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Citation: Duku, E.; Mattah, P.A.D.;
Angnuureng, D.B.; Adotey, J.
Understanding the Complexities of
Human Well-Being in the Context of
Ecosystem Services within Coastal
Ghana. Sustainability 2022,14, 10111.
https://doi.org/10.3390/
su141610111
Academic Editor: Antonio Boggia
Received: 7 July 2022
Accepted: 8 August 2022
Published: 15 August 2022
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4.0/).
sustainability
Article
Understanding the Complexities of Human Well-Being in the
Context of Ecosystem Services within Coastal Ghana
Eric Duku 1,2,3,* , Precious Agbeko Dzorgbe Mattah 1,2 , Donatus Bapentire Angnuureng 1,2
and Joshua Adotey 1
1
Centre for Coastal Management—Africa Centre of Excellence in Coastal Resilience, University of Cape Coast,
Cape Coast PMB TF0494, Ghana
2Department of Fisheries and Aquatic Sciences, University of Cape Coast, Cape Coast PMB TF0494, Ghana
3Hen Mpoano (Our Coast), Takoradi P.O. Box AX 296, Ghana
*Correspondence: eric.duku@stu.ucc.edu.gh or ericduku33@gmail.com; Tel.: +233-249073711
Abstract:
The understanding of the complexities of human well-being (HWB) within the ecosystem
service (ES) context is fundamental to the development of management plans to sustain the flow of
ecosystem services (ESs) for HWB. However, research on HWB in the context of ecosystem services is
still underrepresented on Africa’s coast. Primary data were collected from 794 household heads in six
communities within Ghana’s eastern coastal zone. A sequential logistics regression model was used to
assess the effect of the interactions between ESs, socio-economic conditions, and contextual factors on
HWB. Respondents’ well-being varied across the study communities, with high well-being reported
by 63% of respondents from Anloga and low well-being by 77% in Kedzi. A strong association was
found between HWB and relevant characteristics of respondents including marital status, years
lived in a community, subjective social position (SSP), main livelihood source, income class, access
to a reliable credit facility, and being a member of a local community group. Gender was not a
significant predictor of HWB levels. For the effect of ESs on HWB, we found that respondents who
had high contentment with provisioning and cultural ESs were more likely to have high well-being
as opposed to respondents who had low contentment. Respondents who had low to moderate
contentment with regulatory ESs were more likely to have high well-being, but the contextual factors
condensed the significance of this relationship. Findings suggest the implementation of deliberate
actions to maintain or restore vital ecosystem functions and services for sustainable well-being in
coastal communities.
Keywords:
human well-being; ecosystem services; community resilience; subjective social position;
Keta Lagoon Complex Ramsar site
1. Introduction
Coastal managers, spatial planners, and environmental scientists have discovered that
a large variety of wetlands located in coastal areas can provide many ecosystem services
(ESs) that are important to humans. These services come in the form of sediment trapping,
erosion control, and concentrated aquifer fills in specific locations [
1
,
2
]. Through their regu-
lation mechanism, wetland ecosystems are sources of freshwater, genetic resources, food,
and hydropower for people [
3
]. Furthermore, wetland ecosystems serve as recreation sites
for humans, critical habitats for a large proportion of avian and other terrestrial and aquatic
species, and water resources for agriculture and industrial usage [
4
,
5
]. Additionally, they
provide unique opportunities for education and tourism [
1
,
6
,
7
]. Because of their enormous
ecological function, wetland ecosystems are described as the ‘kidney of the
earth’ [8,9]
.
Wetland ecosystems are uniquely positioned to offer sustainable
livelihoods [1,10,11]
. More-
over, the commodities and services provided by coastal wetlands are critical for enhancing
human health, well-being, and resilience of coastal communities to numerous climatic
stresses [12].
Sustainability 2022,14, 10111. https://doi.org/10.3390/su141610111 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 10111 2 of 20
However, wetland ecosystems are regularly affected from both sides of the coastal
zone because they serve as a buffer zone between the landscape and seascape. More than
half of wetland area was lost in the 20th century, and wetland ecosystems continue to be
lost and degraded around the world [
13
]. Global wetland ecosystems began to deterio-
rate consistently and rapidly before their immense usefulness was properly known [
14
].
They have become accessible targets for human over-exploitation due to the pursuit of
a “better life” through population growth and advances in science and technology [
15
].
This lends weight to the claim that the function and services offered by complex ecological
processes and structures, such as the Keta Lagoon wetland ecosystem, can be connected to
individual or societal well-being [
16
]. Despite the rising need to understand the linkage
at varying landscapes, research on human well-being (HWB) in the context of ESs is still
underrepresented on Africa’s coast. HWB and ESs’ links have been viewed to be multiple
and complex [
17
]. The understanding of the complex links would help relevant stakehold-
ers to develop a comprehensive response plan that will sustain the flow of ESs for HWB
improvement [18].
The efforts of some researchers to understand the patterns and factors associated
with HWB within the context of ecosystems have led to the development of conceptual
frameworks. The first of this kind is the one developed by the Millennium Ecosystem
Assessment (MA) [
12
], which was also the first to unveil the link between ecosystems and
HWB at multiple scales [
17
]. The mediation effects of other significant factors such as
economic, social, and cultural factors on ESs and HWB’s links were also recognized by
the MA. This recognition is very paramount in well-being studies because ES is not the
only factor that affects HWB, but it affects HWB through interaction with socio-economic
conditions [
19
]. Ref. [
20
] also emphasized the relative contribution of ESs to HWB by
arguing that ESs indirectly benefit HWB by interacting with social capital, building capital,
and human capital. While the MA’s framework connects HWB, indirect drivers of change,
direct drivers of change, and ESs, the Driver–Pressure–State–Impact–Response (DPSIR)
framework proposed by [
21
] evaluates the impacts of environmental change on ESs. All
of these show the important contributions from different disciplines that have added
weight to the concept of well-being and have developed credible methods for studying
it [
22
]. This study was based on the MA’s conceptualization of HWB. Based on the MA
conceptualization, HWB, as a complex concept and an objective of development, is defined
by five elements, including security, the basic material for a good life, health, good social
relations, and freedom of choice and action. It then claims that the provision of services is
dependent on the status of the ecosystems in question, with human intervention having the
potential to either increase or decrease the benefits offered to human society. This affirms
the ‘human–nature connection’, as explained by the biophilia hypothesis [
23
] and the
Kaplan and Kaplan model [
24
]. It has additionally been contended that the experience of
ecosystem benefits and, as a result, HWB are contextually and situationally dependent [
25
].
