International Journal of Disaster Risk Reduction 95 (2023) 103896
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International Journal of Disaster Risk Reduction
journal homepage: www.elsevier.com/locate/ijdrr
Assessment of social factors that promote the vulnerability of
communities to coastal hazards in the Volta estuary in Ghana
Memuna Mawusi Mattah a, b, *, Precious Agbeko D. Mattah c, Adelina Mensah b,
Daystar Babanawo c, Emmanuel Brempong c, d, Justice Mensah e, K. Appeaning Addo b
a Department of Environment and Development Studies, Central University, Miotso, Accra, Ghana
b Institute for Environment and Sanitation Studies, University of Ghana, Legon, Ghana
c Africa Centre of Excellence in Coastal Resilience (ACECoR), University of Cape Coast, Cape Coast, Ghana
d Department of Fisheries and Aquatic Sciences, University of Cape Coast, Cape Coast, Ghana
e Directorate of Academic Planning and Quality Assurance, University of Cape Coast, Cape Coast, Ghana
A R T I C L E  I N F O A B S T R A C T
Keywords: Although vulnerability assessments have been widely conducted along the coast of Ghana, they
Estuarine have not focused on the factors contributing to social vulnerability of households and communi-
Social vulnerability ties to disaster risks. Using an indicator-based approach, this study examines the social factors
Climate change that affect the exposure, sensitivity and adaptive capacity of households and communities in the
Disaster
Risk Volta estuary, Ghana. Results indicate that all the communities within the study area were ex-posed to climate change related hazards. However, two communities, Azizanya in Ada East Dis-
trict and Fuveme in Anloga District, were highly exposed with unweighted indexes of 0.50 and
0.76, respectively. Sensitivity among communities was generally high ranging from 0.00 to 0.87
due to several social factors including low household income, high number of children and aged
at home, and lack of social amenities such as roads, health facilities, markets, and schools. The
communities recorded high adaptive capacity and were able to withstand the ravages of the
ocean and the river system. Households and communities developed various adaptation measures
such as relocation to nearby communities or higher grounds, open spaces and packing of their be-
longings on higher objects such as tables, whiles government actions included occasional dredg-
ing and constructions of sea defense structures. An integrated, multi-stakeholder approach in-
volving government, non-governmental community-based organisations, communities, house-
holds, and other stakeholders is proposed to design and implement a comprehensive disaster
management plan to combat climate change related coastal disasters.
1. Introduction
Disasters caused by climate change related phenomena are rampant on the coast of West Africa [1,2]. They are more prevalent
and severe in deltas and estuaries where various rivers flow into the ocean [3–6]. This is because the delta regions or estuaries are ge-
omorphologically dynamic with the sea constantly removing and depositing littoral sediments, most often destroying human habitats
and livelihoods. Given that deltas and estuaries are hotspots of biodiversity and attract huge human populations that depend mainly
on the resources of the wetland [7], impacts of natural hazards are often more disastrous, especially when the populations are unable
to protect themselves, their livelihoods and personal properties [8]. The inability of a society or certain portions of a society to protect
* Corresponding author. P. O. Box 2305, Tema, Ghana.
E-mail addresses: mmattah@central.edu.gh, mmmattah@st.ug.edu.gh (M.M. Mattah).
https://doi.org/10.1016/j.ijdrr.2023.103896
Received 9 March 2023; Received in revised form 8 June 2023; Accepted 25 July 2023
Available online 26 July 2023
2212-4209/© 2023 Elsevier Ltd. All rights reserved.
M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
themselves against natural hazards of such nature raises the concept of human-centred vulnerability among researchers and further
pushes the boundaries of social vulnerability paradigm, which holds the view that vulnerability is socially constructed [8,9]. It is
therefore important to seek to understand the social, economic, political and cultural contexts within which a particular society be-
comes susceptible to climate change related phenomenon [10].
According to the Intergovernmental Panel on Climate Change [11], vulnerability to climate change related phenomenon encom-
passes exposure, sensitivity, potential impact, and adaptive capacity of a system to hazards. [12] connects vulnerability of ecosystems
to climate change to the past, present and future development of human societies. Vulnerability therefore vary with respect to space
and time and it is said to be dynamic in nature [13,14]. Adaptive capacity of a system may also vary with respect to certain socio-
economic variables such as experience, skills, level of income, age, gender, and others [15–21]. Vulnerability assessment, therefore,
comes as a handy tool for researchers and decision-makers to acquire information on the actual or potential damage of hazards on any
system. It also informs the decision maker about the disaster risk from a particular type of hazard and how to mitigate, manage, or re-
duce this risk. Vulnerability assessment therefore, enables the development of location-specific adaptation strategies to reduce risk,
exposure and vulnerability levels [22]. It influences community preparedness, response, mitigation and restoration before and after
disasters.
The Volta delta and its environs of the south eastern coastal area of Ghana have been identified as the riskiest areas of the country
in terms of coastal degradation that threatens lives and properties [23,24]. Increasing risk levels from hazards such as sea erosion,
storm surge, tidal waves and flooding among others in the Volta estuary have been predicted as a result of climate change [24–26]. In
fact, vulnerability studies in the southeastern part of Ghana have mainly focused on the extent of physical vulnerability of coastline
[25]. Babanawo et al. [27] working in the Ketu South Municipality of Southeastern Ghana observed that location specific indicators
are relevant in determining the actual cost of disasters on the livelihoods of coastal dwellers. However, as the ecologically productive
nature of the estuary continues to attract huge human populations [28], communities' vulnerability to disasters will continue to in-
crease. This study therefore employed the indicator base approach through community participation to identify location specific indi-
cators to determine vulnerability levels among communities. It examined the extent of communities’ exposure, their sensitivity, and
adaptive capacity to coastal hazards in the delta area to inform hazard and location specific policy interventions by the government
and other stakeholders such as Non-Governmental Organisations and Community Based Organisations to improve community re-
silience.
1.1. Vulnerability in context
The concept of vulnerability, generally regarded as the extent to which a society may be susceptible to environmental hazards, is
not restricted to a single domain of academic endeavor [29]. It is found in various fields of study including Human Geography, An-
thropology, Disaster Risk Management, Political Ecology, Health and Climate Change Science among others [30,31,32]. This has re-
sulted in various perspectives of the concept. Scholars such as Appeaning [33–37]; conceive vulnerability in terms of the geographic
location of societies, as well as access to resources and conditions at the location that make the society more susceptible to hazards.
