Search Results (365)

Cyber incidents can disrupt many virtual power plant (VPP) assets through shared software and communication services. This study links preventive finance, cyber defense, dispatch, and restoration in one multi-timescale model. An attacker, a VPP operator, a bond vehicle, and a regulator interact in a partially observable stochastic game. The operator controls hardening, dispatch, isolation, and recovery. The bond provides restricted pre-event capital and releases collateral through an auditable index of service loss, control availability, network stress, and recovery delay. A risk-constrained multi-agent policy enforces power-system feasibility, investor impairment, sponsor affordability, and trigger–loss limits. Tests use transparent synthetic VPP-39 and VPP-118 portfolios and 20 out-of-sample seeds. The proposed design lowers normalized social cost to 0.691 and 0.704 and weighted basis risk to 0.065 and 0.071. It also improves critical-load continuity and restoration relative to self-insurance and three bond baselines. The VPP-118 case recovers in 11.8 h, compared with 14.3 h for the closest rule-based benchmark. Ablations separate the effects of finance and control. Removing the coupon–control link reduces verified hardening from 0.672 to 0.519. Removing the safety projection raises unsafe proposals from 0.4% to 5.9%. These results show that stochastic multi-timescale control can support adaptable and resilient VPP operation while keeping the financial mechanism within explicit risk limits. Full article

In the face of the challenges of the dual carbon strategy, emerging digital technologies are reshaping the landscape of the supply chain and are also transforming the long-term stability and adaptability of green innovation in supply chain firms. Drawing on stakeholder theory and limited-attention theory, this study established a theoretical framework of opportunism behavior and environmentalism behavior on digitization and examined the nonlinear relationship between supply chain digitization and firms’ green technology innovation. Additionally, this study used the machine learning method to construct a supply chain data based on 12,466 firm-year observations from manufacturing firms on supply chain networks. The findings suggest that digitization of upstream and downstream firms has a U-shaped effect on green technology innovation of focal firms from the dual perspectives of opportunism behavior and environmentalism behavior. Importantly, opportunism behavior has a negative mediating effect on the relationship between digitization of upstream and downstream firms and green technology innovation of focal firms. Environmentalism behavior has a positive mediating effect on the relationship between digitization of upstream and downstream firms and green technology innovation of focal firms. Also, we find that media attention and institutional investors positively regulate the nonlinear relationship between digitization of upstream and downstream firms and green technology innovation of focal firms. These findings provide theoretical insights and practical references for the study of digital spillover effects from a supply chain perspective and promoting the development of green supply chains. Full article

A key question for investors is how to diversify their clean energy stock holdings amid adverse conditions. Gold has a long history of being an effective diversifier for financial assets. Energy tokens exhibit a low correlation with clean energy stocks and may also serve as a good diversifier. This paper examines whether energy tokens or gold is the most effective diversifier for a clean energy stock portfolio. The analysis uses R² and TVP-VAR measures of return connectedness on a dataset that includes ETFs for wind, solar, nuclear, grid connectivity, electric vehicles, gold, and two energy tokens (POWR and SNC). Network connectedness was highest at the start of the COVID-19 pandemic and during the escalation of the Russia–Ukraine war. R² and TVP-VAR total network connectedness correlate highly (0.93). Grid connectivity is a dominant net transmitter of shocks. Gold is a dominant net receiver of shocks. POWR and SNC have low net connectedness with the other assets. Portfolio analysis reveals that, on a risk-adjusted basis, gold is a more effective diversifier than energy tokens. These results are robust across several portfolio choices (minimum variance, minimum correlation, and minimum connectedness) and representative transaction costs. For three of the four portfolios studied, a portfolio of clean energy stocks and energy tokens has lower risk-adjusted returns than one that invests only in clean energy. Minimum variance portfolios have the highest Sharpe ratios. Full article

