Search Results (598)

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R² = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

This study introduces a fully uncertainty-aware forecasting framework for NBA games that integrates team-level performance metrics, rolling-form indicators, and spatial shot-chart embeddings. The predictive backbone is a recurrent neural network equipped with Monte Carlo dropout, yielding calibrated sequential probabilities. The model is evaluated against strong baselines including logistic regression, XGBoost, convolutional models, a GRU sequence model, and both market-only and non-market-only benchmarks. All experiments rely on strict chronological partitioning (train ≤ 2022, validation 2023, test 2024), ablation tests designed to eliminate any circularity with bookmaker odds, and cross-season robustness checks spanning 2012–2024. Predictive performance is assessed through accuracy, Brier score, log-loss, AUC, and calibration metrics (ECE/MCE), complemented by SHAP-based interpretability to verify that only pre-game information influences predictions. To quantify economic value, calibrated probabilities are fed into a frictionless betting simulator using fractional-Kelly staking, an expected-value threshold, and bootstrap-based uncertainty estimation. Empirically, the uncertainty-aware model delivers systematically better calibration than non-Bayesian baselines and benefits materially from the combination of shot-chart embeddings and recent-form features. Economic value emerges primarily in less-efficient segments of the market: The fused predictor outperforms both market-only and non-market-only variants on moneylines, while spreads and totals show limited exploitable edge, consistent with higher pricing efficiency. Sensitivity studies across Kelly multipliers, EV thresholds, odds caps, and sequence lengths confirm that the findings are robust to modelling and decision-layer perturbations. The paper contributes a reproducible, decision-focused framework linking uncertainty-aware prediction to economic outcomes, clarifying when predictive lift can be monetized in NBA markets, and outlining methodological pathways for improving robustness, calibration, and execution realism in sports forecasting. Full article

For many decades, time-series forecasting has been applied to different problems by scientists and industries. Many models have been introduced for the purpose of forecasting. These advancements have significantly improved the accuracy and reliability of predictions, especially in complex scenarios where traditional methods struggled. As data availability continues to expand, the integration of machine learning techniques is likely to further enhance forecasting capabilities across various fields. Today, hybrid techniques are gaining popularity, as they combine the advantages of different approaches to deliver improved predictive performance and more advanced visualization analytics for decision support. These hybrid approaches can provide better prediction, and at the same time, they can develop a more sophisticated set of visualization analytics for decision support. Recently, the integration of cross-entropy, fuzzy logic, and attention mechanisms in hybrid forecasting models has enhanced their ability to capture complex and uncertain patterns in financial and energy markets. In this study, we propose a hybrid ANN–LSTM deep learning model optimized with cross-entropy, fuzzy logic, and an attention mechanism to enhance the forecasting of financial and energy time series, specifically Ethereum and natural gas prices. Our models combine the feature extraction strength of ANN with the temporal learning of LSTM, while cross-entropy improves convergence, fuzzy logic handles uncertainty, and attention refines feature weighting. Since inaccurate forecasts can lead to greater estimation uncertainty and increased financial and operational risk, improving predictive reliability is essential for effective risk mitigation. These techniques prove effective not only in improving estimation accuracy but also in minimizing financial risks and supporting more informed investment decisions. Full article

High renewable energy penetration in Integrated Energy Systems (IES) introduces significant challenges related to bilateral source-load uncertainty and low-carbon economic dispatch. To address these issues, this paper proposes a novel scheduling framework that synergizes data-driven scenario generation with multi-objective distributionally robust optimization (DRO). Specifically, a deep temporal feature extraction model based on Long Short-Term Memory Autoencoder (LSTM-AE) is integrated with K-Means clustering to generate four typical operation scenarios, effectively capturing complex source-load fluctuations. To further enhance system efficiency and environmental sustainability, a refined Power-to-Gas (P2G) model considering waste heat recovery is developed to realize energy cascading, coupled with a joint market mechanism that integrates Green Certificate Trading (GCT) and tiered carbon pricing. Building on this, a multi-objective DRO model based on Conditional Value at Risk (CVaR) is formulated to optimize the trade-off between operating costs and carbon emissions. Case studies based on California test data demonstrate that the proposed method reduces total operating costs by 9.0% and carbon emissions by 139.9 tons compared to traditional robust optimization (RO). Moreover, the results confirm that the system maintains operational safety even under extreme source-load fluctuation scenarios. Full article

