Effective Disaster Risk Reduction Research Articles

A Systematic Review and Narrative Synthesis of Strategies for Disaster Risk Reduction Education for People With Disabilities.

To identify the components and characteristics of disaster risk reduction education for people with disabilities and to explore the contents and results of disaster risk reduction education. A systematic review and narrative synthesis. The review was conducted in accordance with Tawfik's guidelines and followed the reporting standards outlined in the Preferred Reporting Items for Systematic reviews and Meta-Analysis. The quality appraisal was guided by the mixed methods appraisal tool. The contents of each type of education were narratively synthesised. The literature search was conducted in December 2023 using several electronic databases, including PubMed, EMBASE, CINAHL, Cochrane library and PsycINFO. Eight quantitative studies were selected for this study. Disaster risk reduction education for people with disabilities has focused mainly on fires and earthquakes, and drills were the most used teaching method. Some of the education was conducted by designating a 1:1 ratio of educators and participants or modifying the ratio according to the type of disability. The staff mainly conducted the education, and after education, the effect of disaster risk reduction education was confirmed in terms of the knowledge and response of people with disabilities during drills. This review provides insights for developing disaster risk reduction education for people with disabilities. However, only a few randomised controlled trial (RCT) studies and quasi-experimental studies have been conducted, and several studies have bias risks. Thus, practical, continuous, quality interventions are needed for effective disaster risk reduction education for people with disabilities. This systematic review analysed the research trends of disaster risk reduction education for people with disabilities. The results provide the basis for quality evaluations of the operation, methods and achievements of educational programmes. Efforts are needed to promote collaboration between researcher and the practitioners to conduct further quality research. No Patient or Public Contribution: Systematic review.

For resilient rural shorelines: reviewing Nature-based Solutions for flood risk reduction in small coastal communities

Coastal flood risk poses a serious, existential threat to shoreline populations around the world both now and in the future. Unsurprisingly, global decision makers are considering their options – one of these being Nature-based Solutions – for effective disaster risk reduction which specifically targets coastal flooding. While strides have been made in the field of Nature-based Solutions for coastal flooding, much of this attention has been directed towards the urban setting, with a wealth of scholastic documentation to support this notion. The sizeable rural populations scattered throughout the world's small coastal communities, meanwhile, have been largely neglected in academic literature. Without this information, it is impossible to properly capture the full potential of Nature-based Solutions in (global) flood risk modelling endeavours or understand their role in the future of equitable disaster risk reduction. In light of this gap, we have reviewed the limited amount of existing literature from around the world involving the implementation and effectiveness of Nature-based Solutions in small coastal communities. We analysed 28 peer-reviewed studies to gather common themes and insights about the barriers and opportunities unique to these rural shorelines. Takeaways we have identified include a near consistent scarcity of resources (e.g., technical, financial, institutional) to implement disaster risk reduction measures; an abundance of space and opportune land use regimes which make Nature-based Solutions a highly plausible option; amplified nature contributions to people leading to larger benefits reaped from investments into Nature-based Solutions; and the presence of local knowledge regarding societal norms, climate patterns, and ecosystem capabilities. We argue that these four common themes point to the fact that more attention must be given to coastal flooding-focused Nature-based Solutions in the rural setting. As such, we present this collation as a starting point for future projects of similar setting and scope. We also recommend improving benefit-cost analysis methods as well as including local knowledge and other perspectives in future global assessments of coastal flood risk.

Towards designing a comprehensive composite index for social vulnerability to natural hazards in the big data era: potential challenges and partial solutions

AbstractThis study provides a practical definition and framework to measure social vulnerability to natural hazards, addressing gaps in the literature after three decades of Susan Cutter's Place-Based Model. The current social vulnerability index, designed based on available data such as census data, is limited in capturing all aspects of social vulnerability and spatial inequalities. This research explored and proposed a new theoretical perspective and methodological framework for designing a comprehensive index for social vulnerability to natural disasters using emerging big data, which is practical and feasible and can be applied to social vulnerability studies in general and social vulnerability to natural hazards in particular. This research first defines constructs of social vulnerability to natural hazards, including (1) socioeconomic status or conditions, (2) physical infrastructure or accessibility to facilities and services, (3) ecological-environmental conditions, (4) access to security facilities and crime rate, (5) technological inequalities, (6) health conditions of the citizens, and (7) susceptibility. Then, it proposes potential solutions for developing a comprehensive composite index under ideal conditions without (big) data limitations, using the US and the UK as case examples. These solutions are not just theoretical but also practical and feasible, instilling confidence in their implementation. This research offers valuable insights for researchers and policymakers in diverse sectors, supporting the design of effective disaster risk reduction strategies and intervention programs.

