Search Results (625)

This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Digital twins (DTs) in the energy sector are operational-data-driven models of assets, installations, and networks. Their value grows alongside renewable expansion, electronic communications, and stricter resilience requirements for critical infrastructure. This study evaluates DT applications in Poland’s energy transition, identifying regulatory and cybersecurity determinants required for safe, scalable use. The methodology combines an international literature review, regulatory assessment, and qualitative desk research focusing on DT projects across four Polish energy groups: Enea, Energa, PGE, and Tauron. Each case is assessed using a DT maturity and governance framework covering scope, data coupling, decision support, and security posture. The study identifies four primary deployment types: (1) operational network twins for distribution system operators leveraging SCADA/ADMS, GIS, and state estimation; (2) AI-driven asset performance twins for wind turbines and CHP plants; (3) flexibility twins for hydropower system services; and (4) immersive training twins for the offshore wind sector. Main constraints include data quality, interoperability, fragmented data access regulations, and expanded cyber-attack surfaces from OT/IT convergence. DTs aid spatial planning, mitigating location and land use conflicts. Recommendations emphasize harmonized data governance, cybersecurity-by-design, special determinants, and the creation of regulatory sandboxes to support DT implementation within critical energy infrastructure. Full article

As power grids transition towards highly renewable generation on a global scale, maintaining dynamic stability is becoming a major challenge. Replacing traditional synchronous generators with inverter-based renewables strips the grid of rotational inertia, leaving active distribution networks highly vulnerable to frequency deviations and voltage spikes. To avoid expensive poles and wires upgrades, Battery Energy Storage Systems (BESS) are increasingly being deployed as Non-Network Solutions (NNS). However, the current literature reveals a distinct gap between the macro-scale economic planning of these storage assets and the micro-scale dynamic control actually required to keep the grid resilient. To address this gap, this review proposes a multi-layer deterministic synthesis framework that links physical renewable modelling, degradation-aware techno-economic planning, deterministic forecasting, and EMS dispatch through offline time-domain control validation for AC-microgrid energy storage integration. The research examines how advanced central control units within battery management systems can rigorously and jointly estimate State of Charge (SoC) and State of Energy (SoE) to ensure accurate grid-aware dispatch. Furthermore, the study explores the integration of degradation-aware economic modelling in HOMER Pro with dynamic transient control in MATLAB/Simulink R2025b, driven by hybrid metaheuristic optimization algorithms like Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). This analysis demonstrates that integrating energy storage must be treated as a tightly coupled multidimensional optimization problem to successfully deliver the secure and sustainable infrastructure needed to solve the modern energy trilemma. Full article

This paper presents the INFRARES tool, a fully parameterized, interactive, and freely available tool for the resilience assessment of bridges and tunnels within Greece’s transportation networks, under the impact of single or multiple hazards, including earthquakes and floods. The tool facilitates the application of a comprehensive methodology developed through the INFRARES project: Towards resilient transportation infrastructure in a multi-hazard environment research project. The resilience of each examined asset is quantified for the selected hazard scenario using a resilience index and a corresponding resilience grade. The INFRARES tool introduces two key innovations over previous approaches: first, it incorporates both structural and geotechnical components of bridges, overpasses, and tunnels in the vulnerability assessment step; second, it enables GIS-based visualization of the resilience index across selected single- or multi-hazard scenarios. In this context, INFRARES serves as a proactive decision-support tool, supporting authorities, infrastructure operators, and stakeholders to effectively assess, manage, and mitigate the impacts of diverse hazards on transportation systems, thereby enhancing the safety, reliability, resilience, and sustainability of transportation infrastructure under multi-hazard conditions. The proposed tool and the obtained results may support resilience-informed decision-making, prioritization of mitigation measures, and sustainable management of transportation infrastructure exposed to multiple natural hazards. Full article

