Knowing how to implement emergency material scheduling and transportation during emergency rescues, such as major and critical emergencies, has become a research hotspot in academia and industry in recent years. To leverage the speed and terrain-insensitive advantages of aviation, the weak limitations of geographical conditions must be addressed, and the material scheduling efficiency of aviation rescue centers in disaster-stricken areas needs to be improved. In this study, CRITIC and cloud model theory were integrated to evaluate the urgency of emergency material demands in different flood-stricken areas under catastrophic flood disaster risks. Furthermore, a mathematical model for a single aviation emergency rescue center to dispatch emergency materials to multiple disaster-stricken sites was designed based on the optimized ant colony algorithm. A penalty function was then incorporated to formulate a multi-objective aviation scheduling model, aiming to minimize both total rescue time and total cost. The model was solved using an improved genetic algorithm. Taking rainstorm-induced flood disasters in the megacity of Zhengzhou, China, in 2021 as the empirical research case, the operating paths for the aviation emergency rescue center to serve multiple demand points were optimized. The impact of material demand urgency on scheduling decisions was analyzed. Results revealed that when material demand urgency is considered, aircraft complete deliveries according to urgency rankings and return to the center. All tasks can be completed within the required time via four routes. Although total time increases, economic cost is significantly reduced, and disaster loss is mitigated. The findings obtained from this study provide a decision-making reference for improving the efficiency of aviation emergency rescue and enhancing urban risk management capabilities in response to major and critical emergencies.

Disaster impact evaluation remains predominantly monetary, often overlooking the disproportionate burdens faced by low-asset and marginalized households. This study examines 312 vulnerable landslide-affected households in Idukki District, Kerala, to construct a comprehensive Disaster Loss Index (DLI) that assesses losses across multiple dimensions physical, economic, social, and livelihood. The results reveal an “upside-down” pattern: households with smaller assets report lower absolute monetary losses, yet endure complete destruction and prolonged livelihood disruption. By integrating quantitative index construction with field-level evidence, the study demonstrates that conventional monetary valuation fails to reflect the full scale of disaster-induced deprivation among marginal communities. The findings argue for a shift beyond monetary value toward relational metrics that capture proportional, structural, and livelihood-based dimensions of loss. Such an approach enhances the equity and accuracy of post-disaster assessments and strengthens policy efforts toward inclusive recovery and resilience building.

Abstract Urban pluvial floods have become increasingly frequent under the combined effects of climate change and urbanization, leading to increased disaster losses. This study developed a data-driven urban pluvial flood prediction model using convolutional neural networks (CNNs) to enhance computational efficiency while maintaining simulation accuracy. A high-resolution cellular-based flood model generated the training dataset through systematic patch-based sampling combined with fixed step size selection strategies. The established framework enabled flood simulations through integrated analysis of topographic features and rainfall processes. Shapley additive explanations (SHAP) and Group masking analysis (GMA) were implemented to interpret the decision-making mechanisms of CNN model. The model was validated in a relatively independent drainage area, demonstrating strong agreement with conventional cellular model outputs across six design storm scenarios and two historical rainfall events. Computational experiments showed that the CNN model reduced simulation time from minutes to seconds compared to process-based approaches, while maintaining low absolute errors in water depth predictions. Both SHAP and GMA interpretation revealed that topographic features, particularly building, digital elevation model (DEM), and aspect, exert dominant influence on model predictions. This data-driven framework established an efficient computational paradigm for urban flood modeling, with SHAP and GMA analysis guiding input variable selection while explaining model behavior. The methodology demonstrated potential for real-time monitoring integration, supporting rapid flood risk assessment and resilience enhancement.

