Flooding remains a critical challenge in regions affected by environmental disturbances and infrastructure constraints. The Sidoarjo Mud Control Area in Indonesia represents a complex hydrological environment where ongoing land subsidence, rapid sedimentation, and altered drainage networks increase flood vulnerability. Existing mitigation efforts often rely on fragmented datasets and disconnected analytical tools, limiting their effectiveness for integrated flood inundation simulation for flood-response decision support. This study proposes a Geospatial Digital Twin (GDT) framework that integrates multi-source spatial datasets with dynamic environmental information to support flood-scenario simulation for response decision support. The system architecture is based on a multidimensional geodatabase that incorporates variables such as rainfall intensity and infrastructure operational status. Flood propagation is simulated using a Cellular Automata (CA) approach selected for its computational efficiency in representing flood dynamics across evolving terrains. Model performance was evaluated through comparison with a benchmark HEC-RAS 2D hydraulic simulation. The results demonstrate that the proposed framework can effectively represent spatial flood patterns under different rainfall scenarios. The CA-based simulation showed good agreement with the HEC-RAS 2D benchmark, yielding RMSE values ranging from 0.467 m to 1.126 m. A usability evaluation involving 28 respondents produced an overall score of 85.07
Pharmaceutical waste management (PWM) in Thailand varies across healthcare tiers and institutional actors, yet evidence remains limited on how implementation gaps differ across the system and how they relate to operational and governance arrangements. This study conducted a cross-sectional implementation audit in Songkhla Province (2021–2022) to assess PWM practices across primary care facilities (community pharmacies, private clinics, and primary healthcare centres), hospitals, and relevant government agencies. A harmonised stage-specific knowledge–practice scoring framework was applied across segregation, collection, transportation, and disposal, complemented by interviews and on-site observations. Overall performance differed by facility tier, with primary-care facilities scoring lower than hospitals. Knowledge–practice scores indicated weak segregation performance in primary-care facilities and collection weaknesses in hospitals. Perceived-challenge data showed that disposal was most often reported as problematic by clinics, PHCs, and secondary hospitals, whereas segregation was most often reported by tertiary hospitals and government agencies. Qualitative findings indicate that these patterns reflect operational constraints and fragmented governance arrangements, including inconsistent regulatory coverage, limited access to compliant disposal pathways, and variable awareness of national guidance. The findings suggest that shortcomings in PWM are not confined to individual facility practices, but may also reflect governance, coordination, and procedural gaps across health and environmental sectors. Addressing these gaps is likely to require harmonised operational standards and improved coordination across facility tiers and local authorities to reduce environmental risks associated with pharmaceutical waste. In particular, the results highlight actionable decision points. These include clarifying roles, strengthening enforcement interfaces, and standardising disposal pathways to most efficiently close the cross-tier implementation gap.
This study examines how green logistics, environmental practices, technology adoption, and supply chain resilience are associated with sustainability and economic performance in the logistics and supply chain sector. Drawing on the Natural Resource-Based View and Socio-Technical Systems Theory, the study develops an integrated model of environmental and socio-technical capabilities and their relationships with organisational performance. Survey data were collected from 300 employees in logistics and supply chain firms in Bangladesh, and the proposed relationships were assessed using in-sample partial least squares structural equation modelling (PLS-SEM) based on self-reported measures. The findings show that sustainability performance is positively associated with green logistics, environmental practices, and technology adoption, while economic performance is positively associated with green logistics, technology adoption, and supply chain resilience. The model explains 44.2
Carbon pricing is increasingly recognised as a cornerstone of national sustainability strategies, yet its long-term success depends not only on sound policy design but also on how implementation is perceived by the public. Despite growing research on public acceptance of carbon pricing, limited attention has been given to how implementation characteristics shape multidimensional sustainability evaluations in emerging economies. To address this gap, this study integrates Diffusion of Innovation (DOI) theory with the Triple Bottom Line (TBL) framework to examine the associations between innovation attributes and perceptions of economic feasibility, social legitimacy, and environmental credibility. Using survey data from Malaysia (n = 918), multiple regression analyses and diagnostic assessments reveal substantial interdependence among DOI attributes, suggesting that implementation characteristics may operate as an integrated perception structure rather than being evaluated as entirely independent implementation mechanisms. The findings indicate consistently positive associations between DOI-based implementation characteristics and perceived economic, social, and environmental sustainability outcomes, reflecting an integrated pattern of public policy evaluation. The observed multicollinearity, strong intercorrelations, and principal component analysis further suggest that respondents’ evaluations may reflect an integrated perception of implementation quality rather than completely separable evaluations of individual mechanisms. The study contributes to diffusion and sustainability policy research by demonstrating how integrating DOI and TBL offers a more comprehensive perspective on public evaluations of carbon pricing implementation. The findings highlight the importance of coherent implementation strategies incorporating pilot implementation, policy communication, transparency, and complementary governance mechanisms to strengthen policy legitimacy in emerging economies.
