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Socio-Cultural Vulnerability Index (SVI) Approach for Social Impact
Assessment among the Coastal Community Management Due to Climate
Change
Zaini Sakawi
1,2,
*, Sofia Ayup
3
, Ricky Kemarau
1
, Christoper Perumal
4,5
1
Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM)
2
Geography Programme, Centre of Social, Development and Environmental (SEEDS), Universiti
Kebangsaan Malaysia (UKM)
3
Pusat Citra, Universiti Kebangsaan Malaysia (UKM)
4
Environmental Management Program, Faculty of Social Sciences and Humanities, Universiti
Kebangsaan Malaysia
5
Faculty of Social Sciences and Humanities, Universiti Malaysia Sarawak
DOI:
https://doi.org/10.51584/IJRIAS.2025.100900083
Received: 10 September 2025; Accepted: 17 September 2025; Published: 22 October 2025
ABSTRACT
Climate change is one of the effects of global warming that affects the social and physical environment. The
effects of climate change on coastal communities are expected to have significant implications for society,
culture, economic and theirs well-being. Coastal communities play an important role in sharing information and
should be consulted and included in all efforts to promote climate action and adaptation. Local knowledge are
an important component for climate planning and development programmes of action. Social-cultural assets and
practices are valuable. The loss and damages of social-cultural parameters should be considered in valuations of
climate change impacts. This paper is attempts to indicate the socio-cultural factors that may increase sensitivity
to coastal areas. It is also to develop a comprehensive Socio-cultural Vulnerability Index (SVI) to determines
the vulnerability of coastal areas to climate change phenomena such as coastal flooding and erosion due to sea
level rise, heavy rain, storm surge, high tide, and wave action. Data collection to develop SVI are based on
selected parameters indicate during the survey process. Finding indicate that housing charateristics and
demographic vulnerabilities are the most significant factors influencing community sensitivity to climate
impacts. In contract, cultural and historical components contributed the least. This research contributes a novel,
locally contextualized methodology for evaluating social vulnerability and serves as a practical decision-making
tool for municipal planners, policymakers, and climate adaptation stakeholders. Integrating SVI outcomes into
development planning will support more equitable and effective resilience building among coastal communities.
Keywords: Socio-Cultural Vulnerability Index, coastal community, climate change, sea level rise, Malaysia
INTRODUCTION
The natural environment is strictly a living supporter of all human systems. Successful environmental
management will be the basis for the success or failure of the economy and social system throughout the country
[1]. Climate change is a global matter. Impact and adaptation of climate chance might have a different effects
on several countries, but nevertheless the changing of our climate is a problem that will influence the whole
World. Climate change is one of the effects of global warming that affects the environment, especially coastal
areas. This suggests that climate change has the potential to change the environment on a large scale. These
changes will have an impact on human social and economic systems. Thus, climate change is considered one of
the risks to the coastal environment. Among the potential climate change incidents are sea level rise and the
increase will continue to increase during the 21st century. Visually, the global average sea level rise is expected
to continue for several centuries reaching 2100, with the total increase depending on future greenhouse gas
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emissions [2]. Therefore, it is clear that coastal areas are most susceptible to climate change impacts especially
sea level rise [3]-[4]. The coastal area is one of the most densely populated areas with a high proportion of
vulnerable groups such as women, the elderly and children [5]-[6]. Large numbers of residents and property
have already been exposed to coastal risk. Improvements in terms of population growth, economic development
and future urbanization processes also have the potential to further enhance existing exposure. The risk from
climate change and extreme climate events have direct and indirect impacts on socio-cultural. According to [7],
the loss and damage caused by climate change extends to cultural factors, including direct material losses as well
as losses of mobility, displacement,loss of territory, loss of cultural heritage, or loss of local knowledge and
language elements among others. The capacity and willingness to engage with socio-cultural change during the
climate change or sea level rise impact is intimately tied to cultural beliefs and roles.
Most Asian countries, especially in the lower regions, face challenges and pressures according to the fourth
assessment report (AR4) Intergovernmental Panel on Climate Change (IPCC). Malaysia as one of the countries
located in the Asian region as well as developing countries must face many obstacles especially in terms of
development resources to deal with this risk. The sea level rise is expected to affect coastal community especially
those living on the coast of Pahang, East Malaysia. The position of Pahang, located on the east coast of the
monsoon season each year, adds to the challenges facing the coastal communities of Pahang. Besides, facing the
ongoing survival of the coastal community will also be further burdened by the challenge of the SLR
phenomenon.