This school of thought stems from environmental determinists approach which holds the view that the physical environment (e.g., cli-
mate, landscape and other environmental factors) influences the way of life (culture) of society [33–37]. Another school of thought,
such as that of [29,38,39]; relates societies' vulnerability to the failures or weaknesses of the macro (political and economic) level
and/or micro (households and community) level systems. Here, vulnerability is self-imposed as a result of systemic failures because
human societies have constructive and destructive abilities to determine what happens to them [38,39]. It is therefore important to
understand various aspects of society to estimate the extent of the impact of a hazard on that society [40–42]. This dichotomy some-
what differentiates between biophysical vulnerability and social vulnerability [43], with the former delving more into environmental
factors that expose society to hazards. Social vulnerability on the other hand deals with social, economic, political, cultural, and other
related factors that render society vulnerable. In literature, this dichotomy has other nomenclatures, for example, top-down and bot-
tom-up approaches [44], as well as start-point and end-point aproaches [45]. Some scholars, however, hold the view that a merger of
the two approaches in an integrated manner is important and useful [9]. They claim that bringing together the physical and the social
vulnerability may present a more comprehensive approach to studying the issues of vulnerability [9]. The merger of the two ap-
proaches is also important because there is always social, economic, cultural and political content in any society's decision to settle or
reside in any physical environment [42,46,47]. Agglomeration of the two approaches is therefore very relevant in deciphering perti-
nent factors that differentiate the magnitude and the impact of hazards on society [38,48].
1.2. The case for social vulnerability
Since hazards and disaster risks are peculiar within time and space, the systematic assessment of vulnerability across different sec-
tors, space and time scales are therefore difficult to achieve, hence limited understanding of vulnerability patterns across scale [49].
Factors contributing to social vulnerability are crucial for disaster risk reduction and management. According to Refs. [50–52]; social
vulnerability occurs because of so many inequalities in society. Vulnerability of any society to hazard is most often reflected in the
economic losses, injuries and fatalities associated with natural hazards [9,25].
Literature reveals that factors that contribute to social vulnerability are numerous. Some of these factors include social class, edu-
cational attainment, employment (types and stability), income, human settlement, family structure, population growth, extent of dis-
ability in the population among others [53]. [9] had previously suggested some general factors which include the lack of access to
certain resources like information, knowledge as well as technology, limited social capital (networks and connections), frailty and
physical disability, limited access to political power, representation and lobbying skills, beliefs, customs and traditions, and limited
availability of infrastructure among others. The role of institutions in creating and perpetuating vulnerability through unfavourable
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
power structures among communities have also been well documented [49]. [51,54] concluded that social vulnerability is “conceptu-
ally located at the interaction of nature and culture”. Its contributory factors are therefore multi-dimensional and portray linkages be-
tween social, economic, political, cultural, environmental, personal and other related aspects of society. In every society or commu-
nity there are variations regarding the extent of vulnerability as some people are more vulnerable to disasters than others. The varia-
tions are determined by age, income, gender inequality, linguistic minority, health, educational attainment, disproportionate impact
of disasters, race and ethnicity, among others [55]. Bergstrand et al. [32] observed a correlation between vulnerability and resilience
indicating that communities with high vulnerability most often turn out to be those with low resilience.
1.3. Measuring social vulnerability
The multidisciplinary nature of social vulnerability analysis influence the definition and selection of indices based on the re-
searchers focus. [56] emphasized the need for methodological robustness to ensure the validity of the indices for social vulnerability
studies. Several methodologies have been employed in assessing social vulnerability at different scales and systems. The indicator-
based approach has been commonly used in different studies to address specific hazards [8,57–60]. Mavhura et al. [60] assessed spa-
tial variation of social vulnerability to flood hazards in northeast Zimbabwe using the principal component analysis (PCA) technique
to develop the social vulnerability index (SoVI). [59] developed social vulnerability to natural hazards of Zeeland using Factor Analy-
sis. [58] used an indicator-based approach to assess social vulnerability of coastal areas to sea-level rise and flooding in the city of
Bandar Abbas in Iran. [58] particularly employed the Min-Max technique for standardization of variables and the analytical hierarchy
process (AHP) and fuzzy AHP model were employed after to weight indicators and vulnerability components. [8,57] both determined
social vulnerability using the PCA. The later used census data in Botswana to determine social vulnerability at the district levels and
the PCA model was used to weight the indicators. [8] measured social vulnerability to natural hazards in the Yangtze River Delta re-
gion in China using PCA to construct the SoVI with empirical and proxy data.
The indicator-based approach allows for evaluating different aspects of coastal vulnerability [61]. According to Ref. [58]; the indi-
cator-based approach method enables the researcher to combine qualitative, quantitative and proxy variables as alternative indica-
tors where a variable cannot be measured. It has also been used extensively in various national, regional, and local studies to assess
coastal vulnerability, especially in Europe [38,61]. Notable studies in various parts of Africa have used the indicator-based approach
to assess the vulnerability of communities to various types of hazards such as floods and coastal erosion [62–65].
Using the indicator-based approach however requires a framework to develop the data collection method for the vulnerability in-
dicators [66]. For example [65], adopted the IPCC framework to assess coastal communities' vulnerability to flooding in Ghana,
whilst [63] used the MOVE framework to apply the indicator-based approach to coastal vulnerability assessment to erosion in the
Gambia. In both cases, a stepwise participatory approach was used to identify indicators for community exposure, sensitivity and
adaptive capacity to floods and erosion respectively among communities.
1.4. Assessing vulnerability to climate change-related disasters in coastal communities
Disaster risk management requires understanding risk from all vulnerability perspectives as stated in priority one of the Sendai
Framework, such as capacity, exposure of persons and assets, hazard characteristics, and the environment [67]. Several studies have
been done to evaluate coastal community vulnerability and disaster risk issues using indicators and other parameters to influence
policies and reduce risk across the globe [68–74].
In West Africa, the coastal vulnerability index has been used to inform policy decisions in various countries including Ghana [35,
75,76]. A study in Ghana assessed the coast of Accra using biophysical parameters [33]. In another study by [35]; population data
from the Ghana Statistical Service were combined with the biophysical parameters to study the vulnerability of the coastline of Accra.
Even though the latter integrated population data, it did not involve the local communities in the identification of location specific in-
dicators. The indicators were not also adequately disaggregated into the vulnerability components as specified in the IPCC's fourth re-
port to enable the researcher to quantify the contribution of each component to the total vulnerability of communities. [31,63] ex-
plain the advantages of the indicator-based approach, which facilitates the multi-faceted nature of vulnerability to be assessed. It al-
lows for community input into identifying indicators for vulnerability assessment. The approach accounts for the critical vulnerability
factors of vulnerability (exposure, susceptibility, resilience, and hazards) as defined in the IPCC fourth report. [77] framework al-
though considered as the old paradigm for vulnerability assessment [21], it is widely used for climate related vulnerability assessment
[78–81]. It is continuously used by many researchers because it takes cognizance of the system's exposure to hazards, sensitivity and
adaptive capacity [21,78] and captures the multi-dimensions of vulnerability, covering the social, cultural, economic, environmental,
and institutional dimensions. Studies based on the new paradigm change [12,11] are based on the risk centred approach which “can-
not answer questions on the what factor” in the vulnerability assessment [21,78]. A study conducted in the southeastern coast of
Ghana [27], emphasized on the importance of the use of the [77] framework because it offers the opportunity for tailed policy inter-
ventions based on the type of hazards, the exposure, sensitivity levels of affected communities.