Organizational sustainability assessments currently face challenges such as fragmented data, complex system boundaries, inconsistent sustainability metrics, and limited integration with decision-making processes. Based on the review, existing assessment models often lack comparability and practical applicability at the organizational level. This study presents a systematic literature review of sustainability assessment models as an information system problem to examine research trends and conceptual approaches and to identify the need for integrated data architectures, analytics capabilities, and decision support mechanisms within organizational information systems. This study follows the Kitchenham and PRISMA to conduct data analysis, evaluation and reporting, 75 peer-reviewed journal articles published between 2022 and 2025 were analyzed. The findings reveal that digital technologies are increasingly adopted, but analytical capabilities remain dominated by descriptive analytics, while predictive and prescriptive analytics linked to decision-making are underexplored. This study reconceptualizes sustainability assessment as an integrated, analytics-driven organizational system. The proposed framework provides a conceptual basis for shifting from a reporting-oriented practice to dynamic organizational processes that support strategic planning and decision-making for achieving the Sustainable Development Goals (SDGs).
The rapid increase in Waste Electrical and Electronic Equipment (WEEE) generation, driven by accelerated consumption and shortened product lifespans, has become a critical global environmental challenge. Reliable forecasting tools are essential to support waste management and policy planning, particularly in developing regions where data availability is limited. Using cross-sectional data from 2022, this study proposes a linear-log regression model to estimate WEEE generation across 18 Latin American countries using gross domestic product (GDP) per capita as the primary explanatory variable. Unlike data-intensive approaches such as material flow analysis and time-series models, the proposed model offers a simplified and scalable alternative that requires minimal data inputs, addressing a key limitation in developing contexts. The model demonstrated strong predictive performance, achieving a coefficient of determination (R²) of 0.904 and a mean absolute error (MAE) of 0.870, while satisfying key residual diagnostic checks. Applied to the study sample, predicted values ranged from 1.17 kg per capita (Haiti) to 13.46 kg per capita (Uruguay), closely tracking observed WEEE generation across the full income spectrum of the region. This study demonstrates that regionally calibrated parsimonious regression models can provide reliable WEEE forecasts in data-constrained environments. The model enables policymakers and waste management stakeholders to estimate future WEEE generation and support infrastructure planning and reverse logistics strategies. Future research should incorporate additional socio-economic variables, test the model in other regions, and compare its performance with more complex forecasting techniques.
Wildfire response effectiveness depends on strategically prepositioning resources to balance accessibility and hazard exposure, as wildfires can escalate rapidly and delayed access to equipment in the early stages and initial attack increases operational risk and firefighter casualties. This study presents a geographic information system (GIS) and multi-modal transportation network-based optimization framework that integrates probabilistic fire spreading potential with multi-modal travel time accessibility to support resilience-informed wildfire resource allocation. Fire risk is estimated using a spatial spreading model based on forest distribution and elevation, and accessibility is calculated over an integrated road–forest network including drivable, walkable, and off-trail routes. Candidate cache locations are selected using a p -median facility location formulation under both travel time-only and risk-weighted strategies. Results show that travel time-only optimization can neglect high-risk regions, whereas risk-weighted optimization reduces the expected response time to a randomly occurring early-stage fire from 34 to 31 min, approximately a 10 percent improvement, while also lowering extreme response times from 117 to 97 min. The framework is modular, reproducible, and adaptable to improved data inputs, providing a transparent approach for resilience-oriented wildfire resource prepositioning.
