key: cord-0077690-sfkdysam
authors: Roukounis, Charalampos Nikolaos; Tsihrintzis, Vassilios A.
title: Indices of Coastal Vulnerability to Climate Change: a Review
date: 2022-05-03
journal: Environ
DOI: 10.1007/s40710-022-00577-9
sha: 6c2dce26e96d3f88e5c218f8856a0bf8b4e5892b
doc_id: 77690
cord_uid: sfkdysam

The objective of this paper is to identify and analyze relevant research of index-based methods for the evaluation of climate change vulnerability and resilience of coastal areas. We searched, retrieved, classified and reviewed papers on climate-change hazards, impacts, vulnerability and resilience of coastal water systems and relevant infrastructure. For this, Scopus, Science Direct, Thompson-Reuters Web of Science, Google Scholar, PubMed and other relevant databases were used. The analysis of the state-of-the-art presented in this paper acknowledges that using vulnerability and resilience indices in climate vulnerability research is effective, providing a solid, efficient and user-friendly framework. However, selection of index variables should be part of a holistic as well as dynamic approach to identify not only areas in danger, but also the level of social vulnerability.

Climate change (CC), alongside with rapid population growth and natural and manmade disasters, require modern approaches to be addressed; forward-looking design can have a profound impact on safety and prosperity of individuals and communities as a whole (Rus et al. 2018) . In everyday life, the term "vulnerability" is widely used. Comte et al. (2019) claim that a consistent definition of vulnerability is not available. It is difficult for a definition to include every assessment context, as it is used in different policies and systems exposed to diverse hazards (Nguyen et al. 2016) . However, related to CC, the Intergovernmental Panel on Climate Change (IPCC 2014) definition of vulnerability has been acknowledged by researchers (e.g., Li et al. 2019; Gargiulo et al. 2020 ). It is defined as "the propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts and elements including sensitivity or susceptibility to harm and lack of capacity to cope and adapt".

According to Gargiulo et al. (2020) , coastal vulnerability can be considered as the integration of sensitivity and social vulnerability of coastal areas. For this purpose, physical, socio-environmental and socio-economic aspects of coastal areas are taken into consideration. The analysis of vulnerability is occasionally carried out using complex indices . Vulnerability is also strictly linked with adaptation, as the adaptive capacity is described as the ability of systems, institutions, humans and other organisms to adjust and respond to potential damage, for example due to weather extremes (IPCC 2001 (IPCC , 2014 .

In terms of social vulnerability, characteristics of communities as well as inequities indicate an important factor in the ability of groups to respond to natural hazards and climate challenges (Cutter et al. 2003) . Especially in coastal areas, social inequities, income, race, ethnicity, and demographic data are key factors in people's vulnerability to environmental hazards . Economic losses might be direct, affecting buildings, crucial infrastructure and their operations (Asariotis et al. 2017) , or indirect, affecting local economy, causing issues in the supply chain, unemployment, investment decisions. The use of susceptibility functions (or damage functions) is the most frequently applied method (Meyer et al. 2013) to assess the direct costs related to natural hazards (e.g., Pistrika et al. 2014) . Also, to assess indirect costs, economic models at local, regional or national level are used (Meyer et al. 2013) . Financial costs vary from one place to another, as developed countries have higher property values and disaster insurance is affordable (Christian Aid 2021). However, it is rather difficult to define disaster cost, as it highly depends on the purpose of the assessment and the consequential uncertainties regarding the indirect cost (Hallegate and Przyluski 2010) .

One question that needs to be addressed is "Why to use index-based approaches?" Indices are "measurable, observable quantities that serve as proxies for an aspect of a system that cannot itself be directly adequately measured" (McIntosh and Becker 2017) . Abstract concepts like vulnerability and resilience can be visualized in spatial scales, using observable variables (McIntosh and Becker 2019) . Within this approach, vague factors like "social factors" could be specified, and thus, quantified using multiple variables (e.g., age, education, unemployment) (Malone and Engle 2011) .

In this study, a systematic review of assorted approaches applied globally is presented. In Sect. 2, the methodology followed in the literature review is analyzed. Section 3 discusses climate change vulnerability and resilience indices for coastal areas found in the literature. Section 4 briefly presents aggregation tools for the evaluation of the abovementioned indices. Finally, Sect. 5 presents the main conclusions and highlights gaps in the literature.