The involvement of local coastal community knowledge and practices are an important component of climate
planning and adaptation process. Local knowledge must be recognized as valuable, identified and documented.
It is also included in the climate change and disaster risk management planning processes. Social and cultural
changes can result from many co-occuring factors in addition to climate change. Generally, this study aims to
identify the socio-cultural factors that may increase susceptibility to coastal areas and discusses the process of
developing a comprehensive Socio-cultural Vulnerability Indev (SVI).
SVI is calculated by integrating the
parameter value difference. Each SVI parameter has been identified by weighted scores regarding its importance
and relevance in determining coastal vulnerability to flooding, erosion and coastal flooding due to sea level rise,
heavy rainfall, storms, tides, and tidal action. This study was conducted along the coast of Kuantan in Pahang,
in the East cost of Malaysia (Fig.1).
SOCIO-CULTURAL VULNERABILITY INDEX (SVI)
The vulnerability can be defined as the state of the system before a catastrophic event. The vulnerability can also
be defined as the likelihood of systemic losses measured in the form of economic or human losses [8]. Contextual
vulnerability looks at how the current contextual situation of a system can affect its vulnerability to current and
future climate change [9]. Measuring vulnerability to natural hazards in general and flood hazards in particular
is not an easy task [10]. Index production is one of the most widely used methods of measurement by many
researchers. The vulnerability index has been developed as a fast and consistent method for characterizing the
relative vulnerability of different regions [11]. The various indices are derived from different methods such as
Physical Vulnerability Index (PVI), Economic Vulnerability (EVI), Social Vulnerability Index (SoVI) and
Coastal Vulnerability Index CVI as a result of the combination of two indices, the Physical Vulnerability Index
( PVI) and Socioeconomic Vulnerability Index (SVI)[12],[13]-[14]. Economic susceptibility (EVI), used to
assess relative degrees The vulnerability of the National economic structure as a result of increasing concern
among the international community on the Susceptibility of developing small island countries [12]. It clearly
shows that the Vulnerability Index has been developed in different ways to measure vulnerability to natural
hazards. However, the focus of this study is on the Socio-cultural vulnerability index is the vulnerability index
that focuses on the social and cultural aspects of coastal communities along the coast of Cherating to Tanjung
agas. The resulting Vulnerability Index can generally reflect the current state of the community in responding to
the effects of coastal flooding, erosion, and erosion caused by sea level rise, heavy rainfall, storms, high tides,
and waves.
The use of parameters is required in the calculation of the Socio-Cultural Vulnerability Index. Parameters are
essentially a way of including complex realities in one construct [15]. To determine the Socio-Cultural
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Vulnerability Index among the measured parameters are Demographic Characteristics, Family Structure,
Education Level, Occupation, Health, Residential Features, Neighborhood, Culture and Historic Sites and
Access to Social Media
MATERIALS AND METHODS
Study Area
Socio-cultural Vulnerability Index to coastal flooding, erosion, weathering caused by SLR, heavy rainfall, storm
surges, high tide, and wave action have been conducted in Pahang. Position of Pahang on the East Coast of
Malaysia. Pahang is the largest state in the north of Malaysia with a population of 1,501,900 [16]. The study
area is focused on villages within 1 Km of the coastline. Data collection was carried out in 36 villages (from
Cherating Beach to Tanjung Agas) involving 2 districts in Pahang and Pekan and Kuantan. Most of the villages
involved in this study are traditional villages that still retain a lot of local cultures. The study area was divided
into 18 Management Unit (MU). Fig. 1 shows a map based on MU where MU is the length of the slope that has
a relationship between the characteristics of the natural processes of the beach and land use [17].
Pahang’s coastal zone is particularly vulnerable due to its exposure to the northeast monsoon, sea level rise, and
recurring storm surges. Many of the villages rely on coastal resources for income and maintain strong cultural
identities, making them ideal for assessing socio-cultural vulnerability.
Fig. 1 Map of Study Area by MU
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Data Collection and Analysis
The method of questionnaire survey among the head of households has been conducted face-to-face interview.
