Search Results (94)

This study investigates whether targeted progress on Sustainable Development Goals (SDG 7, 8, and 9) can cushion the impact of oil dependence on unemployment in Gulf Cooperation Council (GCC) economies. Using panel data for six countries from 2000 to 2021 and regression models with country fixed effects and system GMM, we incorporate interaction terms between oil rents and both disaggregated and composite SDG indicators. The results show that SDG 8 (Decent Work) exerts the strongest stabilizing effect, significantly reducing unemployment sensitivity to oil rents. SDG 7 (Clean Energy) exhibits transitional dynamics, with short-term adjustment costs during early stages of the energy transition. SDG 9 (Infrastructure) does not display consistent short-run effects. A composite SDG index also moderates the oil–unemployment link, though this effect is largely driven by SDG 8. Overall, the findings suggest that inclusive labour institutions and clean energy reforms enhance labour market resilience in resource-dependent economies, reducing vulnerability to external shocks and supporting more sustainable development pathways. Full article

The coordinated scheduling of diesel generators, photovoltaic (PV) systems, and energy storage systems (ESS) is essential for improving the reliability and resilience of islanded microgrids in remote and mission-critical applications. This review systematically analyzes diesel–PV–ESSs from an “energy symbiosis” perspective, emphasizing the complementary roles of diesel power security, PV’s clean generation, and ESS’s spatiotemporal energy-shifting capability. A technology–time–performance framework is developed by screening advances over the past decade, revealing that coordinated operation can reduce the Levelized Cost of Energy (LCOE) by 12–18%, maintain voltage deviations within 5% under 30% PV fluctuations, and achieve nonlinear resilience gains. For example, when ESS compensates 120% of diesel start-up delay, the maximum disturbance tolerance time increases by 40%. To quantitatively assess symbiosis–resilience coupling, a dual-indicator framework is proposed, integrating the dynamic coordination degree (ζ ≥ 0.7) and the energy complementarity index (ECI > 0.75), supported by ten representative global cases (2010–2024). Advanced methods such as hybrid inertia emulation (200 ms response) and adaptive weight scheduling enhance the minimum time to sustain (MTTS) by over 30% and improve fault recovery rates to 94%. Key gaps are identified in dynamic weight allocation and topology-specific resilience design. To address them, this review introduces a “symbiosis–resilience threshold” co-design paradigm and derives a ζ–resilience coupling equation to guide optimal capacity ratios. Engineering validation confirms a 30% reduction in development cycles and an 8–12% decrease in lifecycle costs. Overall, this review bridges theoretical methodology and engineering practice, providing a roadmap for advancing high-renewable-penetration islanded microgrids. Full article

The European Union air transport sector has been repeatedly exposed to major disruptions such as the 2008 financial crisis, the COVID-19 pandemic, the war in Ukraine, and volatile energy prices. Strengthening resilience has, therefore, become a strategic priority. This study examines how strategic energy investments—covering renewable energy, sustainable aviation fuels (SAFs), electrification, hydrogen technologies, and advanced infrastructure—contribute to the resilience of the EU air transport system. The methodology integrates both primary and secondary data from EU policy documents, ICAO and IATA databases, Eurostat, and national statistics. A multi-criteria evaluation was applied using four key performance indicators: emission reduction efficiency (ER), annual exposure index (AEI), investment performance index (IPI), and net present value (NPV). Projects were assessed through Simple Additive Weighting (SAW) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), complemented by sensitivity analysis. The results show that the Pioneer project delivers the strongest environmental and financial outcomes, ranking first in ER, AEI, and NPV. Hermes performs best in job creation and social impact, while BioOstrand achieves substantial absolute CO₂ reductions but lower cost efficiency. TULIPS shows limited effectiveness across all indicators. Sensitivity analysis confirmed that rankings remain robust under alternative weighting scenarios. The findings underscore that project design and alignment with resilience objectives matter more than investment size. Strategic energy investments should, therefore, be prioritized not only for decarbonization but also for their ability to reinforce both technological and socio-economic resilience, providing a reliable foundation for a sustainable and crisis-resistant EU air transport sector. Full article