Effects of Alternative Stress of Drought–Flood on Summer Maize Growth and Yield

The present study aims to assess the responses of growth, development, and yield of summer maize to the effects of drought–flood abrupt alternation through comparative tests under single flood, single-drought, and drought–flood abrupt alternation treatments with varying degrees from the elongation to the tasseling stage during the 2021 and 2022 growing seasons. In addition, a water production function model for summer maize was preliminarily established based on the results obtained under the drought–flood abrupt alternation scenarios. The results indicated that drought–flood abrupt alternation with early moderate drought had a certain restricting effect on summer maize, while early moderate drought followed by waterlogging had a compensation effect on the cultivated summer maize. Furthermore, both mild and severe drought followed by waterlogging exert a significant combined constraint on the normal growth and development of summer maize, leading to a sharp decline in maize yield, necessitating a shorter timeframe for mitigating and reducing the effects of waterlogging. Additionally, the water production function model established through a multiple linear regression equation exhibits a high degree of fit and demonstrates a strong linear relationship. This study provides crucial insights for agricultural practices and water resource management strategies, particularly in the evaluation of the integrated impacts of drought and waterlogging on crop yields and the formulation of effective disaster risk reduction and mitigation measures in response to these impacts.

The ERMES chatbot: A conversational communication tool for improved emergency management and disaster risk reduction

The increased occurrence and severity of worldwide disasters, exacerbated by the impact of climate change, pose significant challenges to emergency management, calling for novel and more effective Disaster Risk Reduction (DRR) approaches. Despite the phenomenological differences of diverse hazards and the heterogeneity of local policies and governance structures, effective communication systems are needed to better manage the available resources in all phases of the emergency management cycle, especially in the case of cross-border events. In this work, we present the design, implementation, and validation of a novel tool for emergency management, i.e., the ERMES Chatbot: a mobile-based conversational communication system developed to facilitate real-time bidirectional communication between control centres, in-field forces, and citizens. The methodology used to create the ERMES Chatbot is grounded on the user-centred design approach, which entails end-user involvement in all key phases of the development, including the definition of the end-user requirements, the technical design, and the final validation of the proposed solution. The co-design approach involved several organisations belonging to the DRR domain, which includes a gamified experience for citizens and is iteratively validated through four in-field demonstration events based on realistic emergency scenarios with the involvement of emergency practitioners, volunteering organisations, and citizens.

Simulation of smallholder farmers’ adaptation to irrigation-induced landslides: An ABM application in human-land-policy interaction

Understanding the dynamic interaction between smallholder farmers' adaptation decision-making behavior and the policy environment in the context of human-induced disasters is vital for developing effective policies to enhance farmers' participation in disaster risk reduction (DRR). From the perspective of bounded rationality theory, this study constructs the farmer-land-policy interaction (FLPI) model, taking Heifangtai in Loess Plateau, China, as a case study. Based on first-hand micro data, this study combines static econometric model analysis with dynamic simulation techniques, achieving a combination of micro decision-making and macro policies to understand the interaction between farmers and the policy environment. The FLPI model simulates farmers' future adaptations under multiple policy scenarios (baseline, mandatory policy, and subsidy policy scenario with a combination of different policy tools) from 2021 to 2030. Compared with the simulation results of the baseline scenario, the results indicate that mandatory and subsidy policies are effective interventions incentivizing farmers’ adaptation behavior for effective DRR. Mandatory policies rely on policy intervention to ensure that farmers adopt drip irrigation and to promote the generation of long-term behavior of farmers. And the results also propose a suitable subsidy range of 700–900 yuan and with a period of more than 2 years for subsidy policy. This study enriches the research on the interaction simulation between farmer behavior and policy environment under the background of human-induced disasters. And the FLPI model uses survey data as input, which could provide behavior rules closer to real situations than that of simplifying assumptions. The research indicates that the “bottom-up” approach of the FLPI model proven more appropriate to presenting micro-level adaptation and dynamic changes than traditional statistical models.And the agent-based model is very suitable for simulating the behavioral decision-making changes of farmers in policy environments.