Additionally, it has been suggested that HWB is the result of the interactions between
ecosystems and socio-economic conditions [
19
]. As in in most developing countries, a
high dependency on natural ESs, particularly provisioning services in Africa, has been
well recognized [
26
]. On the Ghanaian coast, where wetland habitats are prevalent, 50%
of income-generating activities and 37% of food production, respectively, directly depend
on these ecosystems [
27
]. For instance, over 100,000 people distributed in six district
assemblies within the boundaries of the Keta Lagoon Complex Ramsar Site (KLCRS)
depend directly or indirectly on the wetland ecosystem of the KLCRS for their livelihoods
and well-being [
28
]. Their dependence comes in the form of shelter, fishing, salt harvesting,
farming, recreation, and tourism among other economic activities [
29
]. The dependency
on the local ecosystems goes along with changes in the socio-economic conditions that
invariably could have consequences on HWB. However, most studies focusing on HWB in
Africa failed to investigate the extent to which the social, economic, and contextual factors
influence the effect of ESs on HWB. It was based on the foregoing background that this
study was contextualized in Ghana’s eastern coastal zone to understand the complexities
Sustainability 2022,14, 10111 3 of 20
of HWB in the context of ESs by measuring and determining the effect of the interactions
between ESs, socio-economic conditions, and contextual factors on HWB. These interactions
in a complex and rapidly growing socio-ecological landscape could better be understood
quantitatively by using sequential logistics regression (SLR). The SLR builds models that
allow researchers to assess the contribution of a predictor with the influence of other
predictors removed [
30
]. With this, we will be able to assess the actual influence of wetland
ESs on HWB to reinforce the prospects for the proper use and sustainable management of
coastal wetlands in Africa.
2. Materials and Methods
2.1. Study Area
The study was carried out in Keta and Anloga districts within the KLCRS (latitudes
5
◦
45
0
N to 6
◦
05
0
N and longitudes 0
◦
50
0
E to 1
◦
08
0
E) in the Volta Region of Ghana. It is
bordered to the west by the Volta River and to the south by the Gulf of Guinea (Figure 1).
The largest lagoon system (Keta Lagoon) in Ghana is found in this socio-ecological land-
scape. The Keta Lagoon is located to the east of the Volta River estuary, separated from
the sea to the south by a small sandbar. It has an estimated surface area of approximately
300 km
2
[
31
]. Some of the Volta River tributaries, Todzie River, and streams such as the
Belikpa and Aka all drain into the lagoon basin. The Avu Lagoon and the Volta estuary are
part of the Ramsar Site. The area has huge mangrove stands, scrub, marsh, fig trees, and
farmlands [28,32].
Sustainability 2022, 14, x FOR PEER REVIEW 4 of 21
Figure 1. Map of the eastern coast of Ghana showing Keta Lagoon and its surrounding floodplain,
district boundaries, and study communities.
2.3. Research Instrument and Measurement Items
In the questionnaire, the consent information and the geographic coordinates of the
respondents’ houses were captured in the first section. The second section entailed socio-
demographic and other household characteristics of respondents such as gender, age,
years lived in the community, residential status, educational level, marital status, main
livelihood source, income, subjective social position/status (SSP), and access to basic util-
ities. We adopted a single Likert scale question with responses ranging from 1 (bottom) to
10 (top) to measure respondents’ SSP [34]. The Likert scale is a psychometric scale that
allows respondents to select from a variety of categories to express their ideas, attitudes,
or feelings regarding a certain topic [35]. Questions about the level of contentment with
ESs that respondents derived (use or experience) from the KLCRS were captured under
Section 3 of the questionnaire. This consisted of 19 Likert scale (0−10) questions. In Section
4, we constructed 20 measured items for the five well-being constituents proposed by the
MA [12]: basic material for a good life (five items), health (four items), security (four
items), good social relations (four items), and freedom of choice and action (three items).
The development of the questions was aided by previous studies [18,36–39]. The basic
material for a good life focused on respondents’ access to basic goods (such as food, cloth-
ing, living conditions, and transportation), access to a comfortable place to live, having a
regulated life environment, and the ability of a household to afford enough food to keep
alive and healthy. The health dimension of well-being included feeling well and having a
healthy physical environment, which is achieved when the individuals secure clean air,
Figure 1.
Map of the eastern coast of Ghana showing Keta Lagoon and its surrounding floodplain,
district boundaries, and study communities.
Sustainability 2022,14, 10111 4 of 20
The population density of the area is high, especially on the narrow sandbar, which
separates the ocean from the lagoon and stretches from Anloga District to Keta Municipality.
The Ramsar Site is in Ghana’s southeastern coastal strip and is part of the dry tropical
equatorial climate area. In addition, it falls within the low-lying eastern coastal plain
of Ghana. Rainfall in the area ranges from 800–1000 mm, with an average temperature
of 30
◦
C [
33
]. Over 100,000 people depend directly or indirectly on the KLCRS wetland
ecosystem for their sustenance. Their dependence comes in the form of shelter, fishing, salt
harvesting, farming, recreation, and tourism among other economic activities [29].
2.2. Research Design and Study Population
The positivist philosophical ideology governed the study, and this informed the
adoption of a quantitative cross-sectional approach. The study randomly sampled and
interviewed 794 household heads from six communities in the two districts. Anloga
(
n= 133
), Woe (n= 132), and Tegbi (n= 132) were the communities selected from Anloga
District while Keta (n= 133), Kedzi (n= 132), and Anlo-Afiadenyigba (n= 132) were the
communities selected from Keta Municipality. These communities are distributed along
the narrow sandbar that separates the Keta Lagoon from the Atlantic Ocean (Figure 1). The
study started with a reconnaissance survey in all six communities of the KLCRS to win
the trust and confidence of the study population. It also helped the researchers to observe
the physical landscape and various ecological aspects of the Keta Lagoon basin, hence
gaining familiarity with the study area. A semi-structured questionnaire was designed,
vetted by experts, and pre-tested before the actual data collection. The vetting and pre-
testing of the questionnaire were to test its validity and reliability. The instrument was
deployed on the KoboCollect tool and administered to the selected communities by trained
enumerators. The interviews were conducted in the local language of the respondents.
Using the fishnet tool in ArcGIS software 10.7, houses were randomly selected and located
on the field with the help of the Garmin GPSMAP
®
62 21E001502 (Model 01102381, Taiwan).
From each of the selected houses, a household head from a randomly selected household
was interviewed.