A sample size of 400 respondents was selected using [18] formula for a population of approximately 11,466,
ensuring a 95% confidence level. Pilot studies based on a set of questionnaires were tested and modified before
being used in actual field studies. The questionnaire design based on information such as demographics, family
structure, occupation, health, housing, neighborhood relationships, cultural and historical sites, and access to
social media. Using this criteria collected from the survey, SVI parameters had been developed. Fig. 2, show the
SVI calculations and the output for vulnerability distribution analysis.
Fig. 2. Methods
The first step is to select the study area. The selection of this area takes into account the area in accordance with
the study conducted. Area selection also identifies locations according to MU. Next is to define parameters and
sub parameters to assess coastal vulnerability. The selected parameters and sub parameters are based on studies
by previous researchers who have produced vulnerability indices. In addition, the parameters selected are in
accordance with the study area or represent the current situation of the community in the study area. The selection
of parameters and sub parameters takes into account the actual state of the study area to ensure that the resulting
index truly reflects the Vulnerability of the study area. The next step is to select the criteria for the position of
coastal vulnerability. This selection is intended to set the criteria for each sub parameter on a scale of 1-5 where
1 is the lowest position value and 5 is the highest position. The selection of criteria for this sub parameter is
carefully done so that the criteria laid down for each position value are to be fully compliant. Next is the most
important part of the SVI calculation performed after the results of the questionnaire were analyzed? This SVI
calculation is based on the equation that the researcher has chosen. Vulnerability distribution analysis is the final
step in which the resulting analysis is presented in the form of a vulnerability map to show the vulnerability
values of each MU involved.
RESULTS
Socio-Cultural Parameters
The selection of parameters to measure Vulnerability is based on several selection features that guarantee the
importance of each of these parameters. Transparency is the first feature that comes to mind during parameter
selection. Objectivity and transparency are issues that cause vulnerability researchers. Transparency is intended
to justify action based on the underlying subjective vulnerability [9]. Next, the selected parameters must be
representative. Emphasize that each of the parameters selected in the socio-cultural study should represent the
current state of the community in the study area. The parameters to be selected must also be measurable by both
Selection of study area
Determine parameters and sub-parameters to
assess coastal vulnerability pantai.
Selection of criteria for coastal vulnerability
Criteria for coastal vulnerability and weight
determination
SVI calculation
Vulnerability distribution analysis
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quantitative and qualitative methods. Besides, the selected parameters are easy to accept and explain to
policymakers. The parameters selected in this study are:
a. Demographic Features
i. Age: This study takes into account the age of the respondent and the age of the householder in the
study area. The most vulnerable ages are children (0-12 years) and the elderly (61 and older).
ii. Distance of Residences: Distance of the respondent's residence from the beach. Where there are 4
positions (Rank) which are determined namely the highest vulnerability (0-200 m), high
vulnerability (201-600 m), simple vulnerability (601-800 m) and lowest vulnerability (801-1000 m).
iii. Density of Population: The total number of people living within one square kilometer (km / sq.).
b. Family Structure
i. Family size: The number of household members per household, where large family sizes are
households with large family members and more at risk (Rasch 2015).
ii. Dependency: The number of householders who depend on working-class households, with low-
income households is often poor and vulnerable compared to low-income households [19].
iii. Gender: Number of women in the household compared to men. Women belong to vulnerable social
groups other than children and the elderly [20]-[21].
iv. Level of Education: Respondent's education level. Education. The higher the number of contents of
houses with higher education the less vulnerability [22].
c. Occupation
i. Type of Occupation: Respondent's occupation. The work owned by the respondent determines the
quality of life of the respondent. Therefore, the lower the quality of life of respondents the higher their
vulnerability
ii. Dependency on coastal resources: Respondents and householders rely on beach resources for their
source of income. The impact of the deterioration of climate change on the community is related to
the extent to which the community relies on local resources. Climate change can affect the ecosystem
in terms of physical and biological components. The components can be added, subtracted, lost, and
modified. These changes can have a negative or positive effect on society depending on the
components [23].
d. Health: The physical health of respondents and household members. Individual physical health also
contributes to the ability to escape and / or recover from floods [10].
e. Housing features
i. Type of house: respondent type of house. The type of home affects vulnerability through two
approaches - whether the house type determines the age and severity of the house or is viewed from a
population density standpoint. For example, terrace type houses and apartments show higher
population density than traditional bungalow and traditionally built houses.
ii. Type of building structure: The type of building materials used in home construction. The quality of
the home walls determines how easily the home is damaged by the flood. Such as Blocks, Zinc and
Wood [16]. The materials are divided into wooden, wooden and brick, brick without plaster and brick
with plaster.
f. Neighborhood Relationships
i. Neighborhood relationships: the connection and friendship of the community in the neighborhood.
