Search Results (1,400)

NFT prices are shaped by heterogeneous signals including visual appearance, textual narratives, transaction trajectories, and on-chain interactions, yet existing studies often model these factors in isolation and rarely unify multimodal alignment, temporal non-stationarity, and heterogeneous relational dependencies in a leakage-safe forecasting setting. We propose MM-Temporal-Graph, a cross-modal temporal graph transformer framework for explainable NFT valuation and information-centric risk forecasting. The model encodes image, text, transaction time series, and blockchain behavioral features, constructs a heterogeneous NFT interaction graph (co-transaction, shared creator, wallet relation, and price co-movement), and jointly performs relation-aware graph attention and global temporal–structural transformer reasoning with an adaptive fusion gate. A contrastive multimodal alignment objective improves robustness under market drift, while a risk-aware regularizer and a multi-source risk index enable early warning and interpretable attribution across modalities, time segments, and relational neighborhoods. On MultiNFT-T, MM-Temporal-Graph improves MAE from 0.162 to 0.153 and R² from 0.823 to 0.841 over the strongest multimodal graph baseline, and achieves 87.4% early risk detection accuracy. These results support accurate, robust, and explainable NFT valuation and proactive risk monitoring in Web3 markets. Full article

Sustainable finance and carbon risk have attracted substantial interest from both practitioners and scholars. This paper integrates the income-based environmental responsibility framework with financial asset pricing models to investigate how carbon transition risk propagates along value chains and impacts asset returns. By utilizing the Ghosh supply-driven input–output model to quantify downstream value chain carbon emissions as a proxy for the dependence of a company’s revenue streams on high-carbon downstream clients, we construct a novel downstream carbon risk factor (DMC) by sorting stocks into portfolios based on this exposure and forming a factor mimicking long short portfolio. We then integrate this DMC factor into the Fama–French five-factor framework to propose a six-factor model capable of capturing value chain risk transmission. Empirical results of Chinese A-share listed companies demonstrate that firms with high DMC exposure, being vulnerable to carbon transition shocks such as carbon pricing, offer a significant risk premium even after controlling for traditional financial characteristics. This finding provides robust evidence for the carbon premium hypothesis in the world’s largest emerging market and contributes a theoretically grounded and empirically implementable framework for integrating value chain carbon risk into asset pricing analysis. Full article

Domestic and foreign capital and consumption goods are imperfect substitutes in production and demand functions of the growth model by Bardhan–Lewis. We extend the model by introducing exogenous technical progress and allow for foreign debt dynamics without dropping domestic capital goods. Trade and growth are mutually affecting each other. Trade may speed up or decrease growth in theory with and without technical progress in comparison with the Solow–Swan model. Steady-state growth rates include that of world income, and the income and price elasticities of export demand. The dynamic process of the economy is analyzed in terms of exports and foreign debt, and both as a share of a stock of imported capital goods. There are multiple steady states where imported capital goods are paid for by high exports and debt, low debt and low exports, or even negative debt and low exports. A stable VAR with data for Brazil shows that the high-debt steady state is relevant for this country. Steady states with high and low debt are saddle-point stable and the steady-state medium debt is stable. Neoclassical standard results appear as two special cases. We link the model to several strands of literature. Full article

Crypto asset markets are often described as globally integrated. However, empirical evidence suggests that they remain segmented across exchanges and jurisdictions. One notable example is the Korean premium (i.e., Kimchi premium), which refers to persistent price gaps between Korean exchanges and offshore venues. The Korean market accounts for a substantial share of global crypto trading activity. Therefore, this segmentation can affect price discovery and create opportunities for systematic trading. Motivated by the Korean premium, this study introduces the Korean Venue Share Indicator (KVSI). Based on the price discovery literature, KVSI is an interpretable venue-level indicator that uses the relative trading volume share between Korean and global exchanges. This study integrates KVSI into the state space of multiple reinforcement learning algorithms to evaluate whether venue-level information improves trading decisions. The results show that the proposed model with KVSI achieves statistically significant improvements in cumulative return (CR), Sharpe ratio (SR), and maximum drawdown (MDD) compared to the baseline model without KVSI. It also achieves higher CR and mixed effects on risk metrics (SR, MDD) relative to benchmark strategies. Additional analyses indicate that the performance gains from KVSI are market-regime-dependent. Overall, the findings have practical implications for developing cross-market systematic trading strategies by leveraging a venue-level indicator as a proxy for market segmentation. Full article