Refined risk assessment of geological hazards is vital for disaster prevention and loss reduction. World Natural Heritage Sites are Earth’s most precious natural assets, yet research on refined geological hazard risk assessment from the perspective of heritage conservation and management is scarce. Taking the Libo-Huanjiang Karst World Natural Heritage Site as the study area, this paper discusses an assessment method focusing on protecting its Outstanding Universal Value (OUV) and draws three conclusions: ① The risk pattern features local high concentration and overall low risk: extremely high-risk areas account for 2.32% (sporadic in the northwest), low-risk areas 68.29% (covering the core zone), and high/medium-risk areas concentrate in the west with developed faults and intense human activities. ② It clarifies the non-linear synergistic mechanism of susceptibility-hazard-vulnerability, with stratum lithology, distance to roads and water systems as core drivers, and low vulnerability acting as an ecological buffer. ③ It correlates risk patterns with heritage protection, verifying the overlap between high-risk areas and key karst landscapes, and defines key prevention zones. The results support risk management and heritage protection, providing a replicable reference for similar World Natural Heritage Sites globally.

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions.

Improved adaptation capacity offsets the precipitation-induced mountain hazard risks in Sichuan-Tibet region.

Summary: Three years after their initial release, the United Nations Office for Disaster Risk Reduction (UNDRR) and the International Science Council (ISC) are undertaking a review of the UNDRR/ISC Hazard Information Profiles (HIPs) ahead of the UNDRR Global Platform for Disaster Risk Reduction that will take place in June 2025. These HIPs provide an authoritative reference on the scope, name, and definitions of hazards of relevance to the Sendai Framework for Disaster Risk Reduction. The HIPs were hailed as ‘groundbreaking’ in the Report of the Midterm Review of the Sendai Framework in 2023 and continue to provide extensive information to various stakeholders across different sectors, including disaster risk reduction planning, monitoring, training, and research. They are widely utilized by intergovernmental bodies, national governments, disaster management agencies, statistical offices, private sectors, and academic institutions, fostering a more comprehensive and unified approach to disaster risk monitoring, recording, and planning. For example, the World Health Organization (WHO) and the International Organization for Migration (IOM) have incorporated these profiles in their reference systems and are employing them in some of their trainings globally. Additionally, UNDRR uses these profiles for monitoring disasters, with the HIPs supporting a new hazardous event and disaster losses and damages tracking system developed by UNDRR, UNDP, and WMO in partnership with many. Many other stakeholders use them as foundational tools for disaster planning and response efforts, research, and teaching. In this review cycle, particular emphasis will be placed on the ‘multi-hazard context’, aiming to enhance understanding of the interplay between different hazards, which can result in cascading, compound, and complex events. This will facilitate the utilization of the profiles for multi-hazard risk assessment and early warning systems. Leveraging the latest advancements in machine learning, efforts have been made to make the HIPs more machine actionable, thereby expanding their usability and applications

Soybean lodging severely impairs yield and quality, and its precise grading is a key prerequisite for intelligent agricultural management and loss assessment in agricultural insurance. Most existing studies have focused primarily on soybean lodging identification and lodging resistance evaluation, whereas methods for the precise differentiation of lodging grades remain to be refined. This study presents an improved AlexNet model integrated with a Local Feature Aggregation (LFA) attention mechanism and a dynamic optimization strategy for the accurate grading of soybean lodging. RGB imagery of soybean canopies during the grain-filling to early maturity stages was acquired via a multispectral unmanned aerial vehicle (UAV). A dynamic Dropout strategy was adopted to enhance model stability and mitigate overfitting, and the Particle Swarm Optimization (PSO) algorithm was employed to intelligently optimize key hyperparameters of the model. The results demonstrate that the optimized model achieved an overall accuracy of 94.23% on the test set, with an average loss of 0.0682 and an inference speed of 0.422 s/step. In independent field validation, the grading accuracies for the five lodging grades were 90.12%, 86.35%, 89.47%, 88.93%, and 92.76%, respectively, with a mean accuracy of 89.53%. The proposed model enables the rapid and precise grading of soybean lodging under field conditions, thereby providing effective technical support for intelligent field management and disaster loss assessment in soybean production.

