles [58,62]. The camp perspective also reveals that the
lived realities do not only differ between countries but also within a country.
7 out of the considered 16 countries in the data of [54] show camps with
different accesses to electricity.
A detailed representation of the lived experiences can however not be made due
to several reasons. First, the database for this analysis [54] differentiated
between three categories of access – access, partial access, and no access. The
source does not specify what “partial access” exactly means though. For a
detailed representation of the electricity access for displaced persons, for
example with the World Bank Multi-Tier Framework [25], further and more detailed
information is required. The second reason is the fact that this study only
focusses on refugee camps, leaving out IDPs, asylum seekers and other people of
concern. Conclusions made from data on refugees living in camps cannot be
transferred and used for an accurate view of energy access for all displaced
persons.
Clean cooking
From the camp perspective, the most visible finding is the high dependency on
firewood. With 96%, the vast majority of the considered camps in the dataset of
[54] are either completely or partially dependent on firewood, emphasizing the
previously discussed low development state in clean cooking access. The camp
perspective also illustrates considerable differences between and within the
countries. While five out of the 20 selected countries only host firewood
dependent camps, three countries have at least four different types of cooking
fuels used in their respective camps, indicating that clean cooking access
differs also considerably within the same country.

4.4. LIMITATIONS AND CONSIDERATIONS OF THE AVAILABLE DATA

In the previous subsections of this article, we discussed insights that
available data reveals about the state of energy access in displacement
contests. This study confirms that already today an information basis exits that
goes beyond the global cumulative indicators for energy access. It is evident
that the relevant transnational data identified is limited in its scope.
Nevertheless, the data gives some important indications that may be utilized to
efficiently progress towards universal access to clean energy in displacement
settings.
Although the scope of data is limited and our quality assessment has revealed
fundamental shortcomings, it is essential that stakeholders use all available
data to improve the relevance of decision-making, especially considering the
urgent need and urgency highlighted in this study. However, the overall lack of
data and evidence also risks overestimating the informational value of the
evidence. The existing data contains fundamental simplifications and assumptions
that need to be critically reflected upon for each application of the data. A
comprehensive understanding of the degree of uncertainty is essential.
Another risk associated with the utilization of the data is the
oversimplification of the subject matter. The presented data is predominantly
quantitative. While the data enables a high-level evaluation of the state of
energy access and may inform the overall scope of the challenges at hand, it
does not provide a sense of context. The inability to describe the relevant
local contexts of settlements and camps contains the risks of neglecting the
context in decision making. This is in stark contrast to the growing body if
scientific literature calling for an acknowledgement of the fundamental
implications of the diversity in contexts (e.g., [18,28,29,63]). In simple
terms, this suggests that the less contextual insights are required, the more
relevant the existing data is. The data may therefore be more relevant to inform
high-level policy decisions than to support the design of energy programs or
interventions on settlement and camp level.
Activities that necessitate a comprehensive contextual understanding of
energy-related lived realities require a more through information base than what
is available to enable meaningful decision making. For example, knowledge of the
dominant type of cooking fuel in a settlement or a camp may give the impression
that the relevance of higher-tier cooking intervention can be assessed for a
settlement or camp. However, assessing the relevance of energy intervention
necessitates much for detail understanding of the context. In the case of
cooking this understanding may include on a household level e.g., individual
cooking preferences [27], energy priorities [19], aspirational energy needs [6],
and the household’s income [21], on a settlement of camp level the availability
of alternative cooking fuels [19] and the type of environment (emergency vs.
protracted situation) [24]. In addition, the type of fuel alone is also
insufficient to determine the tier of energy access of displaced persons. The
stated indicators are only exemplary for the energy-related lived realities of
displaced persons and more insights are needed to inform systemic change
processes.
The need for more updated, more comprehensive, and granular data is apparent. It
is up to international organizations and the scientific community to facilitate
processes to further develop approaches to systematically collect, share, and
evaluate data. Novel conceptual framework and indicators are needed to capture
the state of access to energy in displacement contexts. Future research holds
promise to enhance the field if it first focuses on a more comprehensive
understanding of how energy is intertwined with the lives of displaced people
before deriving a set of energy indicators. It is important that IDPs are
meaningfully included in the research.


5. CONCLUSIONS

The results of our study have shown remarkable differences in the access to
electricity for displaced persons across countries on the African continent. For
both electricity and clean cooking, the availability for displaced persons
ranges from nearly no access at all up to an access rate of 100%. More
strikingly, the results also show that besides South Africa, and the selected
countries in the Maghreb region, the access to both clean cooking and
electricity for displaced persons is considerably low. Particularly in the case
of clean cooking, the very low shares have become apparent. From a country
perspective, it can be concluded that the access to energy for displaced persons
does not only depend greatly on the energy situation in the host country but is
still at a very low level in general.


