Search Results (164)

Natural hazards and their negative impacts on assets are increasing because of a variety of causes, including climate change, population expansion, and urbanization. Moreover, several areas are susceptible to multiple hazards that interact spatially and/or temporally, necessitating a multi-hazard assessment to adequately mitigate their effects. The goal of this study is to investigate the direct monetary losses produced by the simultaneous interaction of two independent hazards in Lisbon’s city centre, i.e., earthquake and pluvial flood. Seismic hazard has been assessed in terms of macro-seismic intensity, while flood scenario allows for the prediction of water depth for different return periods through a hydrologic-hydraulic model in HEC-RAS software. The seismic and flood vulnerability of the urban investigated compound was evaluated through MCDM methodology—specifically, AHP and TOPSIS methods. A framework for multi-hazard analysis was subsequently developed, explicitly accounting for the interaction between the two hazards and their joint occurrence probabilities based on historical data from the case study area. The results demonstrate that multi-hazard losses are 108 M€ for a 2-year return period and 232 M€ for a 475/500-year scenario, emphasizing that floods contribute more across all return periods in the research area; however, for longer return periods, the earthquake contribution increases significantly. Full article

Accurate tail-risk measurement in carbon markets is challenging because carbon allowance prices are shaped not only by heavy-tailed return dynamics, but also by policy changes that can alter the underlying risk dynamics. Models that ignore such structural shifts may perform reasonably well in normal periods while still understating downside risk when market conditions change. To address this issue, this paper proposes a break-regime generalized autoregressive score model with Student-t innovations, denoted as BR-GAS-t, for one-step-ahead forecasting of Value-at-Risk and Expected Shortfall. Using daily spot data from China’s carbon market, we compare BR-GAS-t with historical simulation, GARCH-N, GARCH-t, and regime-free GAS-t benchmarks. The results show that carbon returns are strongly heavy-tailed and that the post-break regime is characterized by stronger shock sensitivity, lower persistence, and a higher long-run conditional scale. Out-of-sample evidence further indicates that BR-GAS-t delivers the strongest overall VaR backtesting performance and the lowest average Fissler–Ziegel (FZ) loss in joint VaR–ES evaluation. Its advantage is most pronounced at the 2.5% and 1% tails, where downside risk is hardest to forecast. Robustness checks based on alternative break dates, window lengths, recursive schemes, and distributional assumptions confirm that the main conclusion is stable. The findings suggest that explicitly incorporating observed policy breaks improves tail-risk forecasting in policy-driven carbon markets. Full article

Decentralized finance (DeFi) has been studied mainly as a financial and technological system, while the role of digital entrepreneurial capability in shaping sustainable user traction remains underexplored. This study repositions DeFi as a digitally mediated entrepreneurial ecosystem and examines whether retention-oriented user behavior is associated with three capability dimensions—entrepreneurial visibility, network embeddedness, and organic acquisition efficiency—together with ecosystem-finance conditions such as total value locked and decentralized-exchange activity. Using an exploratory, correlational design with monthly aggregated data from five incumbent DeFi platforms during the post-FTX recovery period (October 2022–September 2023), the analysis combines canonical correlation analysis, partial least squares regression, and ridge regression. Results indicate a significant multivariate association between ecosystem-finance conditions and the entrepreneurial-capability block, and show that returning-visitor behavior is more coherently linked to the predictor set than broad visitor inflow. Entrepreneurial Visibility Capital and Network Embeddedness emerge as the most stable positive correlates of user retention, while Organic Acquisition Efficiency shows a directionally mixed pattern. Because the sample is small, the findings are interpreted as preliminary evidence rather than confirmatory claims. Overall, the study offers an integrative framework that connects DeFi, digital entrepreneurship, and sustainability-oriented business-model research, and identifies the joint configuration of digital capability and financial conditions as a promising direction for future, larger-scale investigation. Full article

Flood risk assessment is essential for flood control and disaster mitigation in arid and semi-arid river basins, where conventional univariate and bivariate frequency analyses struggle to capture nonlinear dependence among flood variables and often underestimate extreme synergistic risks. This study focuses on the Wulanmulun River Basin in Inner Mongolia and employs long-term observations from the Zuanlongwan and Wangdaohengta hydrological stations. A trivariate D-vine Copula model was constructed to jointly characterize peak discharge, total flood volume, and water level. Optimal vine structures differ between the stations (Qp–H–W and W–Qp–H) and outperform traditional Copula models in representing extreme joint risks. The ternary joint return periods reveal two distinct flood risk transmission modes, “jump” and “accumulation”, and joint exceedance probabilities under low, medium, high, and ultra-high-risk scenarios are 6.4%, 31.95%, 37.64%, and 5.75% at Zuanlongwan, and 4.7%, 35.24%, 45.78%, and 0.53% at Wangdaohengta, indicating concentration in medium-to-high risk ranges. The validation at Longtouguai Station showed an error RSME of 0.0630 and an R² of 0.905, confirming the reliability of the model framework. These results indicate that the proposed framework can effectively capture multivariate flood dependencies and provide a scientific basis for flood control design, risk zoning, and emergency management of small and medium rivers in arid and semi-arid regions. Full article