Close neighborhood relationships can help to disseminate information effectively.
ii. Participation in community activities: frequency of respondents to community activities. Community
activities became an effective medium of information dissemination such as village meetings. The
frequency of respondents in community activities increases the potential for respondents to obtain
such information.
g. Cultural and Historical Sites
i. Cultural events: Frequency of cultural activities such as water festivals, water festivals, folk sports
and fishing festivals are held on the coast. These cultural activities have become local identities by
assuming that the more often these activities are carried out, the higher the probability
ii. Historical sites: the existence of a historical site in the area studied is the existence of a high cultural
heritage unit, the higher the threat [11]. Assuming that the beaches have heritage sites, the higher the
risk.
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h. Access to Social Media: Frequency of use on social media such as Facebook, YouTube, Instagram,
Twitter, Wechat, Whatsapp, Online blogs and more throughout the day. The frequent use of social media
is considered to be that individuals will have quick access to disaster-related information.
Based on the parameters described in Table 1 shows the determination of criteria based on the position of the
vulnerability. Where position 1 is a very low vulnerability position and 5 is a very high vulnerability position.
TABLE 1 DETERMINATION OF CRITERIA
Sub Parameter
1
Very low
3
Moderate
4
High
5
Very High
Age
41-60 years old
13-19 years old
<12 years and >
61 years old
Distance of
residence
601-800 m
801-1000 m
201-400 m
401-600 m
0-200 m
Population
density
<50
person/sqkm
2
80-200 person
/sqkm
2
201-300 person
/sqkm
2
>300 person
/sqkm
2
Family size
4-5 person
6-7 person
>7 person
Liability
3-4 person
5-6 person
>6 person
Gender
There are no
women in the
household
Women who
have stable
employment,
education and
good health.
Women who do
not have stable
employment,
education and
good health
Old woman
Education
University
level
Secondary
school
Primary school
No formal
education
Type of
Profession
Government and
private workers
Farmers, retired,
fishermen,
traders, taxi
drivers and
trucks and self-
employed
Unemployed,
housewife and
student
Health
Be healthy
Be healthy with
minor illnesses
Unwell with
major illnesses
Sick and
disabled people
Type of house
Shop house
Condo /
Apartment
Traditional
house and
terrace
Types of home
construction
materials
Bricks without
plaster
Wood and brick
Wood
Neighborhood
relations
Very good
Moderate
Not Good
Not to Good
Participation in
community
activities
Moderate
Rarely
Very rarely
Cultural event
Never held
Rarely do
Regularly
Very often
Historic site
No historical
sites available
401-600 m
601-1000 m
0-200 m
201-400 m
Frequency of
social media
usage
3-4 times a day
1-2 times a day
No social media
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SVI Calculation
The SVI calculation method is carried out by involving the use of equations constructed from the adaptation of
the DID Report equation (NAHRIM) and also used by [24]. The final SVI score for each Management Unit
(MU) is calculated using the following formula used in the equation below:
Equation:
SVI= a(w1)+b(w2)+c(w3)+d(w4)+e(w5)+f(w6)+g(w7)+h(w8)+i(w9)
_______________________________________________________
9
Where:
[a] to [i] are parameter values (averaged sub-parameters scores)
[w1] to [w9] are weights (based on VIV values)
a = demographic characteristics
b = family structure
c = education level
d = occupation
e = health
f = home features
g = neighborhood relationships
h = culture and historical sites
i = access to social media.
The resulting SVI scores were classified into five vulnerability levels:
a. very low (1.0 – 1.8)
b. low (1.81 – 2.6)
c. moderate (2.61 – 3.4)
d. high (3.41 – 4.2)
e. very high (4.21 – 5.0)
Weighting of Parameters
The purpose of the SVI is to be comprehensive, requiring a broad set of indicators in each sub-index category.