Regional Rainfall Damage Functions to Estimate Direct Economic Losses in Rainstorms: A Case Study of the 2016 Extreme Rainfall Event in Hebei Province of China

Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.

Evaluation of Geohazards Research in the World and Pacific Region: A Comparative Bibliometric Study

Geohazards are natural phenomena that endanger infrastructure, the environment, and human activity. Understanding and mitigating them is crucial for public safety, disaster preparedness, and sustainable land and resource management. This research objective is to analyze geohazard research trends globally and then compare them with countries around the Pacific Ring. Ninety-six geohazard publications were analyzed using bibliometric analysis and the VOSviewer application. The general trend of geohazard research increased slightly. It is critical in dealing with the expanding threats posed by geohazards, adapting to changing conditions, protecting vulnerable people, and leveraging technological improvements for more effective hazard management and disaster risk reduction. The Annual Offshore Technology Conference Proceedings and Engineering Geology are the most popular scholarly journals for authors to publish research on geohazard. The most popular topic area for geohazard papers is Earth and Planetary Sciences, followed by engineering, environmental science, and computer science. It is consistent with the growing use of technology in geohazard research. This study also includes an authorship profile. This research implies enhancing disaster risk reduction strategies and contributing to global efforts to address the challenges posed by geohazards.

Determining factors influencing flood preparedness among citizens in Jakarta: A protection motivation theory approach

The Jakarta Metropolitan Area is one of the cities heavily affected by floods in Asia. Effective disaster risk reduction strategies require deep understanding of what motivates its citizens towards preparedness and mitigation actions. This study aimed to discern these motivating factors among Jakarta's residents through the Protection Motivation Theory (PMT) approach. Through an online survey, 304 participants provided insights into how the extended PMT factors shape their Protection Motivation (PM) towards flood preparedness. A total of nine (9) variables and 41 constructs were considered in this study. PMT factors were comprised of PM, Perceived Severity (PS), Perceived Vulnerability (PV), Response Efficacy (RE), and Perceived Self-Efficacy (SE) along with additional factors such as Media (M), Government Preventive Actions (GA), Flood Experience (FE), and Geographical Perspectives (GP). Partial Least Square Structural Equation Modeling (PLS-SEM) revealed that SE had the most substantial impact on influencing PM (β = 0.522) followed by PV, PS, and RE. The results also showed the significance of M and GA to both SE and RE. Meanwhile, FE illustrates positive impact to both PV and PS. Interestingly, GP was found to be only significant to PV and not to PS. External factors M and GA demonstrated a significant indirect effect on PM through SE, whereas GP exhibited a significant indirect effect on PM through PV. These findings provide actionable insights for policymakers and disaster management practitioners, emphasizing the power of media and the need for comprehensive strategies that address both the psychological and practical aspects of flood disaster readiness in Jakarta and potentially other flood-prone regions.

Comprehensive analysis of characteristics of dry–wet events and their transitions in Uttar Pradesh, India

Understanding the occurrence and characteristics of dry and wet events is crucial for effective disaster prevention, resource management, and risk reduction in vulnerable regions. This study analyzed the spatiotemporal patterns of dry–wet events and their transition characteristics in Uttar Pradesh, India. The standardized precipitation evapotranspiration index (SPEI) at a monthly timescale was utilized to identify hotspot regions vulnerable to concurrent and frequent dry and wet events and their transitions. The severity, duration, and intensity of dry and wet events were characterized with the run theory over SPEI time series data from 18 synoptic stations in Uttar Pradesh over 48 years (1971–2018), sourced from the Indian Institute of Tropical Meteorology and the India Meteorological Department. Multiple assessment methods were utilized to examine the interaction of these extreme events, considering characteristics such as wet–dry ratio, average transition time, and rapid transition times from wet to dry events and from dry to wet events. Average wet durations ranged from 1.27 to 1.58 months, and average dry durations ranged from 1.29 to 1.82 months. Rapid transition times from dry to wet events ranged from 2.5 to 4.1 months, and those for wet-to-dry events ranged from 2.1 to 5.3 months. The eastern region experienced a significantly high number of dry events, while the western and Bundelkhand regions experienced more intense dry events. In contrast, the eastern region had intense wet events. This research on the occurrence of dry–wet events and their transitions can provide valuable insights for government decision-making and disaster prevention and reduction efforts.