AUTHOR CONTRIBUTIONS

Conceptualization, T.B., L.S., B.H.; methodology, T.B., P.B., B.H; software,
T.B., P.B.; validation, T.B., L.S., B.H.; formal analysis, T.B., P.B.;
investigation, T.B., B.H.; resources, T.B., P.B.; data curation, T.B., P.B.;
writing—original draft preparation, T.B., P.B., B.H.; writing—review and
editing, T.B., L.S., B.H.; visualization, T.B., P.B.; supervision, L.S., B.H.;
project administration, L.S., B.H.; funding acquisition, L.S., B.H.. All authors
have read and agreed to the published version of the manuscript.


DATA AVAILABILITY STATEMENT

Not applicable.


ACKNOWLEDGMENTS

We acknowledge support by the German Research Foundation and the Open Access
Publication Fund of Technische Universität Berlin.


CONFLICTS OF INTEREST

The authors declare no conflict of interest.


APPENDIX A

Countries in this study Number of displaced persons Algeria 102753 Angola 55981
Burkina Faso 1917317 Burundi 99251 Cameroon 1473294 Central African Republic
527348 Chad 1080557 Congo 97074 Cote d'Ivoire 937027 Democratic Republic of the
Congo 6063761 Djibouti 30197 Egypt 358523 Ethiopia 4208422 Kenya 1078815 Libya
206330 Malawi 56560 Mali 441449 Mauritania 106370 Mozambique 1060234 Niger
716412 Nigeria 3379779 Rwanda 149218 Somalia 3002276 South Africa 150912 South
Sudan 2167672 Sudan 4685356 Tanzania 247196 Uganda 4144589 Zambia 81090 Zimbabwe
23063

Countries without data or
a displaced population below 20000, and not in this study Number of displaced
persons Equatorial Guinea no data Mauritius no data Sao Tome and Principe no
data Seychelles no data Cape Verde 115 Sierra Leone 324 Benin 2639 Gabon 280
Gambia 3883 Ghana 11048 Guinea 2252 Guinea-Bissau 54 Liberia 1441 Namibia 7268
Senegal 12062 Togo 9876 Tunisia 8929 Botswana 900 Comoros 17 Eritrea 136 Lesotho
545 Madagascar 245 Eswatini 2161 Morocco 18066 Sum 74973 Total number of
displaced persons in Africa 36059666 Share of excluded countries 0,2%

Data Quality Assessment – evaluation of the considered sources

Tracking SDG 7 – The Energy Progress Report Dimension Assessment Score
Timeliness The latest publication of the Energy Progress Report is from 2022 and
represents the results for the year 2021. 1 Completeness 29 from the 30
countries that have been selected for our work have also been considered in the
Energy Progress Report. 2 Accuracy The Energy Progress Report is based on the
collection of census and survey data. However, the data sources lack of
information for some regions and some surveys are not updated regularly. The
missing data is therefore estimated by using modelling tools (for example the
nonparametric modelling). 1 Coherence The methodology used for the creation of
the data is common. The data collection is done by desk research while several
modelling tools are used to fill the missing data for the creation of the
dataset. The report is updated on a regular basis, allowing the resulting
dataset to be compared to other sources. 2 Interpretability The reporting source
gives access to a detailed description of the methodology as well as further
background information on the work. The additional information allows to
interpret and use the data correctly. 2 Total score 8

Humanitarian Energy Data Platform Dimension Assessment Score Timeliness The
source only mentions when the dataset was published (2020). It is not possible
to calculate the number of years elapsed since the data was collected. 0
Completeness The country coverage varies depending on the considered subject.
For instance, the section “Country Market Analysis” covers 21 countries whereas
the section “National Energy Data” covers 51 countries. The minimum coverage
corresponds to 16 out of 30 countries that are considered in our analysis. 0
Accuracy The dataset was created by using different sources with different
levels of accuracy and methodologies (data survey and models). The reporting
source states that the work does not represent a complete picture of the
humanitarian energy situation but rather an overview of factors that influence
the current trends of the humanitarian energy environment. 1 Coherence The
dataset is based on common methods (e.g. surveys, interviews). A comparison with
sources however shows that the data is incoherent. Any interpretation deriving
from the analysis of the data and any use with other sources should be done with
the knowledge that the dataset is not coherent. 1 Interpretability A description
of the methodology is available. The objective of the analysis as well as the
used sources and contributors are mentioned. However, the link to the data is
missing which makes it difficult to find the exact data source. 1 Total score 3

Refugee Settlements Electricity Access (RSEA) Dimension Assessment Score
Timeliness The dataset was published in 2021 and shows data collected in 2020. 1
Completeness The analysis includes 21 out of the 30 countries considered in our
analysis. 0 Accuracy The dataset is based on data from existing literature
(academic articles, white papers) as well as field research (surveys,
interviews). The collected data was cross-referenced for consistency. 2
Coherence The dataset was created by using common methods including desk
research, field data collection and interviews with stakeholders. The reporting
source has published a paper which explains the work and its purpose in detail.
We have not found any incoherence in the data and therefore conclude that this
source can be used in combination with other sources. 2 Interpretability The
work is described in detail in a research paper allowing a clear view on the
used methodology. The purpose as well as the limitations of the work are
explained so that the data can be interpreted correctly. 2 Total score 7

