Search Results (1,031)

Farmland management rights (FMR) mortgage lending has been advanced as a central instrument of rural credit reform in China, yet the program has consistently failed to sustain itself in the absence of direct government facilitation. Drawing on five national and provincial pilot counties in Henan Province, this study investigates the structural factors underlying this sustainability failure. We employ a sequential mixed-methods design: grounded theory analysis of in-depth interviews, policy documents, and media reports from five focal sites to inductively construct a constraint framework, followed by structural equation modeling (SEM) validation using 1055 survey responses. Our grounded theory analysis identifies three internal constraint categories—property rights insecurity, a thin secondary land market, and subject-level agricultural risk—and one external environmental constraint, which together produce a state of mutual non-recognition: neither financial institutions nor farming households regard FMR as legitimate collateral. Notably, the effect of collateral acceptance on farmer mortgage willingness is statistically insignificant, revealing that demand-side barriers are more deeply entrenched than supply-side institutional improvements alone can resolve. These findings challenge the premise that legal formalization of land rights is sufficient to generate market-driven credit activity, and call attention to the equally important role of institutional ecosystem development—encompassing land markets, appraisal capacity, supervisory infrastructure, and rural credit culture. The insights carry direct relevance for developing economies exploring land-backed agricultural credit as a rural finance strategy. Full article

The rapid integration of electric vehicles (EVs) and decentralized renewable energy sources is transforming urban power systems, while simultaneously increasing the complexity of real-time coordination across charging infrastructure, distributed energy resources, and grid-support devices. This systematic review synthesizes recent research on multi-agent reinforcement learning (MARL) for EV–grid coordination, with emphasis on four emerging dimensions: forecasting-informed control, safety-constrained learning, explainability and interpretability, and trustworthy decentralized coordination. A systematic literature search was conducted in IEEE Xplore, Scopus, Web of Science, ScienceDirect, MDPI, and arXiv, covering primarily the period 2016–2025, with selected early-2026 studies retained where relevant, with selected earlier foundational studies retained for context. The review was conducted and reported in accordance with the PRISMA 2020 framework. A total of 412 records were identified through database searching; after duplicate removal and screening, 58 studies were included in the final qualitative synthesis. The reviewed literature shows that MARL is increasingly being applied to EV charging coordination, demand-side management, community energy systems, transactive energy, and ancillary grid services. The evidence further indicates that forecasting integration improves anticipatory control, safety-aware formulations enhance operational reliability, and explainability-oriented designs help address transparency and trust barriers in safety-critical grid environments. However, the literature remains limited by heterogeneous benchmarks, inconsistent evaluation metrics, and a lack of real-world deployment evidence. This review provides a structured synthesis of current methodologies, identifies critical research gaps, and outlines future directions for the development of safe, interpretable, and deployment-ready MARL frameworks for urban energy systems. Full article

The low-carbon transition of the power sector is fundamental to achieving “Dual Carbon” goals, where demand-side management plays an increasingly vital role in transforming flexible loads into renewable energy accommodation and active emission-reduction resources. However, existing low-carbon demand response mechanisms based on dynamic carbon emission factors only reflect average system states and fail to quantify the incremental carbon impact of marginal load changes. To address this limitation, this paper proposes a novel marginal carbon emission factor-driven low carbon demand response mechanism. Unlike traditional methods, the proposed mechanism utilizes marginal carbon emission factors as a high-sensitivity guiding signal to inform users of the real-time emission and renewable energy consumption variations caused by their consumption adjustments. Furthermore, considering the forecasting errors of high-penetration renewable energy, the uncertainty of marginal carbon emission factors is explicitly considered. Case studies are conducted to compare the proposed method with the conventional method through comparative analyses based on the modified PJM-5 system. Results demonstrate that the MCEF-driven approach provides more precise carbon-reduction and renewable energy utilization signals to achieve superior system-wide decarbonization performance and sustainable development. Full article