1.5. Conceptual framework
This study adopted the Methods for the Improvement of Vulnerability in Europe (MOVE) which was developed based on the 2007
IPCC framework. [31] developed and used the MOVE framework to assess a system's vulnerability to single and multiple natural haz-
ards. The exposure, sensitivity, and adaptive capacity of systems were assessed considering the social, environmental, cultural, and
economic indicators [63,82,83] to assess the vulnerability of coastal communities to different hazards.
While using the MOVE framework in The Gambia [63], adduced to the multi-faceted nature of the vulnerability in coastal commu-
nities and called for the need to adopt a flexible framework that has the potential of assessing all the critical components of vulnera-
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
bility (exposure, susceptibility, and adaptive capacity or lack of resilience) to hazards at different scales. The authors considered the
social, cultural, economic, environmental, and institutional dimensions of vulnerability thereby emphasizing the multi-dimensional
ability of the model. Similarly [82], drew on the MOVE framework to assess the vulnerability of a small island in the Southwest Pa-
cific by incorporating the historical vulnerability dimension of a community. [82] indicated that vulnerability could be linked to a
community's past events, which exposed them to risks thereby resulting in migration to a new place. In Côte d’Ivoire [83], also used
the MOVE framework to analyse the social vulnerability of urban areas to flooding, where local indicators were identified through a
participatory approach. Findings from Ref. [83] indicated that exposure and susceptibility contributed most to the phenomenon of
flooding in the coastal urban area.
2. Materials and methods
2.1. Study area
The study was conducted among nine communities in the two districts (Anloga and Ada East) located to the immediate east and
west of the Volta estuary (Fig. 1). Anloga District located at the immediate east of the Volta delta has three (3) communities selected
for the study and they included Fuveme, Agorkedzi and Attiteti. At the immediate west of the delta is the Ada East District which has
communities such as Kewunor, Azizanya, Ayigbo, Lolonyakope, Otrokpe and Azizakpe. These communities were purposively selected
because they were geographically located within five (5) kilometers east and west of the Volta delta (Fig. 1). The two districts consti-
tute part of disaster hotspots on the eastern coast of the country and are prone to coastal flooding and erosion [28].
2.2. Study approach
The study employed the mixed-method approach, which involves the use of both qualitative and quantitative techniques to
gather, analyse and interpret research-oriented data [84–86]. The exploratory sequential design was employed through which quali-
tative data was gathered to pre-identify local indicators that promote communities' susceptibility to disasters. This design ensures that
data is collected progressively from the qualitative to quantitative phase. The qualitative data was gathered through Focus Group Dis-
cussions (FDG) and in-depth interviews with selected stakeholders, including community leaders, and local government officials. The
quantitative instrument was then developed using information from the qualitative data. The structured interview guide was pre-
tested and administered to selected households in the communities. The quantitative data explored communities’ exposure, sensitiv-
ity, and adaptive capacity to climate change-related disasters and disaster risks.
Fig. 1. Map of the study area showing the communities.
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
2.2.1. Data collection procedure
Data collection began with a reconnaissance survey for the research team to acquaint itself with the study area and to identify con-
tact persons at the respective district offices to provide support to the study. With the help of the contact persons from the district of-
fice of the National Disaster Management Organization (NADMO), communities for the study were identified and listed for the study.
Subsequently, community entries were made to identify the leaders and other community members to participate in the study. Ap-
pointments were later scheduled with dates to commence focus group discussions and interviews. The focus group discussion instru-
ment was grouped under three main subtopics: exposure, sensitivity, and adaptive capacity.
Questions on the structured interview instrument included respondents’ knowledge of climate change-related hazards, households
and community vulnerability to these hazards, community perception of risks and disasters, responses to the risks, knowledge on ex-
isting organizational structures and services as well as policies on disaster risk reduction and community involvement in the formula-
tion and implementation at the local government level.
2.2.2. Targeted population and sampling of household heads as respondents
Table 1 shows the estimated population of all the communities (Ghana Statistical Service (GSS), [87]) and the number of house-
holds per community. Using the proportional to size sampling technique after [88]; the total number of households sampled for each
community is shown. Overall, 324 households were sampled for the study.
N
n =
1 + N( 2𝛼)
1716
n = = 324
1 + 1716(0.05)2
Where N = sample frame,
n = desired sample size and.
‘α’ = margin of error (5%).
A systematic sampling technique was employed to select the housing structures and the households to participate in the study. In
each community, household sampling started from the west and ended at the eastern part of the community, given that housing struc-
tures in all communities selected were having east to west orientation following the direction of the coastline. Every 3rd housing
structure was selected and where there are more than one household in the housing structure, the household head of every first
household in the structure was interviewed.
2.2.3. Determination of social vulnerability index
Local indicator variables to determine the Social Vulnerability Index (SoVI) of the communities were adapted from the works of
various authors [31,63,65,82,89]. Indicators from studies by [31,63,65,82] guided the community FGDs. Reflective local community
indicators were identified through the FGDs and validated using in-depth interviews.
The Indicator-based Approach (IBA) [65], widely used for vulnerability assessments, was adapted to assess communities' social
vulnerability to climate change-related disasters and risks from hazards because of sea-level rise (SLR). The flexibility of the indicator-
based approach also allows for it to be combined with other approaches [58,62–64,82,90]. The approach has proven efficient because
it comprehensively captures the multi-dimensions of vulnerability and the critical factors defined in the IPCC fourth report and used
by several studies [31,63,89]. The indicator–based approach measures community vulnerability in terms of their exposure to hazards,
sensitivity, the potential impact of disasters, and adaptive capacity. For coastal disaster studies, some authors [35,71,76,91,74] prefer
to use coastal vulnerability index (CVI) [61]. However, others [58,63,65,82] consider the inadequacy of CVI to quantify the level of
vulnerability of specific place/s in terms of their exposure, sensitivity, or adaptive capacity/resilience. The adoption and use of IBA in
this study, therefore, has the aim of discovering specific components or factors which account for location-based (community) vulner-
ability.
Using the MOVE framework to augment the IBA, a total of 45 sub-indicators were identified and grouped under six sub-titles in-
cluding: (i) historical data on climate-related disasters from 2005 to 2020, (ii) characteristics of households (iii) community profile on
Table 1
Number of households sampled for the study.