Even in the context of the induction theory, each indicator has a strong foundation. However, there is no reason
to suggest that their roles are the same. Therefore, the weighting is appropriate. Weight estimation is intended
to determine the parameters of importance. This study uses the Weighted Average Model. The model is where
the average significant score (ASSi) of parameter i, Xj is the parameter value of parameter i, assuming positions
1-5 where 1 is very low, 2 is low, 3 is position while moderate, 4 is high and 5 is highly susceptible to
vulnerability. Socio-cultural. Nij is the number of respondents that falls in the Xj position of parameter i and N
is the total number of respondents.
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This calculation is done by converting the position score to a numerical value. For example, position 5 is an
equation with value 5, position 4 is an equation of value 4 and so on positions 3, 2 and 1 to parameter i. Next is
to determine the parameters involved by importance. This study used a combination of the weighted average
and coefficient of variation. The coefficient of variation measured with the Vulnerability Index Value (VIV)
model is: where VIVi is the coefficient of variation for the vulnerability parameter i, ASSi is the average
significant score of the vulnerability parameter i and δi is the standard deviation of the important score for
parameter i. After the VIV calculation, the study shows the significant position of the vulnerability between the
parameters (RVIV).
Equation of Weightage:
N
NX
ijjj
i
ASS
5
1
i
i
i
i
ASS
ASS
VIV
Source: Customized and processed from [25]
The summary results of the weighting calculations for each parameter are shown in Table 1 below. The use of
significant average scores (ASS) is considered to be less suitable for determining the position of all socio-cultural
vulnerability parameters. This is because the ASS is a weighted average value calculation that is often identified
as weak, which does not take into account the degree of variation between individual responses. Therefore, a
combination of weighted average values and coefficient of variation is appropriate to determine the position of
socio-cultural vulnerability parameters. Typical techniques are used to reduce the weakness of the attribute level
by weighted average value and apply it to a measure called variance coefficient, obtained through weighted
average distribution by the standard deviation [25].
The calculation of parameter values for vulnerability index value (RVIV) values is as shown in Table 2. The
lowest significant average score (ASS) value was recorded by cultural and historical parameters with a value of
1.43. This indicates that cultural parameters and historical sites are the lowest contributors to socio-cultural
vulnerability. The highest significant average score (ASS) was for the home feature parameter with a value of
4.03 and the second highest significant average (ASS) score was the demographic feature parameter with a value
of 3.46. The high mean values (ASS) of these two parameters indicate that household characteristics and
demographic characteristics are the most significant contributors to socio-cultural vulnerability. Whereas the
results show that the value of VIV is the home feature parameter with a value of 7.54 and the lowest value of
VIV is the cultural and historical parameter with a value of 2.84. The results of the study between VIV and ASS
did not show significant differences in determining the socio-cultural vulnerability parameter.
TABLE 2 COMPARISON OF PARAMETER VALUES FOR POSITIONING OF VULNERABILITY INDEX
VALUES
Parameter
Significant
Average Score
(ASS)
Standard
Deviation
(δ)
Vulnerability
Index Value
(VIV)
Significant position of
the vulnerability
between the parameters
(RVIV)
Demographic Characteristics
3.46
0.97
7.02
2
Family Structure
2.75
0.78
6.27
5
Level of Education
3.11
0.87
6.68
3
Occupation
2.92
1.65
4.68
7
Health
1.15
0.51
3.41
8
Housing features
4.03
1.15
7.54
1
Neighborhood relationship
2.67
1.30
4.72
6
Cultural and Historical sites
1.43
1.01
2.84
9
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Access to social media
3.62
1.24
6.54
4
Finally, to accurately capture the relative importance of each parameter in the construction of the Socio-Cultural
Vulnerability Index (SVI) a Weighted Average Model was employed. This model integrates two key
components:
i. Average Significance Score (ASS): This metric was derived by aggregating the frequency of responses
associated with each sub-parameter and their corresponding vulnerability levels. It represents the central
tendency of perceived significance across respondents.
ii. Vulnerability Index Value (VIV): To complement the ASS, the VIV was calculated using the coefficient
of variation (CV), which quantifies the degree of dispersion in responses. The VIV thus reflects both the
relative importance and the consistency of each sub-parameter across the surveyed population.