Background/Objective: Emergency Departments (EDs) face persistent challenges with overcrowding, unpredictable patient arrivals, and difficulty forecasting short-term demand. Precise hourly arrival predictions are crucial for effective staffing, optimal resource management, and minimizing entry delays. Methods: This paper develops and evaluates a forecasting framework comparing six approaches (a Seasonal Naive baseline, Exponential Smoothing (ETS), Ridge Regression, LightGBM, a hybrid Temporal Convolutional Network (TCN), and a hybrid Long Short-Term Memory (LSTM) network) using de-identified hourly patient arrival records from an ED in Madinah, Saudi Arabia, covering January–November 2024. A set of 183 engineered features is constructed from cyclical time encodings, weekend and public-holiday indicators, structured autoregressive lags, and volatility measures, with all lag-based features verified to use strictly retrospective information. Models are optimized using Bayesian hyperparameter search and trained under an asymmetric loss function that penalizes underprediction to reflect operational risk. Results: Results on a 14-day hold-out test set show that Ridge Regression achieves the lowest MAE (3.75, R² = 0.52), with TCN and LSTM essentially tied (MAE 3.80 and 3.85). Diebold–Mariano tests confirm that Ridge, TCN, and LSTM are statistically indistinguishable from one another and that Ridge is marginally significantly better than LightGBM ($p=0.028$); all four ML models significantly outperform ETS and the Seasonal Naive baseline ($p<0.001$). On the asymmetric metric, TCN achieves the best AsymRMSE (5.59), reflecting its tendency to err on the safe side of staffing decisions. Robustness is confirmed through sensitivity analysis across penalty factors, feature ablation demonstrating the contribution of each feature group without overfitting, expanding-window cross-validation across three independent monthly test periods, and conformal prediction intervals achieving well-calibrated coverage. Conclusions: These results demonstrate that combining engineered temporal features with either a lightweight linear model or a hybrid sequence model yields accurate hourly ED arrival forecasts; whether the achieved accuracy is operationally sufficient for staffing decisions remains a site-specific question that requires clinical validation beyond the scope of this single-center study. Full article

Extreme weather events have raised the frequency of power outages, posing critical challenges to the sustainability and resilience of modern power systems. In such cases, distributed energy resources (DERs) can effectively support the re-establishment of sustainable power supply for critical loads within the distribution network and reduce power outage losses. In this paper, a sustainable fault recovery framework based on an intentional islanding scheme is proposed to partition the distribution system in order to optimize the priority restoration of critical loads, while taking the operational constraints of the system into consideration. In addition, a blockchain-based P2P insurance mechanism is applied to mitigate the outage losses of the network’s users with a higher degree of security and transparency. By linking technical restoration decisions with financial risk-sharing mechanisms, the proposed framework improves economic sustainability and social equity among network users. For this purpose, a multi-layer, multi-objective optimization algorithm is proposed for optimal partitioning of the distribution network, management of DERs, and demand side management of flexible loads in order to minimize the outage losses and the insurance premium, while maintaining satisfactory performance of the network. To validate the feasibility of the proposed algorithm, the 45-node distribution network of Alexandria, Egypt is used. The results show that a reduction in peak load, outage losses, and operational costs are achieved, with an overall saving of 17.34%, in addition to a premium reduction of 41.3%. These results highlight the effectiveness of the proposed framework in enhancing the environmental, economic, and operational sustainability of distribution systems under outage conditions. Full article

The shift toward Smart Cities heavily relies on adopting energy-efficiency strategies to meet ambitious decarbonization targets. However, the rebound effect, where improvements in technical efficiency are partly offset by increased energy consumption, often reduces the expected environmental and economic benefits. Traditional Marginal Abatement Cost Curves (MACC) often ignore this behavioral feedback, which can lead to an overestimation of mitigation potential. This paper introduces a data-driven probabilistic framework for assessing the influence of the rebound effect on a portfolio of urban mitigation strategies by integrating behavioral feedback into a bottom-up MACC. By combining Monte Carlo (MC) simulations to address parametric uncertainty with Bayesian Networks (BN) for conditional inference, the robustness of nine strategies is examined across residential, commercial, and transportation sectors. The results demonstrate that even a moderate rebound effect ($\eta =0.5$) causes a $10.09\%$ decrease in total net abatement, dropping from 24.86 to 22.35 tCO₂e, and significantly raises costs. Notably, the number of strictly cost-effective strategies ($MAC<0$) decreases from six to three, highlighting the fragility of certain “win–win” measures. This framework introduces the concepts of Financial Backfire Probability (FBP) and Environmental Backfire Probability (EBP) as new metrics for urban planning. These findings emphasize that rebound tolerance is a critical factor in climate policy, indicating that additional measures, such as Internet of Things (IoT)-based monitoring and demand-side management, may be necessary to prevent performance erosion amid behavioral uncertainty. Full article

