West Java Province is being exposed to a high risk of natural disasters, especially hydrometeorological disasters such as floods and landslides, hence hindering potential economic growth. The increasing frequency of disasters has shed light on the issue of regional resilience, an important concern for public authorities. Therefore, efforts to assess and strengthen regional resilience are crucial to reducing disaster risks and supporting the achievement of sustainable development. However, up till recently there has been no practical and applicable methodology for resilience assessment, which has become more complicated at the regional level, taking into account the economic, social, ecological, infrastructural, and institutional dimensions. The present paper proposed a composite indicator-based approach to evaluate the level of regional resilience to disasters in West Java Province. To describe the current conditions of resilience in each regency/city in the province, this study adopted 17 indicators that were adjusted for measurement in the actual context. The composite index combined by the macro-regional indicators in five main dimensions were calculated using arithmetic, geometric, harmonic, entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The integrated Regional Disaster Resilience Composite Index (RDRCI) scores across the 27 regencies/cities ranged from 6.33 to 33.98, with 9 regions recording values above the provincial mean of 15.26. The results of this analysis could be employed by policy-makers to evaluate the resilience of a region to natural disasters. Furthermore, the findings highlight the necessity of incorporating all dimensions into policy formulation to strengthen regional resilience to disasters.
Landslides remain one of the most critical natural hazards, posing significant threats to infrastructure, the environment, and human life. Traditional approaches to landslide risk prediction, such as rainfall threshold models and image-based classification, often face limitations including data imbalance, low generalizability, and poor performance in capturing medium- to high-risk scenarios. This study introduces a predictive framework that integrates synthetic data generation with a multiple logistic regression model to improve landslide risk assessment in the Malaysian context. The model was trained on balanced datasets and evaluated through confusion matrices, performance metrics, and validation using unseen data across three distinct scenarios. Results demonstrate that a multiple‑logistic‑regression model trained on this balanced data achieved an overall accuracy of 0.73, precision of 0.73, recall of 0.73, and a Receiver Operating Characteristic-Area Under Curve (ROC‑AUC) of 0.80. In three validation scenarios using unseen data from 2015–2024 (three months before, during, and three months after known landslide events), the model correctly identified medium and high‑risk periods when other machine‑learning models defaulted to low‑risk predictions. The study highlights the trade‑off between accuracy and generalization in machine‑learning‑based early warning systems and underscores the importance of class‑balancing and rigorous validation for real‑world applicability. Our findings, therefore, demonstrate that the logistic‑regression model, when paired with synthetic data augmentation, can serve as a cost‑effective, interpretable pre‑screening tool for regional landslide risk assessment in Malaysia.
To respond to global climate change, promote climate governance, and develop a master plan for sustainable development, carbon neutrality has become a common goal and vision of both developed and developing countries. In view of this objective, the interaction among energy transition, energy intensity, economic growth, and financial development is considered an important tool to harmonize economic development and environmental governance. This study aims to investigate the impacts of these four variables on Vietnam’s carbon neutrality objective during the period of 1995–2022. The Johansen cointegration analysis and a Vector Error Correction Model (VECM) were employed to disentangle short- and long-run relationships among the variables; the results of these analyses revealed asymmetric temporal effects. Energy transition and economic growth were found to increase CO₂ emissions in both the short and long run, hence suggesting that expansion of renewable energy could not effectively substitute fossil fuels and that economic growth remains energy intensive. In contrast, energy intensity and financial development reduced CO₂ emissions in the short run but contributed to rising emissions in the long run. This indicates the presence of rebound effects and scale-driven financial expansion without green investment target. It was concluded that Vietnam should plan and implement appropriate low carbon-intensive policies to achieve its carbon neutrality objective in the years ahead.
Marine debris is one of the major environmental concerns in the 21st century, owing to its impact on the ocean ecosystems, the biodiversity of marine inhabitants, and human well-being. Through the utilization of automated content analysis (ACA) and graph theory in the context of a systematic literature review (SLR), the purpose of this investigation is to comprehensively map and assess the global research landscape concerning marine trash. Leximancer was used in this study to extract semantic links among important ideas, which were then displayed as directed acyclic graphs (DAG). The research used 357 Scopus-indexed papers that were published between 2017 and 2024. Core conceptual clusters relating to microplastics, plastics, and soil were identified through the ACA method. These clusters each reflected a different aspect of marine pollution that was interrelated with the others. The utilization of graph theory enabled the identification of structural links and core nodes that were shared by several themes. These connection points might be quantified by adjacency matrices and normalized grouping was accomplished by k-means analysis. According to the findings, phrases such as “waste”, “plastics”, and “marine” were the most prominent notions, and they served as the foundation for study on marine debris on a worldwide scale. These findings not only contribute to the advancement of automated environmental informatics but also highlight how graph-based content analysis may be used to identify hidden patterns in scientific knowledge. Taking into account both theoretical and methodological considerations, this study have implications for academics who use computational bibliometric analysis in the field of environmental science.