12 Communities Male Female Total No. of Households Sample
Size
Anloga Agorkedzi – – 74 15 3
Atitteti 419 525 944 202 38
Fuveme 400 436 836 232 44
Ada East Azizanya 765 759 1524 242 46
Kewunor 207 199 406 83 16
Ayigbo 649 657 1306 233 44
Lolonyakope 1160 1283 2443 435 82
Azizakpe – – 590 107 20
Otrokpe 438 445 883 167 31
Total 9006 1716 324
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
social amenities/infrastructure, (iv) socio-economic characteristics (v) ability to manage disaster and (vi) social capital. In order to
further summarise the data, the indicators were grouped into three (Exposure, Sensitivity, and Adaptive Capacity) and the min-max
method [65] used to calculate the community vulnerability index for each (SoVI). Table 2 lists the 45 sub-indicators, grouped into
three - exposure, sensitivity, and adaptive capacity, as well as provides the justification for their use in the study. The table also pro-
vides the functional relation of each sub-indicator regarding whether they contribute positively or negatively to the social vulnerabil-
ity of communities.
Social Vulnerability Index (SoVI) was calculated according to procedures in Ref. [65] using the standardized min-max method and
equal weighting assigned to all the components. The vulnerability indices developed at the community levels were transformed into
values that range between 0 and 1 where 0 is the worst score and 1 being the best [65,27,90,92].
S − Smin
Isc =
Smax − Smin
Where:
Isc is the standardized index for each community €,
S is observed value for each community, and.
Smax and S_min are the observed maximum and minimum values, respectively.
The mean index of each of the factor was then estimated as:
1∑
f ISC,mean = ISC
n
Where:
f is either exposure €(E), sensitivity (S) or adaptive capacity (A) index, and
n is the total number of indicators for the factor.
The potential Impact was determined as follows:
Pcmean = EIscmean + SIscmean
and the composite vulnerability was determined by subtracting the adaptive capacity index from the potential impact index as fol-
lows:
IPCCIsc = PIscmean − AIscmean
where:
IPCCIsc is the Composite Vulnerability Index,
PIscmean is the Potential Impact Index and
AIscmean the Adaptive Capacity Index.
The values of the vulnerability factors were aggregated without weighting since the problem of weighting and different variable
units and dimensions was eliminated using the standardization method [9,27,38,93].
The results obtained were interpreted as follows: Exposure and sensitivity were ranked as low (<0.18), medium (0.18–0.29), and
high (≥0.30). The potential impact is the interplay of the extent of exposure and sensitivity which contributes to the level of commu-
nity vulnerability. It was determined as the summation of the sensitivity and exposure values and ranked as low <0.5, medium
(0.5–0.69) and high (≥0.7) while the composite vulnerability which is potential impact less the adaptive capacity of the community
was ranked as 0–0.33 least vulnerable, 0.34–0.66 vulnerable, and 0.67–1 highly vulnerable [65,27,94]. The composite vulnerability
scores are concluded on relative statements [95]. The exposure component is critical because it contributes to the potential impact of
a susceptible ecosystem and resulting in the extent of vulnerability.
2.3. Ethical considerations
Ethical clearance was sought from the College of Basic and Applied Sciences at the University of Ghana, Legon. ECBAS 058/19–20
was obtained to clear the research to be conducted.
3. Results
3.1. Socio-demographic characteristics of communities
Female headed households constituted over 60% of all households in Atitteti, Agorkedzi and Otrokpe (Table 3). All households in
Agorkedzi had at least 5 members who were below five (5) years. Over 75% of households in Lolonyakope and about 69% in Kewunor
had one (1) to five (5) children. More than 63%, 56% and 50% of respondents in Otrokpe, Azizanya and Fuveme respectively had no
formal education. Ayigbo (34%), Agorkedzi (33%), Atitteti (31%) and Lolonyakope (30%) had higher proportions of households with
at least one person at the age of 65 years and above. Households with children in basic schools were more in Fuveme (34%),
Agorkedzi (33%) and Kewunor (31%). At least a child under the age of five (5) was found in 33% of households in Agorkedzi, Az-
izanya (32%), Kewunor (31%) and Atitteti (30%). All households in Agorkedzi had at least one (1) to four (4) migrating from the
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
Table 2
Local community identified and validated indicators for assessment of social vulnerability.
No. INDICATORS JUSTIFICATION FUNCTIONAL
VULNERABILITY RELATION
COMPONENTS
EXPOSURE 1. Historical data on climate Change- +
related Disasters from 2005 to 2020
1. The proportion of the population displaced This explains the proportion of the population exposed to climate +
from tidal waves change-related disasters and the risks of disasters. The higher the
2. The proportion of casualties from tidal numbers, the higher the exposure levels. +
waves
3. The proportion of injuries from tidal wave +
disasters
4. The proportion of the displaced population +
from floods
5. The proportion of casualties from floods +
6. The proportion of injuries from flood +
disasters
7. The proportion of the population displaced +
from storm surge
8. The proportion of casualties from storm +
surge
9. The proportion of injuries from the storm +
surge
SENSITIVITY 2. Characteristics of Household
10. The proportion of female-headed households Female HH are more vulnerable +
11. Proportion of household heads with no Literacy influence HH disaster management decisions +
formal education
12. The proportion of unemployed and people Employments based on primary activities such as fishing and +
with primary occupations such as fishing, farming are more often less lucrative and vulnerable
farming, gathering etc.
13. Average Household size Households with higher membership are more vulnerable +
14. The proportion of the population >65 The aged, children and infants constitutes the categories of +
15. The proportion of the population <12 people that require much support and are more vulnerable in the +
16. The proportion of the population <5 event of a disaster +
3. Community Profile on Social
Amenities/infrastructure
17. The proportion of the population with no Communities without infrastructure and amenities are more +
access to electricity vulnerable during and after disasters
18. The proportion of the population with no +
access to potable water
19. The proportion of the population with no +
access to a health facility.
20. The proportion of the population with no +
access to a communication network
21. The proportion of the population with no +
access to public toilet facilities
22. The proportion of the population with no +
access to market
23. The proportion of the population with no +
access to basic schools
24. The proportion of the population with no +
access to road network.
ADAPTIVE 4. Socio-Economic Characteristics
CAPACITY
25. Household income It increases the resilience of the HH +
26. Proportion of literates +
27. Proportion of household members who +
migrated
28. Proportion of households who receive +
remittances
29. No. of household members working +
5. Communities and households' ability to
manage disasters
30. Indigenous early warnings It reduces the number of casualties +
31. Household preparedness. +
32. Community intervention. It indicates the level of DRR among communities. +
(continued on next page)
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
Table 2 (continued)
No. INDICATORS JUSTIFICATION FUNCTIONAL
VULNERABILITY RELATION
COMPONENTS
33. Households' satisfaction with community +
intervention
34. Government construction of coastal +
protection
35. Satisfaction with government intervention +
36. Households that benefited from training on Influence DRR among communities. +
disaster management and climate change-
related issues.