Amidst Portugal’s ongoing housing crisis, particularly pronounced in the Lisbon Metropolitan Area, thousands of residential units remain vacant. This article investigates why property owners often refrain from placing these homes on the rental market, despite high demand and rising prices. Drawing on empirical data from successive editions of the ALP (Lisbon Landlords Association) Barometer and framed by the literature on housing financialization, institutional trust, and patrimonial ownership cultures, the study shows that vacancy is not merely a result of speculation or neglect. Rather, it emerges as a rational response to a complex interplay of regulatory instability, legal mistrust, and deeply rooted socio-cultural norms. Landlords act not only as economic agents but also as custodians of family heritage, navigating uncertainty in a legal and symbolic environment increasingly perceived as hostile. The article argues that mobilizing Lisbon’s empty housing stock requires more than tax incentives or coercive measures. It demands rebuilding trust, ensuring legal predictability, and acknowledging the cultural meanings that shape property decisions. Policy recommendations include stabilizing rental legislation and designing culturally sensitive engagement strategies for small landlords. Full article

Raw material price volatility, freight rates, and foreign exchange all pose significant uncertainty for lithium-ion battery manufacturers, jeopardising procurement planning and financial stability. In this paper, we formulate a discrete Conditional Value-at-Risk (CVaR) optimisation model to design implementable robust hedging strategies for multi-factor cost exposure. Unlike conventional continuous hedge models, which are often severely parameter-sensitive and require frequent rebalancing, the discrete approach takes hedge ratios to be fixed at a finite implementable grid (0%, 50%, 100%) and simultaneously minimises the expected cost and tail risk. We conduct two case studies: the first evaluates the model behaviour under stochastic price shocks using a multi-market simulation data set, and the second subjects the model to stress testing on correlation drift and tail amplification in order to examine systemic robustness. Our results show that, compared with an OLS-based hedge or a fully hedged benchmark, the discrete CVaR framework yields smoother hedge patterns, lower tail losses, and improved liquidity stability; in addition, our results indicate that, when combined with tail-risk penalisation, decision discretisation can endogenously confer robustness to the industrial procurement horizon. This work contributes to the stochastic optimisation literature and provides a practical tool for mitigating volatility in the global lithium supply chain. Full article

A virtual power plant (VPP) faces multiple uncertainties and temporal coupled decisions when participating as an independent entity in electricity and green markets. A multi-level electricity–green coupled market framework is constructed for a VPP participating as an independent market entity. To address uncertainties in renewable energy outputs and market prices, a risk management method based on conditional value at risk entropy weight method information gap decision theory (CVaR-EIGDT) is proposed. To address the temporal coupled challenges in VPP participation across multi-level electricity–green coupled markets, a multi-stage rolling decision-making method coordinating annual, monthly, and daily scales is proposed, achieving deep coupling in the decision-making sequence of multi-level electricity–green coupled markets. Results show that the proposed model enables adaptive decision-making under varying risk preferences, with decisions exhibiting strong practical adaptability while balancing real-time adjustments and long-term planning. The multi-level electricity–green coupled market framework enhances VPP profitability and resilience, while the CVaR-EIGDT method effectively improves decision-making efficiency across multi-level electricity–green coupled markets. Full article

This paper presents a two-stage stochastic programming model for the joint optimization of fleet deployment and sailing speed in liner shipping under fuel price volatility and carbon tax uncertainty. The integrated framework addresses strategic fleet planning by determining optimal fleet composition in the first stage, while the second stage optimizes operational decisions, including vessel assignment to routes and sailing speeds on individual voyage legs, after observing stochastic parameter realizations. The model incorporates nonlinear fuel consumption functions that are approximated using piecewise linearization techniques, with the resulting formulation being solved using the Sample Average Approximation (SAA) method. To enhance computational tractability, we employ big-M methods to linearize mixed-integer terms and introduce auxiliary variables to handle nonlinear relationships in both the objective function and constraints. The proposed model provides shipping companies with a comprehensive decision-support tool that effectively captures the complex interdependencies between long-term strategic fleet planning and short-term operational speed optimization. Numerical experiments demonstrate the model’s effectiveness in generating optimal solutions that balance economic objectives with environmental considerations under uncertain market conditions, highlighting its practical value for resilient shipping operations in volatile fuel and carbon pricing environments. Full article

This study investigates a large language model driven (LLM) framework for intelligent preprocessing and short-term electricity price forecasting in the Australian National Electricity Market (NEM). By integrating unstructured news features, weather signals, and cyclical calendar variables, the model captures both physical and informational drivers of price volatility. A hybrid approach combining quantile regression with conformal calibration achieves statistically significant improvements in accuracy and uncertainty calibration. The framework demonstrates the potential of integrating LLMs into operational forecasting pipelines to support electricity market decision-making and risk management. Full article

