Search Results (773)

The coffee industry in developing countries has long been locked into the lower end of the global value chain, and external governance mechanisms such as fair trade have struggled to fundamentally reverse this situation. This study adopts a mixed-methods research design, using the coffee industry in Yunnan, China, as a case study to reveal the micro-level mechanisms by which domestic demand drives industrial upgrading. Building on these case-derived mechanisms, it then uses panel data from 96 major coffee-producing and coffee-consuming economies from 2012 to 2024 to examine their macro-level implications, including national-level relationships, nonlinear patterns, and cross-country heterogeneity. The study shows that both factors generally drive export upgrading and exhibit a “promotion—inhibition—re-promotion” pattern characterized by a dual-threshold nonlinearity. The case study reveals that the inhibition phase stems from a mismatch between market expansion and demand structure upgrading, where homogeneous capacity growth temporarily offsets the effects of quality upgrading. Heterogeneity manifests in three distinct patterns: producing countries exhibit only a significant domestic market absorption effect; traditional consuming countries are primarily characterized by path dependence; and emerging consuming countries achieve dual-driven growth in both scale and quality. By bridging macroeconomic effects and microeconomic logic, this study clarifies the conditions under which domestic demand drives product-end upgrading along the value chain, providing a reference for different economies to formulate differentiated upgrading strategies. Full article

Whether machine-learning models extract predictive signal from ETF returns across markets at different efficiency levels and whether statistical accuracy translates into trading value, remain contested. We examine this for iShares MSCI Brazil (EWZ) and iShares Core S&P 500 (IVV) from January 2010 to July 2026, training through December 2022 and testing thereafter. Random Forest, XGBoost with random search, XGBoost with Bayesian optimization, LSTM, GRU and an LSTM + XGBoost ensemble, are compared against historical mean, random walk, and AR(1) benchmarks at one-day (h = 1), five-day (h = 5) and monthly (h = 21) horizons using ten technical indicators. Every model is also evaluated against the classifier that predicts the majority class, and risk-adjusted performance is reported with bootstrap intervals. No model exceeds that trivial classifier in any combination examined. The two markets fail by distinct mechanisms: collapse onto the majority class in the developed market, and dispersed but unprofitable signals in the emerging one. Under Diebold–Mariano tests with autocorrelation-consistent variance and false-discovery control, no model is superior to the historical mean. No strategy outperforms Buy-and-Hold, and in the emerging market, no Sharpe ratio is distinguishable from zero. Where directional significance does appear, at the monthly horizon in the emerging market, it delivers no economic value. Together, these results argue for evaluating financial forecasting models simultaneously on regression metrics, economic performance, and regime stability rather than on any single criterion. Full article

Cryptocurrency holdings in conventional portfolios challenge the empirical adequacy of standard tail-risk estimators. This study identifies a calibration mechanism that brings feature-based machine learning to supervisory-grade value at risk (VaR) coverage, improves its joint VaR and expected shortfall (ES) record relative to volatility filtering, and measures the value of tail-oriented allocation. Ten risk models are evaluated on equity, cryptocurrency and mixed portfolios across 1397 out-of-sample trading days, covering several distinct market phases. Three of these models are variants of a single learner, sharing the same feature set and estimation protocol, and differing only in how the predicted quantile is placed. Uncalibrated gradient boosting understates the tail in every portfolio, yielding violation rates as high as 10.81% against a 5% nominal level, and volatility filtering does not correct the shortfall. Split-conformal calibration keeps forecasts in the green zone of the generalised traffic-light criterion throughout and under every initialisation, yet 47 of the 49 significant loss comparisons still favour a classical benchmark. Separation is only modestly stronger on the cryptocurrency book, at 19 significant comparisons against 15 for each of the other two portfolios. Minimum conditional value at risk (CVaR) allocation reduces realised tail loss by 65% and maximum drawdown by 64% without improving risk-adjusted return. Thus, the evaluated machine learning models require calibration to achieve adequate coverage, whereas the econometric benchmarks retain an advantage in predictive accuracy. Full article