The goal of the review was to identify and analyze relevant research of index-based methods for the evaluation of climate change vulnerability and resilience of coastal areas. Understanding and quantifying the risk is the main challenge that occurs, and is the first step in the development of an effective framework to address real-world cases. The PRISMA 2020 methodology by Page et al. (2021) was followed, as it includes both systematic reviews and meta-analyses. We searched, retrieved, classified and reviewed 1 3 29 Page 4 of 25

It is widely believed that coastal zones are exposed to diverse natural hazards under a changing climate. Coastal areas and infrastructure should be designed to address these challenges (Malliouri et al. 2017) . The exposure level is affected by the land use of coastal areas, the population and social factors, the shoreline type and the existing (and planned) infrastructure. Current and future socio-environmental and socio-economic data are used to develop different scenarios (Fig. 2) . Also, there is a significant variety of presumed socio-economic impacts of climate change among key coastal sectors. The environment is described with hydrologic, atmospheric and geologic variables. As a result, vulnerability components could be identified in spatial scale (Bevacqua et al. 2019 ). Using different projections for future scenarios could also add a temporal scale to the vulnerability assessment. Important components affecting coastal areas are the astronomical tide, the storm surge, the wave run-up, the weather extremes and the sea-level rise. CC affects the frequency of storm surge events and the magnitude of wave heights, having crucial impact on coastal and port infrastructure (Tsoukala et al. 2016; Afentoulis et al. 2017) . The main impacts of these phenomena are coastal erosion and coastal flooding. The majority of the literature analyzes risk caused by SLR and storm surges, while wave data have only been used by a few researchers (e.g., Gornitz et al. 1994; Dawson et al. 2009; Toimil et al. 2017) . The use of historical weather data, as well as climate projections downscaled at the regional level have been used extensively in the literature to model the impacts of CC in various case studies (Fig. 3) . However, climate projections and bias correction methods still have significant uncertainties (Kourtis and Tsihrintzis 2021) .

Social variables include population, nationality, economic status, age, gender, household type, housing quality, and health. Coastal areas are particularly exposed to climate change and extreme weather events, and the dependence of many regions on touristic and recreation activities intensifies the vulnerability level (Bevacqua et al. 2019 ). This also creates an uncertain environment for touristic activities (Uyarra et al. 2005 ).

The majority of the approaches in the literature promote a static understanding of human-environment interactions. Ford et al. (2018) raised similar concerns. The recent crisis caused by the COVID-19 pandemic is an example of the importance of dynamic approaches when social indicators are used. Hinkel (2011) also supported that, as useful as they can get, vulnerability and resilience indices solely cannot direct decision makers and stakeholders to raise public awareness, propose adaptation measures and monitor adaptation policy and funds.

A holistic approach is necessary to overcome the abovementioned concerns. This creates the urge to aggregate the divergent aspects of geophysical and social vulnerability into a sole index, or a limited group of indices. Integrating data from diverse sources is frequently necessary to understand potential relationships among them (Preston and Jones 2008) . Aggregating variables reduces the amount of complexity, and the indices, often described as a "vulnerability score," visualize the climate change vulnerability (Nguyen et al. 2016) .

Flood risk management and defense projects interact with various sectors (socio-economic, environmental, etc.) of an area wider than the affected one. Different stakeholder groups are involved and expected to have diverse opinions about the projects' magnitude and the alternatives which seem viable. Therefore, policy and decision-making on coastal flood defense and risk management at strategic level depend on many criteria and stakeholders (Maragoudaki and Tsakiris 2005) .

Weighting and ranking methods are used to express the relative importance of different individual variables in a system. With Multi-Criteria Decision Analysis (MCDA), it is rather possible to overwhelm many of the disadvantages of conventional evaluation methods. It allows for factors that cannot easily be expressed in monetary terms or quantified, but nevertheless play a decisive role in shaping policies, such as environmental, spatial and social impacts of a project, social justice etc., which should always be taken into consideration (Roukounis et al. 2020; Roukouni 2016) .

Especially the Analytic Hierarchy Process (AHP) by Saaty (1987) is frequently met in the literature. Ellen et al. (2016) suggested that in climate planning, multi-criteria analysis has the ability to perform more efficiently than cost-benefit analysis and working group approaches during the decision-making process, as they are more comprehensive and scenario-driven.