Despite advances, spatial resilience planning remains constrained in its integration of complex system principles to address slow-variable disturbances. This study provided a methodological test of a novel multi-faceted and network-based hybrid resilience assessment that examined rural shrinkage in paired regions of Germany (Lüneburg) and Türkiye (Trakya). The method integrated Specified Resilience Assessment (SRA) and General Resilience Assessment (GRA) under Socio-Ecological Systems (SES) and Complex Adaptive Systems (CAS) lenses and operated through five steps. SRA employed (i) a multi-faceted survey to identify prioritized factors, solutions, and institutional roles/success and (ii) Relational Network Analysis (RNA) to assess complex factors and leverage points; GRA computed (iii) Spatial Network Analysis (SNA) to identify physical connectivity as hubs and sub-clusters; (iv) correlation analysis to determine significant variables among socio-demographic, land-use, facility, and network variables, and (v) k-means clustering to map shrinkage urgency levels. The synthesized outputs generated two operational strategies: strengthening sub-centers and connecting shrinking settlements to these hubs. While the strategies of Germany focused on the needs of the elderly and innovative digital solutions (wd ≈ 28), examples of Türkiye emphasized ecological concerns and the support of cooperatives as a leverage (wd = 54). GRA highlighted weighted degree (up to r = 0.79) and urban-industrial land cover (r ≈ 0.6) as critical drivers of stability; meanwhile, distance to the center (r ≈ -0.55) significantly correlated with shrinkage. Despite limitations of sample size and manual network construction, the study operationalized SES/CAS concepts for slow variables and integrated both qualitative and quantitative insights. It advances resilience research in sustainable spatial development by demonstrating a proof-of-concept and transferable decision-support workflow, while scaling and automation point to the directions for future research.
Climate change poses significant challenges for developing countries, where coastal communities are the most vulnerable. However, adaptation policies targeting coastal populations often remain ineffective because top-down governance structures fail to integrate social-ecological systems adequately. This study empirically examined an integrated Collaborative Governance-Social Ecological Systems (CG-SES) model encompassing principled engagement, shared capacity, joint action, multi-level fit, and community resilience. The analysis was based on the survey data collected through a structured questionnaire from 411 coastal residents in Padang City, Pesisir Selatan Regency, and the Mentawai Islands Regency, West Sumatra. Partial Least Squares-Structural Equation Modelling (PLS-SEM) was employed to assess relationships among various governance dimensions and resilience outcomes. The results indicated that principled engagement had a positive and significant effect on shared capacity (β = 0.936, p < 0.001) and community resilience (β = 0.651, p < 0.001). Shared capacity also positively influenced joint action (β = 0.472, p < 0.001) and community resilience (β = 0.342, p < 0.001). These relationships enhanced the predictive power of the model (R² = 0.926 for multi-level fit; R² = 0.941 for community resilience), indicating that governance variables explained a substantial proportion of variation in resilience outcomes. The findings further suggested that shared capacity functioned as a key mediating mechanism linking principled engagement to community resilience. At the same time, multi-level fit and joint action did not demonstrate direct or moderating effects. Overall, the results highlight the importance of co-produced knowledge, institutional trust, and collective capacity in shaping climate adaptation outcomes in the coastal regions of the Global South.