Driven by energy transition strategies, distributed resources are being extensively integrated into the distribution network (DN). However, sufficient coordination among these resources remains challenging due to their diverse ownership structures. To address this, a hybrid game-theoretic economic scheduling method for the distribution network based on grid–storage–load interaction is proposed. A two-layer game framework, “distribution network–shared energy storage–microgrid alliance (MGA)”, is established to enable coordinated utilization of flexible resources across the grid, storage, and load sides. The upper-layer distribution network determines time-of-use electricity prices to guide the energy strategies of storage and microgrid alliance. The lower-layer agents engage in a two-stage interaction: Stage 1, multiple microgrids (MGs) form an alliance to lease shared energy storage to smooth net-load profiles. The shared energy storage operator (SESO) then utilizes its surplus capacity to assist the distribution network in peak shaving, thereby maximizing its own revenue. Stage 2, the alliance facilitates mutual power support and implements demand response (DR), reducing its energy costs and assisting the system in peak shaving and valley filling. Case analysis demonstrates that, compared to baseline without coordination, the proposed method reduces the distribution network’s electricity procurement cost by 11.28% and lowers the system’s net load peak-to-valley difference rate by 56.53%. Full article

Driven by the national policy of total water resources control and efficiency improvement, the behavior of water resource utilization reduction by firms is widespread, which may have an impact on the value of firms. This study integrates dynamic capability theory and signaling theory to construct a dual-path analytical framework, systematically investigating the impact of water utilization reduction on firm value and its intrinsic mechanisms. Based on data from Chinese A-share listed companies spanning 2012–2023, fixed-effect models, mediation-effect tests, and heterogeneity analysis are employed for empirical verification. The results reveal that water utilization reduction exerts a significant dual-path promoting effect on firm value: it enhances financial performance (ROA) primarily through technological innovation, reflecting the process of resource orchestration and dynamic capability construction; concurrently, it boosts market performance (Tobin’s Q) mainly by improving ESG performance as a signaling channel, mirroring the capital market’s positive pricing of green signals. Further heterogeneity analysis indicates that these effects are more pronounced during the policy deepening stage, in non-water-intensive industries, and in humid/sub-humid regions. This study contributes theoretical support and empirical evidence for firms’ green transformation and the formulation of differentiated water resource policies by the government, highlighting the synergistic development of high-quality economic growth and ecological civilization construction. Full article

This study examines the tension between “substance” and “form” in standardized sustainability reporting within an emerging market context. Using 21,964 firm-year observations from Chinese A-share listed companies (2018–2023), we investigate whether the adoption of the Global Reporting Initiative (GRI) framework enhances substantive Environmental, Social, and Governance (ESG) and creates firm value. While baseline regressions suggest a positive link between GRI and ESG performance, rigorously applying Propensity Score Matching (PSM) reveals a critical nuance: the effect of mere framework adoption attenuates after controlling for selection bias, whereas independent assurance remains a robust driver of substantive governance quality. Furthermore, mediation analysis using bootstrap resampling documents a distinct “Labeling Effect”: GRI adoption directly enhances market valuation (Tobin’s Q), yet the indirect path via ESG scores is statistically insignificant. This indicates that investors utilize GRI as a heuristic signal of legitimacy rather than pricing granular performance metrics. We also identify a “Valuation Latency”, where substantive ESG improvements significantly boost operational profitability (ROA) but are not yet fully incorporated into stock prices. Heterogeneity analysis shows these effects are stronger for non-state-owned enterprises (Non-SOEs), supporting the view that private firms leverage standardized reporting and verification to mitigate legitimacy deficits. These findings provide empirical evidence for regulators and investors to distinguish between the “label” of adoption and the “substance” of verification. Full article

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, ${\mathsf{\sigma }}_p$), internal friction (management fee, $w_g{g}_t$), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (${\mathsf{\sigma }}_p$) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