River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting from climatic variability is essential for effective water quality management and protection of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers: Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. The Weibull-3P and Generalised Extreme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of eighteen different parametric copula classes was evaluated. The Gaussian copula provided the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions has identified notable spatial variability in compound hydrological and thermal extreme hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed Yamuna River had the longest joint RPs and the lowest conditional probabilities. This suggests that simultaneous extreme drought and heat events are less likely in the Yamuna basin, although significant risks remain for less severe thresholds. Full article

The stability of solar-dependent energy systems is vital for urban sustainability, but it is increasingly threatened by compound energy risks (CERs), events where low photovoltaic generation coincides with high electricity demand. This study addresses a critical knowledge gap by disentangling the co-evolving structural and magnitude drivers of these events to identify their propagation pathways and the most vulnerable districts. To achieve this, a novel hybrid framework was developed to provide a high-resolution, spatiotemporal assessment of both risk dimensions across Singapore’s 41 districts. Structural risk was mapped by integrating an undirected co-occurrence network, quantified using Mutual Information (MI), with a directed influence network derived from Bayesian Network Theory (BNT). Concurrently, magnitude risk was assessed through a copula-based analysis of joint probabilities for historical and future climate conditions, using Singapore’s new V3 dataset under multiple Shared Socioeconomic Pathways (SSPs). The results reveal a significant shift in the compound energy risk landscape. Structurally, the network of risk propagation evolves from a historically diffuse configuration to a consolidated system dominated by clusters of 8 to 9 highly interconnected districts under the SSP245 scenario. Under the high-diffusion SSP585 scenario, this evolution is expanded by the addition of 4 more districts. At the same time, the magnitude of risk intensifies across identified hotspot districts. This synthesis uncovers a critical feedback dynamic: districts such as 29, 36, and 40 not only serve as key structural hubs but also experience sharp increases in event probability, with their return periods for extreme compound events collapsing from over 50 years historically to the 10–20-year range. This forms a self-reinforcing loop of systemic vulnerability. These findings indicate that Singapore’s energy security will become increasingly exposed to climate-driven risks that propagate through this consolidated network, requiring targeted spatial adaptation to ensure long-term grid sustainability. Full article

Under global climate change, flood processes exhibit significant non-stationarity due to multiple driving factors, rendering traditional frequency analysis methods based on stationarity assumptions inadequate for accurate risk assessment. This study, focusing on the Kuitun River Basin and utilizing observed data from the Jiangjunmiao Hydrological Station (1959–2014), develops a joint design approach that addresses both non-stationarity and multivariate dependence. The approach integrates the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) with copula functions and employs a parametric bootstrap to quantify the impacts of marginal parameter estimation and sample size uncertainty on design floods. The results indicate that flooding in the Kuitun River is influenced by precipitation, temperature, and snowmelt, with summer precipitation having the greatest impact. Marginal parameter uncertainty is significantly amplified at high return periods, and the confidence intervals of design values expand as the return period increases. In the joint framework, the OR criterion is more sensitive to parameter perturbations, with the 100-year flood peak and flood volume design values approximately 24.2% and 19.8% higher than those of the AND criterion, respectively. Increasing the sample size significantly reduces uncertainty; when the sample size increases from 56 to 500, the HDR area and confidence interval width decrease by approximately 60–70%, and the stability of joint flood design estimates improves significantly. The research findings can provide a scientific basis and technical support for flood analysis and risk management in the Kuitun River Basin under changing environmental conditions. Full article