Financial markets evolve through recurring phases of stability, turbulence, and structural transformation. Standard Hidden Markov Models (HMMs) assume fixed transition probabilities, which limits their ability to capture such higher-order changes in market behavior. This study introduces an Adaptive Hierarchical Hidden Markov Model (AH-HMM), where regime transitions depend on an unobserved meta-regime that reflects the broader macro-financial environment. Each meta-regime defines its own transition matrix across market states such as bull, bear, and turbulent phases. In this way, the model adapts dynamically to structural changes arising from crises, policy shifts, or variations in investor sentiment. Using weekly data for major equity indices, aggregated from daily prices, together with macro-uncertainty indicators, we show that the AH-HMM identifies key turning points including the Global Financial Crisis, the COVID-19 shock, and the post-2022 tightening cycle. In our empirical application, where we approximate the latent structural layer by low- and high-uncertainty environments defined from the VIX, the adaptive model attains a higher in-sample likelihood and delivers competitive out-of-sample forecasts and Value-at-Risk coverage relative to conventional HMMs and time-varying transition alternatives. Overall, the results highlight a mechanism of structural learning within market regimes and offer tools for risk management and policy analysis under uncertainty. Full article

This study develops a weather-driven bio-economic optimization framework for agricultural planning in Guyana by integrating weather simulation, crop modeling, and multi-objective optimization. Precipitation was modeled using a first-order Markov chain with fitted distribution, while temperature and relative humidity were simulated using stochastic differential equations. Reference evapotranspiration was estimated using an artificial neural network. These simulated weather variables were then used as inputs to AquaCrop to estimate rice, maize, and soybean yields across multiple planting intervals. A multi-objective optimization model was then applied to optimize gross profit, economic water productivity, and land use efficiency. Validation at the Rose Hall Estate showed strong accuracy for rice and maize (MAPE < 10%) and moderate accuracy for soybeans. Scenario analyses for the 2024–2025 season, assuming 25% and 50% export targets, revealed that rice–maize double cropping produced the highest profitability, while soybean–maize combinations were less favorable. The framework replaces static yield assumptions with dynamic, simulation-driven models that incorporate price forecasts and allow substitution of alternative forecasting or crop simulators to enhance precision. The scenario-based design provides a flexible decision-support platform for optimizing crop selection, planting intervals, and resource allocation under climate variability and market uncertainty. Moreover, the framework is scalable and well-suited for evidence-based agricultural planning. Full article

Market power remains a persistent challenge in liberalized electricity spot markets, where generators can manipulate bids to distort prices and extract rents. Traditional monitoring approaches—such as structural indices or simulation-based models—offer partial insights but fail to capture the nonlinear, spatially correlated propagation of strategic behavior across transmission-constrained networks. This paper develops a diffusion neural learning framework for market power risk assessment that integrates welfare optimization, nodal pricing dynamics, and graph-based deep learning. Specifically, a Graph Diffusion Network (GDN) is trained on simulated spot market scenarios to learn how localized strategic deviations spread through the network, distort locational marginal prices, and alter system welfare. The modeling framework combines a system-wide welfare maximization objective with multi-constraint market clearing, while the GDN embeds network topology into predictive learning. Results from a case study on an IEEE 118-bus system demonstrate that the proposed method achieves an R² of 0.91 in predicting market power indices, outperforming multilayer perceptrons, recurrent neural networks, and Transformer baselines. Welfare analysis reveals that distributionally robust optimization safeguards up to 3.3 million USD in adverse scenarios compared with baseline stochastic approaches. Further, congestion mapping highlights that strategic bidding concentrates distortions at specific nodes, amplifying rents by up to 40 percent. The proposed approach thus offers both predictive accuracy and interpretability, enabling regulators to detect emerging risks and design targeted mitigation strategies. Overall, this work establishes diffusion-based learning as a novel and effective paradigm for electricity market power assessment under high uncertainty and renewable penetration. Full article

With increasing renewable energy integration, electricity spot markets exhibit high volatility and uncertainty, making it difficult to balance profit and risk. To address this challenge, this paper proposes Joint (Version 1.0), a multi-agent closed-loop framework that integrates forecasting, strategy, and feedback for coordinated decision-making. The Prediction Agent learns statistical patterns of price spreads to generate distributional forecasts, directional probabilities, and extreme-value indicators; the Strategy Agent adaptively maps these signals into executable bidding ratios through a hybrid mechanism; and the Feedback Agent incorporates settlement results for performance evaluation, CVaR-based risk control, and preference-driven optimization. These agents form a dynamic “forecast–strategy–feedback” loop enabling self-improving trading. Experimental results show that Joint achieves a monthly profit of 146,933.46 CNY with strong classification performance (Precision = 53.25%, Recall = 40.45%, AA = 56.05%, SWA = 57.36%), and the complete model in ablation experiments reaches 157,746.64 CNY, demonstrating the indispensable contributions of each component and confirming its robustness and practical value in volatile electricity spot markets. Full article