The widespread adoption of EVs is expected to intensify peak demand, stress distribution networks, and increase carbon emissions if traditional grid-based charging models continue. Therefore, this study proposes a smart-grid-connected microgrid (MG) architecture for EV charging stations (EVCSs) that integrates renewables and energy storage, while incorporating incentive-based response (iDR) and net energy metering schemes into a single optimization framework. To maintain the quality of EV charging services, optimize grid interactions and MG reliability, and explore the socioeconomic impact of establishing such infrastructures, a comprehensive 4E optimization framework incorporating energy, environmental, employment, and economic metrics is developed. The suggested framework is applied to an urban case study in Riyadh, including realistic load profiles, tariff structures, EV charging behavior, network outage scenarios, NEM conditions, and an assumed iDR incentive scenario. A grid-dependent BES/Conv/Grid configuration optimized without iDR is adopted as the common reference for consistently evaluating all system configurations. Compared with this common reference, the results show that while renewable hybridization improves performance, the assumed iDR scenario provides further economic and operational benefits. The optimal iDR-enabled configuration achieves an 87.5% reduction in TNPC and a standard HOMER Grid LCOE of $0.0287/kWh, corresponding to a 67% reduction relative to the common grid-dependent reference. When electricity exports are excluded from the LCOE normalization, the corresponding load-serving LCOE is approximately $0.0372/kWh, which remains approximately 57.3% lower than the reference value of $0.0872/kWh. In addition, the optimal MG generates $63,128 in revenue/year through participation in iDR events without degrading EV charging service performance relative to the non-iDR scenario. Employment analysis indicates that the optimal system supports approximately 49 job-years of direct project-associated employment over the 25-year project lifetime through infrastructure deployment, operation, and maintenance, while ecologically, it limits annual grid-related CO₂ emissions to approximately 280.18 tons, representing about an 84.7% reduction relative to the reference scenario. These findings confirm that coordinated demand-side flexibility and intelligent storage dispatch can partially substitute for infrastructure oversizing, enabling cost-effective, low-carbon, and investor-attractive EVCS-based MGs aligned with sustainability targets. Full article

Breakthroughs in strategic core technologies depend on the coordinated functioning of policy institutions, capital markets, knowledge networks, and firm decision-making. This study examines whether government certification can mobilize these interdependent elements within an innovation ecosystem to promote key core technological innovation (KCTI). Using the staggered implementation of the Little Giant certification program and panel data on Chinese A-share listed firms from 2014 to 2023, we employ a staggered difference-in-differences design to estimate the effect of certification on KCTI. The results show that government certification significantly enhances firms’ KCTI. Further analyses show that certification reduces managerial myopia, promotes knowledge diversification, and improves firms’ access to patient capital, providing evidence consistent with the proposed mechanisms. The effect of certification varies across institutional environments and firm characteristics. Investor attention further strengthens its innovation effect by facilitating the reception and interpretation of certification signals. These findings highlight government certification as an institutional mechanism that connects government guidance, market responses, and firm-level innovation within the broader innovation ecosystem. Full article

The large-scale integration of variable renewable energy sources (RES) such as solar and wind into transmission networks poses significant challenges to grid stability, operational efficiency, and economic dispatch. Battery Energy Storage Systems (BESS) offer a flexible solution, but their optimal placement remains critical to maximizing technical and economic benefits. This paper presents a multi-index, scenario-based framework for optimal BESS siting in a modified IEEE 118-bus transmission system under high renewable penetration. Six complementary indices are employed: Voltage Deviation Index (VDI), Fast Voltage Stability Index (FVSI), Line Congestion Index (LCI), Bus Congestion Index (BCI), Z-bus Sensitivity Index (ZBSI), and nodal price difference (Δλ). Eight extreme scenarios, combining high/low solar, wind, and hydro generation under peak load, are used to identify vulnerable buses. Five weighting case studies reflect different stakeholder priorities: voltage stability, congestion relief, energy arbitrage, loss reduction, and equal weightage. Results show that the congestion relief case achieves the lowest daily operating cost (approx. $8000 less than the base case) and the highest net economic benefit, while the loss reduction case delivers the greatest reduction in active (34 MW) and reactive (169 MVAr) power losses, compared to the base case. The proposed framework demonstrates that integrating technical and market-based indicators enables more robust and economically attractive BESS placement. This work provides a practical, data-driven planning tool for grid operators and investors aiming to enhance transmission system sustainability under high-RES variability. Full article