This article examines whether the informational efficiency of green bonds and sustainability-oriented equity indices changes across crisis regimes. Methods: We analyze the S&P Green Bond Index and eight S&P sustainable equity indices from 16 February 2016 to 10 November 2025 using static and rolling-window multifractal detrended fluctuation analysis. Rolling inefficiency, defined as the absolute deviation of the singularity-spectrum peak from 0.5, is then modelled with crisis-regime indicators, asset effects, dependence-robust inference, and window-level risk controls. Results: The S&P Green Bond Index remains among the most efficient and least volatile assets in the sample. Crisis-labelled windows display higher multifractal inefficiency than the pre-COVID benchmark, although the robustness checks show that part of the raw effect is transmitted through volatility and tail-risk conditions. Conclusions: Sustainable-finance efficiency is nonlinear and regime dependent, and the evidence is interpreted as a conditional association rather than causal identification. Green bonds are comparatively resilient, but not informationally invariant during systemic, geopolitical, and policy-driven stress. Full article

Supply chain disruptions have become a major source of macro-financial stress, yet their implications for green finance remain underexplored. This paper investigates the state-dependent connectedness between supply-side bottlenecks and the green finance market, represented by clean energy equities, green bonds, and carbon prices. Using daily data on regional Supply Bottleneck Indices (SBIs) for China, the United States, and the euro area, we first construct a global Supply Bottleneck Index (GSBI) by principal component analysis and then estimate pairwise quantile-on-quantile connectedness (QQC) between supply bottleneck indicators and each green finance submarket. The results show that connectedness is strongly nonlinear, asymmetric, and time-varying. For the global indicator, connectedness intensifies at both joint and cross-tail quantile combinations, while mid-quantile states exhibit weak coupling. Regional results reveal clear heterogeneity: China and the United States display the strongest connectedness with clean energy equities in extreme upper-tail states, whereas the euro-area indicator is most tightly linked with the carbon market. Across many extreme states, supply bottleneck indicators show positive net connectedness with green finance markets, but green finance markets, especially carbon prices, can dominate the bilateral connectedness relation under calmer or intermediate regimes. Robustness checks based on average and quantile-rank GSBI constructions, a post-2023 subsample, and alternative QQC tuning choices support the tail-dominance pattern. These findings suggest that supply bottlenecks are not uniformly related to all green assets; rather, they are associated with state-dependent changes in the internal connectedness architecture of the green finance system. The paper contributes to the literature on financial connectedness and sustainable finance by showing how a real-economy disturbance is associated with changes in the connectedness and resilience of green financial markets. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

Over the past decade, state-led waterfront projects in the Gulf region have emerged as an intriguing urban phenomenon, repositioning World Heritage Sites (WHSs) located along coastal zones within large-scale urban development. In a region increasingly exposed to urban transformation and a hot-arid climate, these waterfront transformations also intersect with the emerging need for climate-adaptive urban resilience. While such projects are often framed as a means of contemporary extension of heritage assets, their implications for WHSs remain underexamined. This study investigates how state-led waterfront developments in the peripheries of the World Heritage Sites function as socio-spatial mediators that reconnect maritime heritage with these sites while enhancing or undermining urban resilience and climate adaptability. Drawing on comparative case studies from the Historic Jeddah (inscribed in 2014) and the Pearling Testimony of an Island Economy, Muharraq, Bahrain (inscribed in 2012), the data collection comprises literature synthesis, policy and planning documents, state-led waterfront proposals, and site observations, analyzed through the Comprehensive Waterfront Development Index (CWDI) framework. The analysis expands the existing six dimensions of the CWDI framework to eight dimensions and dissects two of the dimensions to a further two more. This expansion and dissection contextualize waterfront developments as socio-spatial mediators re-connecting maritime heritage with the WHSs. Findings reveal that while both cases integrate CWDI dimensions, social integration is limited, whereas climate adaptation remains absent, diminishing the urban resilience of the WHS. By doing so, this study contributes to broader debates on sustainable development and climate-adaptive urban resilience in rapidly transforming peripheries of WHSs. Full article

