Multidimensional vulnerability and risk analysis of the Andaman coast of Thailand: A coupled model-based approach

The contemporary development discourse is largely concerned with mitigating vulnerabilities, building resilience, and achieving sustainability. The interconnection of the various factors involved in vulnerability, sustainability assessments, and resilience building necessitates a comprehensive analysis. However, bringing together these factors for a multidimensional analysis of vulnerability or sustainability poses significant challenges. Thus, it is imperative to establish a framework that combines multidimensional vulnerability and sustainability analysis to overcome vulnerabilities and achieve sustainable development. This article presents a framework integrating sustainability into multidimensional vulnerability analysis to establish sustainable intervention. Firstly, we review the key concepts and multidimensional frameworks of vulnerability analysis. Secondly, we discuss the various domains and drivers of vulnerabilities and the role of sustainability in analysing the impact of multidimensional vulnerability and framing sustainable intervention strategies. Lastly, we argue that mitigating vulnerability drivers is central to the preliminary framework, which can be achieved by aligning sustainable intervention strategies with the sustainability goals (SDGs).

Vulnerability studies have often centered on climate change and catastrophic climatic events. Contrary to this trend, this study explores the multidimensional vulnerabilities faced by small-scale fisheries (SSFs) in the Jaffna peninsula of Sri Lanka. We employed the Integrated Vulnerability Analysis for Sustainability (IVAS) Framework for this analysis. Data was collected through focus group discussions and key informant interviews using semi-structured questionnaires. We analyzed our data following Reflexive Thematic Analysis using ATLAS.ti software and identified 15 vulnerability drivers across five domains specific to the local context, revealing that SSFs in the Jaffna peninsula are highly susceptible to multiple vulnerabilities. Notably, the analysis brought to light a previously uncharted domain of vulnerability stemming from the residual effects of civil war and the lingering root causes of ethnic conflict. However, the interplay between multiple drivers of vulnerabilities, spanning from societal to environmental factors, underscores the importance of adopting multidimensional approaches in vulnerability analysis and policy formulation. This study may be a pioneer of its kind, as the findings provide new insights into multidimensional vulnerability analysis while providing a comprehensive and holistic approach for future studies.

China suffers frequent and severe floods. A lot of studies have been done in the field of flood disaster, including flood vulnerability assessment. This paper develops assessment models of multidimensional flood vulnerability based on the data envelopment analysis method and identifies multidimensional flood vulnerability—population, death, agriculture and economy—at the provincial scale in China using flood damage data and socioeconomic statistical data from 2001 to 2010. Based on the characteristics of multidimensional flood vulnerability of each province, some suggestions for flood prevention and mitigation are suggested. The assessment models of multidimensional flood vulnerability are simple and can be used for vulnerability analysis of natural disaster at regional or national levels. The assessment of multidimensional flood vulnerability can provide multifaceted information that contributes to a deeper understanding of the flood vulnerability and provides a scientific base for the policy making and implementation of flood prevention and mitigation designs.

Considering the increasing impact of natural hazard-related disasters in Malaysia, understanding and evaluating integrated disaster risk must consider multi-hazard and multidimensional vulnerability at the local level, particularly in developing industrial nations like Malaysia. Therefore, this study proposes an improved methodology for conducting an integrated disaster risk assessment index (IDRI) mapping to measure disaster risk within local administrative boundaries in Malaysia. In this study, multi-hazard spatial overlapping combined two common hazards in Malaysia, which are floods and landslides. This study used multidimensional vulnerability, which encompasses six dimensions, 16 subdimensions, and 54 indicators. Researchers applied this approach in three urban districts of Selangor, Malaysia: Sepang, Kuala Langat, and Hulu Langat, which are located within the Langat River catchment and consist of 17 subdistricts. Overall, 32.9% of the total study area was found to be at risk, with 4.3% in the very high-risk area. In comparison with the latest flood events in 2021, the IDRI components were highly correlated with disaster impact. In conclusion, the contribution of this study provides a novel perspective on disaster risk assessment by addressing several types of hazards and multidimensional vulnerability, as compared to the previous methodology focusing on a single hazard and a physical vulnerability factor.

• Integrated and multidimensional approaches are key to identifying vulnerability underlying causes. • Validation of vulnerability indices can be tackled through sensitivity and uncertainty analysis. • Integrated and multidimensional vulnerability analysis is key to tackling integrated flood risk management. Over the last twenty years, numerous strategies and policies (e.g., 2030 Agenda for Sustainable Development) have emerged to promote flood risk reduction that is compatible with the conservation or restoration of river ecosystems, with the ultimate goal of achieving sustainable development. In this context, vulnerability analysis is considered a key aspect in flood risk reduction, and the most widespread methodology for its characterization is the construction of indices. However, such indices have been obtained so far through a fragmented approach, either because they do not consider all vulnerability dimensions (i.e., social, economic, ecosystem, physical, institutional and cultural) or components (i.e., exposure, sensitivity and resilience). Moreover, indices developed on a regional scale focusing on areas prone to flash floods are rarely validated, as the necessary information is often not available and flash floods do not occur simultaneously in all urban areas of a given region. This paper addresses the above two knowledge gaps and describes the construction at regional level of a flash flood Integrated Multidimensional Vulnerability Index (IMVI). Vulnerability here is characterized holistically, integrating in the index all the dimensions and components involved. Subsequently, an uncertainty and sensitivity analysis was performed to validate the IMVI. Lastly, vulnerability regional spatial patterns were identified through a Latent Class Cluster Analysis. The methodology implemented here allows one to identify vulnerability sources and their underlying causes, helping to improve flood risk management. Moreover, validation outputs enable to determine index uncertainty sources, thereby encouraging decision-makers to design more cost-effective vulnerability reduction strategies.

