Search Results (31)

Smallholder and family farms in the Western Balkans face intertwined production, market, institutional, human resources and financial risks. This systematic review maps agricultural-risk evidence across seven Western Balkan countries and five enterprise types: crops, livestock, poultry, beekeeping and mixed systems. Following PRISMA 2020, SciSpace was used as a discovery interface for Scopus-indexed literature and was complemented by regional literature compilation, citation tracking and manual verification. From 189 records identified before deduplication, 68 studies published between 2000 and 2025 met the eligibility criteria. Studies were coded by country, enterprise type, risk category and method, and synthesised through a 5 × 5 evidence-gap matrix and complementary risk-overlap analysis. Evidence is concentrated in production risk for crops and livestock, market risk in beekeeping and institutional risk in mixed systems. The main gaps concern human resources risk in crops and poultry and financial risk in poultry. Although 22 studies address multiple risks, only six cover all five major farm risks, mostly descriptively rather than through interaction modelling. The review is limited by uneven regional source visibility and judgement-based evidence coding. The findings support portfolio-based risk management for sustainable rural development rather than isolated hazard responses. The review was not prospectively registered. Full article

Physical climate hazards—floods, storms, heatwaves, and wildfires—are increasingly disrupting banking operations and generating growing litigation and legal-risk exposures, yet operational risk remains one of the least studied channels through which climate change may affect financial institutions. This paper provides a critical review of the emerging literature on climate-related operational risk in banking, covering both physical disruptions and the legal risk dimension explicitly recognised within the Basel operational risk framework. We map the empirical evidence, critically evaluate the methodological toolkit—event studies, fixed-effects regressions, difference-in-differences, dynamic panel estimators, and logit models—and assess their suitability for a domain characterised by data scarcity, rare events, and non-linearity. Building on this assessment, we outline a conceptual methodological roadmap intended to guide future research, organised around three stages: (i) machine learning-based variable selection and anomaly detection applied to operational loss and climate databases; (ii) econometric modelling of climate-related operational events with explicit identification strategies; and (iii) agent-based modelling to simulate system-wide propagation of climate shocks. Each stage can be conceptually related to elements of the Basel operational risk framework, offering a structured research programme for academics and a diagnostic toolkit for supervisors and risk managers. Full article

The Tomarrazón-Camarones Basin (La Guajira, Colombia) is characterized by frequent, widespread flooding and, anthropogenically, by intense instream sediment mining. Mapping flood hazard is hence essential to develop effective flood management plans, and a knowledge of the water regime (duration curves) is also essential to estimate sediment transport and carry out sediment budgets to inform on the impacts and sustainability of the mining activity. However, neither water levels nor discharges are monitored by official gauging stations, and only a few rainfall gauging stations are available in the area, with daily records often affected by data gaps. Therefore, a first challenge is to reconstruct discharge time series by an affordable effort, scaled to the financial-labour resources available in that challenging context. This paper presents an integrated approach that combines satellite-derived rainfall data with ground observations. A semi-distributed hydrological model (HEC-HMS, SCS-CN method) is used to reconstruct the full flow-rate time series once calibrated and validated with data derived from automatic sensors and field measurements. The model is fed with hourly data derived from daily data at ground gauging stations temporally downscaled by adopting the spatially distributed hourly rainfall patterns obtained from satellite records. Before that, observed water levels in three stations equipped with water level sensors were translated into discharge time series using analytical relationships based on field-measured geometric and physical characteristics. Then, these event-based hydrographs were used to calibrate and validate the model. Results show good agreement with observations, with R² = 0.981 and a relative RMSE of 40% for overall hydrograph reproduction, and R² = 0.87 for peak flow estimation, supporting a reasonable confidence in the approach. The calibrated model is then applied to long-term datasets (1973–2024) to retrieve duration curves and return periods of peak discharges. Full article

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process. Full article

Energy transition regions (ETRs) face simultaneous pressures as decarbonisation policies intersect climate hazards, land-use constraints, and economic uncertainty. Although research on renewable energy deployment, climate vulnerability, spatial planning, and investment behaviour has expanded, these topics often remain disconnected, limiting their usefulness for guiding regional energy strategies. This review applies a structured, PRISMA-informed (but not protocol-registered) search and screening process, combining bibliometric mapping with qualitative thematic synthesis. In total, 231 peer-reviewed studies published between 2015 and 2025 were analysed to identify how climate-related risks, financial conditions, and territorial constraints jointly influence energy system choices in ETRs. Four major themes emerge: climate risk and infrastructure vulnerability, investment dynamics and policy stability, land-use governance and siting conflicts, and renewable energy system integration. Across these areas, common challenges include the impact of extreme events on system reliability, the influence of policy uncertainty on capital flows, and the role of land scarcity in shaping technology mixes. To link these dimensions, this study proposes the Resilience–Investment–Land Nexus (RILN), a framework that describes how climate exposure, investment risk, spatial suitability, and social acceptance interact to shape transition pathways. The results highlight the need for climate-informed planning, stable regulatory environments, and stronger spatial decision-support tools. It also identifies gaps in integrating climate risk, land-use modelling, and investment analysis and offers directions for future work on resilient, region-specific energy transitions. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