2.3. Research Instrument and Measurement Items
In the questionnaire, the consent information and the geographic coordinates of
the respondents’ houses were captured in the first section. The second section entailed
socio-demographic and other household characteristics of respondents such as gender,
age, years lived in the community, residential status, educational level, marital status,
main livelihood source, income, subjective social position/status (SSP), and access to basic
utilities. We adopted a single Likert scale question with responses ranging from 1 (bottom)
to 10 (top) to measure respondents’ SSP [
34
]. The Likert scale is a psychometric scale that
allows respondents to select from a variety of categories to express their ideas, attitudes,
or feelings regarding a certain topic [
35
]. Questions about the level of contentment with
ESs that respondents derived (use or experience) from the KLCRS were captured under
Section 3of the questionnaire. This consisted of 19 Likert scale (0–10) questions. In Section 4,
we constructed 20 measured items for the five well-being constituents proposed by the
MA [12]
: basic material for a good life (five items), health (four items), security (four items),
good social relations (four items), and freedom of choice and action (three items). The
development of the questions was aided by previous studies [
18
,
36
–
39
]. The basic material
for a good life focused on respondents’ access to basic goods (such as food, clothing, living
conditions, and transportation), access to a comfortable place to live, having a regulated
life environment, and the ability of a household to afford enough food to keep alive and
healthy. The health dimension of well-being included feeling well and having a healthy
physical environment, which is achieved when the individuals secure clean air, have access
to clean water, and feel comfortable among others [
1
]. Security was conceptualized as
an individual’s ability to access natural and other resources, personal safety, and security
against natural and man-made disasters, while good social relationships centered on
Sustainability 2022,14, 10111 5 of 20
respondents’ mutual respect, social cohesion, network, and the ability to help or receive
help from others as well as support children, the aged, and people with disabilities. The
opportunity of individuals to achieve what they value doing was captured by the freedom
of choice and action dimension of well-being [40].
2.4. Statistical Analysis
To ensure the internal consistency of the measured items for the five elements of
HWB and those for the level of contentment of ESs that respondents derived from the
KLCRS, the widely used index of the reliability of a scale or set of survey items, Cronbach’s
alpha (
α
), was computed for each construct using Stata SE 14.0. The good reliability of the
measures is depicted by an
α
of more than 0.7 [
41
]. The following are the calculated ‘
α
’
for the five well-being elements: basic material for good life (five items;
α
= 0.945), health
(four items;
α
= 0.973), security (four items;
α
= 0.977), good social relations (four items;
α= 0.977
), and freedom of choice and action (three items;
α
= 0.969). A composite well-
being index (dependent variable) categorized into “low well-being” (0–3.99), “moderate
well-being” (4.0–6.99), and “high well-being” (7.0–10) was constructed. Each of the three
ESs had an ‘
α
’ above 0.7, with 0.932 for provisioning services (eight items), 0.908 for cultural
services (four items), and 0.864 for regulatory services (seven items). Contentment levels
for provisioning ecosystem services (PESs), regulatory ecosystem services (RESs), and
cultural ecosystem services (CESs) were categorized into low (0–3), moderate (3.1–6), and
high (6.1–10). Considering the nature of the current study, the ESs were the key predictor
variables in the SLR model.
The other independent variables were grouped into socio-demographic, economic
characteristics, utilities and sanitation-related characteristics, and contextual factors. The
relevant socio-demographic variables included in the study were gender, age (categorized
into young adult, <35 years; middle-aged adult, 35–55 years; and old-aged adult, >55 years;
see Armah et al. [
42
]), years of living in the community, religious affiliation, educational
level, marital status, member of local community group (yes or no response), a beneficiary
of welfare intervention (yes or no response), and SSP (using the percentile, this was grouped
into bottom, <5; middle, 5–6; and top, 7–10). Using the median of GHC 700 (note that
at the time of data collection, GHC 5.97 = US$1.00)), income measured on a continuous
scale (as part of the economic characteristics) was grouped into three classes: low class
(<GHC 700), middle class (GHC 700–999), and high class (>GHC 999). Household age
composition (dependent: ages 0 to 14 and 65+, and the productive ages: 15 to 64) was
used to calculate the dependency ratio (DDR) for each household (see Hadley et al. [
43
]).
This was also captured as an economic variable together with the main livelihood source
of respondents, engaged in other economic activities (no = 0, yes = 1), and access to a
reliable credit facility (no = 0, yes = 1). The utilities (cooking fuel and drinking water)
and household toilet facilities were captured under utilities and sanitation facilities. These
household characteristics were considered in the study because they are fundamental to
good health [
44
]. Using the classification system from the 2013 and 2017 WHO/UNICEF
Joint Monitoring Programme (JMP) report, the status of cooking fuel was categorized as
“clean” and “unclean”, and the status of a drinking water source and the type of sanitation
facilities were categorized as “improved” and “unimproved” [
45
,
46
]. The final independent
variable (contextual factors) was community.
The first model (Model I) of the sequential logistics regression contained the ESs only.
Models II and III included respondents’ socio-demographic and economic characteristics,
respectively, while Model IV included the variables related to utilities and sanitation
facilities plus all the variables in Model III. The final model (Model V) included all the socio-
demographic, economic characteristics, utilities and sanitation facilities-related variables,
and contextual factors. The results for all models were presented using the odds ratio (OR)
(a measure of effect size between the outcome and predictors) at a 95% confidence interval
(CI) [
30
]. The significance of the odds ratios and Pearson chi-square test results was set
at an alpha value
≤
0.05. Stata SE 14.0 (StataCorp, College Station, TX, USA) was used to
Sustainability 2022,14, 10111 6 of 20
perform all the statistical analyses. The Kolmogorov–Smirnov test of normality indicated a
close to a normal distribution of well-being scores, and the variance inflation factor (VIF)
revealed the absence of high multicollinearity between the independent variables (mean
VIF = 1.61). All the processed data from the cross-sectional survey were imported into the
ArcGIS 10.7 software, and a spatial map of the well-being index was developed to reveal
significant patterns identified.
3. Results
3.1. Respondents’ Background Characteristics and Well-Being Levels
The study included 794 household heads distributed in the six selected communities,
with approximately 92% of them being indigene and 8% being migrants. More than half
(63%) of the respondents had lived in the study area for more than 30 years (Table 1).
Respondents who had lived in the study area for 25–30 years had the highest (54%) pro-
portion among those with low well-being, while those who had lived in the study area for
more than 35 years were the highest (47%) in the high well-being category (Table 2). The
percentages of male and female household heads sampled from the six communities were
63% and 37%, respectively. In terms of well-being levels concerning gender, 64% of the
females and 55% of males had moderate to high well-being. Respondents who mentioned
that they were affiliated with a Christian religion dominated the sample, consisting of
59%, with the smallest percentage (3%) of respondents affiliated with the Islamic religion.
As indicated in Table 2, those who were affiliated with the Islamic religion represented
the highest percentage (38%) in the high well-being category, while those with a tradi-
tional faith had the highest percentage (45%) in the low well-being category. A majority
(73%) of the respondents indicated that they were members of a community group. Those
who did not belong to any community group constituted the highest (52%) proportion
among the respondents with low well-being, compared to 38% of those who were members
of local associations. The study sample was predominantly (56%) middle-aged adults
(
35–55 years
), with 24% being old-aged adults and young adults making up 20% of the
respondents. Among those whose well-being was high, old-aged adults had the highest
(44%) proportion while young adults had the lowest (29%) proportion among those with
low well-being.