The massive integration of variable renewable energy sources (vRES) in Central Europe is fundamentally transforming electricity price formation and straining transmission grids. However, existing academic metrics, such as the RES Capture Price, offer only a static view of investor revenues and fail to capture dynamic market rigidity and systemic risks during periods of high instantaneous vRES penetration. This study addresses this literature gap by introducing two novel and transparent methodological parameters: Market Exposure to Green Overproduction (MEGO) and the Price Divergence Coefficient (PDC). Formulated as conditional non-parametric indicators, the MEGO index quantifies the conditional probability of price collapse and the loss of market elasticity during hours when vRES penetration exceeds critical thresholds (α = 0.50 to 0.80) of systemic load. Conversely, the PDC index measures the frequency of substantial price non-convergence across neighbouring bidding zones (CZ, PL, FR) relative to the German reference market (DE). Based on an extensive dataset spanning from 2015 to mid-2026—capturing the transition to 15 min market time units— the empirical results reveal a distinct change in market behaviour. While the frequency of price collapse during high-vRES periods was lower in earlier years and temporarily reduced during the 2022 energy crisis, the post-crisis period (2024–2026) exhibits substantially higher MEGO values, with periods in which wind and solar generation exceeded 80% of instantaneous system load being associated with prices at or below 0 EUR/MWh in up to 60% of the evaluated intervals. Concurrently, the PDC analysis reveals persistent spatial price non-convergence, particularly in France and Poland. These patterns coincided with major changes in European electricity-market conditions, including the implementation of Core Flow-Based Market Coupling, variations in nuclear availability and evolving cross-border network conditions; however, the PDC indicator alone does not permit causal attribution to any individual factor. The proposed MEGO and PDC parameters provide policymakers, transmission system operators (TSOs), and investors with an intuitive diagnostic framework for dimensioning grid flexibility, energy storage, and cross-border infrastructure in the decarbonization era. Full article

Property condition assessment is a critical step in residential real estate investment underwriting, motivating after-repair value (ARV) estimates and rehabilitation cost projections. Traditional approaches rely on in-person inspections or manual photo review by experienced investors—processes that are time-consuming, subjective, and do not scale. Prior computer vision work on building analysis has focused on structural defect detection using convolutional neural networks but has not addressed the holistic, room-level condition assessment needed for residential investment decision-making. This paper presents a per-room analysis framework that leverages multimodal large language models (MLLMs) to assess the condition of residential properties from photographs. The framework analyzes each photo independently at the room level—detecting the room type, condition category, condition score, material features, and visible issues. Condition output is intended to feed a separate downstream rehabilitation cost and ARV estimation model that is outside the scope of this paper; the present empirical evaluation is restricted to per-photo condition assessment and inter-rater agreement with human experts. I evaluate the framework on two complementary datasets: (i) a primary per-image condition evaluation on 57 photographs from 14 real off-market properties in the Memphis, TN MSA, spanning three condition tiers (Fixer, Outdated, Standard), with independent labels from two experienced real estate investors; (ii) a secondary room classification evaluation on the public REI Dataset (51 attempted, 39 successful, 12 HTTP-503 failures). The room classification accuracy was 76.5% intention-to-analyze on REI (100% per-protocol on the 39 successful calls; 23.5% API failure rate) and 82.5% on the concierge dataset. The inter-rater agreement on the concierge dataset, with 95% bootstrap CIs (5000 resamples) and Spearman’s $\rho$ as primary score statistic, was as follows: Cohen’s $\kappa =0.773$ ($95\%$ CI $[0.64,0.90]$) between Labeler A and the MLLM (weighted $\kappa =0.853$ $[0.76,0.94]$; $\rho =0.906$); and $\kappa =0.502$ $[0.35,0.66]$ between Labeler B and the MLLM ($\rho =0.858$); both bracket the human–human reliability of $\kappa =0.590$ $[0.42,0.74]$ ($\rho =0.807$). The MLLM’s $\kappa$ asymmetry across the two labelers is statistically significant ($\Delta \kappa =0.271$, $95\%$ bootstrap CI $[0.115,0.429]$, $p=0.0004$), which I attribute to plausible training distribution and labeling style differences. A blind re-labeling sensitivity analysis on a stratified 15-image subsample yields anchoring-corrected $\kappa$ estimates of approximately $0.65$ (Labeler A) and $0.35$ (Labeler B); the headline anchored values therefore sit at the upper bound of plausible blind-equivalent agreement. Failure modes concentrate at the Outdated tier and at the Outdated–Standard boundary, where humans themselves disagree most, indicating intrinsic taxonomy ambiguity rather than a model artifact. I make no claim to multi-market generalization and present multi-market extension as ongoing work. Full article