As a widely recognized measure of corporate sustainability, ESG performance exerts a significant influence on corporate exports and risk-coping capabilities. Using product–destination-level panel data of Chinese listed firms from 2009 to 2016, compiled from the China Customs Database and the CSMAR Database, this paper employs the High-Dimensional Fixed Effects (HDFE) model to empirically examine the impact of ESG performance on corporate export resilience and its underlying mechanisms. The findings reveal that improved ESG performance significantly strengthens firm export resilience, a conclusion that remains robust after a series of robustness checks and addressing endogeneity concerns. ESG performance directly enhances export resilience through its environmental, social, and governance dimensions, with the social dimension exhibiting the strongest effect. Mechanism analysis indicates that ESG performance enhances export resilience by promoting diversification in both export products and export markets. Heterogeneity analysis reveals that the effect varies significantly across countries, products, and firms. Specifically, the positive effect is more pronounced for exports to developed countries, Belt and Road Initiative (BRI) participating countries, and coastal countries. Moreover, ESG performance contributes more strongly to the export resilience of final goods, high-technology products, and products with comparative advantages. At the firm level, the enhancing effect is more evident for state-owned enterprises, capital-intensive firms, and large-scale enterprises. Further analysis reveals that ESG performance and export resilience exhibit a positive joint effect in enhancing overseas market profitability and overseas revenue sustainability. These findings offer actionable implications for policymakers and exporters aiming to embed ESG principles into their export operations, which may further facilitate the sustainable and high-quality transformation of foreign trade. Full article

Vehicle-to-Grid (V2G) enables energy and information exchange between electric vehicles (EVs) and the power grid. Large amounts of distributed data are generated in V2G systems. The charging data of EVs connected to charging piles (CPs) are aggregated by charging stations (CSs), charging service operators (COs), and load aggregation platforms. The aggregated results support grid regulation and electricity market trading. However, some CPs within a station are idle or offline due to hardware failures or communication disruptions, preventing their data from being aggregated in time. Consequently, traditional schemes that rely on full-pile participation result in incomplete ciphertext aggregation results due to the absence of data contributions from some CPs, making correct decryption unreliable. In addition, most existing data aggregation schemes only provide a single result. They cannot satisfy the differentiated data access demands of V2G business entities. To address these challenges, a user-driven differentiated fault-tolerant data aggregation scheme for V2G interaction is proposed. First, the EC-ElGamal encryption scheme is employed to preserve data confidentiality, while the ECDSA batch signature verification mechanism is adopted to ensure data integrity. Second, a mask compensation mechanism is designed to restore the completeness of the aggregated ciphertext under the failures of some CPs. Finally, on-demand differential de-aggregation and controlled authorized decryption are designed based on Shamir’s secret sharing scheme. Data users (DUs) are allowed to access target ciphertexts on demand, only upon obtaining sufficient authorization from CPs. Security and performance analyses demonstrate that the proposed scheme effectively resists chosen-plaintext and key-collusion attacks with practical efficiency. The proposed scheme provides a secure and reliable solution for V2G data aggregation. Full article