To elucidate how exogenous regulators mitigate the impact of mechanical leaf damage on maize, field experiments were conducted on two sowing dates (S1, S2) using two cultivars (XY335, ZD958). Severe leaf damage at the six-leaf stage significantly reduced kernel number, ear number, and 100-kernel weight, causing yield losses of 21.9–48.9%. Foliar application of melatonin (MT), brassinolide (BR), and urea (UR) substantially alleviated these losses, increasing yield by 14.1–52.2% compared to damaged controls, with UR and BR being most effective, especially in ZD958. These regulators restored leaf area index (LAI) by promoting leaf width and delaying senescence, improved photosynthetic performance (Pn, Gs, Ci, and Tr), enhanced post-silking dry matter accumulation by up to 31%, and accelerated grain filling through increased maximum and mean filling rates. Structural equation modeling confirmed that kernel number and 100-kernel weight were the primary yield determinants. These findings reveal the physiological mechanisms underlying damage recovery and demonstrate the potential of targeted regulator applications—urea as a cost-effective option, brassinolide for improving kernel number under sustained stress, and melatonin for broad resilience. This study provides not only theoretical evidence but also a feasible strategy for mitigating yield loss in maize production under field conditions where leaf damage commonly occurs. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

Assessing energy security in the context of sustainable development, as well as the current geopolitical climate, is a highly important, timely, and complex challenge. Addressing this issue, this paper introduces a new multi-barrier methodological approach to evaluation based on the Multi-Barrier Energy Security System (MBEES) model. This model incorporates five barriers (dimensions) influencing energy security. The MBEES model, along with the developed methodology, was applied to assess the energy security of the EU-27 countries for the period of 2014–2023, in line with EU policy objectives such as Fit for 55 and the Green Deal. The Criteria Importance Through Intercriteria Correlation and Entropy methods, combined with the Laplace criterion, were employed to determine the weights of the model’s sub-indicators. This multi-criteria decision-making (MCDM) approach enabled a synthetic overall evaluation of both the general energy security status of the EU-27 countries and the performance of each barrier examined. The study also identified the weakest elements (barriers) within national energy systems that could potentially threaten their stability and resilience. This identification is essential for effective energy risk management and for enhancing the resilience of energy systems against disruptions. Due to its broad scope—covering availability, self-sufficiency, diversification, energy efficiency, energy costs, as well as environmental and social aspects—the study delivered a comprehensive evaluation of energy security in the EU-27 during the examined period. The findings reveal significant spatial and temporal variations in energy security levels among the EU-27 countries. Scandinavian and Western European nations achieved the highest scores, whereas Central, Eastern, and Southern European countries showed lower MBEES index values, reflecting persistent structural, social, and environmental vulnerabilities. The results hold strong potential for practical application, offering guidance for EU policymakers in aligning national strategies with overarching policy frameworks such as REPowerEU and the European Green Deal. Full article

This study develops a computational framework for the simultaneous quantification of seismic resilience and economic losses in corrosion-affected coastal continuous girder bridges. The proposed model integrates adjustment factors to reflect delays in post-earthquake repairs and cost increments caused by progressive material degradation. Finite element methods and nonlinear dynamic time-history simulations were conducted on an existing coastal continuous girder bridge to validate the proposed model. The key innovation lies in a probability-weighted resilience index incorporating damage state occurrence probabilities, which overcomes the computational inefficiency of traditional recovery function approaches. Key findings demonstrate that chloride exposure duration exhibits a statistically significant positive association with earthquake-induced structural failure probabilities. Sensitivity analysis reveals two critical patterns: (1) a 0.3 g PGA increase causes a 11.4–18.2% reduction in the resilience index (RI), and (2) every ten-year extension of corrosion exposure decreases RI by 2.7–6.2%, confirming seismic intensity’s predominant role compared to material deterioration. The refined assessment approach reduces computational deviation to ±2.4%, relative to conventional recovery function methods. Economic analysis indicates that chloride-induced aging generates incremental indirect losses ranging from $58,000 to $108,000 per decade, illustrating compounding post-disaster socioeconomic consequences. This work systematically bridges corrosion-dependent structural vulnerabilities with long-term fiscal implications, providing decision-support tools for coastal continuous girder bridges’ maintenance planning. Full article

This study evaluates three pairs trading strategies—the distance method (DM), mispricing index (MPI) copula, and mixed copula—across the Chinese equity market from 2005 to 2024, incorporating time-varying transaction costs. To enhance computational efficiency, a novel two-step methodology is proposed that first selects candidate pairs based on the sum of squared differences and then applies copula models to capture nonlinear and asymmetric dependence structures between stocks. Pre-cost monthly excess returns are 84, 30, and 25 basis points, respectively, dropping to 81, 23, and 15 basis points post-costs. While the DM consistently delivers higher returns, copula strategies offer advantages in stability and resilience, especially in volatile markets. The Student-t copula proves particularly effective in capturing dependence structures with fat tails and asymmetric correlations. Although copula methods face challenges such as unconverged trades—instances where spreads fail to revert within the trading horizon—they nonetheless highlight the diversification and risk mitigation potential of advanced dependence-based approaches. Enhancing trade convergence and controlling downside risk could further improve copula strategy performance. Overall, the results highlight the diversification and risk mitigation potential of advanced copula-based pairs trading models under dynamic market conditions. Full article