Energy management of multi-carrier energy hubs (MCEHs) is a challenging task, particularly when fuel cell electric vehicle (FCEV) stations are included, due to the stochastic nature of FCEV demand, system loads, and integrated renewable energy resources (RERs) such as wind turbines (WTs) and photovoltaic (PV) systems. This paper aims to optimize the energy management of an MCEH-based microgrid to simultaneously minimize total operating costs and emissions. To this end, a novel enhanced quadratic interpolation optimization (EQIO) algorithm is proposed. The proposed EQIO algorithm incorporates two key improvements: a best-to-mean quasi-oppositional-based learning (BMQOBL) strategy and an evaluation mutation (EM) strategy. The performance of EQIO is evaluated using the CEC 2022 benchmark functions, and the obtained results are compared with those of other optimization techniques. Three case studies are investigated: (i) energy management of the MCEH microgrid without RERs, (ii) sustainable operation (with RERs), and (iii) sustainable operation with RERs combined with the application of demand-side response (DSR). Moreover, the proposed framework explicitly supports long-term sustainability goals by enhancing renewable energy utilization, reducing the carbon footprint, and promoting cleaner transportation through efficient integration of FCEV infrastructure. The results demonstrate that integrating RERs reduces operating costs and emissions by 51.47% and 59.69%, respectively, compared to the case without RERs. Furthermore, the combined application of RERs and DSR achieves cost and emission reductions of 55.26% and 53.93%, respectively, compared to the case without RERs. Full article

Background: Surgical management of adult patients with post-dysplastic coxarthrosis using total hip arthroplasty is technically demanding and carries an increased risk of complications. In cases of high iliac dislocation classified as Crowe type IV, restoring the acetabular component to the anatomical hip centre often requires femoral shortening osteotomy to enable safe reduction in the prosthetic joint. Nevertheless, long-term evidence on functional outcomes and prosthesis survival with this approach is limited. Methods: A retrospective cohort study included 19 patients with 22 cases of Crowe type IV post-dysplastic hip osteoarthritis treated with uncemented total hip arthroplasty (Pinnacle/S-ROM, DePuy, Warsaw, IN, USA) combined with transverse subtrochanteric femoral shortening osteotomy. Patients underwent serial clinical follow-up, including assessment of range of motion, measurement of limb-length discrepancy, and functional evaluation using the Harris Hip Score and the WOMAC questionnaire. Radiological assessment included evaluation of osteotomy union, implant positioning, and osteolysis on standardized radiographs. Vertical distances of the centre of rotation (CR), the tip of the greater trochanter (GT), and the tip of the lesser trochanter (LT) from both reference lines were measured bilaterally, and inter-side differences were calculated. The reference lines consisted of the line connecting the inferior margins of the ischial bones and the teardrop (TD) line. Results: All osteotomies united at a mean of 5.57 months, with a mean follow-up of 129 months. Mean limb-length discrepancy decreased from 5.27 cm to 1.5 cm, and mean hip flexion improved from 82.9° to 106°. Functional outcomes improved significantly, with mean WOMAC increasing from 55.4 to 80.1 (p < 0.001) and mean Harris Hip Score from 49.8 to 84.66 at up to 3 years of follow-up (p < 0.001). Osteotomy length correlated strongly with lesser trochanter–teardrop distance (p = 0.00000048). Complications included distal femoral fissure (27.3%) and revision (18%), with no infection or permanent neurological deficit. Conclusions: Total hip arthroplasty combined with subtrochanteric femoral shortening osteotomy for Crowe type IV post-dysplastic hip osteoarthritis appears to be a feasible and effective procedure in an experienced centre, providing reliable osteotomy healing and significant early functional improvement that is sustained over time. Limb-length discrepancy was reduced and satisfactory biomechanical restoration was achieved, with an acceptable complication profile and implant survival of 81.3% at long-term follow-up. The LT–TD parameter was identified as a potential predictor of osteotomy length, enabling the proposal of a preoperative planning equation. However, given the limited sample size and lack of validation, these findings should be interpreted cautiously. Further studies are needed to confirm their broader applicability. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