Purpose: Informal businesses contribute to more than half of employment and livelihoods in developing countries, but we do not know much about the extent to which natural disasters affect them. In Sri Lanka, informal businesses arguably represent 96% of commercial establishments although there have been questions about who they are and their flood vulnerability in terms of loss and damage. This paper attempts to better understand informal businesses of Sri Lanka with more details, revealing current data discrepancies. It also clarifies what factors affected past flood loss and damage. Methodology: To better understand informal business owners’ perceptions about flood vulnerability, we conducted a questionnaire survey among 180 small business owners in Colombo and Gampaha districts from February to March 2023. The questionnaire was designed based on the vulnerability function developed by IPCC and had 32 questions. The participants were randomly selected from six divisional secretariat divisions (DSDs) in each district. Additionally, interviews and online discussions were conducted among the main government stakeholders before and after the questionnaire survey. The data were analyzed using Microsoft Excel and SPSS and presented in descriptive forms, including tables, figures, and graphs. Findings: The results show that Gampaha business owners had relatively higher flood vulnerability than Colombo, mainly due to low education achievements, single-owned businesses, lower annual turnover and asset value and lack of mitigation measures. We found that business registration practices at government agencies played a significant role in leaving many businesses informal, and our survey shows that only 16.7% in Colombo and 12.2% in Gampaha had operated registered/formal businesses. We calculated an annual average flood loss and damage among these businesses to be in the range of LKR 50,000-100,000 per business. Those businesses that had experienced higher loss and damage exhibited low disaster risk awareness/preparedness and financial difficulties. Our factor analysis found that education, ownership and size of businesses, registration status and availability of government compensation led to regional differences in terms of flood preparedness and vulnerability. Unique Contribution to Theory, Practice and Policy: This study shows the specific vulnerability factors faced by informal business owners in dealing with floods. To better reflect the community vulnerability to floods, it is recommended to incorporate the above vulnerability factors in disaster loss and damage assessment and related policies and strategies. Further, it is necessary to adopt a more flexible framework for business registration and formalization to minimize disaster vulnerability.

Accurate estimation of flood-induced economic losses remains a critical challenge in flood risk assessment due to uncertainties associated with vulnerability modeling and exposure representation. This study presents a comparative evaluation of depth-damage estimation approaches by integrating high-resolution building inventory data with hydraulic model outputs. A two-dimensional hydrodynamic model was developed to simulate spatial flood depth distributions, which were subsequently intersected with structure-level building datasets to enable object-based damage assessment. Three depth-damage estimation approaches were evaluated: an empirical curve model, a parametric relative damage function, and a multi-parameter vulnerability model incorporating structural attributes. Model performance was assessed using statistical metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), bias ratio, and sensitivity analysis under controlled hydraulic depth perturbations. Results indicate that high-resolution exposure data significantly influence loss estimation magnitude and spatial distribution, reducing aggregation bias commonly observed in traditional approaches. Depth-only formulations were found to systematically overestimate damages under shallow flooding conditions, while the multi-parameter model demonstrated improved robustness and reduced sensitivity to hydraulic uncertainty. Comparative analysis further revealed that vulnerability formulation contributes substantially to overall uncertainty in flood loss prediction, often exceeding hydraulic modeling variability. The findings highlight the importance of integrating asset-level exposure datasets and locally calibrated vulnerability relationships for reliable economic loss estimation. The proposed framework provides methodological guidance for flood risk practitioners, supports improved disaster loss accounting, and contributes to resilience-oriented urban planning. Future research directions include adaptive artificial intelligence–based damage functions and real-time digital twin systems for dynamic flood loss estimation.

After rainfall-induced landslides enter the creep deformation stage, timely mitigation is often challenging, making reasonable and effective early warning critical for reducing disaster losses. This study focuses on the Tanjiawan landslide, introducing the concept of “a single rainfall process” to characterize the rainfall process affecting landslide deformation. Based on a detailed analysis of deformation characteristics such as displacement and displacement rate under rainfall, the least squares method is used to identify the “failure inflection point” and “stable inflection point” on the “step-like” deformation curve to determine the accelerated deformation interval. This approach further establishes the antecedent rainfall threshold (Pe), current rainfall (P), and displacement rate threshold (V). Subsequently, a refined dynamic early warning model is developed by integrating the function F(V, P, Pe) with a Logistic regression model. The findings indicate: (1) The deformation of the Tanjiawan landslide is closely correlated with rainfall processes, with cumulative displacement curves exhibiting distinct “step-like” characteristics and displacement rates showing a “lagged attenuation” phenomenon. (2) Finer monitoring cycles enable more precise capture of dynamic landslide deformation, resulting in more reliable displacement rate thresholds. (3) The landslide early warning model can dynamically adjust monitoring cycles based on the evolutionary characteristics of deformation stages, achieving adaptive monitoring optimization.