The transition of thermal power systems toward lower-carbon electricity raises a critical strategic question: whether to rely on cross-border electricity imports or reactivate domestic nuclear capacity under supply constraints. This study examines the trade-offs between these alternatives within a sustainable finance framework. A contingent-claim model is developed in which a life insurer provides long-term financing to a biomass-energy supplier, a thermal power plant, and a nuclear power plant operating under carbon-pricing regulation. The framework links electricity-market decisions with financial risk valuation, allowing the joint effects of biomass utilization, carbon regulation, electricity imports, and nuclear-security risks to be evaluated. The results show that biomass integration and tighter carbon regulation reduce short-term profitability in thermal generation but support long-run decarbonization. Cross-border electricity imports improve system flexibility and reduce operational volatility, strengthening the financial position of thermal producers. In contrast, nuclear-security disruptions significantly increase default risk for nuclear assets, reflecting their exposure to operational and regulatory uncertainty. By integrating energy-transition strategies with contingent-claim valuation, the analysis highlights the role of financial intermediation in shaping investment incentives and risk allocation in the electricity sector. The findings suggest that coordinated policies combining market integration, low-carbon transition strategies, and stable financing mechanisms can enhance system resilience. Full article

This study examines the relationship between environmental performance, digital integration, information asymmetry, and default risk among European firms. It seeks to understand how sustainability and digitalization jointly enhance corporate financial stability. The sample comprises 1303 non-financial firms from 20 European countries over the period 2016–2023. This study uses a Thomson Reuters sample composed of European publicly listed companies with ESG (environmental, social, and governance) ratings. Europe represents an ideal setting for this analysis due to its dual green and digital transition, supported by some of the most advanced regulatory policies in the world. Methodologically, the analysis employs a dynamic panel model estimated using the two-step system GMM approach, complemented by a robustness check based on 2SLS-IV estimation to address potential endogeneity concerns. The empirical findings reveal that both environmental performance and digital integration significantly reduce default risk whereas information asymmetry increases it. Moreover, sustainability and digital transformation attenuate the adverse effect of information asymmetry on financial stability, confirming their complementary role as resilience-enhancing mechanisms. These results underscore the critical importance of transparency, innovation, and organizational capabilities in mitigating financial risk. Overall, the study makes an original contribution to the literature on sustainable governance by demonstrating that environmental performance and digital integration are not merely regulatory requirements but constitute strategic intangible assets that strengthen financial soundness and reduce default risk within the European context. Full article

This study examines the relationship between environmental, social, and governance (ESG) criteria and profitability in the global insurance sector from two distinct perspectives. The System GMM analysis measures the associations between ESG criteria and asset profitability. The analysis, conducted using the CRADIS method and weighted by the CRISUS, MAXC, and NMV methods, determines the companies’ multidimensional performance rankings. Thus, the financial outcomes of companies’ sustainability investments are comprehensively revealed. According to the System GMM estimation results, environmental and social variables are negatively associated with asset profitability, whereas the governance variable and return on equity are positively associated with asset profitability. The leverage ratio and firm size are negatively associated with profitability. While asset profitability and return on equity stand out as the most significant factors compared with environmental, social, and governance variables, environmental and social variables have become increasingly prominent in decision-making processes since 2020. According to the NMV method, return on equity is the decisive criterion, whereas the CRISUS-MAXC integrated method identifies return on assets as the decisive criterion; in both methods, the leverage ratio remains variable and has the lowest impact. According to the CRADIS method rankings, Admiral Group and Zurich Insurance were identified as having the highest performance and the lowest volatility. CNA Financial, Great Eastern, and Hanwha Corp were identified as the lowest-performing companies. Sensitivity analysis results indicate that the NMV-CRADIS method is more resilient to changes in weights. Full article