Mapping the risk reduction benefits of coral reef conservation – Hawaiʻi case study

Effective disaster risk reduction measures are vital to coastal communities around the world. While nature-based solutions provide coastal communities with a promising alternative to traditional engineering-based solutions; these solutions are often overlooked by communities when planning and implementing disaster risk reduction measures. This study builds upon the literature that demonstrates the effectiveness of coral reef conservation to mitigate coastal flood risk. Our approach utilizes freely available tools and data to quantify the economic value of coral reef conservation for the Hawaiian Islands. We explore a scenario that depicts coastal flooding if the upper 1 m of the coral reef were to be lost. The study analyzes the Average Annual Loss (AAL) and losses avoided based on a series of 4 coastal flood scenario return periods with and without coral reefs. This case study finds that the preservation of the upper 1 m of coral reefs for the main islands of Hawaiʻi provides the state with $629 million in annual losses avoided to buildings. A hot spot analysis of the losses avoided identifies areas where conservation efforts could be prioritized. Our findings provide additional support to the use of nature-based solutions as an effective disaster risk reduction measure, and provides communities and stakeholders with a methodology that can be implemented using readily available data and tools.

Risk perception and vulnerability of communities in Nepal to transboundary glacial lake outburst floods from Tibet, China

Transboundary glacial lake outburst floods (GLOFs) pose severe risks to downstream communities and infrastructure across international borders. However, previous GLOF research has largely overlooked the transboundary aspects of national and sub-national level GLOFs, focusing instead mostly on inventorying glacial lakes and assessing their hazards. The lack of cross-border coordination could have dire consequences for vulnerable communities and potential disaster preparedness. Assessing risk perception and vulnerability among downstream populations exposed to GLOFs is crucial for effective disaster risk reduction and management. This study examined the risk perception and vulnerabilities of Nepali communities in Bhotekoshi/Sunkoshi (Poiqu in Tibet) and Trishuli (Gyirong in Tibet) river basins, exposed to transboundary GLOFs originating from Tibetan basins. Household-level data were collected from 90 respondents in the villages along those two basins. The findings highlight high risk perception in the Bhotekoshi basin and medium perception in the Trishuli basin, along with corresponding vulnerability levels. Key factors influencing risk perception and vulnerability are explored, emphasizing the need for public awareness and policy formulation at national and regional levels. To reduce vulnerability to transboundary GLOFs, extending early warning systems, enhancing cross-border cooperation, supporting socioeconomically disadvantaged populations, and ensuring resilient infrastructure are essential.

Unveiling the Recipe for Disaster Vulnerability: A Multidimensional Analysis in Dumaguete City, Philippines

Disaster vulnerability is not just a condition but a consequence of the interactions of natural and social factors. From this viewpoint, it is imperative to consider both natural and social factors when planning effective disaster risk reduction strategies. This study sought to determine factors that predict disaster vulnerability. Specifically, this research determined the associations between several socio-ecological dimensions (e.g., environmental, social, and economic profile), coping capacity, disaster preparedness, and disaster vulnerability of selected households in Dumaguete city. Using snowball sampling, a descriptive correlational design was used to collect data from self-report surveys of non-probable samples of 150 households from different disaster-prone barangays. Frequency count, weighted mean, and standard deviation were used to describe the data, and a multiple linear regression analysis was done to test the study's hypotheses. Findings show that the respondents manifested moderate levels of coping capacity and disaster preparedness while having high levels of disaster vulnerability. The study revealed that disaster preparedness and coping capacity were negatively correlated to disaster vulnerability. Aside from coping capacity and disaster preparedness, environmental, social, and economic profiles were also noted to predict disaster vulnerability significantly. Environmental profile was the most vital determinant of the participants’ perceived disaster vulnerability. To better prepare for disasters, it is suggested that city disaster risk reduction strategies focus on helping vulnerable barangays develop better adaptive capacities and manage environmental hazards. This could include providing training on essential hazard management and creating income-generating opportunities to offset the adverse effects of disasters.