The Philippines confronts an escalating climate crisis, with annual disaster losses projected to reach 7.6% of GDP by 2030. Despite the proven efficacy of nature-based solutions (NbS), their integration into national development planning remains marginal, as epitomised by the flagship “Build Better More” (BBM) project. This study diagnoses this implementation gap as a fundamental systems failure: the systematic undervaluation of critical natural capital. This capital is conceptualised as a synergistic socio-ecological life-support system—the integrated integrity of ecological resilience, abiotic surroundings, and biotic communities (A+B+C=D). Consequently, NbS are treated as peripheral add-ons, perpetuating a self-reinforcing vicious cycle of maladaptive grey infrastructure investment and costly reconstruction. Employing systems thinking and causal loop modelling, we demonstrate how the BBM Project’s current structure actively degrades this foundation (A+B+C=D), locking development into a path of accelerating risk. A quantitative system dynamics model proves that a hybrid NbS-grey portfolio yields vastly superior economic and resilience outcomes. We therefore prescribe a transformative triple-track intervention: (1) innovative finance, for example, resilience bonds; (2) governance restructuring to integrate environmental mandates; and (3) legally embedded equity safeguards such as mandatory Social Equity and Distributional Impact Assessments. This integrated blueprint is designed to dismantle the vicious cycles and initiate stabilising virtuous cycles of socio-ecological resilience. We conclude that the BBM Project’s objectives are unattainable without this foundational institutional shift. While grounded in the Philippines, our diagnostic framework of vicious and virtuous cycles provides a transferable roadmap for climate-vulnerable nations to transition from costly degradation to a climate-resilient future.

Abstract Historic urban districts have unique historical value and cultural significance, but their narrow, low-connectivity, and enclosed spatial structures can lead to crowding, restricted evacuation routes, and increased exposure risk and disaster losses during emergencies. This study aims to develop a High-Sensitivity Exposure Unit (HSEU) identification framework based on dynamic evacuation responses and static population distribution verification, to identify high-sensitivity exposure areas in historic urban districts, analyze the relationship between spatial form and exposure risk, and provide a theoretical basis for disaster preparedness and evacuation optimization in historic districts. Using the multi-agent simulation software AnyLogic, an emergency evacuation model for the historic district is constructed, selecting 12 key nodes with typical traffic functions or terrain constraints, and recording the dynamic density variations and congestion durations at these nodes. By combining the peak density of nodes and congestion time, the risk level is calculated and analyzed in relation to street types and spatial form characteristics. The study identifies low, medium, and high-risk areas within the historic district, revealing that evacuation risk is not only concentrated in geometrically constrained spaces, but that wide arterial roads with insufficient visual guidance may also become high-exposure hotspots. This study proposes differentiated evacuation optimization strategies for historic districts, such as one-way flow arrangements for narrow spaces, optimization of target accessibility at traffic hubs, and expansion and phased evacuation strategies for entrance and exit areas. These strategies help alleviate congestion pressure in high-risk areas and improve evacuation efficiency. By combining dynamic density evolution with static load validation, this study provides a scientific basis for exposure risk assessment, disaster preparedness, and resource prepositioning in historic urban districts.