A hazard zoning map is the most essential tool during the crisis management cycle’s prevention and risk reduction phase. In this study, a geographic information system (GIS) is applied to the crisis management of ports through the preparation of a risk zoning map in Jazan Port, Saudi Arabia, using a novel integrated model of the fuzzy hierarchical analysis process and emotional artificial neural network (FAHP-EANN). The objective is to more accurately identify the highly potential risk zones in the port through hybrid techniques, which mitigates the associated life and financial damages through proper management during a probable hazard. Prior to creating the risk zoning map, every potential port accident is identified, categorized into six criteria, and assigned a weight through the utilization of a machine learning algorithm. The findings indicate that the three most effective criteria for the risks of Jazan Port are land fires, pollution and dangerous substances, and human behavior, respectively. A zoning map of all risks in Jazan Port was generated by using the weights obtained for each of the major accidents. This map may be utilized in the development of crisis prevention measures for the port and in the formation of crisis management units. Full article

The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains. Full article

The present study is part of the context in which Romania adopted the European Parliament Directive 2007/60/EC on flood risk assessment and management. Therefore, the aim of this research is to assess the risk induced by a hydrological hazard, expressed by a financial value estimation, for the Turcu River in the northern sector of the Bran–Dragoslavele transcarpathian corridor (Romania), an important tourist axis where the pressure on land has increased considerably. As a result, the intra-village areas of Moieciu de Sus, Cheia, Moieciu de Jos, Bran and Tohanu Nou have also expanded into areas vulnerable to flooding. There are currently no studies on the areas potentially affected as well as the extent of the possible damage. For this reason, we proceeded to model the water level corresponding to the maximum flow value with a probability exceeding 1%, using HEC-RAS and ArcGIS software. The results of the implementation of the spatial analysis model resulted in the delineation of the floodplain and the assessment of the potential financial loss related to the minimum market value of the land with the related real estate infrastructures. The research reveals that in the 1% band area (78.7841 ha) with water depth > 0.5 m, more than 433 infrastructures are at high risk of flooding, most of them with high real estate value, i.e., 5.61 km of roads for which a cost of EUR 3,402,666.90 was calculated for restoration. A knowledge of financial vulnerability to flooding becomes important for the community; local authorities involved in making decisions for insuring real estate at risk and planning/managing investments work to prevent/combat the effects of flooding. Full article

Earthquakes are one of the most costly and deadliest natural disasters. This perspective paper presents a discussion focused on the role of seismic design codes in risk perception and seismic risk reduction in Europe. The seismic design codes are a key component for both the design of new buildings, as well as for the vulnerability assessment of existing ones. The impact of seismic design codes on seismic risk reduction is discussed using as case-study countries, Italy, Turkey, Greece, and Romania, which according to the recent European seismic risk model 2020 have the largest expected mean annual losses due to earthquakes. The evaluation of the seismic exposure of the four countries shows that from the entire population of more than 170 million people, about 130 million live in buildings designed using no or low level seismic design. The mean annual expected losses due to earthquakes are of the order of 0.1–0.2% of the national GDP. Moreover, the mean annual death probability due to earthquakes is 10⁻⁶ which represents a risk level not of great concern to the average people. However, large earthquakes in Europe from the past 50 years have produced losses in excess of 10 billion Euros and several hundred thousand affected people. A solution for a better communication of seismic risk in order to increase seismic risk perception might be to provide exceedance probabilities of specific macroseismic intensity levels for time frames of 10 or 20 years, instead of annual values. Macroseismic levels from past earthquakes might be used in order to have a better understanding of the results and should complement the seismic design maps. In addition, in the case of seismic vulnerability, the use of simple terms (e.g., inhabitable or uninhabitable) along with their associated occurrence probabilities in the same time frame as in the case of the seismic hazard, might be a solution. Financial incentives for seismic strengthening, as well as a clear definition of an earthquake-prone building are also very useful for increasing seismic risk perception. Full article