UNHCR refugee data finder Dimension Assessment Score Timeliness The data was
published in 2023 and shows the results of the analysis for the year 2022. The
dataset is updated every six months. 2 Completeness All the countries selected
for our analysis are covered by the dataset. 2 Accuracy The work is based on
different data sources which all represent real world data, including population
censuses, surveys and administrative records. Statistical frameworks
specifically developed for the analysis of forcibly displaced persons are used
to complement the data analysis. 2 Coherence The source uses methodologies that
are common for a population count (statistical analysis based on population
censuses, surveys, administrative data records, etc.). There has not been found
any incoherence during our analysis of the data. 2 Interpretability A detailed
description of the used methodology is available and allows to interpret and use
the data correctly. The source also gives access to further documents with
detailed descriptions of the analysis that lead to the creation of the dataset.
2 Total score 10

Regulatory Indicators for Sustainable Energy (RISE) Dimension Assessment Score
Timeliness The dataset was published in 2022 and represents the results for the
year 2021. 1 Completeness The source does not have the same country coverage for
each section. The minimum coverage is 26 out of 30 countries considered in our
analysis. 1 Accuracy The dataset is based on desk research and field data
(surveys). The reporting sources gives access to the used data sources and
additional information related to the analysis. It should be noted that the
results are not representing any real-world data but rather a score which is
based on the specific framework that was developed for this analysis (RISE
framework). A certain subjectiveness should therefore be attributed to the work
which also influences its accuracy. 1 Coherence The used methodology is
explained in detail. Any incoherence in the dataset was not found. 2
Interpretability The reporting source gives access to the methodology and
further information that allows a clear understanding of what the dataset can be
used for. The sources that were used for the creation of the dataset are also
shared on the web page of the reporting source. 2 Total score 7

Moving Energy Initiative Dimension Assessment Score Timeliness The information
was obtained from different sources with the oldest dating back to 2014. 0
Completeness The source does not have the same country coverage for each
section. The lowest coverage is 18 out of 30 countries considered in our
analysis. 0 Accuracy A scientific article was published which describes the
methodology of the work in detail. It is stated that the dataset is based on a
simple model which does not lead to accurate results. 0 Coherence The dataset
was created with common methods and can be used with other datasets if it is
understood that the data represents more an indication than a detailed picture.
The dataset shows some incoherencies (e.g. description for type of cooking fuel,
see section 2 GPA UNITAR). 1 Interpretability A detailed description of the
methodology used in this work is presented in the scientific article. It is
clear what the work is intended to show and how the data should be interpreted.
2 Total score 3


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Figure 1. Evolution of the number of refugees, asylum-seekers, IDPs and other
persons of concern. Own compilation based on data from UNHCR [1].

Figure 2. Number of displaced persons by country in third quarter of 2023, in
thousands. Own compilation based on data in [48], accessed in January 2024.

Figure 3. Share of displaced persons in the total population of the host
country. Own compilation based on data in [48], [61], accessed in January 2024.

Figure 4. Changes in the share of displaced persons in the total population of
the host country and for the period of 2020 to 2022. Own compilation based on
data in [48], [61], accessed in January 2024.

Figure 5. RISE overall score on the country regulatory and policy environment in
2021. Own compilation based on data in [59], accessed in January 2024.

Figure 6. RISE score for the pillar renewable energy in 2021. Own compilation
based on data in [59], accessed in January 2024.

Figure 7. RISE score for the pillar electricity access in 2021. Own compilation
based on data in [59], accessed in January 2024.

Table 1. List of countries with more than 200,000 displaced persons in January
2024 [48], 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

Table 2. Evaluation matrix.

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 One year 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

Table 3. Selected sources for the visualization of current data on energy access
in displacement settings.

Level Information Indicator Source Country General Energy access in rural and
urban areas [55] Regulations and policies for energy access [59] Specific to the
displacement context Electricity access for displaced persons [56] Access to
clean cooking for displaced persons Access to lighting for displaced persons
Number of projects [54] Camp or Settlement Specific to the displacement context
Camp population [54,56,57] Electricity access for displaced persons [54] Access
to clean cooking for displaced persons [54,56] Access to lighting for displaced
persons [56] Livelihood [54]

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

Source Score per dimension Total score Timeliness Completeness Accuracy
Coherence Interpretability ESMAP [55] 1 2 1 2 2 8 GPA [54] 0 0 1 1 1 3 EU [57] 1
0 2 2 2 7 UNHCR [48] 2 2 2 2 2 10 World Bank [59] 1 1 1 2 2 7 UN OCHA [56] 0 0 0
1 2 3

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Table of Contents

1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References

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