GIS can be used to produce different kinds of hazard and vulnerability maps, as researchers integrate indicators regarding various aspects of vulnerability. To locate 'hot spots' of vulnerability, measures of exposure, sensitivity and adaptive capacity are combined and mapped. On the other hand, uncertainties in the model could also be identified (Malone and Engle 2011).

In order to establish new approaches in vulnerability and risk analysis, the development of solidly identified and quantified variables could be effective (Brooks 2003) . Index-based methods offer a simple way for the classification of alternative options during decision making procedures (Giannakidou et al. 2020) .

One major challenge in climate vulnerability research is to define the subject of interest. Initially, research was focused on the physical profile of climate change vulnerability (e.g., Thieler and Hammar-Klose 1999) , developing robust indices that estimate the exposure of coastal areas. However, the understanding that climate change is a concern of humankind globally (UN 2015) had raised the question "who is vulnerable" rather than "what". The assumption that people lacking wealth and resources are more susceptible to be damaged by climate change impacts has been common among researchers (Malone and Engle 2011) . Indeed, Islam and Winkel (2017) illustrated how social inequality affects the ability of disadvantaged groups to respond to climate change hazards.

In order to address the abovementioned challenges, indicators should be consistent and coherent, based on robust data, using multiple criteria in the selection process. A holistic approach is crucial in order to assess coastal vulnerability, as excluding features may lead to unrealistic results . Furthermore, high spatial resolution is crucial for the effectiveness of vulnerability indices , taking into consideration factors that cannot be evaluated at large scales (Torresan et al. 2008) .

The index-based approaches have been the most widely accepted methods to assess vulnerability (Debortoli et al. 2019; Blasiak et al. 2017; Nguyen et al. 2016; Jun et al. 2011; Baker 2009 ). In complex human and other natural systems, more than one factors are present. The exposure and vulnerability of these environments is estimated by integrating multiple factors of every part of the system. As a result, these approaches are adopted more frequently than single-factor methodologies (Giri et al. 2020 ). In the present research various vulnerability indices were identified. The literature review showed that the majority of vulnerability indices were developed considering the geomorphological and socio-economic factors as main features.

In order to evaluate the geophysical characteristics of coastal areas and to allocate the future impacts of climate change among divergent coastal sections, the Coastal Vulnerability Index (CVI) was developed . Different versions of the CVI were applied first on the coasts of the USA, Canada and Mexico (Gornitz et al. 1994; Thieler and Hammar-Klose 1999) . This index was also applied, after modifications matching local preferences and data limitations in various other locations Table 1 .

A variety of physical characteristics have been included to these indices, in order to evaluate vulnerability levels of coastal areas to different climate change-related hazards. Geomorphological (slope, elevation, shoreline type, rock type, solid and drift geology, coastal erosion/accretion rate), sea (SLR, tidal range, storm surge), wave (wave height, wave run-up) and weather data, alongside with information regarding land cover, existing coastal flood protection measures and other environmental agents are used for this purpose. A summary of the physical variables used in the literature is presented in Table 2 .

