Search Results (3,252)

This study evaluates deterministic flood fatality models using a harmonised dataset of river and flash flood events in Europe (1980–2024). The objective is to quantify differences across data sources and critically assess the applicability of commonly used prediction models for hydrological floods, with particular emphasis on flash floods, which remain poorly represented in existing methodologies. The analysis integrates large-scale databases on flood fatalities (HANZE, EM-DAT) with detailed event-based studies containing hazard and other indicators, enabling a combined evaluation from different sources. Three model groups are assessed by comparing predicted and observed fatalities: Damage–Fatality, Depth–Fatality, and Depth–Velocity–Fatality approaches. Results confirm discrepancy between exposure and mortality: river floods dominate in terms of affected population (87%) and economic losses (71%), whereas flash floods account for nearly half of all fatalities despite affecting only 13% of people. All evaluated models show significant limitations for prediction of flash floods fatalities; single-parameter approaches perform poorly, while multi-parameter models remain highly sensitive to uncertain hydraulic inputs. The study demonstrates that current methods are not transferable to flash flood conditions and highlights the need for integrated, multi-variable approaches supported by consistent and high-quality datasets. The main contributions of the study are the first systematic validation of widely used models against historical river and flash flood events, revealing their uncertainties, and a comprehensive assessment of their robustness and sensitivity to key input indicators. Full article

This study addresses the sustainable supply chain network design (SSCND) problem by integrating economic and environmental dimensions through a multi-objective, multi-echelon stochastic mathematical model. The proposed model focuses on simultaneously optimizing total cost, carbon emissions, water footprint, and renewable energy utilization. Strategic solar energy investment alongside facility location and sizing decisions are considered under uncertain conditions. Initially, a multi-product stochastic model is developed and solved utilizing the augmented epsilon constraint (AUGMECON2) method to obtain Pareto-optimal solutions for small-scale instances. For validation purposes, the exact solutions obtained using AUGMECON2 were used as the benchmark for the small-scale instance, while the proposed hybrid NSGA-II algorithm generated near-optimal solutions with deviations of 0.30%, 1.53%, 0.03%, and 0.0006% for total cost, carbon emissions, renewable energy use, and water footprint, respectively. Compared with the cost-oriented solution, the renewable energy-focused solution increased total cost by 76.33% while reducing the water footprint by 6.36% and carbon emissions by 3.57%. For medium- and large-scale instances, where exact solutions became computationally impractical, the hybrid NSGA-II algorithm remained applicable and generated feasible Pareto solutions within 59.05 s and 309.62 s, respectively. Overall, the presented framework provides a scalable decision-support tool for sustainable supply chain planning under uncertainty. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

Introduction: Placental malaria increases the risk of adverse birth outcomes. Current preventive measures are undermined by poor coverage, growing resistance to chemo-preventive and therapeutic drugs, and vector eliminating insecticides. Candidate placental malaria (PM) vaccines (PAMVAC and PRIMVAC) have shown safety and immunogenicity in Phase I trials, but empirical evidence on their potential population-level value is lacking. This study modelled the expected cost-effectiveness of a PM vaccine administered before pregnancy. Methods: A decision-analytic model compared two strategies from the provider’s perspective: vaccinating women of childbearing age versus no vaccination. The model incorporated gravidity-specific risks of PM, neonatal mortality and the malaria attributable fractions from the literature. Since the efficacy of a PM vaccine for malaria prevention is unknown, we assumed a 40% efficacy and varied this estimate widely in sensitivity analyses. Primary outcomes were incremental cost-effectiveness ratios (ICERs) per perinatal disability adjusted life years (DALYs) averted. Baseline, best-case, and worst-case scenarios were analysed. One-way and probabilistic sensitivity analyses were used to assess parameter uncertainty. Cost-effectiveness was defined as an ICER below half of sub- Saharan Africa’s 2025 GDP per capita ($1556). Results: The vaccine was most cost-effective among primigravidae. Under baseline assumptions (40% efficacy; 30% uptake; $5 dose price), the ICER was $321 per perinatal DALY averted for primigravidae versus $4444 for multigravidae. Best-case assumptions further improved cost-effectiveness ($225 vs. $3148). Sensitivity analyses showed robust cost-effectiveness for primigravidae across all plausible parameter ranges, while ICERs in multigravidae were highly sensitive to programme costs and vaccine efficacy. Cost-effectiveness acceptability curves demonstrated that vaccination becomes favourable for primigravidae at relatively low willingness-to-pay thresholds. Conclusions: A placental malaria vaccine delivered before pregnancy has high potential to be cost-effective in endemic areas when targeted to protect primigravidae. These findings support prioritised deployment strategies and highlight the value of early economic modelling to inform vaccine development and policy planning. Full article

Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO₂ metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO₂ metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO₂ emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article

Energy transition includes the substitution of centralized energy systems with decentralized variable renewable energy sources (vRES), the growth of which brings drawbacks such as grid congestion and intermittency. These issues are increasingly troublesome in many local energy systems, including in The Netherlands. Biogas may provide options to provide backup renewable energy in times of energy supply uncertainty. In The Netherlands, the consideration of biogas in such functions is limited. Meanwhile, local energy initiatives (LEIs) are spearheading the adoption of vRES. Because of concern over local grid balancing, LEIs may want or need to innovate and diversify their activities. Such innovation could include bioenergy in general, and biogas specifically. However, only a small number of LEIs consider bioenergy, and Dutch LEIs seem hesitant to venture into biogas specifically. In this paper we explore the question of what hinders adoption of biogas in The Netherlands in general, and by LEIs specifically, deploying an approach based on the technological innovation systems (TIS) concept. In that approach, we take insights from current and expected policy in The Netherlands juxtaposed with insights from similar countries surrounding The Netherlands. We conclude that historic developments in biogas already created a moderately supportive platform for large-scale biogas development, but some essential factors remain inadequately developed. Key barriers to biogas innovation, especially for LEIs, are insufficient mobilization of financial and knowledge resources, and insufficient attention to alleviating preconceptions. Dependable support and attention for socio-economic factors in policymaking would improve conditions associated with resources, preconceptions and resistance, and the situation for LEIs to explore the potential of biogas. However, it remains uncertain whether such measures would be sufficient to improve the potential of local biogas utilization in The Netherlands in a way that opens a role for biogas in solving energy transition challenges such as energy system balancing. Full article

Fugitive greenhouse gas (GHG) emissions in industrial parks are characterized by high opacity and spatial dispersion. Existing localization and quantification methods often rely on idealized meteorological assumptions and low-precision mobile monitoring data, making it difficult to achieve accurate source characterization. This study focuses on the Nansha Economic and Technological Development Zone in Guangzhou—one of the first pilot zones for synergistic pollution and carbon reduction in China—to develop an atmospheric inversion model based on multi-site fixed monitoring. By integrating GHG concentrations with multi-dimensional meteorological parameters, the model couples an atmospheric dispersion framework with a Bayesian inversion algorithm. Specifically, site-specific conditions and high-frequency meteorological data are utilized to constrain dispersion parameters, effectively reducing model uncertainty driven by meteorological variability. Within the Bayesian framework, the model enables the simultaneous inversion of both the locations and emission strengths of multiple sources. Results identified three distinct fugitive emission sources: one primary source in the International Auto Industrial Park with a CO₂ emission intensity of 103.15 g/s and two sources in the Western Industrial Park with intensities of 0.051 g/s and 0.26 g/s, respectively. Overall, this research framework significantly enhances the accuracy and spatial resolution of emission inversion, providing robust technical support for precision carbon management and the development of targeted mitigation strategies for key industrial processes. Full article

The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems. Full article

Economic entropy, as an emerging concept in econophysics, has gained increasing relevance in the analysis of complex systems characterized by uncertainty, nonlinearity, and out-of-equilibrium dynamics. However, its integration into conventional economic modeling—particularly in production functions such as the Cobb–Douglas function—remains fragmented and lacks systematic empirical validation. This study conducts a scientometric analysis of 345 Scopus-indexed documents (1973–2024) addressing the intersection between entropy, econophysics, and production functions, with the aim of mapping the intellectual structure of the field, characterizing its growth trends, identifying its core contributions, and highlighting its main research gaps. The results reveal that the field has experienced sustained growth since 2004, with a notable acceleration between 2020 and 2023, although it exhibits a fragmented authorship structure that does not conform to Lotka’s Law, suggesting that the field is still in a stage of scientific consolidation. The Cobb–Douglas function emerges as a niche topic within the econophysics literature, with limited integration between entropy-based approaches—informational, thermodynamic, and maximum entropy—and the empirical modeling of production. Furthermore, weak citation linkages between econophysics and conventional economics are observed, confirming the interdisciplinary fragmentation of the field. These findings provide a structured reference for researchers interested in advancing toward analytical frameworks that explicitly incorporate uncertainty, information, and physical constraints into economic analysis, thereby contributing to the development of econophysics as an integrative discipline. Full article