With the rapid expansion of renewable energy and the acceleration of electricity market reforms, coal-fired units are facing increasing difficulty in recovering fixed costs due to marginal cost-based bidding competition and depressed clearing prices caused by low-cost renewable integration, circumstances in which reasonable returns and investment incentives for coal-fired power plants are not guaranteed. To address this issue, this paper proposes a capacity cost compensation mechanism for coal-fired power in the electricity market environment. First, a joint clearing model for the electricity spot market considering both energy and reserve services is established, and annual market operation simulations are conducted to obtain unit output schedules, clearing prices, and annual revenues. Second, based on the long-term simulation results, the marginal clearing probability and fixed cost recovery deficit of each coal-fired unit are calculated, and a capacity compensation pricing method based on marginal clearing probability weighting is proposed to determine the system unit capacity compensation price. Subsequently, the compensated capacity is determined using the availability factor method, comprehensively reflecting each unit’s actual contribution to system capacity adequacy. Finally, case studies conducted on a modified IEEE 30-bus system validate the effectiveness of the proposed mechanism. The results demonstrate that following the implementation of the proposed mechanism, the investment payback periods of all coal-fired units are reduced to within the planned 20-year horizon, thereby ensuring the sustainable operation of coal-fired units and maintaining adequate reliability margins in the power system. Full article

The Xiamen Bay area is frequently impacted by typhoons and is characterized by a complex hydrodynamic environment. The combined action of waves, currents, and storm surges threatens the construction of the Third Eastern Link. Traditional design methods often overlook the correlations among hydrological variables, potentially leading to overestimated design standards. To address this issue, we developed a high-accuracy multi-driver hydrodynamic numerical model for Xiamen Bay. A high-resolution dataset of waves, currents, and storm surges spanning nearly 20 years was established. Based on the Copula function, a trivariate joint probability distribution of wave–current–storm surge was constructed. The results indicate that the Gamma distribution is the most suitable marginal distribution for the individual variables, and the Clayton Copula function best captures the dependence structure among the three variables. For the same return period, the design values of wave height, current velocity, and water level obtained using the Copula method are lower than those derived using traditional standard methods. The research findings can provide a more scientific and economical design basis for the Third Eastern Link project and serve as a reference for multivariate joint probability modeling in similar sea areas. Full article

Background/Objectives: Hip hemiarthroplasty (HHA) for femoral neck fractures (FNFs) can be performed via the posterolateral approach (PLA), straight lateral approach (SLA) or direct anterior approach (DAA). However, the optimal approach remains unclear. This study evaluated mortality and return-to-home rates following an institutional transition from SLA to DAA. Methods: This retrospective observational cohort study included patients who underwent primary cemented unipolar hip hemiarthroplasty for FNF during a period of transition in surgical approach (2015–2023). Clinical outcomes between the straight lateral and direct anterior approach were compared. Primary outcomes were the mortality and return-to-home rates. Secondary outcomes included perioperative parameters and complications. A subgroup analysis was performed using Fracture Mobility Score (FMS) and Katz activities of daily living (ADL) index to compare functional outcomes. Results: Over a 9-year period, a total of 762 HHA were performed, of which 411 SLA and 333 DAA. Mortality at 90 days (14.1% vs. 8.7%, p = 0.029) and 1 year (26.5% vs. 17.7%, p = 0.005) were significantly higher in the SLA group. Among patients living at home preoperatively, return-to-home after surgery was lower for SLA compared to DAA (23.2% vs. 41.4%, p < 0.001). In terms of complications, SLA had significantly lower rates of periprosthetic joint infections (SLA n = 6 (1.5%) vs. DAA n = 15 (4.6%), p = 0.024). The decline in Katz ADL score at three months was significantly greater in the SLA group than in the DAA group (ΔKatz ADL −0.73 ± 1.57 vs. −0.11 ± 1.60, p = 0.036). Conclusions: Transitioning from SLA to DAA in HHA was associated with improved preservation of independent living, higher return-to-home rates and lower 90-day and 1-year mortality. However, DAA was also associated with higher rates of PJI. Full article

In recent years, the increasing frequency of strong and super typhoons has been attributed to rising sea surface temperatures due to global warming. This study utilized the Weather Research and Forecasting (WRF) and Simulating WAves Nearshore (SWAN) models to analyze 30 years of wind and wave data for the Yangjiang sea area in China. The accuracy of the numerical simulations was validated using observed data from typhoons Ty201213, Ty201522, Ty201822, and Ty202118, along with wind and wave data from December 2024. This study utilized the P-III distribution to analyze design wind parameters. At a height of 10 m, the 3 s and 10 min mean wind speeds for the 100- and 50-year return periods were 62.21 m/s, 47.85 m/s, 57.99 m/s, and 44.61 m/s, respectively. At hub height (170 m), the corresponding values were 80.27 m/s, 61.75 m/s, 74.84 m/s, and 57.57 m/s. Furthermore, this study successfully applied a 2D-KDE approach to construct a joint probability model and derive environmental contours for extreme environmental assessments. The HS and TP at project point P for the 100- and 50-year return periods are 13.61 m and 15.91 s, as well as 12.39 m and 15.07 s, respectively. Full article