This study maps competency requirements for two representative frontline marketing roles in China’s new energy vehicle (NEV) sector, NEV sales consultants and livestreaming hosts, and examines their alignment with current vocational education standards. Using a market-oriented, data-driven design, recruitment texts were collected from Zhaopin across more than 20 major Chinese cities. Latent Dirichlet Allocation (LDA) identified competency themes, which were then organized into work-process task domains and visualized as position–task–competency mappings. Mapping these demand-side requirements to national teaching standards reveals relatively strong alignment for sales in market insight and sales strategy, but also gaps in omni-channel lead operations, customer experience management, and operational coordination; livestreaming roles show systematic gaps across the entire work process, particularly in on-air control, customer conversion process design, and data-driven optimization. Building on the identified gaps, the study proposes a position–task–competency-to-curriculum translation pathway to support modular updates in NEV marketing talent development within vocational education and training. Full article

This paper proposes a multi-objective optimal scheduling model for integrated energy systems (IESs) that incorporates a tiered carbon emissions trading mechanism and load-side demand response (LDR) to promote sustainability. First, a reward–penalty-based tiered carbon cost model is embedded within the IES scheduling framework, internalizing carbon constraints and providing differentiated carbon price signals for emission reduction. Second, a refined demand response model is introduced, categorizing electrical and thermal loads to enhance flexibility in system operation. The demand response strategy allows for temporal load shifting and load reduction, optimizing the overall energy management. Third, the augmented epsilon-constraint method (AUGMECON) is employed to minimize both total operating costs and carbon emissions. Scenario-based simulations are conducted to evaluate system performance under different configurations: the integrated carbon trading and LDR model, a carbon-trading-only approach, and a baseline scenario. The results show that the proposed model achieves the best performance, reducing operating costs by 13.6% and carbon emissions by 7.0% compared to the baseline. Additionally, the combined approach improves renewable energy utilization and reduces reliance on high-carbon energy sources, demonstrating the effectiveness of integrating carbon trading and demand response strategies for low-carbon and sustainable energy system management. Full article

Climate change is increasingly affecting groundwater resources in the Carpathian Basin, while rising temperatures are likely to increase irrigation demand and pressure on aquifers. We assessed climate- and pumping-driven impacts on the Nyírség recharge–discharge system (north-eastern Hungary) by combining shallow groundwater monitoring (1970–2022) with hydroclimate indicators from CHIRPS precipitation and ERA5-Land air temperature and snow depth (1981–2024). Using these datasets, we developed and calibrated a MODFLOW groundwater-flow model for representative wet (2010) and dry (2022) conditions, incorporating permitted abstraction and scenario-based estimates of unregistered pumping. We then ran scenario simulations to evaluate mid-century (2050) conditions and managed aquifer recharge (MAR) options. Precipitation exhibits strong interannual variability, but the region shows marked warming and a pronounced decline in snow storage, implying reduced cold-season buffering and higher evaporative demand. Simulations reproduce the observed post-2010 decline in shallow groundwater, with the largest decreases in higher-elevation recharge areas, whereas increased pumping mainly intensifies localized drawdown near major well fields. Scenario results indicate that climate-driven reductions in recharge dominate basin-scale declines by 2050, while MAR provides primarily local benefits; direct subsurface injection performs best among the tested options. These findings support practical groundwater management by prioritizing measurable and enforceable abstraction (including unregistered withdrawals), demand-side irrigation efficiency and adaptive caps in recharge areas, and targeted subsurface MAR where source water and infrastructure are available. Full article