Despite growing research on sustainable aviation, multi-airport systems, and environmentally constrained capacity allocation, critical gaps persist. Existing studies often treat passenger choice, airline competition, and airport regulation in isolation, or evaluate environmental policies such as carbon taxation only as macro-level constraints. Consequently, the endogenous feedback among pricing, capacity reallocation, and regulatory intervention in shaping equilibrium outcomes within multi-airport systems remains underexplored, particularly within a unified dynamic framework that links low-carbon policies to operational decision-making. This study develops such a dynamic framework to support the sustainable transition of carbon-constrained multi-airport regions. Focusing on the Beijing–Tianjin multi-airport system and China’s “Dual Carbon” goals, we construct a three-layer iterative equilibrium game integrating passenger airport choice (modeled using a multinomial logit specification), airline capacity reallocation (formulated as an evolutionary game internalizing carbon taxes), and airport slot regulation (implemented through a multi-objective mechanism balancing economic revenue, hub connectivity, and environmental performance). An agent-based simulation of the Beijing/Tianjin–Nanchang route demonstrates robust convergence to a stable systemic equilibrium. Intensified competition reduces fares and improves accessibility, while capacity shifts from higher-cost Beijing airports to Tianjin Binhai Airport, whose market share rises from 10.6% to 34.0%. Airport utilization becomes more balanced, total airline profits increase slightly, and both total and per-passenger CO₂ emissions decline, indicating improved carbon efficiency despite demand growth. The results further identify a range of carbon-tax levels that jointly promote emission reduction and traffic rebalancing with limited profit loss. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

Stock price prediction is a core challenge in quantitative finance. While machine learning has advanced the modeling of complex financial time series, existing methods often rely on single-target predictions, underutilize multidimensional market information, and are disconnected from practical trading systems. To address these gaps, this research develops a hybrid machine learning framework for flexible target forecasting and systematic trading of major American technology stocks. The framework integrates Ensemble Models (AdaBoost, Decision Tree, LightGBM, Random Forest, XGBoost) with Fusion Models (Voting, Stacking, Blending) and introduces a Transfer Learning method enhanced by Dynamic Time Warping to facilitate knowledge sharing across assets, improving robustness. Focusing on ten key stocks, we forecast three distinct momentum indicators: next-day Closing Price Difference, Moving Average Difference, and Exponential Moving Average Difference. Empirical results demonstrate that the proposed Transfer Learning approach achieves superior predictive performance and trading simulations confirm that strategies based on these predicted momentum signals generate substantial returns. This research demonstrates that the proposed hybrid machine learning framework can mitigate the high information entropy inherent in financial markets, offering a systematic and practical method for integrating machine learning with quantitative trading. Full article

Shiitake mushrooms (Lentinula edodes) are widely consumed as a key health food in the Republic of Korea. However, they face declining production value and consumption, necessitating a shift from production-focused research to an understanding of consumer demand. The aim of this study was to quantify Korean consumers’ trade-offs among key shiitake attributes and to derive actionable marketing strategies to expand domestic consumption. We conducted an online survey (n = 500) to quantify consumer utility for four key attributes: cap size (two levels), cap color (two levels), origin (two levels: domestic (Korean) and imported (Chinese)), and price (four levels per 500 g). The results identified price as the most important attribute (relative importance = 46.41%), followed by origin (19.85%), cap color (17.10%), and cap size (16.64%). Utility analysis (part-worths) revealed a distinct dual preference: consumers value both low-priced shiitake (KRW 4000 (USD 2.9)/500 g) for personal consumption and high-priced options (KRW 13,000 (USD 9.5)/500 g) for gifting. Consumers showed a clear preference for dark-colored caps, while the aggregate-level utility difference between origin levels was small. A Logit model simulation indicated the highest predicted shares for profiles priced at KRW 13,000 (15.9%) and KRW 4000 (15.7%), consistent with a polarized value–premium structure. These findings indicate that Korean producers should adopt a dual strategy: developing low-cost products to stimulate general consumption while simultaneously marketing high-quality, dark-colored, domestically produced shiitake as premium gift items, thereby establishing effective food choice strategies in a competitive market. Although the empirical setting is the Republic of Korea (with ‘Chinese’ included only as an imported-origin level representing the main foreign competitor), the findings speak to broader specialty-food contexts where import competition and dual-purpose purchasing (everyday use vs. gifting) shape attribute trade-offs. Full article