Whole value at risk for flood damage estimates through spatial data analysis

Effective disaster risk reduction (DRR) for flooding requires a comprehensive estimate of the whole value at risk (WVAR) to inform appropriate and proportionate mitigation expenditure. Conventional flood risk estimation methods focus on the direct effects of inundation on community value and generally ignore collateral effects on assets and populations outside the flooded area. Consequently, conventional methods tend to underestimate the cost of flooding, leading to an underestimate of the return on DRR investment. Using spatial data analysis in an urban case study for Toronto, Canada, we identify and capture the collateral value at risk (ColVaR) to estimate the WVAR more comprehensively. In our case study, ColVaR (mean estimate) amounts to 70% of direct losses (ColVar = $344 M; direct losses = $475 M CAD), ranging from 20%–150% (ColVar $100–$740 M) when spanning the 90% confidence intervals of our Monte Carlo simulations. Thus, we demonstrate that if the collateral value at risk is ignored, WVAR can be significantly underestimated, potentially leading to reduced disaster risk reduction resource allocations and thereby adding risk exposure for communities. We present an accessible, seven-step process using existing spatial analysis tools and techniques that infrastructure stakeholders and planners can use to estimate ColVaR and better formulate DRR measures for their communities.

Climate Change-Related Disaster Risk Mitigation through Innovative Insurance Mechanism: A System Dynamics Model Application for a Case Study in Latvia

This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model’s structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model’s plausibility. The case study’s findings support the innovative insurance mechanism’s dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation.

Automated geovisualization of flood disaster impacts in the global South cities with open geospatial data sets and ICEYE SAR flood data

Flooding is a highly destructive natural disaster affecting millions of people annually, causing substantial global economic losses. Especially in the Global South, vulnerable populations with limited resources for flood protection and recovery suffer the most. However, effective flood impact analysis and standardized geospatial data processing and visualization is lacking, hindering effective disaster risk reduction. This study employs Python to develop a standardized, automated analysis and visualization model to address flood impacts after a high magnitude event anywhere in the world by using global geospatial data sets.In order to test its applicability two relevant cases, with different locations and scales, were selected, (Bangkok, Thailand, and Tula de Allende, Mexico). In both cases the automated flood model efficiently processed flood extent and depth data from ICEYE SAR images, added the population estimates from the German Aerospace Center, and key infrastructure elements from OpenStreetMap. The output of the automated process is presented as web-based interactive maps, offering insights into flooding severity and impacts with minimal user input. This quick approach (processing time between 41 s and 10,5 min) facilitates timely responses by first responders, with great potential to aid and improve the efficiency of international humanitarian efforts.By leveraging global high-resolution geospatial data for local-level analysis, this study demonstrates the versatility and time-saving benefits of this automated analysis and visualization. Automated models standardize results, minimizing human errors, and enabling consistent historical flood data comparison. Even though the databases are gathered from trusted sources, additional evaluation of uncertainties of the results with field data should be considered in further development of this tool. Nevertheless, this research highlights the worldwide potential, and especially the Global South, of automated global geospatial analysis to improve disaster impact reduction by enhancing efficient response efforts.

An optimization-based risk management framework with risk interdependence for effective disaster risk reduction

Effective risk management is crucial in the field of disaster management to mitigate the impact of disasters. It involves proactive planning, resource allocation, and the implementation of response measures. By identifying and assessing risks, stakeholders can take preventive measures and establish contingency plans, fostering resilience, coordination, and efficient disaster response and recovery processes. In the context of globalization, organizations face numerous uncertainties and risks, necessitating the implementation of efficient risk management plans. While the conventional risk response analysis assumes independence among risks, in reality, they are interconnected. Therefore, evaluating risk interactions becomes vital in comprehensive risk management evaluations. This study proposes an innovative framework for disaster risk management based on a review of relevant literature, global project management standards, and the insights gained from previous studies dealing with disaster management projects. To identify appropriate risk response strategies, an optimization model is developed that incorporates a maximization utility function and directly captures potential risk interdependence. An exact optimization method and two metaheuristics are proposed to solve the developed optimization model for the selection of risk response strategies. A detailed assessment of the considered solution algorithms in terms of various performance indicators and sensitivity analysis of model parameters are conducted through a comparative study. This analysis provides valuable insights into the effectiveness and practical applicability of the proposed methodology. The outcomes of this study will benefit practitioners and policymakers involved in disaster management by improving their ability to make informed decisions and allocate resources effectively.