Jordan has built an expanding architecture of climate commitments, strategies, reporting instruments, and institutional arrangements, yet the political processes through which these are translated into domestic governance remain thinly analyzed in the peer-reviewed literature. This article maps the literature on climate change in Jordan to examine what the field explains and neglects. Drawing on a systematic mapping review of studies published between 1998 and 2025, it distinguishes research substantively addressing climate change in Jordan from the narrower subset treating political and governance mechanisms as central to explanation. The review identifies 75 studies, but only 8 analyze authority, distribution, coordination, contestation, or implementation politics as causal dynamics. This imbalance indicates uneven scholarly visibility into climate politics, not the amount or intensity of political bargaining in practice. Jordan is treated as a strategically selected, theory-building case in which a disciplinary imbalance intersects with severe resource scarcity, dependence on external finance, concentrated executive authority, and fragmented implementation. Building on depoliticization scholarship, the article introduces aid-embedded climate depoliticization as a conceptual proposition. It proposes that, where external finance and reporting architectures interact with concentrated political brokerage and fragmented implementation, policy thickening may develop alongside uneven visibility of political processes, including bargaining over priorities, distribution, and accountability. It then develops a mechanism-centered research agenda around distributional allocation, bureaucratic implementation, framing and depoliticization, transnational finance, and multi-level authority. The agenda specifies researchable mechanisms, evidence needs, and applications for decision-making to build a cumulative field of climate politics in Jordan with value for comparable governance settings.
Cascading urban disruptions, from routine roadworks to regional floods, affect mobility in ways that cut across employment, health services, and economic productivity. Yet no standardised methodology exists to synthesise heterogeneous disruption data across scales for sustainable city planning. This paper develops and applies a replicable faceted classification scheme (hereafter “taxonomy”) and a multi-source integration workflow for urban events affecting mobility. The methodology rests on three pillars: a systematic literature review (123 papers), a pan-European citizen survey (n = 140), and analysis of official disaster databases (the Emergency Events Database [EM-DAT], 4,179 events; the Copernicus Emergency Management Service [CEMS], 962 activations). Standardised crosswalk tables and thematic comparison across sources enable consistent aggregation across these sources under the common taxonomy. Pilot patterns from the application indicate that transport-related disruptions are consistently salient across sources: 85.0 κ≥ 0.75 . Preparedness baselines suggest limited readiness: only 19.9
The increasing urgency of climate change, biodiversity loss, and resource depletion has positioned Nature-Based Solutions (NbS) as critical instruments for sustainability transitions. However, decision frameworks for prioritizing NbS investments within circular economy (CE) contexts remain limited, particularly under uncertainty and multidimensional trade-offs. This study develops an integrated sustainability decision framework that operationalizes a five-dimensional evaluation architecture comprising ecological effectiveness, circularity performance, economic viability, social co-benefits, and governance and scalability. The Fuzzy Best–Worst Method (FBWM) and Interval-Valued Fuzzy TOPSIS (IVF-TOPSIS) are employed as complementary analytical tools to support systematic NbS investment prioritization. The criteria architecture was validated through a two-round Delphi process involving 23 experts (Kendall’s W = 0.76), and eight NbS typologies were evaluated with robustness assessed through 5000 Monte Carlo simulations. Results identify Urban Constructed Wetland Systems (CC = 0.716) and Agroforestry Integration Programs (CC = 0.680) as the highest-priority investments, reflecting their balanced performance in ecological effectiveness, regenerative circularity, and implementation readiness. Carbon sequestration, regenerative design, and monitoring feasibility emerged as the most influential evaluation criteria. The findings indicate that NbS prioritization within CE investment contexts depends not only on ecological performance but also on the integration of circularity, governance, and financial implementation capacity. Sensitivity analysis demonstrates high ranking stability (mean Spearman’s ρ = 0.889), supporting the robustness of the prioritization results under criteria-weight uncertainty. The study contributes by establishing an integrated NbS–CE evaluation framework that explicitly incorporates circularity as a core investment dimension and provides a transparent, uncertainty-aware approach for sustainability investment decision-making.