Financial markets are increasingly shaped by heterogeneous information sources, including trading behaviors, order-book dynamics, textual events, investor sentiment, and macroeconomic conditions. These signals are often asynchronous, noisy, partially missing, and associated with different degrees of reliability, which makes trustworthy financial risk early warning challenging. To address these issues, this study proposes an uncertainty-aware multimodal financial sensing network, termed UAMF-Net. The model treats price series, trading volume, order books, news texts, investor sentiment, and macroeconomic variables as financial sensing signals and integrates them through three task-oriented modules. First, the asynchronous multimodal temporal alignment module uses absolute time encoding, relative interval modeling, event-lag representation, temporal gating, and target-time-guided cross-scale attention to align cross-frequency financial signals according to their relevance to the prediction time. Second, the risk-aware multimodal soft fusion module estimates modality-level risk contribution and signal reliability by combining fuzzy risk membership, modality confidence weights, and cross-modal consistency constraints. Third, the uncertainty-aware risk early warning module adopts evidential learning to generate nonnegative class evidence, derive risk-category probabilities from Dirichlet parameters, estimate predictive uncertainty from total evidence strength, and jointly predict continuous risk intensity. Experimental results show that UAMF-Net achieves the best overall performance, with Accuracy, Precision, Recall, F1-score, Macro-F1, ROC-AUC, and PR-AUC reaching $0.882$, $0.864$, $0.849$, $0.856$, $0.839$, $0.941$, and $0.824$, respectively, while ECE and Brier score are reduced to $0.037$ and $0.096$. Under severe temporal asynchrony, UAMF-Net maintains an Accuracy of $0.849$, a Macro-F1 of $0.797$, and a PR-AUC of $0.774$. Under the missing multiple modalities setting, it achieves an Accuracy of $0.842$ and a Macro-F1 of $0.788$. The uncertainty analysis further shows that Risk Precision@90% reaches $0.889$. Validation on FNSPID, Daily News, and StockEmotions also confirms its generalization ability across public financial benchmarks. These results indicate that UAMF-Net improves financial risk early warning by jointly modeling temporal asynchrony, modality reliability, and predictive uncertainty. Full article

Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape. Full article

Visibility graphs map time series to networks whose combinatorial structure encodes fractality, recovering the Hurst exponent of self-affine processes. We ask what the visibility construction carries beyond this fractal content. We lift the visibility graph to a 1-varifold, a measure on position and direction space from geometric measure theory, and equip it with a multiscale positive definite kernel. The lift embeds visibility graphs of unequal size in a common Hilbert space and yields a channel-resolved measure of cross-series geometric alignment. On a 25.8-year daily panel of thirteen commodity and currency layers, we define a relative alignment contrast that compares commodity currencies and safe haven currencies in their geometric alignment with the commodity complex. During global risk-off episodes the contrast is large and positive: commodity currencies import commodity shock geometry far beyond a time-shift independence benchmark, while the Japanese yen remains near geometric independence and the franc is confounded by a managed regime. The contrast is significant under three stress definitions with autocorrelation robust inference, holds as a continuous dose response, survives the removal of any single crisis, withstands moment, fractal, and topological controls, is direction-consistent across sixteen specifications, and collapses under a time-shift placebo. Detrended fluctuation analysis explains only two percent of it, so the reconfiguration is geometric information beyond fractality at this horizon, and a scaling exponent of the kernel mass separates a fractal-free component from a fractal-driven one. For investors, financial institutions, and policymakers, the contrast is a real-time structural diagnostic of flight to safety: it marks when commodity currencies stop diversifying the commodity complex while genuine safe havens still do, signaling through a channel that second-moment risk measures are built to miss. Full article

A smart grid is an advanced electricity network that uses digital technology to manage the electrical energy in real time to improve global efficiency and to facilitate the integration of renewable energy sources into it. Therefore, energy management is a real challenge in smart grids with distributed generation units, as they have an intermittent production nature, which affects the stability of the grid and economic benefits for investors. In this paper, this challenge is analyzed for the integration of a wind farm, an electrolyzer, and a fuel cell into an electrical network. Some scenarios are studied based on electrical energy availability and/or economic assumptions in the smart grid. For this purpose, the macroscopic energy representation (EMR) formalism tool is used for modeling the elements of the studied system. EMR is useful for simplifying complex multi-domain systems, helping with design control strategies and providing a clear physical interpretation of system behavior. The different scenarios were evaluated and implemented in the MATLAB-Simulink environment, and the simulation results are presented. Finally, a conclusion is developed based on the different aims and obtained results. Full article