Adaptive capacity to reduce disaster risks in informal settlements.

This study strengthens the intergovernmental structures and public participation to reduce disaster risks in communities. This study discourages a silos mentality and encourages coordination between government departments to identify root causes by applying the pressure and release model for effective disaster risk reduction.

Resilience through Disaster Risk Reduction Initiative: A Case Study on Disaster Resilient Habitat in Coastal Region of Bangladesh

Due to spatial geomorphological and climatological conditions, Bainpara is susceptible to tropical cyclones along with tidal surge. Most adverse effects of climate change are anticipated here in the form of extreme weather events like tropical sea cyclones along with tide flow. The study area is carrying a huge burden of population as compared to its resource potentialities and employment opportunities. Cyclone Aila hit the south-western coast of Bangladesh leading to large scale damages to lives, infrastructures, livelihoods and economy of the area. Moreover, weak economic condition along with inadequate infrastructure and higher dependency of natural resources in this area makes the situation more vulnerable. Within this context, CDMP-II has come forward to rebuild the community with the new concept of Disaster Resilient Habitat (DRH)- Bainpara Model Village (BMV). This research investigates the multifaceted relationship between resilience and disaster risk reduction (DRR) in the context of coastal Bangladesh, highly vulnerable to climatic disasters. Employing a mixed-methods approach involving quantitative and qualitative techniques. this study explores how localized DRR strategies influence community resilience in disaster-prone habitats. Primary and secondary data was collected to meet the objective of the study. However, primary data has been collected through questionnaire survey, Focus Group Discussions (FGD), Key Informant Interview (KII) and case study. Relevant published and unpublished document has been analyzed in brief for collecting secondary data. The research assesses community vulnerability to hazards such as cyclones, tidal surge, coastal erosion, water logging and salinity intrusion as well as explores the adaptive capacities of households and communities, Central to this inquiry, identification of effective DRR practices can mitigate disaster risks and improved livelihoods, ultimately leading to sustainable development. It is evident that implementation of BMV bring positive changes in the lives and livelihood for the beneficiaries of the BMV. It is revealed that DRH holds significant promise for protecting poor and excluded people against current and future weather extremes and tackling increasing levels of risk and vulnerability. It is found from the study that the community of BMV transformed themselves from being vulnerable victims of disasters to examples of resilience. It is now the reality that BMV project provides a sustainable solution, which tackle up deep-rooted disadvantage of coastal poverty and underlying vulnerability of the community. This could be the example of Build Back Better (BBB) the fourth priority of Sendai Framework for Disaster Risk Reduction (SFDRR) 2015-2030 after Hyogo Framework for Action (HFA) 2005-2015. The findings highlight the importance of community involvement, incorporating local knowledge and considering socio-economic factors in shaping resilience outcomes. Moreover, the research underscores the necessity for inclusive policies that align DRR and CCA efforts, ensuring sustainable development in vulnerable coastal regions. It is expected that this study will contribute to the growing body of literature on disaster management, providing actionable insights for policymakers, development partners and practitioners working for strengthening resilience in similar contexts. BMV-the DRH model could be replicated in another vulnerable coastal areas as this represents a positive change in the lives and livelihood of the community.

The key role of risk perception in preparedness for oil pipeline accidents in urban areas: A sequential mediation analysis

Effective disaster risk reduction requires conducting research within communities that gave recognized natural or technological risks, to foster the development of more resilient societies. However, limited information is available regarding risk perception and preparedness for technological hazards in disaster-prone urban areas of South America. The aim of this study is to investigate the factors that influence public risk perception regarding the Trans-Ecuadorian Oil Pipeline System and its relationship with household preparedness for a potential accident in the Metropolitan District of Quito (Ecuador). We assess the link among knowledge, trust in authorities, risk perception, intention to prepare, and preparedness. Results from a sequential mediation analysis reveal that risk perception partially and positively mediates the relationship between knowledge and preparedness and acts as a negative full mediator between the latter and trust. These findings provide valuable information for future risk governance and communication strategies, aimed at enhancing risk perception and improving individual preparedness of individuals, as well as risk mitigation procedures.