This dual-weighting approach addresses the limitations inherent in using the ASS alone, which fails to account
for variability in the data. By incorporating the VIV, the model unsures that parameters with higher response
variability often indicative of localized sensitivities of differential exposure are approximately weighted. This
methodological refinement enhances the robustness and contextual sensitivity of the SVI (Rawshan et al., 2007).
Overall the SVI results show significant variation in vulnerability levels across the 18 MU along the coastal zone
of Kuantan and Pekan. The calculated SVI values fall within a range from 2.84 (low) to 7.54 (very high),
indicating both low-risk and high-risk communities within the same coastal corridor (refer Table 2).
Most and Least Vulnerable Parameters
The analysis revealed that housing features represent the most significant contributor to socio-cultural
vulnerability within the study area, with a Relative Vulnerability Index Value (RVIV) of 1.00. This high
weighting reflects the prevalence of substandard building materials, aging infrastructure, and informal housing
designs, all of which increase physical exposure to climate-related coastal hazards such as flooding and erosion.
Demographic characteristics, particularly age distribution and the proximity of residential areas to the coastline,
also emerged as critical determinants of vulnerability. These factors influence both physical exposure and the
ability of residents to respond effectively to environmental stressors. In addition, low levels of formal education
were identified as a limiting factor in community adaptive capacity, reducing the uptake of early warnings,
hazard awareness, and disaster preparedness initiatives.
In contrast, cultural and historical sites were found to contribute the least to overall SVI scores. This outcome
may suggest either lower direct exposure to climate-related threats or a general lack of integration between
cultural heritage considerations and physical risk assessments. Similarly, health-related parameters showed
relatively low influence on the index. However, this result should be interpreted cautiously, as it may reflect
issues such as underreporting of chronic health conditions or limited access to healthcare facilities, rather than a
true indication of community resilience.
Spatial Distribution of Socio-Cultural Vulnerability
The spatial distribution of the Socio-Cultural Vulnerability Index (SVI), as derived from Geographic Information
System (GIS)-based mapping, indicates a clear pattern of vulnerability clustering along the coastal margin.
i. High-vulnerability zones are predominantly located within 0–400 meters of the shoreline, with particular
concentration in Management Units (MU) 03, 07, and 11 in the Kuantan district. These areas are
characterized by older, traditional housing structures, limited flood protection measures, and
underdeveloped infrastructure, all of which significantly heighten exposure to coastal hazards such as
sea level rise, storm surges, and coastal erosion.
ii. Moderate-vulnerability zones are generally found in semi-urbanized villages that exhibit a transitional
housing landscape, comprising both traditional and modern structures. These areas tend to have
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comparatively better access to public services, early warning systems, and information dissemination
channels, contributing to a moderate level of adaptive capacity.
iii. Low-vulnerability zones are typically situated in inland MUs located 800 to 1000 meters from the
coastline. These areas are often associated with improved socio-economic indicators, including higher
education attainment, more durable housing structures, and better access to basic infrastructure and
services. As a result, communities in these zones exhibit a relatively lower degree of socio-cultural
vulnerability to climate-related coastal impacts.
IMPLICATIONS FOR COMMUNITY RESILIENCE AND POLICY
The findings of this study offer several important implications for enhancing community resilience and
informing evidence-based policy interventions in coastal regions vulnerable to climate change.
i. Municipal Planning and Land Use Management. Management Units (MUs) identified as highly
vulnerable should be prioritized in local development planning. Specific measures may include the
designation of coastal buffer zones, retrofitting of critical infrastructure, and, where necessary, the
provision of relocation incentives for at-risk populations. Furthermore, housing policies must incorporate
climate-resilient building standards, particularly in traditional settlements that are currently characterized
by aging structures and informal construction methods.
ii. Community Awareness and Education. The relatively high scores associated with education levels and
access to social media present both a risk and an opportunity. These factors suggest a strong potential for
implementing targeted digital awareness campaigns aimed at improving preparedness and adaptive
behavior, particularly among younger populations who are more digitally engaged. Strengthening climate
literacy at the grassroots level can significantly improve anticipatory responses to coastal hazards.
iii. Localized Adaptation Programming. The SVI provides a critical tool for guiding community based
adaptation efforts. Interventions should be tailored to the needs of the most at risk groups, notably the
elderly, low income households, and informal sector workers. These groups exhibit reduced adaptive
capacity due to limited economic resources, mobility, or access to institutional support mechanisms.