In an era marked by recurrent floods, droughts, and climate-induced crises, Nigeria’s disaster management system is challenged by fragmented institutions and weak community engagement. Indigenous Knowledge Systems (IKS), embodied in local environmental observations, traditional governance, and social learning, provide time-tested strategies for anticipating, responding to, and adapting to hazards. However, limited integration of IKS with scientific early warning systems and formal disaster institutions has restricted understanding of its potential to enhance trust-building, participation, and response effectiveness. This study proposes a hybrid framework that integrates IKS with scientific and institutional disaster preparedness systems to strengthen humanitarian logistics and community resilience in Nigeria. It draws on qualitative field evidence from selected communities, supported by conceptual simulation and disaster preparedness scenario analysis. Using a mixed-methods conceptual approach, the study synthesises existing literature, policy frameworks, and field evidence from three ecologically distinct regions: the Niger Delta floodplains, the Benue agricultural valley, and the Northern drylands. Guided by Socio-Ecological Systems (SES) theory and knowledge co-production frameworks, it examines how indigenous forecasting, communal resource sharing, and local governance can complement data-driven early warning and formal logistics. Preliminary findings indicate that rainmakers’ seasonal predictions, ancestral floodplain mapping, and rotational resource allocation enhance trust, participation, and adaptive responses. Nonetheless, their limited incorporation into formal systems constrains broader application. The proposed Hybrid Indigenous–Scientific Disaster Preparedness Framework (HIS-DPF) outlines pathways for institutional integration, data interoperability, and participatory logistics planning. This paper contributes to the discourse on sustainable humanitarian logistics by demonstrating how localised knowledge systems, when integrated with scientific approaches, can enhance disaster preparedness, inclusivity, and resilience in the risk environments of sub-Saharan Africa.
Critical infrastructure systems increasingly rely on real-time coordination and adaptive governance to manage disruption under growth and structural constraint. In airport environments, Integrated Operations Centers (IOCs) represent resilience-by-intervention: coordinated operational response that mitigates cascading performance degradation without expanding physical capacity such as lanes, curb frontage, or terminals. This contrasts with resilience-by-design, in which redundancy and excess capacity are embedded in the physical system itself. Despite growing adoption of such operational centers, empirical methods for quantifying their resilience contribution remain limited. This study develops a quantitative framework to assess resilience-by-intervention using airport surface transportation as a high-sensitivity subsystem. Using Dallas-Fort Worth International Airport (DFW) as a case study, we construct a worst-case scenario (WCS) delay model that integrates quantile regression with Extreme Value Theory to estimate statistically grounded upper-bound traffic delays under stress conditions. Observed surface traffic performance across three high-disruption days in 2025 is benchmarked against modeled worst-case trajectories to evaluate delay mitigation and emissions differentials. Across the three days, observed cumulative delay remained 55 to 62
Decarbonization outcomes often differ across regions despite similar levels of foreign direct investment (FDI), yet existing studies largely estimate average effects and provide limited explanation for why environmental responses vary across institutional and structural contexts. This study addresses this gap by examining whether the relationship between FDI and environmental performance reflects conditional system responses rather than a uniform association across regions. Using a balanced panel of 63 Vietnamese provinces over the period 2005–2023, we estimate fixed-effects, interaction, and quantile regression models to examine conditional associations across institutional contexts and emission regimes. Three consistent findings emerge. First, FDI is negatively associated with emissions per capita on average, although the estimated relationship is modest. Second, this association varies systematically with institutional coordination capacity. The interaction between FDI and the Provincial Competitiveness Index (PCI), used as a proxy for institutional coordination capacity, is negative and statistically significant, indicating that the association between FDI and lower emissions becomes stronger in provinces with higher coordination institutional capacity but remains limited where institutional coordination is weaker. Third, the estimated associations differ across the emissions distribution, becoming progressively stronger in higher-emission systems while remaining statistically insignificant at lower emission levels. These findings suggest that uneven decarbonization reflects heterogeneous and context-dependent system responses rather than a uniform relationship between FDI and environmental performance. By integrating institutional conditionality with distributional heterogeneity, the study offers a systems-based explanation for mixed empirical evidence and provides a conceptual framework to support adaptive environmental system design and decision-making under institutional heterogeneity.