Table 1. Respondents’ background characteristics.
Variables n% Variables N%
Gender Engage in other economic activity
Male 502 63 No 707 88
Female 292 37 Yes 92 12
Age group Member of local community group
Young adults: <35 years 153 20 No 213 27
Middle-aged adults: 35–55 443 56 Yes 581 73
Old-aged adults: >55 years 192 24
Years lived in community Residential status
25–30 294 37 Indigene 731 92
31–35 75 9 Migrant 61 8
More than 35 425 54 SSP
Educational level Bottom 317 40
No formal education 188 24 Middle 285 36
Basic 296 37 Top 192 24
Secondary 176 22 Dependency ratio (DDR)
Post-secondary 134 17 Low 372 47
Religious Affiliation Middle 120 16
Christian 474 59 High 292 37
Traditionalist 150 19 Beneficiary of previous
welfare intervention
Islamic 21 3 No 680 86
Do not belong 149 19 Yes 114 14
Sustainability 2022,14, 10111 7 of 20
Table 1. Cont.
Variables n% Variables N%
Marital status Income class
Unmarried 146 18 Low (<GHC 700) 383 48
Married 511 64 Middle (GHC 700–999) 145 18
Divorced/separated 69 9 High (>GHC 999) 266 34
Widowed 68 9 Access to a reliable credit facility
Main livelihood source No 705 89
Fishing 217 27 Yes 89 11
Farming 119 15 Cooking fuel status
Services and sales work 199 25 Unclean 441 56
Public or private
professional/manager work
144 18 Clean 353 44
Craft and related trade 49 6 Drinking water status
Salt extraction 28 3 Unimproved 50 6
Pension 26 4 Improved 744 94
Other occupations 12 2 Sanitation facilities
Unimproved 486 61
Improved 308 39
NB: The exchange rate at the time of the household survey and data analysis (October 2021) was GHC 5.97
= US$1.00.
Table 2. Percentage distribution of human well-being (HWB) by independent variables.
Human Well-Being Levels
Variable Low Well-Being Moderate Well-Being High Well-Being Chi-Square
% % %
Gender
Male 45 17 38 X2= 10.507 *,
Cramer’s V = 0.115
Female 36 26 38
Age group
Young adults 29 30 41 X2= 21.822 *,
Cramer’s V = 0.117
Middle-aged adults 46 20 34
Old-aged adults 42 14 44
Residence status
Indigene 42 20 38 X2= 3.948,
Cramer’s V = 0.071
Migrant 30 27 43
Years lived in community
25–30 54 20 26 X2= 39.068 ***,
Cramer’s V = 0.157
31–35 40.0 27 33
More than 35 33 20 47
Religious affiliation
Christian 43 20 37
X2= 8.171,
Cramer’s V = 0.072
Traditional 45 17 38
Islamic 19 38 43
Do not belong 38 21 41
Educational level
No formal education 37 15 48
X2= 19.798 *,
Cramer’s V = 0.112
Basic 43 20 37
Secondary 49 22 29
Post-secondary 35 27 38
Marital status
Unmarried 19 21 60
X2= 74.080 ***,
Cramer’s V = 0.216
Married 50 21 29
Divorced/separated 29 26 45
Widowed 37 7 56
Dependency ratio
Low 31 24 45 X2= 29.603 ***,
Cramer’s V = 0.137
Moderate 47 18 35
High 52 17 32
Income class
Low 49 13 38 X2= 44.491 ***,
Cramer’s V = 0.167
Middle 40 19 41
High 31 33 36
Sustainability 2022,14, 10111 8 of 20
Table 2. Cont.
Human Well-Being Levels
Variable Low Well-Being Moderate Well-Being High Well-Being Chi-Square
% % %
Main livelihood source
Fishing 57 13 30
X2= 66.730 ***,
Cramer’s V = 0.205
Farming 26 20 54
Services and sales work 38 29 33
Public or private professional/manager work 40 23 37
Craft and related trade 35 28 37
Salt extraction 23 8 69
Pension 36 11 53
Other occupations 75 8 17
Engage in other economic activity
No 42 20 38 X2= 2.426,
Cramer’s V = 0.055
Yes 36 26 38
Beneficiary of previous welfare intervention
No 40 19 41 X2= 18.318 ***,
Cramer’s V = 0.152
Yes 53 27 20
Member of local community group
No 38 15 47 X2= 77.843 ***,
Cramer’s V = 0.313
Yes 52 34 14
SSP
Bottom 75 16 9 X2= 274.004, ***
Cramer’s V = 0.415
Middle 24 19 57
Top 13 29 58
Access to a reliable credit facility
No 45 17 38 X2= 52.666 ***,
Cramer’s V = 0.258
Yes 15 47 38
Cooking fuel status
Unclean 39 18 44 X2= 7.8504 *,
Cramer’s V = 0.0994
Clean 45 24 31
Drinking water status
Unimproved 72 8.0 20 X2= 20.665 ***,
Cramer’s V = 0.161
Improved 39 22 39
Sanitation facilities
Unimproved 46 21 33 X2= 12.540 *,
Cramer’s V = 0.126
Improved 34 18 47
Contentment with PESs
Low 61 15 24 X2= 170.371 ***,
Cramer’s V = 0.328
Moderate 18 28 54
High 0 7 93
Contentment with RESs
Low 61 6 33
Moderate 27 30.0 43 X2= 120.622 ***,
Cramer’s V = 0.276
High 72 16 12
Contentment with CESs
Low 64 8 28 X2= 172.661 ***,
Cramer’s V = 0.313
Moderate 18 34 48
High 45 15 40
Community
Anloga 1 36 63
X2= 429.488 ***,
Cramer’s V = 0.501
Woe 0 37 63
Tegbi 75 4 21
Keta 67 18 15
Kedzi 77 23 0.0
Anlo-Afiadenyigba 29 5 67
*p≤0.05; *** p≤0.001.
In terms of marital status, a majority (64%) of the respondents were married and
were living with their partners. However, in the high well-being category, the unmarried
respondents had the highest percentage (60%), followed by those who were widowed (56%).
Additionally, the unmarried respondents had the highest (29%) proportion in the high-class
category of the SSP whereas the married dominated the low-class category (Figure 2b).
We also observed marital status differences in respondents’ main source of livelihood,
with most of the unmarried involved in fishing and public or private professional and
Sustainability 2022,14, 10111 9 of 20
managerial work (Figure 2a). A high proportion (39%) of the respondents were into services
and sales as their source of livelihood, followed by the married and widowed (Figure 2a).