37. Disaster Volunteer Groups (DVGs) +
38. Relief Items +
39. Estimated total household income Influence level of HH disaster management cycle +
40. Remittances from family and friends abroad +
41. The proportion of the relocated population Contributes to households/communities' resilience +
42. The proportion with government support for +
relocation
43. The proportion of self-support for relocation It indicates wealth +
6. Social Capital
44. The proportion of the population with social Social capital increases community resilience +
support for relocation
45. The proportion of the population with social +
support for reconstruction
Table 3
Socio-demographic characteristics of households.
District Community Proportion of Households with membership that have: N
Female at least 1–5 no at least one person at least one child at least one child at least 1–4 members
heads membership education above 65 years of age in basic school under age five (5) who migrated
Anloga Atitteti 65.8 52.6 42.1 31.6 23.7 28.9 47.4 38
Fuveme 47.7 63.6 50 18.2 34.1 22.7 38.6 44
Agorkedzi 66.7 100 0 33.3 33.3 33.3 100 3
Ada Azizakpe 35.0 20 40 40 25 15 75 20
East Lolonyakope 46.4 75.4 46.4 30.4 23.2 18.8 82.6 69
Ayigbo 29.5 54.5 45.5 34.1 22.7 27.3 22.7 44
Azizanya 47.8 54.3 56.5 28.3 23.9 32.6 47.8 46
Otrokpe 63.3 20 63.3 16.7 13.3 23.3 46.7 30
Kewunor 56.3 68.8 31.3 31.3 31.3 31.3 62.5 16
community because of the hazards. More than 82% of households in Lolonyakope, 75% in Azizakpe and 62% in Kewunor also had be-
tween one (1) to four (4) members migrating.
3.2. Prevalent environmental hazards in the delta area
Qualitative data revealed that five different but interrelated environmental hazards were prevalent in the communities of the
Volta delta. These hazards included sea erosion, tidal waves, flooding from the rain, flooding from the river, and flooding from the
sea. Table 4 shows the hazards and the extent (low, medium, and high) to which they affected the communities. Fuveme and
Agorkedzi communities were the two most affected communities by sea erosion, tidal waves, and flooding from the sea. Attiteti is oc-
Table 4
Noted environmental hazards and their level of impact on communities.
District Community Sea Erosion Tidal Wave Rain Flooding Sea Flooding River Flooding
Anloga Atitteti 1 2 1 2 1
Fuveme 3 3 1 3 1
Agorkedzi 3 3 1 3 1
Ada East Azizakpe 3 3 3 3 3
Lolonyakope 1 1 3 2 3
Ayigbo 2 1 3 1 3
Azizanya 2 2 3 2 2
Otrokpe 3 3 1 2 3
Kewunor 3 3 3 3 3
Note: 1 = Low 2 = Medium 3 = High.
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casionally affected by tidal waves and floods from the sea. None of the communities in the Anloga District was affected by flooding
from torrential rains. In the Ada-East District, Azizakpe and Kewunor were the most affected by all the known environmental hazards.
As an island in the estuary, Azizakpe is generally low lying such that aside being affected by sea erosion, floods, and tidal waves from
the sea, much of it is flooded when torrential rains occur. Kewunor, on the other hand, experienced all five hazards because of its loca-
tion directly at the mouth of the estuary. It is affected directly by the diurnal changes in the river and the sea. Reports indicate that the
sea defence structure installed by the government of Ghana between 2013 and 2016 (which is Phase 1 of the proposed coastal man-
agement measures of the estuary) in portions of the coast of Ada East have provided some protection to communities such as
Lolonyakope, Ayigbo and Azizanya from sea erosion, tidal waves, and flooding from the sea. However, Otrokpe and Kewunor con-
tinue to experience erosion. Communities generally believed that the inability of the government of Ghana to complete the Phase 2 of
the project was responsible for the intensity of tidal waves and erosion in the area.
3.3. Vulnerability indices for communities in the Volta delta
Table 5 shows the indices for exposure, sensitivity, potential impact, adaptative capacity as well as the extent of vulnerability of
the studied communities. Regarding community exposure, Fuveme (0.76) and Azizanya (0.50) were ranked high, while Azizakpe
(0.24), Ayigbo (0.21), Otrokpe (0.29) and Kewunor (0.19) were ranked medium and the rest including Atitteti (0.09), Agorkedzi
(0.09) and Lolonyakope (0.07) were ranked low. On sensitivity, all the communities were ranked high in exception of Kewunor (0.21)
and Agorkedzi (0.00) which were ranked medium and low respectively. Generally, sensitivity in the study area was high apart from
Agorkedzi and Kewunor, which reported low and medium values of (0.00–0.21) respectively. The potential impact, which is the sum-
mation of exposure and sensitivity to coastal erosion, was also presented in Table 5. The potential impact is calculated to assess the
impact of coastal erosion on all selected communities. Fuveme recorded the highest index for potential impact of 1.40, followed by
Azizanya (1.29), Ayigbo (0.95), Lolonyakope (0.94), Otrokpe (0.92) and Atitteti (0.73), all of which were ranked high. Azizakpe's po-
tential impact was ranked as medium with 0.58 while Kewunor (0.39) and Agorkedzi (0.09) were ranked low. Two of the three com-
munities in Anloga district including Fuveme and Atitteti, therefore, recorded potential impacts that were ranked high.
The data on composite vulnerability of communities is also presented in the table. The scale for the indexes ranged from least vul-
nerable = (0–0.33), vulnerable = (0.34–0.66) to highly vulnerable = (0.67–1). The data shows that Fuveme had the highest index of
0.92, followed by Azizanya with an index of 0.69 and Ayigbo, Otrokpe and Atitteti recorded 0.64, 0.57, and 0.54 respectively. It
therefore means that Fuveme and Azizanya were highly vulnerable communities, while Ayigbo, Otrokpe and Atitteti were vulnerable,
and the rest of the communities were least vulnerable.
The following Figs. 2–6 present the enhanced visualization of the extent of vulnerability for the various variables including the ex-
posure, sensitivity, potential impact, adaptive capacity as well as the composite vulnerability of the sampled communities. Although
Azizakpe is an island community on the Volta River, the possible reasons accounting for the index of medium exposure (0.24), high
adaptive capacity (0.32) and least vulnerable (0.26) is that the community members employed multiple strategies to protect the com-
munity from completely being devasted by the multiple effect of tidal waves and floods from both the sea and the Volta River. These
include the use of 40 feet long sandbags filled with sand, and tyres filled with concrete. Heaping of refuse was also observed along the
banks of the Volta River. The residents indicated that an expatriate who owns a property on the Island provides the community with a
dredging machine and 40 ft long sacks which are filled with sand and used as a protection along the banks of the sea and the Volta
River to reduce the impact of tidal waves and floods Fig. 7.