Climate change and socio-economic development are profoundly connected, as human activities have been the main driver of environmental harm. Socio-economic variables are used to determine the impacts of climate change on the society. These variables broaden physical indices to an inherent cultural bias (McLaughlin and Cooper 2010). About 10% of people who reside at low elevation coastal zones, located up to 10 m elevation of presentday mean sea level (MSL) (Wahl et al. 2018) , face a great threat due to sea level rise (SLR) (McGranahan et al. 2007 ). Thus, identifying social vulnerability of coastal communities is crucial for planning resilience strategies . Social vulnerability may include characteristics of the communities and the built environment, such as the urbanization level, the growth rates, and the economic stability and development (Cutter et al. 2003) . As it is believed that vulnerability depends on the sensitivity of both the natural and social components of a system (Heinz Center 2002), social inequities are key factors in natural hazard vulnerability assessment . Characteristics of communities as well as social inequalities have also been taken into consideration. Socio-economic parameters such as demographic data, education level, population density and land use are constantly used in the literature to estimate social vulnerability. Cutter et al. (2003) used qualitative and quantitative data related to 42 variables (that were then reduced to 11 independent factors) to estimate the level of social vulnerability within American metropolitan coastal areas, creating the Social Vulnerability Index (SoVI). This index has been the baseline for the vast majority of the indices in the literature that take social factors into consideration. Alexandrakis et al. (2014) Table 3 . Composite indices composed of various index types, such as geophysical, social, economic (i.e., socio-economic, socio-enviromental) are also frequently used. From simpler (Wu et al. 2002; Boruff et al. 2005; Chakraborty et al. 2005; Kantamaneni 2016 ) to more complex indices Satta 2014; Zanetti et al. 2016) , recent researches tend to consider multiple factors. More and more variables describing climate data, wave and wind, geomorphology, land use/land cover, education, unemployment, and crucial infrastructure in coastal areas are used, with the proper adjustments for each case study (Briguglio (2009) Greece Population density, share of persons above 65 years in total population, share of children below 5 years in total population, share of foreign-born in total population, share of women in total population, share of low educated in total population et al. 2020; Edmonds et al. 2020; El-Zein et al. 2021) . Especially in urban coastal environments (e.g., Zanetti et al. 2016; Gargiulo et al. 2020) , the combination of different variable types seems to provide a more dynamic approach to human-environment interactions. As index-based methods are believed by some authors (e.g., Ford et al. 2018) to consist a static approach, the use of combined indices is a step to fill this gap, since human-environment interactions in coastal urban environments are highly dynamic. Especially in coastal cities, natural systems are distorted by human actions and co-exist with the built environment (Malvarez et al. 2021 ) in a complex definition of resilience (Masselink and Lazarus 2019) . However, some authors suggest that one single complex index is not representative and prefer the use of multiple single indices instead (e.g., Tragaki et al. 2018) . A summary of composite indices found in the literature is presented in Table 4 , and the socio-economic variables used for social and composite indices are presented in Table 5 . Questionnaire surveys have also been used to evaluate vulnerability and resilience, as in the case of the Port Resilience Index (Morris and Sempier 2016) . Global indices such as the World Risk Index (World Risk Report 2019), the Index for Risk Management (INFORM; Messina et al. 2019) , the Climate Risk Index (CRI; Eckstein et al. 2020 ) and the University of Notre Dame's Global Adaptation Index (ND-GAIN; Chen et al. 2015) have also been frequently used in order to validate custom indices, as they are designed to improve the common data basis for risk management so that the majority of governments and policy makers and other stakeholders can work consistently (Poljansek et al. 2020) .

The CVI has been the ground for many indices with a geophysical approach developed later in the literature, and the SoVI (Cutter et al. 2003) for indices with a socio-economic approach. Past researches included simpler indices and variables were one-dimensional. As years advanced, more and more papers combining physical and socioeconomic approaches have been published. Despite having similarities in variable selection, it was not possible to find consistency between different approaches, especially for complex indices. Also, even though the majority of the variables are quantitative, the evaluation process highly depends on the personal opinion of the researchers. Variable ranking and categorization may differ, taking into consideration specific aspects of each study area. There are even different approaches for the same variable. For example, some researchers suggest that low values for the variable "tidal range" indicate high vulnerability (e.g., Tragaki et al. 2018; Zampazas et al. 2022) , while others suggest the opposite (e.g., Pantusa et al. 2018) . Furthermore, vulnerability assessment using indices seems to be a static approach while human-environment interactions are highly dynamic. The approaches of Zanetti et al. (2016) , Toimil et al. (2017) and Gargiulo et al. (2020) tried to fill this gap with indices matching diverse socio-economic issues; however, there is still room for improvement in this direction. The MHCRI by Satta (2014) included diverse variables that are implemented in both urban and rural environments.

A total of 46 studies that generate a vulnerability index for climate change related hazards in coastal areas has been reviewed. There are studies that focus on physical variables and others that focus on both physical and social factors. An interesting fact is the diversity of the approaches that have been developed. Authors have used different scales of analysis, criteria for variable selection and natural phenomena as main hazards for their research. Especially among social indicators, it is possible that limitations in data availability have led to many different approaches worldwide. Furthermore, the lack of consistency in the selection of variables and indicators (especially socio-economic) is observed, whereas the selection of geophysical indicators is rather standardized. Outcomes of models based on geophysical approaches seem to be more consistent as well. This research demonstrates that a broad range of variables have been used in this context, considering different types of time and space scales for coastal process operation, categorizing into different intervals and ranking each variable result in a large spectrum of vulnerability levels. However, in terms of suitability for vulnerability classification, variable ranking is often controversial, as the evaluation process highly depends on the personal opinion of the researchers. The objective of this review paper was to provide a valid description of the key dimensions of heterogeneity within research on climate change vulnerability. Through this Table 5 Socio-Economic Variables used in the literature review, researchers and policy-makers can determine the gaps in knowledge on climate change vulnerability of coastal areas, and the challenges that need to be addressed in future research.