This study examines the dynamic connectedness and spillover transmission mechanisms among tourism-related exchange-traded funds (ETFs), clean energy markets, oil, geopolitical risks, and tourism transportation using daily percentage returns from 24 March 2017 to 16 March 2026. We employed a battery of econometric methodologies, including the R²-decomposed connectedness approach, a TVP-VAR framework to examine the extent and nature of connectedness and distinguish contemporaneous and lagged spillovers, and the DCC-GARCH specification to assess dynamic conditional correlations. The data exhibit substantial volatility and non-normality, supporting these dynamic econometric approaches. The results reveal that connectedness varies considerably over time and tends to intensify during periods of major economic and geopolitical uncertainty. PEJ emerges as the dominant overall net transmitter of shocks, primarily through lagged spillovers, while Transportation and Clean Energy act as contemporaneous transmitters but become net receivers in the lagged horizon. The decomposition further shows that contemporaneous linkages are relatively weak, while lagged effects are stronger, suggesting that shock transmission occurs gradually rather than instantaneously. The dynamic correlations are time-varying, and ETFs display asymmetric behavior across different market conditions. Overall, this study highlights the time-varying nature of interactions between tourism and clean energy assets, driven by energy market dynamics and geopolitical risks, and provides useful implications for portfolio diversification, risk management, and policy decisions in interconnected tourism and sustainable financial markets. Full article

Cryptocurrency markets trade continuously, but their return dynamics need not be uniform over the 24 h cycle. Using hourly Kraken prices from 2016 to 2025, we divide each day into complementary 12 h sessions and evaluate 25 ordered combinations of cash, long, short, momentum, and reversal positions across all 12 non-redundant hourly boundaries. The full-sample selection identifies Reversal/Reversal at an 08:00 UTC daytime start for Bitcoin (BTC) and Long/Reversal at a 05:00 UTC start for Ethereum (ETH). The return mechanisms differ: BTC is associated with conditional reversal in both sessions, whereas ETH combines positive overnight drift with daytime reversal. The selected rules generate higher realized terminal wealth and more favorable drawdown and Sharpe-ratio outcomes than buy-and-hold along the observed full-sample path. These realized differences, however, are not statistically significant in paired bootstrap tests, and Hansen’s Superior Predictive Ability test does not reject the null after accounting for the search over 300 cutoff–strategy combinations. A chronological holdout exercise, in which selection uses only 2016–2020 and evaluation uses 2021–2025, further shows that the ETH rule persists, but the BTC training-selected rule underperforms buy-and-hold. The findings should therefore be interpreted as evidence of asset-specific historical return structure, not as proof of a stable or readily implementable abnormal-profit opportunity. The 0–2 basis-point cost scenarios are illustrative and exclude slippage, market impact, borrowing, and funding costs. Full article

This study forecasts cumulative log returns at horizons of one to twenty trading days and reconstructs future adjusted prices by exponentiating those return forecasts; it does not optimize a price-level loss. This task is difficult because financial returns are nonstationary, heavy-tailed, horizon-dependent, and subject to rapidly changing volatility. We propose FreqCast, which combines a market-conditioned spectral decomposition, a structured state-space branch for the component designated low-frequency, causal multiscale encoders for the components designated intermediate- and high-frequency, and a horizon-conditioned reliability gate. The gate uses expert representations, predictive scale, and cross-expert disagreement to fuse four cumulative-return estimates. The joint objective covers point loss, an auxiliary directional score, Laplace likelihood, ordered quantile loss, decomposition regularization, and horizon coherence. Experiments use eight large U.S. stocks and a single 2021–2024 test interval. Within that restricted benchmark, the reported point estimates favor FreqCast over the included baselines and show horizon- and volatility-dependent expert allocation. Our main contribution is the coordinated frequency-dependent assignment and reliability fusion of heterogeneous forecasting mechanisms, while broad market robustness and statistical superiority remain to be established. Full article

Balancing decarbonization with grid reliability requires managing renewable volatility through Renewable Portfolio Standards (RPSs) or Capacity Remuneration Mechanisms (CRMs) alongside carbon emission trading (CET). However, their comparative systemic impacts remain unclear. This study evaluates the strategic equivalence of CET-RPS and CET-CRM regulations using a Stackelberg duopoly model. We endogenize grid-side storage investment and parameterize source-storage synergy to establish a rigorous equivalence mapping anchored in a unified macroeconomic penetration target. The results reveal that the CRM exhibits dual systemic impacts contingent on technological readiness. Immature conditions necessitate excessive capacity prices, inducing speculative over-investment. Conversely, mature synergy creates a substitution effect that efficiently offsets public compensation budgets. Furthermore, the dynamic policy phase boundary demonstrates that stricter decarbonization targets require higher technological readiness for the CRM to dominate the RPS in social welfare. Additionally, imposing stringent administrative capacity derating factors on storage fails to alter its physical deployment, instead unintentionally transferring wealth to conventional high-carbon generators. Regulators must therefore synchronize capacity market deployment with storage technology maturity and adopt dynamic capacity accreditation to prevent such distributional distortions. Full article