3.4. Prevailing economic activities in the selected communities
The majority (77.4%) of respondents were engaged in fishing and fishing-related occupations, while 11.6% were involved in petty
trading, 4.2% were unemployed, as civil servants and artisans constituted 3.2% of the sampled population (Fig. 8).
Fig. 8 also shows the average income (in US dollars) of the respondents (household heads) per month in the various communi-
ties. It shows that, except for household heads of Azizakpe with an income threshold between 91 and 178 USD, the rest of the com-
munities’ household heads earned between 0 and 91 USD per month. Most household heads earn well below 100 US dollars per
Table 5
Exposure, sensitivity, and adaptive capacity of communities.
District Community Exposure Sensitivity Potential Impact Adaptive capacity Composite Vulnerability
Anloga Atitteti 0.09 0.64 0.73 0.18 0.54
Fuveme 0.76 0.65 1.40 0.48 0.92
Agorkedzi 0.09 0.00 0.09 0.01 0.08
Ada East Azizakpe 0.24 0.34 0.58 0.32 0.26
Lolonyakope 0.07 0.87 0.94 0.74 0.20
Ayigbo 0.21 0.74 0.95 0.31 0.64
Azizanya 0.50 0.74 1.25 0.55 0.69
Otrokpe 0.29 0.64 0.92 0.35 0.57
Kewunor 0.19 0.21 0.39 0.18 0.21
Ranking for exposure and sensitivity was: Low= (<0.18), Medium= (0.18–0.29) and High= (≥0.30). In the case of potential impact, it was ranked: Low (<0.5),
Medium (0.5–0.69) and High= (≥0.7); and composite vulnerability classifications was ranked: least vulnerable = (0–0.33), vulnerable = (0.34–0.66) and highly vul-
nerable = (0.67–1) after [65,94]. Concerning adaptive capacity, the indices in Ref. [65] ranged as follows: low = (≤0.12), medium = (0.13–0.14) and high =
(0.15–0.16). The adaptive capacity reported for the study area ranged from 0.01 to 0.74. Lolonyakope recorded the highest adaptive capacity index of 0.74 and Az-
izanya, Fuveme, Otrokpe and Azizakpe followed with 0.55, 0.48,0.35 and 0.32 respectively.
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Fig. 2. Extent of exposure to coastal disasters by communities.
Fig. 3. Extent of sensitivity to coastal disasters among communities.
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
Fig. 4. Potential impact of coastal disasters among communities.
month, influencing their preparedness and response to disasters. The prevailing exchange rate at the time of the study in 2020 was
5.6 Cedis to one (1) US Dollar.
Table 6 below shows the proportion of households whose members have people who worked to support the household head. In
Kewunor, 62.5% of households had at least one person engaged in an income-earning occupation. Another 12.6% of the households
had more than four (4) people who worked to support the household head. Similarly, 60% of households in Azizakpe had at least one
person who earned income to support the household head. Another 10% of households in the same community had more than four
(4) people who engaged in certain occupations to support the household. In Ayigbo, over 52% of the households had at least one (1)
person who earned income to support the household.
3.5. Types of housing structures inhabited by the households
The most common housing structures are built with cement and roofed with asbestos roofing sheets (Table 7). All households in-
terviewed in Agorkedzi, and 80% and 73% of those interviewed in Otrokpe and Attiteti, respectively, lived in houses made of cement
and roofed with asbestos. However, in Fuveme, which was affected over the years by tidal waves, 63.6% of households live in houses
built with coconut fronds and roofed by asbestos sheets. The entire Fuveme community had relocated and were living in makeshift
structures mainly made up of coconut fronds and different types of roofs. However, the community FGDs revealed that the type of
housing structures was not an essential determinant of the impact of erosion on communities. When they were asked about how envi-
ronmental hazards impacted on their buildings, some participants said:
“It is not because of the location or the material we used to build our homes but the force of the sea’s tidal waves removing the sand from
under the house, and after some time, the houses collapse, and they are gone.”
The leader of the Fuveme displaced community stated in an in-depth interview that he had an eleven (11) unit house built with ce-
ment, but all had been washed away.
“It is not because the building material was not good. I had 11 unites of houses made of cement blocks and a storeroom and
other facilities attached, but the sea eroded the entire building.”
3.6. Available infrastructure in the communities
The community profiling (Table 8) revealed that the communities generally lacked amenities such as health facilities, roads, elec-
tricity, and potable water. Three communities, Fuveme and Agorkedzi of Anloga District and Azizakpe in Ada East, did not have elec-
tricity. The rest were connected to the hydroelectric power from Akosombo, which is the primary source of energy for the country.
However, all the communities surveyed had a communication network even though the reception was poor at certain places, includ-
ing Agorkedzi and Fuveme. The availability of communication network however, linked the communities to other locations in and
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
Fig. 5. Adaptive capacity of coastal communities to disasters.
outside of Ghana through mobile telephone networks. Only four (4) of the communities had road networks, and the rest of the five (5)
did not. Residents of communities without roads walked or used boats to the nearest communities with roads to travel for commercial
activities, access health facilities or for other activities.
Except for Azizakpe, an island in the Ada-East District, the rest of the communities in the study areas had portable drinking water
supply from the Ghana Water Company. Residents of Azizakpe used the Volta River for domestic purposes, but most people drank sa-
chet water, which they bought from Ada Foah. Only Atitteti in the Anloga District had a public toilet in the form of the Kumasi Venti-
lated Improved Pit (KVIP). The rest of the communities engaged in open defecation along the beaches and nearby bushes. None of the
communities had a health facility. They all travelled to their district capitals or nearest major communities to seek health care. Basic
schools were available in only five (5) communities.
3.7. Interventions aimed at reducing the risk of hazards through sea defence structures
Respondents indicated that there have not been any significant attempts to reduce the risk from the environmental hazards in four
(4) communities, especially Attiteti, Fuveme and Agorkedzi of the Anloga and Azizakpe in the Ada East District (Table 9). The remain-
ing five (5) communities of the Ada East District received some interventions from the Government of Ghana through the construction
of the Phase I sea defence structures. The sea defence structure has protected two- Lolonyakope and Ayigbo-out of the five (5) commu-
nities with less support for the Kewunor community. Azizakpe, Azizanya and Kewunor have resorted to providing self-interventions
such as sandbags, heaping of refuse around their building, and car tyres filled with concretes of cement, to prevent floods from the
Volta River, tidal waves, and further erosion of the limited land area.
Interviews with community leaders and the FGDs with community members revealed that the impact of the climate change-
related events continue to exert enormous pressures on the communities, even with those with sea defence structures. The communi-
ties have general dissatisfaction and displeasure about the lack of proper or effective measures to protect them and their belongings.