Carbon capture and storage (CCS) is widely regarded as a viable pathway for reducing greenhouse gas emissions; however, large-scale deployment remains constrained by project economics and policy uncertainty. This study presents a life cycle costing assessment of a proposed CCS retrofit at the Hunter Unit 3 coal-fired power plant in Emery County, Utah, encompassing carbon capture, transport, and subsurface storage. Results indicate that the project appears economically favorable under the assumptions of this screening-level analysis and under current policy conditions, with an estimated break-even time of approximately five years. The analysis identifies a large upfront capital investment exceeding $600,000,000, offset by planned revenue from federal tax credits totaling several billion dollars over the project lifetime. Sensitivity analyses show that project economics are dominated by capture costs and annual mass of CO₂ sequestration rates, while storage and transport costs play secondary roles. A synthetic policy-perturbation analysis of the $85/ton tax credit further demonstrates that policy volatility materially increases uncertainty in investment returns. These results highlight both the economic potential of CCS retrofits at existing power plants and the critical role of stable long-term policy in enabling investment. Full article

This study investigates how Climate Policy Uncertainty (CPU) influences investments in the Renewable Energy Transition (ET), a relationship widely presumed to be negative, despite the empirical literature reporting mixed and highly heterogeneous results. Using a preregistered systematic review following PRISMA guidelines, we identify seventeen peer-reviewed studies from Web of Science and Scopus. Their quantitative estimates are harmonized using Fisher z-transformations and analyzed within a meta-analytic framework. A global random-effects meta-analysis reveals a small and statistically insignificant average effect of CPU on ET-related investment outcomes, together with extremely high heterogeneity, indicating that a single pooled coefficient is not an informative universal summary. To examine whether part of this dispersion follows an interpretable pattern, we estimate an exploratory mixed-effects meta-regression based on a four-channel transmission framework derived from the reviewed literature. This model accounts for 50.4% of the between-study variance, and only the Macroeconomic channel shows a negative and statistically significant deviation from the reference category ($\beta$ = 1.0700, p = 0.0060). This result should be interpreted cautiously, however, given the small number of studies in each subgroup and the persistence of substantial residual heterogeneity. Overall, the evidence suggests that the CPU does not affect ET-related investment outcomes in a uniform way; rather, the reported relationship varies across contexts, with the strongest negative pattern appearing in studies that capture macroeconomic conditions related to the energy transition, such as foreign direct investment, trade openness, and aggregate green investment. By providing the first meta-analytic quantification of this relationship and a structured mapping of transmission mechanisms, this study offers novel empirical clarity to a fragmented literature. The policy implication is direct, and governments seeking to accelerate the energy transition must prioritize long-term credibility, regulatory stability, and macroeconomic predictability, as these are the domains through which climate policy uncertainty most severely constrains low-carbon investment. Full article

East Africa is highly vulnerable to climate change due to limited adaptive capacity and strong reliance on rain-fed agriculture. Ethiopia, in particular, experiences recurrent socio-economic losses from droughts and floods. This study presents a national-scale assessment of observed (1981–2010) and projected (2041–2100) changes in extreme seasonal precipitation across Ethiopia using ten ETCCDIs. High-resolution Enhancing National Climate Services (ENACTS) observations and bias-corrected outputs from a selected ensemble of CMIP6 models under SSP2-4.5 and SSP5-8.5 scenarios are used to assess historically trends and future extreme precipitation, respectively. Historical trends show increases in extreme precipitation during the Kiremt (JJAS) season, particularly over the northwestern, western, and southwestern highlands; however, most of these increases are not statistically significant. In contrast, the Belg (FMAM) season exhibits widespread declines, which are also largely not statistically significant. Future projections suggest increases in total precipitation (PRCPTOT), heavy (R10) and very heavy rainfall days (R20), very wet days (R95p) and extremely wet days (R95p), and rainfall intensity (SDII) over northwestern, western, southwestern, and parts of northeastern Ethiopia during JJAS. During FMAM, PRCPTOT is projected to increase in the northern and northwestern regions, while decreases are expected in the northeastern and southeastern regions. The Awash and Tekeze basins emerge as key hotspots of change, indicating potential seasonal shifts and an increased likelihood of extreme weather in these regions. Despite inter-model uncertainty, the results highlight the need for flexible, uncertainty-informed adaptation strategies to enhance climate resilience in Ethiopia. Full article