Sustainability 2022, 14, x FOR PEER REVIEW 10 of 21
Figure 2. Main livelihood source by (a) subjective social position (SSP) and (b) marital status.
As shown in Table 1, the dependency ratio (DRR) of most (46.9%) of the respondents
was low. The level of well-being increased with a decreasing level of dependency ratio
(see Table 2). Most (76%) of the household heads had formal education, which included
those who had attained a basic education (37%), secondary (22%), and post-secondary
Figure 2. Main livelihood source by (a) subjective social position (SSP) and (b) marital status.
Sustainability 2022,14, 10111 10 of 20
As shown in Table 1, the dependency ratio (DRR) of most (46.9%) of the respondents
was low. The level of well-being increased with a decreasing level of dependency ratio (see
Table 2). Most (76%) of the household heads had formal education, which included those
who had attained a basic education (37%), secondary (22%), and post-secondary (17%).
Approximately 24% of the household heads were without a formal education. There were
varying occupations among the sampled household heads, ranging from fishing, which
recorded the highest percentage of 27%, to those involved in other occupations (Table 1).
Most (48%) of the respondents were considered low-income earners receiving a net monthly
income of less than GHC 700. Accordingly, they recorded the highest percentage (49%) in
the low well-being category. Respondents identified as middle-income earners had the
highest (41%) proportion in the high well-being category.
With regard to access to a reliable credit facility, 89% of the respondents had no access
to a reliable credit facility to support their businesses or household income avenues. Most
(45%) of those with no access to a reliable credit facility had low well-being, while most
(47%) of the respondents with access to credit had moderate well-being. Again, 87%
out of the 794 respondents claimed they had not previously benefited from any welfare
interventions. However, those who had benefited from welfare interventions had the
highest percentage (53%) among those with low well-being. Nearly 40% of the respondents
were at the bottom of the societal ladder, whereas 24% were those at the top. SSP increased
with increasing levels of well-being (see Table 2).
A significant proportion (95.3%) of the respondents’ households drank from improved
water sources. However, more than half (61.2%) of the respondents used unimproved
sanitation facilities in their houses (Table 1). Based on this sample, 6 out of 10 households
used unimproved sanitation facilities and 9 out of every 10 households were likely to
drink from an improved water source. The main cooking fuel used by more than half
(55.5%) of the respondents could be described as unclean. Differences in drinking water
status, cooking fuel status, and sanitation facilities concerning well-being levels were also
observed (see Table 2).
Considering the contentment with the ESs, approximately 61% and 64% of the respon-
dents who had low contentment with PESs and CESs, respectively, had low well-being.
Similarly, 61% of the respondents who had low contentment with RESs had low well-being
(Table 2). While the majority (93%) of the respondents who had high contentment with
PESs had high well-being, the majority (72%) of the respondents who had high content-
ment with RESs had low well-being. Moreover, 54%, 48%, and 43% of respondents who
had moderate contentment with PESs, CESs, and RESs, respectively, fell within the high
well-being category.
3.2. Univariate Analyses of the Association between Well-Being Levels of Respondents and
Explanatory Variables
A nonparametric Pearson chi-square test of independence was used to calculate
the association between well-being (response variable) and level of contentment with ESs
(provisioning, regulatory, and cultural) derived from the KLCRS and other explanatory vari-
ables (including respondents’ social and economic characteristics, utilities and sanitation
facilities, and contextual factors). We found significant associations between respondents’
well-being and all the explanatory variables, except residential status, religious affiliation,
and engagement in other economic activities (see Table 2). However, the strength of the
association depicted by the Cramer’s V statistic was strong for all the ESs’ types, SSP, main
occupation, access to a reliable credit facility, member of a community group, and commu-
nity. The remaining independent variables showed a weak association with well-being. We
observed differences in the levels of well-being across the study communities. For instance,
the distribution of well-being levels showed higher well-being improvement for most of
the respondents from Anloga, Woe, and Anlo-Afiadenyigba (Table 2, Figure 3).
Sustainability 2022,14, 10111 11 of 20
Sustainability 2022, 14, x FOR PEER REVIEW 12 of 21
Figure 3. Spatial distribution of human well-being (HWB) levels across the study communities.
3.3. Sequential Logistic Regression Analysis of the Effects of ESs and Other Explanatory
Variables on Respondents’ Well-Being
Table S1 shows the results from the sequential logistic regression analysis on the ef-
fect of ESs, socio-economic characteristics, utilities and sanitation facilities, and contextual
factors on HWB in the KLCRS. In Model I, respondents with low and moderate content-
ment with PESs they derived from the KLCRS were 98.8% and 94.6%, respectively, less
likely to have higher well-being compared to those who perceived their contentment with
PESs derived from the wetland to be high. Additionally, compared with respondents per-
ceiving high CESs, those who had low contentment with CESs were 58.1% less likely to
have high well-being (Table S1). However, compared with respondents who perceived
high RESs, respondents whose level of contentment with RESs was low (OR = 17.249; 95%
CI = 5.712–52.083) to moderate (OR = 8.543; 95% CI = 3.143–23.221) were far more likely to
report high well-being (Table S1).
In Model II, respondents who perceived low and moderate levels of contentment
with PESs derived from the wetland were 99% and 92.5%, respectively, less likely to ex-
perience high well-being as compared to those who perceived high contentment with
PESs they derived from the wetland. Similarly, respondents who perceived low CESs
were 55.1% less likely to experience higher well-being levels as compared with those who
perceived high CESs derived from the wetland. In terms of RESs, respondents who per-
ceived them to be low (OR = 25.402; 95% CI = 6.660–96.888) and moderate (OR = 7.880; 95%
CI = 2.350–26.425) were far more likely to have high well-being. With regards to age, mid-
dle-aged adults (OR = 0.546; 95% CI = 0.312–0.956) were less likely to have high well-being
than young adults. Respondents who had spent 25‒30 years (OR = 0.405; 95% CI = 0.259–
Figure 3. Spatial distribution of human well-being (HWB) levels across the study communities.
3.3. Sequential Logistic Regression Analysis of the Effects of ESs and Other Explanatory Variables
on Respondents’ Well-Being
Table S1 shows the results from the sequential logistic regression analysis on the effect
of ESs, socio-economic characteristics, utilities and sanitation facilities, and contextual
factors on HWB in the KLCRS. In Model I, respondents with low and moderate content-
ment with PESs they derived from the KLCRS were 98.8% and 94.6%, respectively, less
likely to have higher well-being compared to those who perceived their contentment with
PESs derived from the wetland to be high. Additionally, compared with respondents
perceiving high CESs, those who had low contentment with CESs were 58.1% less likely
to have high well-being (Table S1). However, compared with respondents who perceived
high RESs, respondents whose level of contentment with RESs was low (OR = 17.249;
95% CI = 5.712–52.083
) to moderate (OR = 8.543; 95% CI = 3.143–23.221) were far more
likely to report high well-being (Table S1).