Participants in the FGD at Lolonyakope and Ayigbo held the view that the indigenes were not involved in constructing the Phase 1 of
the sea defence structure hence the poor work done by the contractors.
“The assembly did not involve us in constructing the sea defence, so the sea defence was done in the wrong way. They are aware that the
people have done the wrong thing, but they do not say it. So, after they left, we still feel the impact of the sea erosion”.
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
Fig. 6. Composite vulnerability among coastal communities.
Fig. 7. Community protective structures observed at Azizakpe.
The leader of the Fuveme displaced community stated that their plight was because of Phase 1 of the sea defence structure con-
structed at Ada. He indicated that when the structure was not there, the impact of the sea was not as severe.
“The major factor that predisposed us to coastal erosion is the sea defence structure constructed at Ada because previously the impact
was not so severe as this”.
The chief fisherman at Atitteti indicated that coastal erosion had been with them since the time of their forefathers. But he men-
tioned that it was not as severe; therefore, they believed that the erosion rate was as a result of the construction of the Ada Sea defence
structure.
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Fig. 8. (A) Occupation of household heads/respondents to the structured interviews, (B) Average income of household heads in US dollars.
Table 6
Number of people in the household working in addition to the household heads.
District Community Number of people in the household working in addition to the household head
Nobody One (1) Person Two (2) Persons Three (3) Persons Four (4) or more persons N
Anloga Attiteti 44.7 44.7 7.9 0 2.6 38
Fuveme 36.4 31.8 27.3 4.5 0 44
Agorkedzi 66.7 0 33.3 0 0 3
Ada East Azizakpe 5 60 10 15 10 20
Lolonyakope 2.9 30.4 39.2 21.7 5.7 69
Ayigbo 4.5 52.3 15.9 20.5 6.8 44
Azizanya 6.5 28.3 30.4 15.2 19.6 46
Otrokpe 10 20 16.7 30 23.3 30
Kewunor 12.5 62.5 12.5 0 12.6 16
Table 7
Types of housing structures in the study area.
District Community Types of housing structures occupied by households interviewed
Cement and Cement and Cement and Mud and Coconut fronds Coconut fronds Wood and Coconut N
asbestos roof aluminium roof thatch roof thatch roof and asbestos and aluminium asbestos roof fronds and
roof roof thatch
Anloga Attiteti 73.7 - 7.9 - - - 2.6 15.8 38
Fuveme 0 - - - 63.6 15.9 - 20.5 44
Agorkedzi 100 - - - - - - - 3
Ada Azizakpe 25 5 15 - - 5 - 50 20
East Lolonyakope 15.9 1.4 13 47.8 1.4 - - 20.3 69
Ayigbo 50 - 25 15.9 - - - 9.1 44
Azizanya 60.9 2.2 10.9 23.9 2.2 - - - 46
Otrokpe 80 3.3 6.7 3.3 - 3.3 - 3.3 30
Kewunor 25 - 25 - 6.3 - - 43.8 16
“This (erosion) has been with our fathers and forefathers, but it was not as severe as it is in our time. So, we believe that the construction
of the sea defence at Ada was not properly done. It was not supposed to be on the Western side. It should have started from the eastern
side to reduce the pressure of the tides coming from the west”.
Interviews with the Ada East District officials confirmed that the change of government has stalled the construction of the Phase II
of the sea defence project, which could have offered some more protection to the communities. According to NADMO officials and the
Planning Officers, Phase II was to be constructed at the eastern side of the estuary, however this could not be implemented.
Except for Azizakpe, community interventions were all ineffective. A participant (the queen mother of the fishmongers) at Atitteti
during the FGD indicated that the tidal waves are so strong that no effort from them could stop its devastating effect.
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Table 8
Basic infrastructure amenities available in communities.
District Community Electricity Communication Network Road Network Potable Water KVIP Health Facility Basic School
Anloga Attiteti 1 1 1 1 1 0 1
Fuveme 0 1 0 1 0 0 0
Agorkedzi 0 1 0 1 0 0 0
Ada East Azizakpe 0 1 0 0 0 0 1
Lolonyakope 1 1 0 1 0 0 1
Ayigbo 1 1 1 1 0 0 0
Azizanya 1 1 1 1 0 0 0
Otrokpe 1 1 1 1 0 0 1
Kewunor 1 1 0 1 0 0 1
Note: 1-means facility is present; 0- means facility is not present, KVIP- Kumasi Ventilated Improved Pit.
Table 9
Attempts at risk reduction interventions.
District Community Government Sea Defense Structure Effectiveness of the Community interventions Effectiveness community
(SDS) SDS (CI) interventions
Anloga Attiteti No NA 1 1
Fuveme No NA 1 1
Agorkedzi No NA 1 1
Ada Azizakpe No NA 3 2
East Lolonyakope Yes 3 1 1
Ayigbo Yes 3 1 1
Azizanya Yes 1 1 1
Otrokpe Yes 1 1 1
Kewunor Yes 1 2 1
Note: 1 = Low 2 = Medium 3 = High.
4. Discussion
Coastal areas worldwide are generally vulnerable to inundation and erosive powers of the ocean [6,24,26,96,97]. Coastal vulnera-
bility is more prevalent in developing countries as compared to the developed countries. The exposure, sensitivity, and adaptability of
communities to severe coastal processes vary among communities and it is influenced by economic, environmental and social factors
[98]. This study explored the social factors that predisposes communities in the Volta delta of Ghana to coastal hazards. Following
[97] suggestions on socio-cultural factors predisposing communities to coastal vulnerability, this study examined factors such as
household characteristics (gender of household heads, occupation, education, population >65, <12 and < 5, average HH size),
community infrastructure and or amenities (road and communication network, electricity, health, toilet and educational facilities,
etc.), and socio-economic characteristics of the communities (household heads income, remittances, migration, rate of dependence in
households, etc.).
The Volta Delta is considered as highly sensitive and extremely vulnerable, however, a few communities are more exposed to
coastal processes. The combination of factors such as the prevalence of female headed households, high number of young children
and/or old people, people with disability, scanty source of income, lack of health facilities, poor or absent road networks, increase the
sensitivity levels of communities to coastal hazards [97,99,100]. [28,101,87] indicate that young children and women dominate the
population of the communities in the Volta estuary because of the migration of men in search of greener pastures. Several vulnerabil-
ity studies including [58,63,65,82,83]; have considered children below 5 and 12, as well as women, as indicators for sensitivity due to
their vulnerable nature during and after disaster events. Whereas some studies [65] have used housing structures (the type of building
and roofing materials) as indicators for sensitivity, this study shows that the type and quality of the building did not matter when con-
sidering the impacts of tidal waves, erosion, and floods from the river or the sea, etc. The use of more flexible housing structures and
materials such as thatch roofs served as an adaptive measure, since during disasters, the thatch buildings could be more easily dis-
mantled compared to cement blocks and asbestos or aluminum roofs. It was economically cheaper and easier to re-erect a thatched
house after a major disaster event.