The clinical profiles of child victims admitted to hospitals following earthquakes, as well as the characteristics associated with hospital stay, are important. This study aimed to analyze the clinical outcomes of pediatric victims admitted following a sudden earthquake to enhance preparedness for accessing essential health services to reduce losses in future disasters. Among 2158 patients referred to the pediatric emergency department (PED), 356 children followed at our tertiary inpatient clinic were included. Demographics, surgical interventions, and the presence of life-threatening conditions like crush syndrome and fasciotomy/amputation were investigated. Crush syndrome was defined as clinical and laboratory evidence of muscle injury accompanied by systemic complications odds ratios (OR) with 95% confidence intervals (CI) were calculated. Among the patients, 56.5% were male. The median length of hospital stay was 4 days (range: 1-120). The most common injury mechanism was entrapment under rubble, and the lower extremities were the most frequently affected injury site. Compartment syndrome developed in 31.7% of patients, and 2.8% underwent amputation. Crush syndrome was identified in 75.3% of hospitalized patients and was significantly more common among children admitted on the second day or later after the earthquake. Elevated creatine phosphokinase (CPK) levels significantly increased the likelihood of developing crush syndrome (OR: 61.7, 95% CI) and the need for fasciotomy (OR: 16.9, 95% CI). Fasciotomy was required in 28.9% of patients. Dehydration was associated with an increased risk of fasciotomy (OR: 7.2) and amputation (OR: 5.4). Elevated levels of myoglobin, uric acid, blood urea nitrogen/creatinine, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were significantly associated with crush syndrome and fasciotomy (p < 0.001). The high burden of crush-related complications in pediatric earthquake victims underscores the need for pediatric-specific trauma protocols in disaster settings. Early identification of risk factors and rapid intervention may reduce severe outcomes such as fasciotomy, renal failure, and amputation.

Nonlinear modeling and interpretation of typhoon disaster losses using machine learning and SHAP values: a case study in Zhejiang Province

Ecological disasters pose severe threats to marine ecosystems and the marine economy, and therefore disaster prevention and mitigation efforts are critically important. We reviewed the occurrence, impacts, and related research progress in disaster prevention and mitigation of marine ecological disasters, including harmful algal blooms, jellyfish blooms, starfish blooms, marine biofouling, and marine biological invasions. Marine ecological disasters exhibit diverse types, where the occurrence of one type may trigger others and even lead to the emergence of new disaster-causing organisms. In the face of complex marine ecological environmental changes, monitoring, early warning, and prevention technologies for marine ecological disasters must evolve with the times. The fundamental principles of "prevention first, combining prevention and control, rational utilization, and effective management" are essential for effectively preventing and mitigating marine ecological disasters. To minimize disaster losses, future efforts should focus on strengthening researches into the interrelated mechanisms of marine ecological disasters, monitoring and early warning systems, effective prevention and control technologies, and the resource utilization of disaster-causing organisms.

Analyzing seismic hazard risk is crucial for comprehensive risk mitigation and seismic emergency planning. A scientific evaluation of seismic hazard risk is also crucial for strengthening pre-disaster preparedness and reducing disaster losses. The Pamir Frontal Thrust (PFT) fault is the most recent deformation zone that is still active in the late Holocene, with a documented history of earthquakes of magnitude 7.0 or higher. The southeastern section of the fault passes through various densely populated townships, and there are multiple vulnerability factors in the region, which make the southeastern section of the PFT fault at high risk of seismic hazards. This study assesses the current seismic risk of the southeastern section of the PFT fault. The potential for earthquakes in the region is evaluated by analyzing characteristics such as historical seismicity and the rate of fault activity. It analyzes the distribution of ground-shaking impacts in near-fault regions by combining stochastic simulations of high-frequency ground shaking with predictive methods for near-fault effects. Building characteristics are surveyed using an integrated space-air-ground approach. Through the integration of these methods, an in-depth assessment of the seismic hazard risk was conducted. The results show that regions exposed to more intense seismic shaking demonstrate correspondingly higher damage indices, and the densely populated townships in the area with intensity of VIII and above should be paid more attention to. Moreover, under identical intensity circumstances, areas with a high proportion of earth - wood - type houses display greater anticipated damage. And a gridded hazard risk assessment was produced. Seismic risk maps for the region are also provided. It provides the basis for effective disaster prevention and preparation.