Accurate river flow velocity estimation is critical for flood risk management and sediment transport modeling. This study proposes an artificial intelligence (AI)-based framework that integrates optical flow analysis and deep learning to estimate flow velocity from charge-coupled device (CCD) camera videos. The approach was tested on a field dataset from Yufeng No. 2 stream (torrent), consisting of 3263 ten min 4 K videos recorded over two months, paired with Doppler radar measurements as the ground truth. Video preprocessing included frame resizing to 224 × 224 pixels, day/night classification, and exclusion of sequences with missing frames. Two deep learning architectures—a convolutional neural network combined with long short-term memory (CNN+LSTM) and a three-dimensional convolutional neural network (3D CNN)—were evaluated under different input configurations: red–green–blue (RGB) frames, optical flow, and combined RGB with optical flow. Performance was assessed using Nash–Sutcliffe Efficiency (NSE) and the index of agreement (d statistic). Results show that optical flow combined with a 3D CNN achieved the best accuracy (NSE > 0.5), outperforming CNN+LSTM and RGB-based inputs. Increasing the training set beyond approximately 100 videos provided no significant improvement, while nighttime videos degraded performance due to poor image quality and frame loss. These findings highlight the potential of combining optical flow and deep learning for cost-effective and scalable flow monitoring in small rivers. Future work will address nighttime video enhancement, broader velocity ranges, and real-time implementation. By improving the timeliness and accuracy of river flow monitoring, the proposed approach supports early warning systems, flood risk reduction, and sustainable water resource management. When integrated with turbidity measurements, it enables more accurate estimation of sediment loads transported into downstream reservoirs, helping to predict siltation rates and safeguard long-term water supply capacity. These outcomes contribute to the Sustainable Development Goals, particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by enhancing disaster preparedness, protecting communities, and promoting climate-resilient water management practices. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

This research proposes a novel multi-objective optimization framework for virtual power plants (VPPs) operating in day-ahead electricity markets. The VPP integrates diverse distributed energy resources (DERs) such as wind turbines, solar photovoltaics (PV), fuel cells (FCs), combined heat and power (CHP) systems, and microturbines (MTs), along with demand response (DR) programs and energy storage systems (ESSs). The trading model is designed to optimize the VPP’s participation in the day-ahead market by aggregating these resources to function as a single entity, thereby improving market efficiency and resource utilization. The optimization framework simultaneously minimizes operational costs, maximizes system flexibility, and enhances reliability, addressing challenges posed by renewable energy integration and market uncertainties. A new flexibility index is introduced, incorporating both the technical and economic factors of individual units within the VPP, offering a comprehensive measure of system adaptability. The model is validated on IEEE 24-bus and 118-bus systems using evolutionary algorithms, achieving significant improvements in flexibility (20% increase), cost reduction (15%), and reliability (a 30% reduction in unsupplied energy). This study advances the development of efficient and resilient power systems amid growing renewable energy penetration. Full article

Objectives: To analyze, across the periods before, during, and after the implementation of Social Isolation and Distancing Measures (IMDIS): (a) changes in the prevalence of non-communicable chronic diseases (NCDs), stratified by age group in the Brazilian population; and (b) the association between physical inactivity (PI), insufficient moderate-to-vigorous physical activity (MVPA), and sedentary behavior (SB) with the occurrence of these conditions. This cross-sectional study used data from VIGITEL (Brazil’s Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey), an annual population-based telephone survey conducted across the country. Data were collected in 2019, 2021, and 2023, with a total sample size of 101,226 participants. Arterial hypertension (AH) and diabetes mellitus (DM) were self-reported, and obesity (OB) was diagnosed using body mass index. PI, insufficient MVPA, and SB were identified via VIGITEL indicators. Chi-square tests assessed differences in prevalence overall and by age group. Logistic regression models estimated odds ratios (ORs) for associations between demographic variables, behavioral factors, and the studied periods. The prevalence of AH and DM was highest among individuals over 60 years, reaching 61% after IMDIS, a period when OB also peaked across all age groups. Individuals aged 30–59 and those over 60 had higher odds of AH, DM, and OB across all periods. Female participants had higher ORs for AH and DM both before and after IMDIS. PI and insufficient MVPA were associated with increased odds of AH, DM, and OB in all periods, while SB significantly elevated the OR for OB at all time points. After IMDIS, there was an increase in the prevalence of AH, DM, and OB among older adults and younger individuals. PI, insufficient MVPA, SB, and advanced age were all associated with a greater likelihood of NCDs at every stage of the study. The high post-IMDIS rates of AH, DM, and OB highlight the need for urgent public health strategies. Low-cost programs, such as live videos and online group sessions, should be included in national physical activity guidelines. These initiatives are affordable, aligned with WHO goals, and reduce PI in IMDIS scenarios. Incorporating them into Academia da Saúde and Agita Brasil strengthens NCD prevention and increases the resilience of the health system for future health crises. Full article