In Model II, respondents who perceived low and moderate levels of contentment with
PESs derived from the wetland were 99% and 92.5%, respectively, less likely to experience
high well-being as compared to those who perceived high contentment with PESs they
derived from the wetland. Similarly, respondents who perceived low CESs were 55.1% less
likely to experience higher well-being levels as compared with those who perceived high
CESs derived from the wetland. In terms of RESs, respondents who perceived them to be
low (OR = 25.402; 95% CI = 6.660–96.888) and moderate (OR = 7.880;
95% CI = 2.350–26.425
)
were far more likely to have high well-being. With regards to age, middle-aged adults
(
OR = 0.546
;
95% CI = 0.312–0.956
) were less likely to have high well-being than young
adults. Respondents who had spent 25–30 years (OR = 0.405; 95% CI = 0.259–0.633) in the
Sustainability 2022,14, 10111 12 of 20
study area and those who had spent 31–35 years (OR = 0.495; 95% CI = 0.265–0.924) were less
likely to have high well-being as compared to those who had stayed for more than 35 years.
Moreover, compared with respondents who were married, the unmarried respondents
(
OR = 2.737
;
95% CI = 1.627–4.606
) and those who were divorced/separated (OR = 1.898;
95% CI = 1. 078–3.342) were far more likely to have high well-being. Respondents who were
not members of a community group were 68% less likely to have high well-being than those
who belonged to a local group/association. Finally, Model II showed that respondents’ SSP
significantly (p< 0.001) predicted their well-being levels, with those at the bottom being
92.1% less likely to have high levels of well-being than respondents at the top.
The inclusion of the economic characteristics, such as income, main occupation/livelihood
source, engaging in other economic activity other than main occupation, access to reliable
credit, and dependency ratio in Model III, improved the significance of the association
between RESs and HWB but did not cause any significant changes in the relationships PESs
and CESs have with HWB. It also moderated the relationship between educational level
and HWB by making it to be significant at an alpha level of 0.05. Respondents’ income
and access to reliable credit facilities had significant effects on their well-being levels but
main occupation/livelihood source, dependency ratio, and engagement in other economic
activities did not have significant effects on respondents’ well-being. Model III revealed that
respondents who engaged in fishing as their main livelihood source/occupation were 59.5%
less likely to have high well-being compared to respondents who were public/private
professional or managerial workers.
In Model IV, the significance and pattern of the association between all the ESs’ types
and respondents’ well-being observed in Model III were not altered. However, it condensed
the influence of age and educational level on HWB by displacing the significance of
the association that age and educational level had with HWB (Table S1). Unexpectedly,
respondents who used unclean cooking fuel were found (Table S1) to be 60.6% more likely
to have high well-being than those who used clean cooking fuel. This was not the same for
drinking water and sanitation facilities.
The final model (Model V, Figure 4) showed the effect of ESs on HWB while controlling
for socio-economic characteristics of the respondents, household utilities and sanitation
facilities, and contextual factors. As with the previous four models, respondents who
perceived PESs derived from the KLCRS to be low and moderate were, respectively, 95.2%
and 0.88.7% less likely to have high well-being levels compared with those who perceived
high PESs (see Table S1; Model V). Similarly, compared with respondents who perceived
a high sense of CESs, respondents who indicated that they had low and moderate levels
of contentment with CESs were, respectively, 82.6% and 51.5% less likely to have high
well-being. However, respondents who considered their contentment level with RESs
to be moderate were 337.9% far more likely to have improved well-being than those
who perceived high RESs. As rightly observed in Model V (Table S1, Figure 4), the
inclusion of the contextual factor (community) intensified the relationship between CESs
and respondents’ well-being but altered the significance of the relationship between RESs
and HWB.
In Model V (Table S1), socio-demographic variables such as gender of respondents,
age, educational level, and ‘benefited from welfare intervention previously’ did not have
significant effects on the levels of well-being. The number of years respondents had lived
in the KLCRS, marital status, membership of local association/group, and SSP all had a
significant effect on well-being levels. For instance, respondents who had stayed in their
neighborhood for 25–30 years were 42.3% less likely to have high well-being, compared to
those who had stayed in their community for more than 35 years. Additionally, respondents
who had never been married (OR = 2.454; 95% CI = 1.334–4.514) were more likely to have
high well-being than those who were married (see Table S1, Model V). Additionally,
respondents who did not belong to any local association or group were 72.9% less likely
to have high well-being, compared to those who were members of a community group.
Finally, compared with respondents who were at the top in terms of SSP, those who were
Sustainability 2022,14, 10111 13 of 20
at the bottom and those perceived to be at the middle were 93.3% and 44.1%, respectively,
less likely to have high well-being.
Sustainability 2022, 14, x FOR PEER REVIEW 14 of 21
Figure 4. Effects of ecosystem services (ESs) on human well-being (HWB) while controlling for so-
cio-demographic, economic, utilities and sanitation-related variables, and contextual factors. Model
V represents the final model of the sequential logistic regression involving all the independent var-
iables.
In Model V (Table S1), socio-demographic variables such as gender of respondents,
age, educational level, and ‘benefited from welfare intervention previously’ did not have
significant effects on the levels of well-being. The number of years respondents had lived
in the KLCRS, marital status, membership of local association/group, and SSP all had a
significant effect on well-being levels. For instance, respondents who had stayed in their
neighborhood for 25‒30 years were 42.3% less likely to have high well-being, compared
to those who had stayed in their community for more than 35 years. Additionally, re-
spondents who had never been married (OR = 2.454; 95% CI = 1.334–4.514) were more
likely to have high well-being than those who were married (see Table S1, Model V). Ad-
ditionally, respondents who did not belong to any local association or group were 72.9%
less likely to have high well-being, compared to those who were members of a community
group. Finally, compared with respondents who were at the top in terms of SSP, those
who were at the bottom and those perceived to be at the middle were 93.3% and 44.1%,
respectively, less likely to have high well-being.
With regard to the economic factors, respondents designated as middle-income earn-
ers were 95.2% more likely to have high well-being than those in the high-income class.
Compared to respondents who were public/private professionals or managerial workers,
farmers were 37.6% more likely to have high well-being whereas salt extraction workers
were less likely to have high well-being (OR = 0.327; 95% CI = 0.107–0.999) (Table S1,
Model V). Moreover, respondents who indicated that they did not have access to reliable
Figure 4.