Global emergency governance prioritizes “efficient response and risk resilience,” yet a critical implementation gap persists at the grassroots level, where policy goals often fail to translate into tangible outcomes. Data from the United Nations Office for Disaster Risk Reduction indicates that 60% of global disaster losses (2015–2024) stem from delayed grassroots responses, a figure exceeding 80% in developing countries due to inadequate governance mechanisms. To address this gap, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) within the Technology-Organization-Environment (TOE) framework to analyze 50 grassroots emergency policy documents (implemented 2019–2021) from China’s three major urban agglomerations (Yangtze River Delta [YRD], Pearl River Delta [PRD], Beijing-Tianjin-Hebei [BTH]). Focusing on institutional design completeness as a foundational precursor to practical effectiveness, two core findings emerge: (1) robust early warning and response capacity (YJ) is a universal necessary condition for high policy effectiveness across all regions; (2) four equifinal configurational pathways to effectiveness are identified, including an integrated administrative coordination pathway, a context-adaptive resource-consolidated pathway (with technology-empowered and organization-driven sub-models in the PRD and BTH, respectively), a grassroots capacity-building (culture-driven) pathway, and a resource-constrained basic response pathway. Theoretically, this study advances the TOE framework’s application in administration-led governance contexts by revealing context-specific configurational interactions of institutional factors. Practically, it provides region-tailored policy templates for translating institutional design into on-the-ground emergency governance efficacy.

Ecological crises in Indonesia, such as rapid deforestation, the spread of mining, loss of biodiversity, climate, and frequent disasters, overlap with each other and require more frequent theological and technical solutions (Margono et al., 2014; Sodhi et al., 2004; Steffen et al., 2015). As the largest archipelagic state in the world, with Christian communities of different denominations widely distributed throughout the country, Indonesian Christian scholarship has begun to develop particular models of ecological theology. However, this new area is still divided, and there is not much systematic systematization of its thematic, methodological, and contextual developments (Golo and Yusuf 2018). In this article, a systematic literature review on the topic is conducted using PRISMA to examine Indonesian Christian ecotheology to from 20002025. Nine databases of academic sources ( Scopus, Web of Science, Atla Religion Database, ProQuest Religion and Philosophy, EBSCOhost, DOAJ, GARUDA, Neliti, and major repositories of theological collections) revealed 1,847 records, and 68 studies were selected after screening and eligibility tools. Quality appraisal was an integration of the Mixed Methods Appraisal Tool (MMAT) (used to assess empirical works) (Hong et al., 2018) and a modified theological assessment framework (Vanhoozer, 2005; Schreiter, 1985). Data synthesis was performed based on descriptive statistics, mapping of key word co-occurrence, and thematic analysis (Sandelowski et al., 2006; Thomas and Harden, 2008). The results indicate rapid growth since 2016, denominational diversity, and five master themes: doctrinal foundations, indigenous cosmologies and land/sea relations, ecclesial practices and eco-spirituality, public and justice-oriented ecotheology, and methodological developments. Indonesian ecotheology also contributes to the global discourse through its adat, archipelagic and disaster experiences, and postcolonial critique, although there are still gaps in Papua-Maluku ecotheology, Pentecostal ecotheology, marine theology and urban theology, and outcome-oriented research.

Abstract Global disaster losses have escalated as a share of GDP, magnifying intergenerational risks and emphasizing the urgency of ethically grounded resilience strategies. This study pioneers a six-decade, cross-national empirical analysis (1960–2020) that distinguishes between the pre-1990 “outcome-proxy” era and the post-1990 institutionalized ESG era, offering the first long-term assessment of ESG ethics in disaster mitigation. Moving beyond purely financial or technical framings, the research conceptualizes ESG investments as morally significant interventions that integrate environmental stewardship, social responsibility, and governance accountability. Using fixed-effects panel regressions with robust errors, sub-period tests, and inter-sectoral interaction terms on datasets from the World Bank and Our World in Data, the study examines how ESG indicators shape both the frequency and economic damages of natural disasters. Results indicate that institutionalized ESG frameworks significantly enhance disaster resilience, while environmental and governance capacities act as key mediators of climate adaptation. By linking ESG performance to intergenerational justice, the study extends sustainability discourse beyond finance into ethics, governance, and policy design. This research thus pioneers a long-term, cross-national analysis connecting ESG ethics to intergenerational disaster resilience, providing original insights for ethical climate governance and sustainable finance policy.