Household bread and bakery product waste constitutes a growing issue in Algeria, with significant economic, environmental, and socio-cultural implications. This research is situated within the framework of sustainable food systems and responds to recent transformations in domestic food practices, driven by increased female labor force participation, time constraints, and the widespread availability of industrial bread, which have reshaped household food management and traditional home bread-making practices. The study aims to (1) review traditional Algerian breads, emphasizing their culinary, nutritional, and cultural significance; (2) examine household behaviors during the month of Ramadan in the city of Constantine, focusing on patterns of consumption, purchasing, waste generation, and strategies for reusing leftovers; and (3) assess the economic implications of these practices using the FUSIONS methodology and explore their contribution to household-level food sustainability. Methodologically, a cross-sectional exploratory survey was conducted among 100 married women, the majority of whom were middle-aged (62%; range: 27–71 years; mean age: 52.0 ± 10.21), well-educated (59% with a university degree), economically active (68%), and living in medium-sized households (63%). The findings reveal pronounced contrasts across bread categories. Industrial breads, particularly baguettes, are characterized by high daily purchase frequencies (4.16 ± 1.31 units/day) and the highest waste rates (12.67%), largely attributable to over-purchasing (92%) and low perceived value associated with subsidized prices, with convenience (100%) remaining the primary factor explaining their dominance. In contrast, traditional breads exhibit minimal waste levels (1.63%) despite frequent purchase (3.85 ± 0.70 loaves/day), reflecting more conscious food management shaped by strong cultural attachment, higher perceived value, and dietary preferences (100%). Modern bakery products, along with confections and pastries, the latter representing of 58% of total household food purchases, comprise a substantial share of food expenditure during Ramadan (2.16 ± 0.46 loaves/day and 12.07 and 7.28 ± 2.50 units/day, respectively), while generating relatively low levels of food waste (5.69%, 4.19%, and 0%, respectively). This suggests that higher prices and symbolic value encourage more careful purchasing behaviors and conscious consumption. Freezing leftovers (63%) emerges as the most commonly adopted waste-reduction strategy. Overall, this work provides original quantitative evidence at the household level on bread and bakery product waste in Algeria. It highlights the key socio-economic, cultural, and behavioral drivers underlying waste generation and proposes actionable recommendations to promote more sustainable food practices, in line with the United Nations Sustainable Development Goal 12 on responsible consumption and production. Full article

This paper examines the impact of industrial energy efficiency on household energy poverty in the twenty-seven Member States of the European Union for the period 2003–2023. Although the literature has widely discussed energy efficiency as an enabler of decarbonisation and economic performance, its direct link to energy poverty at the macro level has rarely been analysed, let alone with respect to structural changes in industry. Filling this gap, this paper evaluates whether reductions in industrial energy intensity result in reduced energy poverty, understood as the share of households unable to maintain adequate indoor thermal comfort. Empirical analysis relies on a balanced panel dataset and uses fixed-effects regression models to take into account unobserved country-specific and time-specific heterogeneity. In addition, potential endogeneity between industrial energy intensity and labour productivity is addressed by the instrumental variable approach using two-stage least squares. The main models also include key macroeconomic and social control variables: real GDP per capita, social benefit expenditure, electricity prices for households, and unit labour costs. The results yield a robust and statistically significant positive link between industrial energy intensity and energy poverty, suggesting that efficiency improvements in industry make a quantifiable difference in household energy deprivation. This effect even increases in strength after the correction for endogeneity, thereby corroborating the causal relevance of productivity-driven efficiency gains. The findings also show substantial heterogeneity between EU Member States, indicating that national structural features will determine baseline levels of energy poverty. However, no strong evidence is found for an indirect price-mediated transmission mechanism or for moderation effects bound to income levels or social expenditure. This study provides sound empirical evidence that industrial energy efficiency is an important but structurally conditioned lever to alleviate energy poverty in the European Union. The results emphasise the integration of industrial efficiency policies with social and institutional frameworks while designing strategies for a just and inclusive energy transition. Full article

Against the backdrop of urban renewal and population ageing in China, elevator retrofitting in older residential compounds has emerged as a critical yet contentious issue, primarily due to uneven cost-sharing and perceived inequities in the distribution of benefits. This study employs a combined empirical framework integrating Difference-in-Differences (DID) and cost–benefit analysis to systematically evaluate the economic impacts of elevator installation in older neighbourhoods of Hangzhou. Using transaction data from 879 housing units across 18 residential compounds between 2018 and 2020, along with actual project cost records, we quantify the premium effects and assess economic feasibility. The results show that elevator retrofitting leads to an overall 5.53% increase in housing prices, with significant vertical differentiation: upper-floor units appreciate by 8.10%, middle-floor units by 4.58%, and lower-floor units by 1.59%. Further analysis confirms that the aggregate increase in property value fully covers installation costs, long-term maintenance, and reasonable compensation for lower-floor residents, thereby achieving a Pareto improvement. The study establishes a floor-gradient linkage mechanism between value uplift and cost-sharing, providing a quantifiable basis for policy design and community negotiation. These findings challenge the prevailing zero-sum view of elevator retrofitting while offering a replicable model for urban renewal that equitably balances stakeholder benefits. Full article