Effects of ecosystem services (ESs) on human well-being (HWB) while controlling for socio-
demographic, economic, utilities and sanitation-related variables, and contextual factors. Model V
represents the final model of the sequential logistic regression involving all the independent variables.
With regard to the economic factors, respondents designated as middle-income earn-
ers were 95.2% more likely to have high well-being than those in the high-income class.
Compared to respondents who were public/private professionals or managerial workers,
farmers were 37.6% more likely to have high well-being whereas salt extraction workers
were less likely to have high well-being (OR = 0.327; 95% CI = 0.107–0.999) (Table S1,
Model V). Moreover, respondents who indicated that they did not have access to reliable
credit facilities to support their businesses or household income were 63.8% less likely to
have high well-being. Apart from engaging in other economic activities and DDR, all the
other economic variables considered in this analysis had significant effects on respondents’
well-being.
Considering the utilities and household sanitation facilities, respondents whose house-
holds used unimproved sanitation facilities were 37.5% less likely to have high well-being
compared to those whose households used improved sanitation facilities. The signifi-
cance of the effect of cooking fuel type and drinking water source status on respondents’
well-being observed in Model IV (Table S1) disappeared with the inclusion of community
(contextual factors). Regarding the study communities, respondents were less likely to
have high well-being in Keta (94.4%), Kedzi (90.5%), and Tegbi (83.6%) compared to the
respondents who resided in Anloga. However, those who were from Anlo-Afiadenyigba
Sustainability 2022,14, 10111 14 of 20
were far more (251.1%) likely to have high well-being than respondents who were residents
of Anloga. It was observed (Table S1) that variables such as gender, beneficiary of welfare
intervention previously, engagement in other economic activities, and DDR did not have a
significant relationship with respondents’ well-being levels in any of the models in which
they were included.
4. Discussion
The study was undertaken to contribute to the growing need to understand the
complexities of wetland ESs and HWB’s linkage, which are fundamental for sustainable
development. Using empirical evidence from the KLCRS in southeastern Ghana, these
relationships were unveiled. In addition to investigating the relationships between ESs
and HWB of residents in the study area, other important factors such as social, economic,
and contextual factors that have the potential to influence HWB were also considered.
The study revealed that respondents who had low contentment with PESs and CESs
reported less improvement in their well-being, as compared to those with high contentment.
This points to the fact that a limited flow and insufficient access to PESs by people are
important determinants in the loss of HWB [
47
]. This is likely to be more prominent
in areas with a high dependency on local ESs. Contrary to PESs and CESs, the well-
being of respondents who perceived moderate contentment with RES was high, compared
to those who had high contentment. This suggested that the well-being of residents
in coastal communities is likely to increase despite the limited flow of RESs. The MA,
based on its global assessment, indicated increased well-being in the face of declining ESs,
particularly RESs [
48
]. Some other studies [
49
,
50
] also reported similar results. A plausible
explanation for the increased well-being despite the low contentment with RESs attributable
to the deteriorated ecosystem could be the availability of close substitutes and other
available technology for the degraded ESs, as explained in
Raudsepp-Hearne et al. [48]
.
Additionally, it has been argued that the well-being benefits associated with PESs, such as
food production, in some cases outweigh the cost of a decline in other ecosystem services.
This study recognizes the fact that HWB is multi-dimensional [
51
] and is influenced
by some important factors that may be independent of the ESs. The findings of this study
demonstrate the extent to which income contributes significantly to respondents’ well-
being levels. Income was found to be a significant predictor of respondents’ well-being in
the last model of the sequential logistics analysis. The middle-income earners were more
likely to have high well-being than were the high-income earners. Even though this was
not expected, there is evidence supporting the fact that some societies tend to be happier
regardless of income, and the disparities in social support and positivity have been the
plausible explanation for this association [52].
Another important finding was the significant effect of respondents’ main occupa-
tion/livelihood source on their well-being. Farmers were marginally more likely to have
high well-being than those who were public/private professional and managerial workers.
This observation could be attributed to the ability of most farming households to acquire
a lot of goods and services directly from the local ecosystem, which helps them to avoid
the cost of buying most of the daily necessities such as food, fish, and fuelwood. This
is supported by the argument that the benefits households obtained from ecosystems in-
cluded both the benefits reflected in their income and the avoided costs not reflected in their
expenditures [53]. Another plausible explanation for farmers’ higher odds of having high
well-being is that most of the non-governmental organizations (NGOs) and government
livelihoods’ intervention programs undertaken in the study area were targeting the farm-
ers. For example, under the Food and Agriculture Sector Development Policy (FASDEP
II) implemented in Ghana, farmers were given some incentives including subsidies and
technical information to boost food production [
54
]. Currently, vegetable and grain farmers
are given close support under the Planting for Food and Jobs program implemented by the
Ministry of Food and Agriculture in Ghana through the District Assembly [
54
]. Farmers are
mostly provided with alternative and complementary livelihood strategies by the NGOs,
Sustainability 2022,14, 10111 15 of 20
and this increases their resource base to have a better life. It was, therefore, not surpris-
ing that individuals who diversified into other economic activities, as well as those who
had access to reliable credit facilities to support their businesses and household income,
were more likely to have higher levels of well-being. In a study in Ghana [
55
], livelihood
diversification was found to have consumption gain in the short run and a long-run wealth
creation effect.
Gender was not found to be a significant predictor of respondents’ well-being. This
suggests that “equality in the capability and freedom of the different gender to pursue
a life of their choosing” [
56
] exists in the study area. In terms of marital status, most of
the respondents who had never married had high well-being. A plausible explanation for
this could probably be that the unmarried may have had low economic pressures in their
quest to provide for their daily needs. This may expand their ability to enjoy a social life.
Additionally, it was observed (Figure 2a) that most of the unmarried respondents were in
good employment as well as in the middle class of SSP and, for that matter, enjoyed higher
well-being. Similar findings were reported by previous studies in Ghana [
57
,
58
]. It was
argued further that the unmarried in Ghana also have access to social support just as the
married, which gives them meaningful relationships [58].
Despite the significant differences in the levels of well-being among respondents
with low, moderate, and high dependency ratios, the dependency ratio did not show
a significant effect on HWB. However, being a member of a community group had a
significant effect on respondents’ well-being levels. As admitted by most participants,
being a member of a community group gives one the advantage of receiving economic
support from NGOs and the District Assembly to boost their businesses and household
income. This is because community groups and associations are considered to be the route
to community mobilization for development by both NGOs and government agencies [
59
].
More than the financial support, the community associations/groups also promote social
connectedness and cohesion, as revealed in some green spaces and HWB studies [
60
–
62
] as
being one of the pathways to improve health and well-being. Consistent with a study in
Beijing [
63
], the current study found that the number of years lived in a community affects
people’s well-being. In a more riparian or natural environment with a large expanse of
vegetated areas and water bodies such as the current study area, the relationship between
years lived in a community and respondents’ well-being can be explained by the biophilia
hypothesis [23] and the Kaplan and Kaplan model [24].