The absence of a specific framework for the selection of indicators was identified, especially regarding social vulnerability. In most of the case studies, indicators have been selected taking into consideration data availability and researchers' aspirations. Moreover, researchers do not acknowledge concerns regarding static understanding of the human environment in index-based vulnerability research. A holistic approach in the selection of variables would be able to overcome these concerns, in order to identify not only areas in danger, but also the level of vulnerability of people and communities at local scales, as damage on crucial infrastructure will affect directly their livelihood.

Doukakis

Erosion and shoreline change Gornitz

Assessing vulnerability of coasts to climate change: A review of approaches and their application to the Australian coast. GIS for the Coastal Zone: A Selection of Papers from CoastGIS

Assessing coastal vulnerability index to climate change: the Case of Accra -Ghana

Coastal processes assessment under extreme storm events using numerical modelling approaches

The socioeconomic vulnerability index: A pragmatic approach for assessing climate change led risks-A case study in the south-western coastal Bangladesh

Natural and human indicators in coastal vulnerability and risk assessment

Port Industry Survey on Climate Impacts and Adaptation, UNCTAD Research Paper No

Multi-criteria evaluation approach to coastal vulnerability index development in micro-tidal low-lying areas

Vulnerability and resilience in natural disasters: a marketing and public policy perspective

A flood vulnerability index for coastal cities and its use in assessing climate change impacts

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

Climate change and marine fisheries: Least developed countries top global index of vulnerability

Spatial and numerical methodologies on coastal erosion and flooding risk assessment

Erosion hazard vulnerability of us coastal counties

Updating and augmenting the economic vulnerability index. Occasional paper, Islands and Small States Institute of the University of Malta

Vulnerability, risk and adaptation: A conceptual framework

Comparative coastal risk index (CCRI): A multidisciplinary risk index for Latin America and the Caribbean

Population evacuation: assessing spatial variability in geophysical risk and social vulnerability to natural hazards

University of Notre Dame global adaptation index country index technical report. ND-GAIN

Counting the cost 2021: A year of climate breakdown

Conceptual advances on global scale assessments of vulnerability: Informing investments for coastal populations at risk of climate change

Social vulnerability to environmental hazards

Integrated analysis of risks of coastal flooding and cliff erosion under scenarios of long term change

An integrative climate change vulnerability index for Arctic aviation and marine transportation

Coastal vulnerability and the implications of sea-level rise for Ireland

Coastal vulnerability and risk parameters

Global Climate Risk Index 2020 -Who Suffers Most from Extreme Weather Events? Weather-Related Loss Events

A new composite climate change vulnerability index

Planning for an uncertain future: can multicriteria analysis support better decision making in climate planning?

Geophysical and social vulnerability to floods at municipal scale under climate change: The cage of an inner-city suburb of Sydney

Vulnerability and its discontents: The past, present and future of climate change vulnerability research

Coastal vulnerability assessment to sealevel rise based on geomorphological and oceanographical parameters: the case of Argolikos Gulf

Coastal areas and climate change: A decision support tool for implementing adaptation measures

Vulnerability to coastal flooding of industrial urban areas in Greece

Climate change vulnerability assessment and adaptation strategies through best management practices

The development of a coastal risk assessment database: vulnerability to sea-level rise in the U

Application of social vulnerability index (SoVI) and delineation of natural risk zones in Greater Lisbon

The economics of natural disasters: concepts and methods. Policy research working paper 5507

A GIS-based framework for high-level climate change risk assessment of critical infrastructure

Human Links to Coastal Disasters. The H

Indicators of vulnerability and adaptive capacity: Towards a clarification of the sciencepolicy interface

Climate Change 2001: Impacts, Adaptation And Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change

Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

Climate Change and Social Inequality. UN Department of Economic and Social Affairs (DESA) Working Papers

Development of spatial water resources vulnerability index considering climate change impacts

Coastal infrastructure vulnerability: an integrated assessment model

Assessment of the sensitivity of the southern coast of the Gulf of Corinth (Peloponnese, Greece) to sea-level rise

Vulnerability of Hampton roads, Virginia to storm-surge flooding and sea-level rise