The Arctic region, one of the most vulnerable areas globally, faces severe climate change impacts, with rising sea levels and temperatures threatening local communities. Modern geoinformation tools provide a reliable, cost-efficient, and time-saving method for assessing these climate changes in Arctic coastal regions. This study focuses on Finland’s Arctic and sub-Arctic diverse coastline. The Coastal Vulnerability Index (CVI) is used to assess the vulnerability of Finland’s coastlines, using advanced geoinformatics tools. Integrating high-resolution data from EMODnet, the National Land Survey of Finland Digital Elevation Model (DEM), and physical sources, the CVI includes six input parameters: geomorphology, coastal slope, shoreline change rates, mean wave height, tidal range, and relative sea-level change. The CVI results reveal pronounced spatial variability: 37% of the coastline is classified with very low vulnerability, primarily in the southern Gulf of Finland, and some northern segments, specifically part of Lapland, exhibit minimal susceptibility to coastal hazards. Conversely, the central Gulf of Bothnia shows high vulnerability (29%), with low and moderate vulnerability zones comprising 27% and 6%, respectively, and very high vulnerability at 1%. This assessment provides essential insights for sustainable coastal management in Finland by offering a replicable model for Arctic coastal assessments. This study supports policymakers and local communities in developing targeted adaptation strategies to enhance resilience against climate-driven coastal hazards. Full article

Multi-energy systems (MES), or energy hubs, offer a technologically viable solution for maintaining resilient energy infrastructure in the face of increasingly frequent disasters, which disproportionately affect low-income and disadvantaged communities; however, their adoption for these purposes remains poorly understood. Following PRISMA 2020, this paper systematically reviews the MES literature from both resiliency and equity perspectives to identify synergies, disparities, and gaps in the context of climate change and long-term decarbonization goals. From 2420 records identified from Scopus (1997–2023), we included 211 original MES research publications for detailed review, with studies excluded based on their scale, scope, or technology. Risk of bias was minimized through dual-stage screening and statistical analysis across 18 physical system and research approach categories. The results found that papers including equity are statically more likely to involve fully renewable energy systems, while middle income countries tend to adopt renewable systems with biofuels more than high income countries. Sector coupling with two energy types improved the resiliency index the most (73% difference between baseline and proposed MES), suggesting two-type systems are optimal. Statistically significant differences in modeling formulations also emerged, such as equity-focused MES studies adopting deterministic design models, while resilience-focused studies favored stochastic control formulations and load-shedding objectives. While preliminary studies indicate low operational costs and high resilience can synergistically be achieved, further MES case studies are needed with low-income communities and extreme climates. Broadly, this review novelly applies structured statistical analysis for the MES domain, revealing key trends in technology adoption, modeling approaches, and equity-resilience integration. Full article

Mechanised agricultural operations are often performed individually, under minimal supervision and across a wide range of unfavourable working conditions, resulting in a complex mixture of hazards and external stressors that severely affect safety conditions. Socio-technical and environmental constraints significantly affect safety culture and require continuous performance adjustments to overcome timing pressures, resource limitations, and unstable weather conditions. This study introduces a FRAM-based safety culture model that embeds the thoroughness-efficiency trade-off (ETTO) in four distinct operational modes that adhere to specific safety cultures, namely, thoroughness, risk awareness, compliance, and efficiency. This model has been instantiated for mechanised ploughing: foreground task functions were coupled with background functions that represent socio-technical constraints and environmental variability, while severity classes for potential incidents were derived from the US OSHA accident database. The framework was also supported by a semi-quantitative Resonance Index based on severity and coupling strength, the Total Resonance Index (TRI), to assess how variability propagates in foreground functions and to identify hot-spot functions where small adjustments can escalate into high resonance and hazardous conditions. Results showed that the negative effects on functional resonance generated by safety detriment on TRI observed between compliance and effective working modes were three times larger than the drift between risk awareness and compliance, demonstrating that efficiency comes with a much higher cost than keeping safety at compliance levels. Extending the proposed approach with quantitative assessments could further support the management of socio-technical and environmental drivers in mechanised farming, strengthening the role of safety as a competitive asset for enhancing resilience and service quality. Full article