Local adaptation programs must therefore prioritize social inclusivity and equity.
iv. Integrating Local and Traditional Knowledge. Although cultural and historical site parameters
contributed minimally to the composite vulnerability score, they remain essential components of
sustainable adaptation strategies. Local knowledge systems, communal practices, and intergenerational
memory serve as valuable assets in strengthening early warning mechanisms, fostering risk
communication, and promoting social cohesion during periods of environmental stress.
CONCLUSIONS
Overall, this study developed and applied a Socio-Cultural Vulnerability Index (SVI) to assess community level
vulnerability to coastal hazards in Kuantan and Pekan, Malaysia. The conclusion of the selected parameters is
based on several characteristics of transparent selection and is based on the suitability of the study area.
Vulnerability is a non-static thing because individuals who are considered 'exposed' at this time do not mean
they will remain. On the contrary, the same thing applies to 'exposed' people. This is because the person may be
exposed to forces or processes such as aging, disease or redundancy, which have nothing to do with adverse
events such as natural hazards. Therefore, Vulnerability analysis can be seen as an overview of dynamic
processes [8].
The methodology based on socio-cultural vulnerability is recognized to quantify and evaluate social
vulnerabilities that have been validated by many authors and has been recognized for use in coastal areas and
many developing areas as it is a relatively simple method. The weighting analysis results show that home
characteristics are the most contributing parameters to the socio-cultural Vulnerability Index while cultural and
historical parameters are the least contributing to vulnerability values. The resulting SVI describes local
heterogeneity in social and cultural aspects. SVI can be used for municipal planning to address the effects of
climate change on local communities. Economic and social factors are among the factors that influence the
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue IX September 2025
Page 847
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community at large and support the impact of dangerous events. Therefore awareness of adaptation and
mitigation of natural hazards is essential.
The findings reveal significant spatial and social disparities levels across 18 coastal management units. Notably,
parameters such as housing conditions, demographic composition, and education levels emerged as the most
critical determinants of vulnerability, while cultural heritage and health factors had relatively lower
contributions. The novelty of this research lies in its multi-dimensional approach that integrates both quantitative
assessments and spatial analysis, enabling a more nuanced understanding of socio-cultural vulnerability. By
applying the SVI at a localized scale, this study provides a practical tool for identifying priority areas for
adaptation planning and resource allocation.
Finally, the methodology and index developed here can be adapted for use in other coastal regions globally,
especially in developing countries facing similar challenges of uneven development, rapid urbanization, and
increasing exposure to climate related hazards. This study contributes to ongoing efforts to localize climate
adaptation and build socially inclusive resilience strategies.
ACKNOWLEDGEMENT
This research was supported by Universiti Kebangsaan Malaysia and funded under Ministry of Higher Education
Malaysia Grant No. ZF-2025-009. This support is gratefully acknowledged. The authors would like to thank
others researchers, lecturer and friends for ideas and discussion.
REFERENCES
1. Doukakis, E. (2005) Coastal Vulnerability and Risk Parameters. Eur Water, 11(12): 3–7.
2. Gregory, J. (2013) Projections of Sea Level Rise. IPCC Fifth Assessment Report. 16.
3. Wang, Q. S, Pan, C. H, Zhang, G. Z., 2018. Impact of adaptation strategies for sea-level rise on Yangtze River
Delta. Advances in Climate Change Research 9, 154-160.
4. Wong, P.P., Losada, I.J., Gattuso, J.-P., Hinkel, J., Khattabi, A., Mcinnes, K.L., Saito, Y., Sallenger, A. (2014)
Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of
the Intergovernmental Panel on Climate Change. 361-409.
5. Wisner, B. (2005). Tracking Vulnerability: History, Use, Potential and Limitations of a Concept. Presented
at the SIDA & Stockholm University Research Conference on Structures of Vulnerability, held in 2005.
6. Nguyen, T. T. X. & Woodroffe, C. D. (2016) Assessing relative vulnerability to sea-level rise in the western
part of the Mekong River Delta in Vietnam. Sustainability Science. 11(4). 645–659.