By examining the significant flaws in multivariate risk analysis and integrated risk analysis, this article introduces a new approach to evaluating the total risk within complex risk systems: the principle of multi-dimensional risk (MDR) analysis. Under this framework, the scope of each individual risk is first defined, and the risk-bearing entity is identified. Each risk is then analyzed independently, and the results are subsequently integrated to provide a comprehensive view of MDR. Multivariate risk analysis becomes increasingly impractical as the number of factors grows, due to the correspondingly large sample size required—often unattainable in real-world conditions. Integrated risk analysis methods, such as weighted combinations and Copula techniques, are heavily influenced by subjective factors, which compromise the reliability of their results. In contrast, MDR analysis involves fewer variables per individual risk, reducing the sample size requirement and making data collection more feasible. Individual risks can be quantified using objective physical indicators such as economic loss or physical injury, enabling more accurate calculations of the total risk across the system. A case study involving two-dimensional risks—flood and earthquake—demonstrated that these events often have vastly different occurrence cycles. When these risks are entangled in conventional analysis, the resulting annual total risk value can be severely distorted. By analyzing individual risks separately, maintaining the focus on overall system risk, and treating the total risk as an MDR problem, a more reliable foundation for policy-making and risk management can be established. There are at least three types of MDR relationships: independent, compounding, and negatively correlated. As a result, no universal MDR analysis model exists.

Gender analysis in vulnerability assessments is needed in disaster risk reduction (DRR). This study examined headship-based household vulnerabilities in the Koshi River Basin of Nepal. This comparative study between male-headed households (MHHs) and female-headed households (FHHs) analyzed the social, infrastructural, economic, and environmental components of vulnerability assessments. A mixed method was used to collect data, including a survey of 216 households, 15 key informant interviews, 40 in-depth interviews, and 8 focus group discussions. The results from the weightage average index (WAI) revealed that the FHHs are more vulnerable in all components. Social and physical components show greater vulnerability for FHHs compared to economic and environmental components. The t-test showed that the difference in multidimensional vulnerability is highly significant (F = 3.423, p-value = 0.000). The WAI calculation showed 42%, 51%, and 7% FHHs and 6%, 35%, 49%, and 10% of MHHs are in very high, high, moderate, and low levels of vulnerability, respectively. Sociocultural norms were the main factors driving the gap which affected households’ ability to respond to and recover from flood disasters and impacted the DRR process. The study suggests that more attention is given to FHHs through increased access to services, capacity building, awareness training, livelihood initiatives, participation in preparedness activities, and inclusion in the DRR process to minimize the impact of floods in the future, particularly for FHHs.

Development of an interactive web-based tool for flood risk analysis and climate–resilient road drainage design: RiskDRAIN

This study assesses the multidimensional livelihood vulnerability of smallholder farmers to climate change-induced shocks in Dinki watershed, northcentral highlands of Ethiopia. The data were collected through a cross-sectional survey conducted on 288 households, six focus group discussions, and 15 key informant interviews, complemented with 30 years of rainfall and temperature data obtained from the National Metrological Agency. The Livelihood Vulnerability Index (LVI) framed within the Intergovernmental Panel on Climate Change and vulnerability (LVI-IPCC) approach was used to measure households’ livelihood vulnerability on the agro-ecology unit of analysis. In each agro-ecology, the LVI and LVI-IPCC were calculated as well as one-way analysis of variance was used to test differences between agro-ecological zones. The findings indicate that the vulnerability dimensions and major components varied between agro-ecological zones (p < 0.001). The result also reveals that increased sensitivity to water and health facilities, recurrent exposure to climate change-induced shocks, poor technology utilization, and limited livelihood diversification practices are principal factors contributing to mounted sensitivity, exposure, and overall livelihood vulnerability of the lowland agro-ecology. The finding suggests that designing resilience-building adaptation strategies based on the agro-ecology unit of analysis is sound to reduce the vulnerability of smallholder farmers to climate change-induced shocks.