Quality of life (QoL) in the Arab world hinges on credible institutions and effective social spending; yet evidence linking governance, health budgets, and environmental pressures remains fragmented and seldom extends beyond 2020. This study clarified these links by assembling a balanced panel of 14 Arab countries from 2000–2020 to examine how institutional quality and public health expenditure shaped QoL, while accounting for carbon emissions, economic expansion, and education expenditures. QoL is proxied by life expectancy, while institutional quality is captured through a composite index constructed by applying Principal Component Analysis to the Worldwide Governance Indicators. The analysis employed country- and year-fixed effects, along with panel-corrected standard errors (PCSE), to address heteroskedasticity and cross-sectional dependence. Results indicated that institutional quality was the dominant driver as the composite index was strongly associated with higher QoL (β = 1.843, p < .01). Health expenditure was also crucial though the effect was economically small (β ≈ 0.0063, p < .05). Education expenditure was weakly negative (p < .10), thus reflecting quality and governance constraints in the education sector. Carbon emissions displayed a small positive coefficient (β ≈ 0.0949, p < .05), which likely implied policy and structural weaknesses rather than genuine welfare gains. Moreover, GDP per capita exhibited a statistically significant yet negligible and slightly negative elasticity (≈−0.000124), indicating rent-dependent growth that failed to translate into improved well-being. Collectively, the findings imply that governance reforms yield the greatest QoL, whereas spending without institutional credibility produces limited returns. Future work should test interaction effects, explore thresholds, and incorporate subjective QoL metrics to guide the sequencing of reforms across Gulf Cooperation Council (GCC) and non-GCC settings.
In East Java, small-scale purse seine fisheries play a critical role in preserving food security, local economies, and coastal cultural systems; however, there are challenges of sustainability due to the dual pressures of ecological responsibility and risks of operational safety. This study evaluated purse seine operations at Tambakrejo Fishing Port, a key landing site in East Java, by integrating Food and Agriculture Organization Code of Conduct for Responsible Fisheries (FAO-CCRF) sustainability indicators with a Frequency–Severity Index (FI–SI) occupational risk assessment. The CCRF indicators were anchored in Articles 6–8 (general principles, fisheries management, and fishing operations) to examine ecological performance and exposure to hazards across distinct operational phases. Data were collected through direct observation, structured fisher interviews, port documentation, and catch monitoring (9 vessels over 15 sampling days). FI–SI scores were assigned by a standardized rubric triangulated with evidence from interviews and port records. Results indicated a highly selective catch composition with minimal bycatch (~0.4%), though species-specific vulnerabilities persisted due to sub-length at first maturity (Lm) retention, particularly in Euthynnus affinis. Risk evaluation showed that the highest hazard exposure occurred during labor-intensive and time-pressure phases such as setting, pursing, and hauling, driven by rope handling, wet-deck dynamics, and repetitive manual tasks. The proposed dual-matrix approach differed from certification-oriented indicator sets (e.g., MSC-type schemes), Ecosystem Approach to Fisheries Management (EAFM) scorecards, and standalone occupational health and safety matrices by linking phase-level ecological signals with task-level safety risk to identify high-risk–low-compliance nodes and prioritise feasible controls. The integration of sustainability and risk indicators suggests that compliant and selective practices could reduce both ecological pressure and hazard exposure, hence upholding the concept that sustainability and safety are mutually reinforcing outcomes. The framework offers practical guidance for adaptive co-management by emphasizing low-cost improvements, training, and procedural discipline, while acknowledging that cross-sectional sampling, seasonal variability of sea states, and local implementation capacity could influence risk profiles and its feasibility.
This study presented a theory-informed bibliometric review that explored the intersection of adaptation finance, vulnerability, and development cooperation within the climate finance literature. Anchored in the vulnerability-resilience framework, the study aims to map the conceptually-aligned financial models on adaptation, particularly how policy-driven instruments such as Official Development Assistance (ODA) have evolved within the world economy and debates about global macroeconomic policy. Utilizing a conceptually integrated search strategy, the analysis combined bibliographic coupling, thematic clustering, and theory-informed mapping techniques. The findings revealed that although adaptation-related concepts held a central place in global policy frameworks (e.g., Sustainable Development Goals (SDGs) 13 and 17), their representations in the academic literature remained uneven and fragmented. Structural clusters reflected the dominance of Global North institutions and mitigation-centered research whereas emerging thematic patterns indicated growing emphasis on context-specific and vulnerability-sensitive adaptation finance. Comparative insights from sectoral ODA data confirmed the thematic gaps identified in the bibliometric analysis and underscored the persistent disconnect between financial flows and local adaptation needs. By linking bibliometric insights with patterns of institutional finance, this study offered an integrative perspective on climate-oriented development and contributed to the agenda of global economic transformation. In doing so, it addressed a significant research gap via combining integrated theory-driven bibliometric mapping with analysis of policy-centered development finance.
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