7. Straza, T., Lui, S. & Burfitt, B. 2018. Effects of climate change on society, culture and gender relevant to the
Pacific Islands. Science Review. 201 – 210.
8. Tapsell, S., Mccarthy, S., Faulkner, H., Alexander, M., Kuhlicke, C., Brown, S., Walker, G. et al. (2010)
Social vulnerability to natural hazards. Contract. 1(11): 524.
9. Brugère, C., & De Young, C. (2015). Assessing climate change vulnerability in fisheries and aquaculture:
Available methodologies and their relevance for the sector. FAO Fisheries and Aquaculture Technical Paper
No. 597. Food and Agriculture Organization of the United Nations (FAO). Rome, Italy.
10. Rasch, R. J. (2015) Assessing urban vulnerability to flood hazard in Brazilian municipalities. Environment
and Urbanization. 28(1), 145–168.
11. Balica, S. F., Wright, N. G. & van der Meulen, F. (2012) A flood vulnerability index for coastal cities and its
use in assessing climate change impacts. Natural Hazards. 64(1): 73-105.
12. Guillaumont, P. (2007) Assessing the Economic Vulnerability of Small Island Developing States and the
Least Developed Countries. United Nations University World Institute for Development Economics Research
(UNU-WIDER).
13. Kashem, S. Bin, Wilson, B. & Van Zandt, S. (2016) Planning for Climate Adaptation: Evaluating the
Changing Patterns of Social Vulnerability and Adaptation Challenges in Three Coastal Cities. Journal of
Planning Education and Research. 36(3): 304-318.
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN APPLIED SCIENCE (IJRIAS)
ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |Volume X Issue IX September 2025
Page 848
www.rsisinternational.org
14. Mani Murali, R., Ankita, M., Amrita, S. & Vethamony, P. (2013) Coastal vulnerability assessment of
Puducherry coast, India, using the analytical hierarchical process. Natural Hazards and Earth System
Sciences. 13(12), 3291–3311.
15. Vincent, K. (2004). Creating an index of social vulnerability to climate change for Africa (Working Paper
No. 56). Tyndall Centre for Climate Change Research / School of Environmental Sciences, University of East
Anglia.
16. Department of Statistics Malaysia. (2011). Population and Housing census of Malaysia 2010. Putrajaya:
Department of Statistics Malaysia.
17. Department of Irrigation and Drainage. (2011). Annual Report Department of Irrigation and Drainage by
State, Malaysia. Kuala Lumpur: 17Department of Irrigation and Drainage.
18. Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and
Psychological Measurement.
19. Chaudhuri, S. (2003) Assessing vulnerability to poverty: concepts, empirical methods and illustrative
examples. Columbia University New York: Department of Economics.
20. Antwi, E. K., Boakye-Danquah, J., Barima Owusu, A., Loh, S. K., Mensah, R., Boafo, Y. A. & Apronti, P.
T. (2015) Community vulnerability assessment index for flood prone savannah agro-ecological zone: A case
study of Wa West District, Ghana. Weather and Climate Extremes. 10, 56–69.
21. Setiadi, N., Taubenböck, H., Raupp, S. & Birkmann, J (2010) Integrating Socio-Economic Data in Spatial
Analysis: An Exposure Analysis Method for Planning Urban Risk Mitigation. 18–20.
22. Akukwe, T. I. and Ogbodo, C. (2015) Spatial Analysis of Vulnerability to Flooding in Port Harcourt
Metropolis, Nigeria. SAGE Open. 5(1): 1-19.
23. Sutherland, M. (2004). Sea-Level Rise and Coastal Zone Management. Published in the proceedings of the
FIG Working Week Conference and Workshop, May 2004, Athens, Greece.
24. Carlos Coelho, Raquel Silva, F. Veloso Gomes, F. Taveira Pinto. (2006). A Vulnerability Analysis Approach
for the Portuguese West Coast. WIT Transactions on Ecology and the Environment, 91: 241–252.
25. Rawshan, A. Begum, C Siwar, R Abidin, JJ Pereira. (2011). Vulnerability of Climate Change and Hardcore
Proverty in Malaysia. Journal of Environmental Science and Technology, 4(2): 112-117.