As fixed charging stations (FCSs) approach saturation, mobile charging stations (MCSs) have emerged as a flexible complement. This study proposes a bi-level optimization framework that integrates dynamic siting of MCSs with coordinated pricing for both MCSs and FCSs. The upper level maximizes the operator’s total net profit by jointly deciding MCS deployment and spatio-temporal prices, while the lower-level models EV users’ charging choices via cost minimization. The bi-level problem is reformulated as a single-level mathematical program with equilibrium constraints (MPECs) by replacing the lower-level with Karush–Kuhn–Tucker (KKT) optimality and complementarity conditions. The nonconvexities are addressed using the Big-M method, auxiliary variables, and piecewise linearization. This reformulation converts the problem into a mixed-integer linear program (MILP). The case studies show that the proposed coordinated strategy substantially improves the operator’s total net profit compared with the fixed sitting benchmark. This improvement is mainly achieved by allowing the MCSs to respond to spatiotemporal demand variations. Compared with the MCS-only optimization benchmark, the increase in total net profit is marginal under the tested scenario. This result suggests that coordinated MCS–FCS pricing mainly improves the investor’s portfolio-level outcome by reducing internal competition between MCSs and FCSs, rather than by increasing the standalone profit of the MCSs. The proposed framework provides an optimization approach for coordinated MCSs and FCSs operation in saturated charging networks. Full article

Forecasting stock market returns in emerging economies remains challenging due to market volatility, structural irregularities, and limited data availability. This study investigates whether discrete wavelet transformation can enhance the predictive performance of deep learning models when applied to financial time series from emerging markets. Using daily returns of the Ghana Stock Exchange Composite Index (GSE-CI) spanning 2011 to 2022, we evaluate three widely used deep learning architectures—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN)—in both their standard form and with preprocessing based on the Daubechies-4 (db4) discrete wavelet transform. The empirical results indicate that wavelet preprocessing consistently reduced forecasting errors across all three deep learning architectures, highlighting its effectiveness as a multiscale feature extraction and noise reduction technique for financial time series. Among the models considered, the Wavelet-LSTM achieved the lowest forecasting error, while the wavelet-enhanced variants consistently outperformed their corresponding baseline models. These findings suggest that the benefits of wavelet decomposition extend beyond a specific neural network architecture by providing a richer representation of nonlinear temporal dynamics in volatile and data-constrained financial environments. As one of the first studies to systematically evaluate wavelet-augmented deep learning models for stock market forecasting in an African equity market, this work contributes to the growing literature on hybrid forecasting frameworks and provides practical insights for researchers, analysts, and investors interested in forecasting emerging financial markets. Full article

Geopolitical instability is increasingly affecting the development and deployment of artificial intelligence (AI) infrastructure by disrupting energy systems, semiconductor supply chains, and digital infrastructure networks. While existing research has explored geopolitical risk, digital sovereignty, and AI governance, little attention has been paid to understanding how geopolitical factors are integrated into the valuation and strategic allocation of AI infrastructure. This theory-building study introduces the concept of geopolitical repricing, defined as the process through which firms, investors, governments, and infrastructure operators revise their assessment of the economic value, risk profile, and strategic importance of AI infrastructure in response to geopolitical instability. Drawing on literature on geopolitical risk, AI infrastructure, digital sovereignty, geo-economics, and investment under uncertainty, the paper develops a four-stage analytical framework that links geopolitical shocks, transmission channels, revaluation, and strategic reallocation. The framework identifies three interconnected transmission channels: energy volatility, supply-chain disruption, and infrastructure vulnerability, and explains how their cumulative effects may influence valuation judgments, investment criteria, and infrastructure allocation decisions. The study further proposes a set of theoretically derived propositions and operational indicators to guide future empirical research. The paper contributes to the emerging political economy of AI by providing a conceptual explanation of how geopolitical instability may shape infrastructure valuation beyond the immediate effects of disruption. It lays the groundwork for future research on the connections between geopolitical factors, infrastructure strategies, and AI development. Full article