Cyber resilience is now central to safety-critical cyber-physical systems (CPS), yet standards and assessments still treat it primarily as a security property. Interventions are implemented without knowing which aspects of resilience they affect, or whether one capability can compensate for another—leaving operators and regulators investing without knowing which operational outcome each intervention actually moves. This paper builds on the concept of a resilience trajectory: the measurable progression of system performance through degradation, detection, response and recovery. Using two industrial case studies and a physical safety-critical testbed, resilience attributes were selectively activated and observed under adversarial and non-adversarial disruption. The results show that resilience attributes do not contribute equally throughout a disruption: different domains performed different resilience functions, each exerting greatest influence at a particular phase while interacting throughout. Targeting the dominant domain for the affected phase produced the greatest improvement, suggesting that investment can be prioritised by identifying where resilience is lost and which domain constrains it. Critically, the domains were not interchangeable; weaknesses in one could not be fully compensated for by strengthening another. Building on these findings, a refined hierarchical taxonomy and cross-domain dependency model are introduced to trace how interventions influence outcomes and surface hidden consequence pathways, giving practitioners a clearer basis for prioritising resilience investment, and indicating where current assessment practice and standards could be strengthened.
Agricultural land abandonment has emerged as a globally significant land-use change that poses complex socioecological challenges. The phenomenon is driven by multiple variables, and comprehensive analysis is required to understand its patterns and implications. In the present study, bibliometric analysis was conducted using R software packages (bibliometrix and biblioshiny) to assess thematic trends, intellectual structure, and future trajectories. A total of 491 Scopus-indexed articles on agricultural land abandonment, published between 1991 and 2024, were included in the analysis. The results reveal a marked increase in this type of literature since the 2000 s, and the focus on keywords such as agricultural land, biodiversity, climate change, and ecosystem services emphasizes the interdisciplinary nature of the field. Emerging trends highlight the increasing use of advanced remote sensing and machine learning technologies for detecting and monitoring abandonment dynamics. The findings map key shifts in agricultural land abandonment research and underscore the critical role of interdisciplinary collaboration and technological advancements in addressing this pressing environmental issue. Based on these insights, key future research directions are identified.
This article synthesizes the academic literature on the credibility of sustainability, ESG, climate, and net-zero claims made by banking institutions, with particular attention to whether prior research links these claims to observable financing conduct, loan-book exposure, governance arrangements, and risk-management practices. Using a scoping-review design with theory-based synthesis, the study applies Population–Concept–Context logic and PRISMA-ScR-informed reporting to map how claim–conduct alignment has been conceptualized, examined, and verified. Systematic searches of Scopus and Web of Science produced a final sample of 70 peer-reviewed studies. The synthesis does not support the general conclusion that all banking sustainability claims are symbolic. Instead, it identifies three recurring evidence patterns: disclosure–practice disconnect, partial or substantive alignment in lending and risk management, and mixed alignment in which climate or ESG information is acknowledged but only weakly translated into portfolio steering or borrower discipline. Credibility is strongest when claims are linked to pricing, covenants, credit allocation, financed-emissions measurement, client transition assessment, board oversight, and external assurance. The article contributes by developing a conduct-based interpretation of banking transition credibility and proposing a provisional Bank Transition Credibility Assurance System for future empirical calibration, supervisory testing, and assurance practice.