One of the outstanding findings from this study is that an individual’s SSP, which may
have not been fully explored in previous well-being studies, was found to be a significant
predictor of respondents’ well-being levels. The likelihood of respondents who consider
themselves to be at the bottom of the societal ladder to report high well-being was less
than those who consider themselves to be at the top. Additionally, the odds of those who
position themselves in the middle reporting high well-being improvement were higher
than those at the bottom. This relationship was observed in all the models of the sequential
logistics analysis. Moreover, the SSP is associated with self-related health [
64
,
65
] and both
psychological and physiological functioning [
34
], all of which are constituents of HWB. In
a happiness study [
66
], it was found that an individual’s SSP is a more important predictor
of happiness than objective measures such as income, education, and labor market position.
In this study, we also argued that SSP offers a better measure of one’s overall status and
progress in society as influenced by money, education, job, and access to ESs than the use
of income.
The findings of the study affirm that access to safe drinking water and improved
sanitation is central to an improvement in human health and well-being [
67
–
69
], and these
are reflected in Goal 3 and Goal 6 of the sustainable development goals. The results show
that the likelihood of respondents with access to improved sanitation facilities to have
improved well-being was more than those who use unimproved sanitation facilities such as
open defecation, shared or own pit latrine, and a flush or pour-flush latrine elsewhere (e.g.,
open gutter) among others. The same can be said for those who use improved drinking
Sustainability 2022,14, 10111 16 of 20
water. Even though there is a general notion that unclean cooking fuel use has a lot of
implications for the user, this study showed that respondents who use unclean cooking fuel
reported high well-being compared to those who use clean cooking fuel. The significance
of this observation was moderated by the contextual factor (communities), which could be
due to the community variations in the cooking fuel type used. The observation made from
the field showed that the use of unclean cooking fuel, especially fuelwood, was outdoors,
which the surrounding vegetation may help regulate. The air pollution regulating the
ability of the ecosystem may limit the health risk associated with household air pollution
from unclean cooking fuel use. However, the increasing use of unclean cooking fuel without
an innovative approach to reducing its pollutant levels will probably cause health risks in
the future as well as in other communities.
In this study, variations in the levels of well-being among the six study communi-
ties were observed. This shows that the location where a household resides is crucial in
determining its well-being within the KLCRS, affirming the doctrine of environmental
determinism [
70
,
71
]. This has been one of the geneses of the growing need for the assess-
ment of the nexus between ESs and HWB at different scales. The inclusion of the study
communities in the final model moderated the significance of the relationship between
HWB and both CESs and RESs. It was further shown that among the six study communities,
a greater proportion of the respondents from Anlo-Afiadenyigba and Anloga reported
higher well-being (refer to Table 2). This trend could be attributed to the multiple livelihood
opportunities such as fishing, salt extraction, farming, and Kente weaving among others
that exist in these two communities, of which a greater part is directly linked to the ESs
provided by the wetland ecosystem of the KLCRS [72].
5. Strengths, Limitations, and Future Research
The use of a relatively large sample size involving households with varying socio-
economic statuses whose livelihoods are either directly or indirectly connected to the
wetland ecosystem of the KLCRS is one of the strengths of this study. We also employed
rigorous statistical analytical techniques to analyze the data. A broad range of socio-
economic and household characteristics was considered in our analysis. Despite these, the
uneven ratio between the categories of some of the variables such as gender may have
made the relationship between such variables and HWB insignificant in the SLR model.
The subjective approaches used in the measurement of well-being and ESs’ contentment
are susceptible to biases from recall and other social desirability issues. Based on these,
future research is required to combine subjective and objective measurement approaches
to well-being and ESs’ contentment. It should also consider the non-proportionality of
variable categories.
6. Conclusions
The current study sought to understand the complexities of HWB in the context of
ESs in a fragile riparian environment within the eastern coastal zone of Ghana. We found
significant community differences in the levels of well-being. This study demonstrated
that people with high contentment with PESs and CESs they derived (use or experience)
from wetland ecosystems are more likely to report high well-being. Marital status, years
lived in a community, SSP, main livelihood source, income class, access to a reliable credit
facility, being a member of a local community group, and type of sanitation facilities
and community were the social, economic, utilities, and sanitation-related factors that
had significant effects on HWB in the KLCRS. The community (contextual factors) was
a significant moderator of the relationship between ESs and HWB. These findings are
clear evidence that ESs contribute to HWB improvement by interacting with people’s
socio-economic characteristics, household characteristics, and contextual factors. To ensure
sustainable well-being and capacity building for all coastal communities on the African
coast, it is recommended that deliberate actions are implemented to maintain or restore
vital ecosystem functions and services. For instance, effective implementation of the
Sustainability 2022,14, 10111 17 of 20
fourth Ramsar Strategic Plan (2016–2024) [
73
] together with marine spatial planning could
help improve HWB while maintaining and improving the ecological integrity of wetland
ecosystems in coastal areas of Africa.
Supplementary Materials:
The following supporting information can be downloaded at https://
www.mdpi.com/article/10.3390/su141610111/s1. Table S1: Sequential logistic regression results of
the effects of independent variables on human well-being in the wetland ecosystem of the KLCRS.
Author Contributions:
The study was conceptualized and executed by E.D., P.A.D.M. and D.B.A.
supervised all aspects of the study. E.D. performed the data collection and analysis. P.A.D.M., D.B.A.,
J.A. and E.D. contributed to revising the manuscript critically for its intellectual content. All authors
have read and agreed to the published version of the manuscript.
Funding:
This research, as part of the first author’s MPhil thesis, received a fieldwork grant from the
World Bank Africa Centre of Excellence in Coastal Resilience (ACECoR) Project (World Bank ACE
Grant Number 6389-G) at the University of Cape Coast (UCC), Ghana. The APC was funded by the
World Bank Africa Centre of Excellence in Coastal Resilience (ACECoR).
Institutional Review Board Statement:
Since the research involved human participants, ethical
approval was provided by the University of Cape Coast Institutional Review Board (UCCIRB),
with reference number UCCIRB/CANS/2021/03. Anonymity and confidentiality were ensured
and upheld.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in
the study
.
Data Availability Statement:
The policies of UCCIRB do not support the sharing of the survey
data publicly.
Acknowledgments:
The authors sincerely thank the Africa Centre of Excellence in Coastal Resilience
(ACECoR) and the University of Cape Coast (UCC), with support from the World Bank, for funding
this research. We, therefore, thank all research respondents for their consent and all the field assistants
for their excellent fieldwork and dedication.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or
in the decision to publish the results.
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