What drives persistent urban–rural tourism satisfaction gaps: whether from promotional over-promising or structural service deficits? This distinction fundamentally determines whether territorial development resources should target marketing sophistication or productive capacity, yet remains empirically unresolved. Text-mining for 33,174 attractions across 349 Chinese cities reveals that both rural and urban destinations systematically under-promise, with description sentiment falling consistently below actual ratings, contradicting the “digital facade” hypothesis. Urban attractions nonetheless generate more positive surprises through superior service delivery (gap = 0.62 vs. 0.55). Sentiment measurement robustness is validated through triangulation of two independent dictionary-based methods ($r=0.58$, $p<0.001$) and cross-paradigm verification using a pre-trained BERT transformer ($\tau =1.000$ ranking stability). SHAP decomposition quantifies the policy implication: controllable service quality indicators, including description quality (23.2%), information richness (30.7%), and price positioning (16.5%), collectively explain over 70% of the variance in satisfaction, while fixed geographic factors (rural classification 14.9% and city-tier 14.7%) account for 29.6%, yielding a controllable-to-geographic ratio of 2.4:1. Propensity score matching with six covariates confirms a 0.074–0.100-point rural penalty persists after controlling for confounders, while non-linear analysis demonstrates that rural attractions face no marginal productivity disadvantage, and the challenge is baseline capacity, not investment efficiency. For policymakers pursuing Sustainable Development Goals 8, 10, and 12 through tourism-led regional strategies, these results mandate redirecting resources from demand-side expectation management toward supply-side infrastructure and workforce development, the true binding constraint on rural competitiveness. Full article

While pricing policy has emerged as a critical demand-side lever for decarbonizing mobility, its bidirectional effects on modal shift remain unexplored. Dynamic pricing in high-speed rail (HSR) creates a double-edged environmental outcome: advance discounts attract passengers from aviation, yet last-minute premiums may reverse these gains. Using 2.4 million price observations from Madrid–Barcelona (2019), we introduce a carbon leakage framework that quantifies this phenomenon within a multi-source validated framework. Our analysis reveals a structural tension: while early-bird pricing attracts 274,431 annual passengers from aviation—saving 23,650 tonnes CO₂/year—last-minute scarcity premiums systematically drive passengers back to air travel. Multi-source calibrated elasticity ($\epsilon =-0.95$, validated through triangulation across CNMC corridor data, meta-analytic evidence, and recent empirical studies within the range $[-1.91,-0.75]$) shows that 22.3% of last-minute tickets exceed the EUR 120 aviation threshold, creating 1511 tonnes CO₂ leakage annually (6.4% offset of gross savings). Critically, this leakage ratio is shown to be structurally independent of elasticity specification, being determined by the price distribution shape rather than demand parameters. Scenario analysis suggests that under static assumptions, price caps at EUR 110–120 would eliminate leakage while preserving an estimated 94% of operator revenue, though general equilibrium effects remain unmodeled. These findings identify illustrative scenario thresholds for carbon-aware revenue management, demonstrating that demand-side decarbonization requires not only attracting passengers to sustainable modes but also preventing their reversal to high-carbon alternatives. Full article

The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the scheduling problem of MG under utility-induced demand side load participation level for residential areas. Our research overcomes the constraints of conventional techniques by utilizing quantum-inspired particle swarm optimization (QPSO) to improve the operational efficiency and resilience of MG’s. In this study, a three-stage stochastic framework is proposed to address the optimal energy scheduling of MGs while taking economic and emission aspects into account. Using real-time meteorological data, five Cases were investigated and simulated using MATLAB/Simulink. Without the involvement of load participation, MG’s producing units in first Case, had carbon emissions of 797.110 kg and an operating cost of 267.10 €. Similar to this, the impact of demand side on the MG was evaluated in the remaining Cases. According to the simulation results, the fifth Case, which has optimal DGs scheduling, is the suggested way to improve MGs efficiency and provide a dependable power supply with low operating costs, emission reduction, and convergence features. This study not only demonstrates the practicality of QPSO algorithms but also paves the way for more resilient, efficient, and sustainable energy systems. Full article