Prolonged downturns in the global economy have simultaneously increased banks’ credit risk exposures and intensified the need for effective liquidity management. This study develops a dynamic agent-based financial network comprising banks, depositors, firms, and the central bank to examine trade-offs in bank resolution under coupled liquidity and credit risks from the perspective of systemic stability. The simulation results show that, for liquidity risk management, when banks adopt the asset-sale strategy, both default probability and expected returns in the banking system exhibit a nonlinear pattern: they first decline and then rise as the asset depreciation ratio increases. Furthermore, at moderate levels of asset depreciation, the asset-sale strategy helps preserve heterogeneity within the banking system, thereby preventing excessive risk concentration, and performs better than the liability-expansion strategy. Regarding credit risk resolution, the debt-relief strategy significantly improves systemic stability, whereas the effectiveness of the debt-extension strategy depends critically on liquidity management conditions. Under liability-expansion scenarios, default risk initially declines but later rises as debt maturity is extended, whereas expected returns move in the opposite direction. Under asset-sale conditions, the debt-extension strategy enhances systemic stability only when the allowable number of debt extensions is sufficiently high. The analysis of strategic trade-offs indicates that combining the debt-relief strategy with the asset-sale strategy generates a positive synergistic effect and strengthens systemic resilience, whereas the interaction between the debt-extension and asset-sale strategies produces offsetting effects. These findings offer useful implications for banks and regulators in designing coordinated and adaptive frameworks for risk resolution and systemic stability. Full article

Periods of geopolitical uncertainty have increasingly shaped the performance of global financial markets, yet the extent to which these risks influence the co-movement of asset markets in South Africa remains unclear. Although co-movement has emerged as a crucial factor for investors seeking portfolio diversification, existing studies present mixed findings, with some suggesting that geopolitical risk strengthens financial integration, defined as the extent to which markets move together in response to global shocks, while others find that it weakens these linkages by triggering market segmentation. Against this backdrop, this study examines the impact of geopolitical risk’s influence on the co-movement of South African asset markets, focusing on how shifts in global uncertainty interact with local market dynamics. Using time-series monthly data from December 2004 to January 2025, the study applies a dual-method approach. The multivariate generalised autoregressive conditional heteroskedasticity asymmetric dynamic conditional correlation (MGARCH-ADCC) model is first employed to estimate time-varying correlations across the equity, bond, and property markets. Thereafter, the autoregressive distributed lag (ARDL) model is used to assess both the short- and long-run effects of geopolitical risk on these co-movement patterns. The results indicate that geopolitical risk significantly increases co-movement between South African asset markets in both the short and long run, thereby diminishing the traditional benefits of diversification. These findings reinforce the view that market participants respond collectively to uncertainty rather than fundamentals. Overall, the study contributes to the empirical understanding of market integration under geopolitical stress and highlights the need for investors and policymakers to incorporate geopolitical risk indicators into investment and policy frameworks to strengthen market resilience. Full article

Everyday heritage objects are often overlooked despite their cultural significance and vulnerability to surface degradation caused by environmental exposure, material ageing, and human interaction. This review examines how surface characterization, digital documentation, additive manufacturing, and extended reality (XR) technologies can be integrated to support the conservation, replication, and inclusive dissemination of such assets. The study synthesizes recent advances in non-destructive surface analysis methods, including spectroscopic and imaging techniques, alongside 3D scanning approaches capable of capturing both geometry and surface condition. These data are linked to additive manufacturing workflows for producing accurate and durable replicas, with particular attention to surface fidelity and material selection. The review further explores how tactile replicas and multimodal interpretation strategies can enhance accessibility for visually impaired users, addressing limitations of visually dominant heritage practices. XR technologies are discussed as complementary tools for interpretation and remote access. The findings highlight that combining surface-focused conservation with digital and fabrication technologies enables more resilient, accessible, and sustainable heritage management. Future research should focus on standardizing inclusive design approaches and improving the integration of surface data into digital and physical reproduction pipelines. Full article