Assessment of coastal vulnerability for present and future climate conditions in coastal areas of the Aegean Sea

Adaptation of urban drainage networks to climate change: A review

Vulnerability assessment of storm surges in the coastal area of Guangdong Province

Adapting climate change challenge: A new vulnerability assessment framework from the global perspective

Evaluating regional vulnerability to climate change: purposes and methods

Integrated design of coastal structures and adaptation to climate change hazards

Storm impacts on a coupled human-natural coastal system: Resilience of developed coasts

Coastal vulnerability assessment of Puducherry coast, India, using the analytical hierarchical process

Flood mitigation planning using Promethee

Defining coastal resilience

The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones

Expert evaluation of open-data indicators of seaport vulnerability to climate and extreme weather impacts for U.S. North Atlantic ports

A multi-scale coastal vulnerability index: A tool for coastal managers? Environ Hazards

Regional geomorphic vulnerability analysis to storms for Catalan beaches

Usage of INFORM GRI in Humanitarian Aid and Development Assistance Initiatives: Results of the INFORM GRI's Impact Survey. EUR 29894 EN, Publications Office of the European Union

Review article: Assessing the costs of natural hazards -state of the art and knowledge gaps

Ports resilience expert committee (2016) ports resilience index: a port management self-assessment 28

Indicator-based assessment of climate change impacts on coasts: A review of concepts, methodological approaches and vulnerability indices

2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Application of coastal vu 0lnerability index. a case study along the Apulian Coastline

Coastal Vulnerability Assessment of Cumberland Island National Seashore (CUIS) to sea-level rise

Flood depth-damage functions for built environment

Imputation of missing values in the INFORM Global Risk Index, EUR 30037 EN, Publications Office of the European Union

A national assessment of the sensitivity of Australian runoff to climate change

Sealevel rise and coastal vulnerability: An assessment of Andhra Pradesh coast, India through remote sensing and GIS

Investigation of innovative financing schemes for transportation projects

A combination of PROMETHEE and Goal-Programming methods for the evaluation of water airport connections

Resilience assessment of complex urban systems to natural disasters: A new literature review

The Analytic Hierarchy Process-what it is and how it is used

An index-based method to assess vulnerabilities and risks of Mediterranean coastal zones to multiple hazards -Un Indice per la valutazione delle vulnerabilità e dei rischi generati da multiple pericolosità sulle zone costiere del Mediterraneo. Doctoral dissertation submitted in October 2014 to the Department of Economics Ca'Foscari University of

Strengthening the Knowledge Base on Regional Climate Variability and Change: Application of a Multi-Scale Coastal Risk Index at Regional and Local Scale in the Mediterranean

Integrated multihazard mapping

National Assessment of Coastal Vulnerability to Sea-Level Rise

Development of a relative coastal vulnerability index in a macro-tidal environment for climate change adaptation

Multi-sectoral, high-resolution assessment of climate change consequences of coastal flooding

Assessing coastal vulnerability to climate change: Comparing segmentation at global and regional scales

Coastal hazard vulnerability assessment based on geomorphic, oceanographic and demographic parameters

An integrated wave modelling framework for extreme and rare events for climate change in coastal areas -The case of Rethymno

United Nations Framework Convention on Climate Change

Island-specific preferences of tourists for environmental features: implications of climate change for tourism-dependent states

Coastal sea levels, impacts and adaptation

Bündnis Entwicklung Hilft and Ruhr University Bochum -Institute for International Law of Peace and Armed Conflict (IFHV)

Vulnerability of coastal communities to sea-level rise: A case study of Cape May County

National assessment of coastal vulnerability to sea-level rise for the Chinese coast

Assessment of the sensitivity of Zakynthos Island (Ionian Sea, Western Greece) to climate change-induced coastal hazards

A climate change vulnerability index and case study in a

Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations

Acknowledgements A graduate scholarship to C.N. Roukounis by the Research Committee of the National

Age of inhabitants Cutter et al. (2003) , Boruff et al. (2005) , Kleinosky et al. (2007) , Tate et al. (2010) , Li and Li (2011) , Satta (2014) , Guillard-Goncąlves et al. (2015) , Zanetti et al. (2016) , Tragaki et al. (2018) , Gargiulo et al. (2020) , El-Zein et al. (2021) Income (inhabitants' annual income) Cutter et al. (2003) , Boruff et al. (2005) , Mackey and Russell (