Purpose(1) How can high-frequency data be utilized more effectively to identify and extract various risks in the cryptocurrency market? (2) Do the risk characteristics of different cryptocurrencies exhibit consistency or variability across multiple dimensions? (3) Based on these risk characteristics, how can more precise risk prevention and hedging strategies be provided to investors?Design/methodology/approach(1) The threshold optimal detection (TOD) model can accurately separate jump and continuous behaviors by setting appropriate threshold values, effectively identifying extreme price fluctuations in the market. (2) The method for separating trend and cyclical behaviors can be achieved through filter design and application. This approach effectively distinguishes between long-term and short-term fluctuations in the cryptocurrency market, enabling a clearer analysis of market risks across different time scales. (3) We use linear regression to estimate the sensitivity of cryptocurrency returns to different risk factors, represented by the β coefficient. (4) The time-frequency domain characteristics of wavelet coherence analysis allow for simultaneous examination of risk frequencies in the cryptocurrency market, providing a more comprehensive understanding of market behavior.FindingsFirst, by integrating high-frequency data with multidimensional risk decomposition techniques, this study systematically identifies and analyzes various risk features within the cryptocurrency market, enriching the existing literature on high-frequency volatility and risk identification. Second, this paper innovatively decomposes continuous risk into trend risk and cyclical risk, providing a more refined framework for managing market volatility risks. Finally, the paper proposes differentiated response strategies tailored to various risk characteristics, particularly in jump risk management, offering practical guidance on the use of derivatives such as options to provide actionable solutions for investors.Originality/valueThis paper proposes a multidimensional risk extraction and analysis method by examining high-frequency data of nine major cryptocurrencies from December 2020 to July 2024. It not only explores the characteristics of jump risk and continuous risk but also further decomposes continuous risk into trend risk and cyclical risk using filtering techniques, revealing the heterogeneous performance of different cryptocurrencies in both long-term and short-term volatility. This multidimensional risk analysis allows for a more comprehensive capture of various market fluctuation patterns, providing investors and risk managers with more effective response strategies.

Measuring Multidimensional Vulnerability in Afghanistan

Complex climate change risk and emerging directions for vulnerability research in Africa

Background A limited ability to identify social vulnerability and community resilience at local scales has been recognised as a critical barrier to both climate adaptation and health risk assessment and planning. This study aims to assess multidimensional social vulnerability to the health impacts of climate change across communities in Australia, quantify its contribution to health inequalities, and identify key drivers of health vulnerability. Methods Informed by a scoping review and the WHO Social Determinants of Health Equity framework, we compiled area-level data from multiple sources on 61 social vulnerability indicators, subsumed under 27 subdomains and 8 domains (demographic profile, economic security, residential environment, infrastructure and services, social stability and community support, population health, governance and policies, climate knowledge and awareness). These indicators were used to construct a Social Vulnerability Index for the Health Impact of Climate Change (SVI-HICC) and scores in each domain. We used dominance analyses to identify the strongest predictors of vulnerability, examined inequalities in mental, physical, and social health associated with extreme weather and climate events across the vulnerability distribution, and tested the capacity of SVI-HICC to predict adverse health outcomes following climate-related extreme events in comparison to alternative social indices. Findings Spatial mapping showed that high vulnerability was clustered in regional and remote areas, with pockets of moderate vulnerability in urban areas. People living in high vulnerability areas experienced significant health losses from weather and climate disaster, this was not seen for people in low vulnerability areas. Infrastructure and services, economic security, and residential environment were identified as the most influential domains contributing to social vulnerability, primarily driven by access to healthcare services, area disadvantage, dwelling condition, and housing precarity. Interpretation An area-level assessment of multi-dimensional social vulnerability makes visible how social and structural determinants contribute to health inequalities in climate change. Such insights can inform climate adaptation policies that are equity-oriented and context-sensitive.

Multidimensional risk evaluation of natural gas pipelines based on a multicriteria decision model using visualization tools and statistical tests for global sensitivity analysis

Accidents involve critical consequences that require an appropriate and efficient form of risk management. A multidimensional risk analysis allows a broader view. MCDM/A approaches enable more consistent decision-making, taking into account the DM’s rationality (compensatory or non-compensatory), DM’s behavior regarding risk (prone, neutral or averse) and the uncertainties inherent in the risk context. This chapter presents numerical applications illustrating the use of multicriteria models in two different contexts: a natural gas pipeline and an underground electricity distribution system. Two different MCDM/A approaches are considered: MAUT (Multiattribute Utility Theory) and the ELECTRE TRI outranking method. In the numerical applications, MCDM/A approach steps for building decision models are presented: identifying hazard scenarios, estimating the set of payoffs, eliciting the MAU function (Multi-attribute Utility function), computing the probability function of consequences and estimating multidimensional risk. Loss functions are introduced in the models to calculate the probability distribution functions over the multiple criteria such as impact on humans, and environmental and financial losses. Therefore, Decision Theory concepts are applied to estimate risk in industrial plants and modes of transportation. Finally, other decision problems related to multidimensional risk analysis, using MCDM/A, are considered in different contexts, such as: power electricity systems, natural hazards, risk analysis on counter-terrorism, nuclear power plant.