Leak detection in aging water distribution networks (WDNs) is a complex engineering challenge influenced by nonlinear hydraulic performance, infrastructure deterioration, spatial heterogeneity, and limited data on confirmed failures. Purely sensor-based and data-driven approaches often face scalability constraints and rely on simplified assumptions, limiting robustness under real operating conditions. This study proposes a hybrid spatial predictive framework that integrates pressure-driven hydraulic simulation, GIS-based Fuzzy Analytic Hierarchy Process (FAHP), and supervised machine learning to identify leakage-prone nodes in large-scale WDNs. A pressure-driven EPANET model incorporating emitter coefficients and pipe aging effects simulates realistic leakage under diurnal demand patterns. A four-rule screening process identifies 4,699 high-risk nodes from over 39,000 nodes, followed by sensitivity–correlation analysis to determine influential nodes for efficient sensor placement. Spatial and infrastructural characteristics are quantified using Fuzzy AHP through a weighted evaluation of six criteria, with pipe age identified as the dominant factor (0.382). In addition, GIS-based fuzzy AHP enables the development of a spatial leakage-risk map, indicating that more than 30
Climate change has intensified the need for more effective analytical tools to support mitigation, adaptation, and long-term sustainability planning. In this context, this study aims to systematically map the intellectual structure, application domains, and governance implications of research on artificial intelligence (AI) in climate governance and sustainability pathways through a combined bibliometric and thematic review. The analysis covers 116 Scopus-indexed publications published between 2010 and 2026. Quantitatively, the corpus is dominated by research articles (88.8
The sacred river Ganga holds immense cultural, economic, and environmental significance in India. This study investigates stakeholders’ preferences for Ganga river cleanliness using multidimensional participation mechanisms and Discrete Choice Experiment (DCE) in Haridwar, India. The DCE preferences were assessed using multiple attributes, such as monetary contribution (Willingness to Pay), time commitment (Willingness to Work), financial compensation (Willingness to Accept), and varying regulatory governance structures. Further, the study uses baseline Conditional Logit Model (CLM), Mixed Logit (MXL), and Latent Class Analysis (LCA) to capture unobserved heterogeneity and segment behavior of 240 respondents. Results reveal that participation is jointly motivated by monetary and non-monetary incentives. Also, the stakeholders exhibit a strong positive preference for decentralized community-driven governance over strictly top-down state regulation. Heterogeneity analysis suggest that the higher-income individuals prefer monetary contributions, while low-income households and residents prefer to contribute through labor work for river restoration. Overall, these findings indicate the importance of hybrid governance models for inclusive, dual-payment policy designs, and the mobilization of local social capital to sustain the ecological health of the Ganga river.
This study develops and empirically tests a structured transition-finance mechanism examining whether transition-plan credibility influences sustainability-linked loan pricing in the European energy sector and whether syndicate participation transmits part of this effect. Using an unbalanced panel of 107 listed energy firms across five major European economies from 2015 to 2024, the study integrates firm-level transition-plan data, syndicated-loan information, and financial fundamentals. The proposed relationships are estimated using static panel models and two-step system GMM, complemented by mediation analysis. The findings show that stronger transition-plan credibility is associated with lower all-in-drawn spreads, greater syndicate participation, and a significant indirect pricing benefit through the syndication channel. These results indicate that lenders reward execution-oriented transition capacity rather than symbolic climate disclosure alone. The study implies that banks and arrangers should institutionalize a Transition-Plan Credibility Scoring and Loan Structuring Engine to embed governance quality, target credibility, capex alignment, and pathway realism into screening, syndication, and loan-pricing decisions.