Ghana's Fourth Communication to the United Nations in 2020 confirmed and emphasized that communities in the Volta delta are
highly exposed to the impact of climate change [26]. Some authors [102–104], have attributed the vulnerable nature of coastal shore-
lines and in particular, estuaries to the increasing sea-level rise from the impact of climate change. The study site, located in a dy-
namic area, experiences as many as five hazards: sea erosion, tidal wave, flooding from rain, flooding from the river and flooding
from the ocean. All the nine communities in one way or the other have been affected by these hazards. However, two communities in-
cluding Fuveme and Azizanya are prominently exposed to the ravages of the ocean and the river because of their location. Azizanya
suffers from both tidal waves and floods from the river. The number of people displaced each year by tidal waves and floods from the
river in the two communities has been high [24].
Over the years, households and communities have adapted to withstand the erosive powers of the ocean. Adaptive capacity in this
regard, is the latent ability of coastal communities to proactively and positively respond or recover from the effect of disaster caused
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M.M. Mattah et al. International Journal of Disaster Risk Reduction 95 (2023) 103896
by hazards [105]. According to Refs. [16,20,106,107]; the adaptive capacity of households and communities require the availability
of resources be it financial, social capital and networks, human resources, natural resources, institutions. The authors also believed
that the types of resources needed for adaptation depend on (i) the context within which adaptation is considered, (ii) the type of dis-
aster risk faced and (iii) the nature of adaptation strategy. At the household level, knowledge of indigenous early warning systems
plays a key role in enhancing the adaptability of households. There are also improvised protective measures by individuals, house-
holds, and communities to withstand the ravages of the ocean. The high adaptive capacity recorded among the communities indicate
that they were aware of the threat from the hazards in their communities and therefore their willingness to adapt to effective mecha-
nisms to minimize their exposure, potential impact and vulnerability levels [108,109]. The impact of hazards on affected communi-
ties reflect the successful adaptation strategies and their willingness to adapt. Aside the willingness to adapt, governments at various
levels-national, regional, and local as well as development agencies and civil society organisations must be willing to invest substan-
tially in protecting the households and communities [110]. Interviews with the government officials at the District Assemblies indi-
cated that, change of government and lack of resources affected the implementation of the Phase 2 of the sea defence. Communities
however averred that the sea defence structure of Phase 1 was not well constructed and for that matter, has worsened the situation in
the eastern side of the estuary. Communities stated that they were not consulted or engaged during the implementation of Phase 1.
They claimed their local and indigenous knowledge could have been of immense help to both the consultant and contractors of the
sea defence structure.
Generally, the communities are of low-income status with many of the households earning below 100 USD per month. Although
the communities are indigenous, poor people worldwide are more likely than others to live and remain at areas of high risk of disaster
impacts [111]. This is because properties in these high-risk areas are far cheaper. Communities of the Volta Delta continue to live in
high-risk areas possibly due to poverty as well as cultural reasons [112] where there is continuous allegiance to their ancestors
[112–114]. Though economic losses of poor households and communities may be relatively low as compared to the rich ones [115],
recovery from disasters is equally difficult for them. Low income communities are often unable to build adaptive measures to disas-
ters. [116] found that losses after disasters, in absolute terms, may be low but the average losses incurred during disasters is equiva-
lent to average size of savings. In effect, the poor most often have all their savings eroded during disasters [117].
The various socioeconomic factors discussed in this paper constitute a spectrum of variables that could affect the vulnerability sta-
tus of households and communities to disasters. According to Ref. [118]; households and communities who are negatively affected by
the socioeconomic factors are often vulnerable and seriously affected by disasters.
4.1. Conclusion
The study set out to assess the social factors that influence the vulnerability of communities within the enclave of the estuary of
the longest and largest river in Ghana, the Volta River, to coastal hazards. The rationale was to understand the vulnerability context to
make evidence-based recommendations regarding the way forward. The findings of the study show that although there were varia-
tions in the levels of communities' vulnerability to climate-related coastal hazards, the whole study area is generally vulnerable. So-
cial factors responsible for the increase in sensitivity included the prevalence of female headed households, high number of young
children and/or the elderly, people with disability, scanty source of income, lack of health facilities, poor or absence of road net-
works. Other factors bordered on cultural issues of the people's continuous allegiance to their ancestors because of their long period of
habitation of the place. Since the socio-cultural drivers of the phenomenon are multifaceted and interconnected, they require a pack-
age of solutions that can tackle the problem from multiple angles. A multi-stakeholder and multi-faceted approach are needed to build
the resilience of communities and ecosystems to prepare, respond, and cope with disasters in the event of their occurrence. It is rec-
ommended that a comprehensive disaster risk management plan be designed to guide the management of coastal hazards in the area.
Such a plan or framework should consider disaster risk models based on eccentric vulnerability factors, hazards warning alerts
methodology, human and infrastructure vulnerability assessment, post-disaster rehabilitation planning, sensitization/education of
the residents, as well as a technology-supported approach to coastal hazards prevention/mitigation/adaptation management. The
onus rests with the government to partner with other stakeholders such as non-governmental organisations, community-based organi-
sations, and households to prosecute this human-centred agenda to bring lasting relief to the communities concerned.
4.2. Limitation and suggestion for further research
This was a case study that looked at only the Volta estuary of Ghana, therefore, the findings may not be generalizable, although
other communities facing similar challenges may learn lessons from the study. Further research may be conducted on strengthening
coastal disaster risk governance, enhancing disaster preparedness for effective response as well as recovery, rehabilitation, and recon-
struction to guide policy and practice in the field of coastal disaster risk management. Such studies should have wider scope and
larger sample size to make the findings generalizable. Further studies should also be conducted based on the AR5 and AR6 framework
to provide basis for comparison. The use of equal weighting for the calculation of vulnerability indices could also be a limitation since
this assumes that all the indicators have equal weighting.
Funders
This study is a PhD thesis, and the authors wish to thank BANGA Africa, University of Ghana, Legon and Central University Ghana,
Miotso for partly funding the research.
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Institutional review board statement
Ethical clearance was sought from the College of Basic and Applied Sciences at the University of Ghana, Legon. ECBAS 058/19–20
was obtained to clear the research to be conducted.
Informed consent statement
Informed consent was acquired from all subjects who participated in the study, following ECBAS's guidelines.
Declaration of competing interest
The authors declare that they have no financial nor personal interest which is competing or influencing the data published in this
study.
Data availability
The authors do not have permission to share data.
Acknowledgments
The authors would like to express their gratitude to BANGA Africa of University of Ghana, Legon and Central University, Ghana
for partly funding this study, all the NADMO, and District Assembly Officials, Field Assistants and Community Members who sup-
ported with the data collection for the study.
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