Water resource management and flood forecasting are crucial societal and financial stakes requiring reliable predictions of flow parameters (depth, velocity), the accuracy of which is often limited by uncertainties in hydrodynamic numerical models. In this study, we assess the effect of two uncertainty sources, namely breach characteristics induced by overtopping and the roughness coefficient, on water elevations and inundation extent. A two-dimensional (2D) hydraulic solver was applied in a Monte Carlo integration framework to a reach of the Loire river (France) including about 300 physical parameters. Inundation hazard maps for different flood scenarios allowed for the highlighting of the impact of the breach development chronology. Special attention was paid to proposing a relevant sensitivity analysis to examine the factors influencing the depth and extent of flooding. The spatial analysis of the vulnerability area induced by a levee breach width exhibits that, with increasing the flood discharge, the rise of the parameter influence is accompanied by a more localized spatial effect. This argues for a local analysis to allow a clear understanding of the flood hazard. The physical interpretation, highlighted by a global sensitivity analysis, showed the dependence of the flood simulation on the main factors studied, i.e., the roughness coefficients and the characteristics of the breaches. Full article

There are still gaps in defining values and category classifications of exposed items in quantitative damage analysis. This paper proposes a framework that refines the development of flood risk analysis at a local scale. This study first performs a quantitative risk analysis, based mainly on secondary data; it then attempts to communicate the results graphically, aiming to reduce the financial and human resources required. We propose an easily standardized database in a GIS environment, analyzing the influence of a reservoir for flood control and the construction of replicable local-scale risk curves. Hydrological (HEC-HMS) and 2D hydrodynamic (HEC-RAS) models were used to simulate hydrographs considering different return periods. For damage estimation, the processing included vectorization of lots, building use definition with Google Street View, classification of standard designs, and a field survey to validate those classes. In monetary value, this study calculated the effect of the construction of a reservoir for damage reduction, showing the potential to determine the effectiveness of measures adopted to mitigate flood impacts. In addition, for each simulated return period, exposure, hazard, and damage maps can be established, making it possible to perform a complete risk analysis. Full article

Environmental justice advocates that all people are protected from disproportionate impacts of environmental hazards. Despite this ideal aspiration, social and environmental inequalities exist throughout greater Los Angeles. Previous research has identified and mapped pollutant levels, demographic information, and the population’s socioeconomic status and health issues. Nevertheless, the complex interrelationships between these factors remain unclear. To close this knowledge gap, we first measured the spatial centrality using sDNA software. These data were then integrated with other socioeconomic and health data collected from CalEnvironScreen, with census tract as the unit of analysis. Finally, structural equation modeling (SEM) was executed to explore direct, indirect, and total effects among variables. The results show that the White population tends to reside in the more segregated areas and lives closer to green space, contributing to higher housing stability, financial security, and more education attainment. In contrast, people of color, especially Latinx, experience the opposite of the environmental benefits. Spatial centrality exhibits a significant indirect effect on environmental justice by influencing ethnicity composition and pollution levels. Moreover, green space accessibility significantly influences environmental justice via pollution. These findings can assist decision-makers to create a more inclusive society and curtail social segregation for all individuals. Full article

Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering the fusion of different information at the data modal level. To exploit the complementary information of different modalities and boost LSM accuracy, this study presents a new LSM model that integrates data modality and machine learning methods. The presented method first groups causative factors into different modal types based on their intrinsic characteristics, followed by the calculation of the pairwise similarity of modal data. The similarities of different modalities are fused using nonlinear graph fusion to generate a unified graph, which is subsequently classified using different machine learning methods to produce final LSM. Experimental results suggest that the presented method achieves higher performance than existing LSM methods. This study provides a new solution for producing precise LSM from a fusion perspective that can be applied to minimize the potential landslide risk and for sustainable use of erosion-prone slopes. Full article

Windstorms are a major disturbance factor for European forests. The 2018 Vaia storm, felled large volumes of timber in Italy causing serious ecological and financial losses. Remote sensing is fundamental for primary assessment of damages and post-emergency phase. An explicit estimation of the timber loss caused by Vaia using satellite remote sensing was not yet undertaken. In this investigation, three different estimates of timber loss were compared in two study sites in the Alpine area: pre-existing local growing stock volume maps based on lidar data, a recent national-level forest volume map, and an novel estimation of AGB values based on active and passive remote sensing. The compared datasets resemble the type of information that a forest manager might potentially find or produce. The results show a significant disagreement in the different biomass estimates, related to the methods used to produce them, the study areas characteristics, and the size of the damaged areas. These sources of uncertainty highlight the difficulty of estimating timber loss, unless a unified national or regional European strategy to improve preparedness to forest hazards is defined. Considering the frequent impacts on forest resources that occurred in the last years in the European Union, remote sensing-based surveys targeting forests is urgent, particularly for the many European countries that still lack reliable forest stocks data. Full article