This paper presents a scenario-based optimization framework for evaluating the life-cycle cost of right-sizing and replacement timing for distribution transformers under demand–growth uncertainty. The proposed formulation jointly considers the discrete commercial transformer ratings, the discounted investment cost, and the monetized iron and copper losses over a 15-year planning horizon. Demand uncertainty is represented by nine scenarios defined by combinations of initial apparent power demand and annual growth rate, with $D_1\in \{45,50,55\}$ kVA and $g\in \{3\%,4\%,5\%\}$. Under these assumptions, the demand envelope evolves from an initial range of 45–55 kVA to approximately 68.1–108.9 kVA in Year 15, while expected demand increases from 50 kVA to about 87 kVA. The optimization results show that the economically optimal policy is to install a 112.5 kVA transformer in Year 1 and maintain that rating throughout the horizon, without triggering any replacement events. The selected transformer maintains expected loading between approximately 0.44 p.u. and 0.77 p.u., while the upper-demand scenario remains below 1.0 p.u. over the entire horizon. These results indicate that, for the demand–growth conditions analyzed, the preferred outcome is a single initial sizing decision rather than a phased replacement strategy. Therefore, the proposed framework provides a consistent scenario-based alternative to deterministic margin-based planning for distribution transformer asset management. Full article

The potential of Trichoderma nordicum (Hypocreales, Ascomycota), a recently described species, for antagonism and use in the biocontrol of oomycete-caused plant diseases is unknown. Trichoderma is a well-known genus for containing microbial antagonists and biocontrol agents. The T. nordicum in this study was isolated from decomposing wood, and rpb2 and tef1 barcode sequencing demonstrated that the isolates were a match to the reference T. nordicum and T. nigricans strains. Since T. nordicum was described before T. nigricans, the isolates were assigned to T. nordicum, although taxonomic uncertainty between these species requires future clarification. In dual-culture confrontation assays, T. nordicum overgrew five economically important oomycete plant pathogens (Phytophthora capsici, P. sojae, Pythium aphanidermatum, P. myriotylum, and Globisporangium ultimum). The inability to recover viable P. aphanidermatum and P. capsici from the parts of the plate overgrown by T. nordicum, coupled with protease and endo-cellulase activities, correlates with T. nordicum having antagonistic abilities. Inoculation with T. nordicum preventively reduced the levels of cucumber seedling damping-off caused by P. aphanidermatum by up to 70%. The T. nordicum biocontrol effects against pepper blight caused by P. capsici were greater than 80%, compared to an autoclaved T. nordicum spore control. T. nordicum could also significantly promote the growth of pepper, with plant weight increased by up to 40%, compared to an autoclaved-spore control. In contrast, T. nordicum could not be used to control Pythium soft rot of ginger caused by P. myriotylum, even though P. myriotylum was overgrown by T. nordicum, suggesting host- or pathosystem-specific factors influence biocontrol efficacy. In summary, T. nordicum is a promising biocontrol agent for use in the control of pepper blight caused by P. capsici, and also has potential for use in the control of other oomycete-caused plant diseases in vegetable production systems. Full article

Due to urbanization and industrialization, there are significant regional differences in carbon emissions, making it increasingly urgent and necessary to conduct an in-depth examination of carbon emission trends from energy consumption across various sectors at the provincial level. Taking Hebei Province, a major carbon-emitting province in China, as a case study, we analyzed carbon emissions from three perspectives: historical emissions, influencing factors, and scenario projections. First, we established a carbon emission inventory for energy consumption. Second, using the integrated LMDI-SD-MC framework, we constructed four subsystems economy, society, energy, and technology and employed three scenarios for forecasting. The results show that: (1) Carbon emissions in Hebei Province from 2003 to 2021 exhibited increased trend year by year, with the share of coal and coke decreasing and the share of natural gas increasing. The industry, residential, and transportation sectors accounted for more than 95% of total carbon emissions. (2) In terms of influencing factors, energy intensity and the level of economic development contributed the most significantly, with contribution rates of −75.97% and 195.97%, respectively. (3) Among the scenario projections, the low-carbon development scenario is the most suitable for Hebei Province, enabling the province to achieve its “Dual Carbon” goals as scheduled. Under the baseline development scenario, the peak is reached in 2040. Under the rapid development scenario, carbon emissions will reach 1130.86 10⁶ tons by 2060. (4) Uncertainty analysis using Monte Carlo simulation for all three scenarios showed errors within ±10%, indicating that the model results are robust and interpretable. This study provides a provincial level emission reduction perspective for China to achieve its “Dual Carbon” goals and sustainable development. Full article