Parametric tropical cyclone models are widely used to generate large wind field ensembles for assessing extreme storm tides and wave heights. The radius of maximum wind (RMW) is a key model parameter and is commonly estimated using empirical formulas. This study shows that uncertainty introduced by the choice of RMW formulas has been largely overlooked in tropical cyclone risk assessments. Using the Pearl River Estuary as a case study, historical wind fields (1981–2024) were generated with a parametric tropical cyclone model combined with eight empirical RMW formulas. Storm tides and wave heights during tropical cyclone events were simulated using a coupled wave–current model (ROMS–SWAN) and analyzed with extreme value theory. The results indicate that, for estuarine nearshore zones, the 100-year return period of water level and significant wave height vary by up to 1.26 m and 1.54 m, respectively, across all the selected RMW formulas. Joint probability analysis further shows that RMW uncertainty can shift the joint return period of the same compound storm tide and wave event from 100 years to 10 years. For an individual extreme event, differences in the RMW formula alone can produce deviations up to 2.11 m in peak storm tide levels and 3.8 m in significant wave heights. Such differences can also change the duration of extreme sea states by 13 h. These results highlight that RMW formula selection is a critical uncertainty factor, and related uncertainty should be considered in large-sample tropical cyclone hazard assessment and engineering design. Full article

Complex water networks face prominent contradictions among flood control, water supply, and ecological protection, and traditional scheduling models struggle to address multi-dimensional water security challenges. To solve this problem, this study proposes a multi-objective coordinated water resources scheduling model for complex water networks, taking the Taihu Lake Basin as a typical case. First, a multi-objective optimization indicator system covering flood control, water supply, and aquatic ecological environment was constructed, including 12 key indicators such as drainage efficiency of key outflow hubs and water supply guarantee rate. Second, a dynamic variable weighting strategy was adopted to convert the multi-objective optimization problem into a single-objective one by adjusting indicator weights according to different scheduling periods. Finally, a combined solving mode integrating a basin water quantity-quality model and a joint scheduling decision model was established, optimized using the particle swarm optimization (PSO) algorithm. Under the 1991-Type 100-Year Return Period Rainfall scenario, three scheduling schemes were designed: a basic scheduling scheme and two enhanced discharge schemes modified by lowering the drainage threshold of the Xinmeng River Project. Simulation and decision results show that the enhanced discharge scheme with the lowest drainage threshold achieves the optimal performance with an objective function value of 98.8. Compared with the basic scheme, it extends the flood season drainage days of the Jiepai Hub from 32 to 43 days, increases the average flood season discharge of the Xinmeng River to the Yangtze River by 9.5%, and reduces the maximum water levels of Wangmuguan, Fangqian, Jintan, and Changzhou (III) stations by 5 cm, 5 cm, 4 cm, and 4 cm, respectively. This model effectively overcomes technical bottlenecks such as conflicting multi-objectives and complex water system structures, providing theoretical and technical support for multi-objective coordinated scheduling of water resources in complex water networks. Full article

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of $H_s$ = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height ($H_{s100}$ = 4.66 m, $T_{p100}$ = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, $H_{s100}$ = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia. Full article

While a number of studies have analyzed the determinants of economic growth in Asia, the research on the synergistic interplay of the quality of governance and the investments in research and development have not received nuanced attention in the scholarly research. This study fills the research gap by looking at the joint effect of governance and R&D investment on economic growth in Asian nations with varying levels of development. Using the fixed-effects model and the generalized method of moments (GMM) model, this study investigated the individual and combined effect of governance and R&D investment in driving economic growth in the static as well as dynamic panel of 34 Asian nations for the period 2000–2024. The study further undertakes a comparative assessment of the lower-middle-income, upper-middle-income, and high-income economies on the continent. The findings reveal that the interaction between R&D and governance is negative and significant in lower-middle-income countries such as India, Indonesia, Philippines, and Tajikistan, showing that weak institutions hinder R&D effectiveness. It turns strongly positive in upper-middle-income economies such as China, Kazakhstan, Malaysia, and Thailand, as governance strengthens, but becomes insignificant, in high-income nations such as Israel, Korea, Singapore, and Qatar, suggesting diminishing returns. The results under dynamic panel estimation show positive and significant effects of the interaction between R&D and governance upon per capita GDP in all countries’ panels. The findings suggest that the diverse and nonlinear progression from technological adoption to creation in Asian nations requires sustained investments in R&D and deliberate policy alignment with national innovation systems. Full article