Expanding offshore wind energy introduces complex marine pollution liability challenges for specific emerging markets like Taiwan. Traditional static regulations often trigger administrative conflicts and struggle to adapt to dynamic stakeholder behaviors. This study develops an integrated socio-technical model combining evolutionary game theory (EGT) and system dynamics (SD) to analyze the strategic co-evolution between regulators and developers. The model is strictly calibrated using empirical data from Taiwan’s offshore wind sector to simulate various scenarios. Phase plane simulations reveal an “excess-liability paradox”. Crucially, the mathematical stability analysis identifies the intangible value of corporate reputation as the structural prerequisite for system convergence. Statutory penalties serve effectively as a complementary deterrent signal, but long-term stability requires normative shifts. We propose combining high statutory penalty deterrence with ESG-linked reputation mechanisms, providing a fundamental theoretical reference for emerging offshore wind markets facing similar initial regulatory and economic trade-offs. Full article

The rapid expansion of embodied intelligent agricultural machinery has not been accompanied by a commensurate increase in market adoption, highlighting a growing disconnect between technological supply and downstream demand. This study develops a dynamic general equilibrium model to examine how government fiscal support and labor-market frictions shape the adoption of embodied intelligent agricultural machinery and structural transformation in agricultural production. Structural parameters are estimated using agricultural machinery purchase data and listed-company recruitment records for 2021–2025, and the estimated parameters are incorporated into dynamic simulations. The results show that the scale and composition of fiscal support operate through distinct mechanisms. An expansion in the scale of fiscal support primarily raises final agricultural output by promoting the accumulation of R&D outcomes and technological knowledge and thereby improving productivity, while exerting only a limited direct effect on the demand structure for embodied intelligent machinery. Changes in the composition of fiscal support, by contrast, affect sectoral output allocation through shifts in government demand. Lower labor-market frictions increase the nominal demand share of embodied intelligent agricultural machinery and induce a reallocation of capital, labor, and output toward the embodied intelligent sector, with relatively limited effects on aggregate agricultural output. Reallocating fiscal resources from current government purchases toward government savings generates an intertemporal trade-off between current support and long-run productivity accumulation. The labor-market-friction mechanism remains robust across alternative elasticities of substitution. Overall, the findings indicate that sustained technology adoption depends on the effective transmission of technological supply into market demand through coordinated fiscal support and improved labor-market allocation. Full article

Against the backdrop of increasing global supply chain disruption risks, supplier diversification has emerged as a critical strategy for enhancing economic resilience and sustainable trade performance. Using matched supplier–customer transaction data for Chinese listed firms and Chinese Customs data from 2009 to 2016, this study examines the impact of supplier diversification on firms’ export product scope and its underlying mechanisms. The results show that supplier diversification significantly expands firms’ export product scope by mitigating resource and technological lock-in effects associated with concentrated supply chains. Specifically, a 0.1-unit increase in supplier diversification is associated with an average increase of approximately 0.074 distinct HS 6-digit product categories exported by a firm. Mechanism analyses indicate that this effect operates primarily through promoting technological innovation and reducing production costs. The positive effect is more pronounced among larger firms, firms facing weaker financial constraints, and firms operating in highly competitive or technology-intensive industries. Further analyses show that supplier diversification facilitates the entry of new products into export markets and supports the continued export of existing products, while having no significant effect on product exit. These findings identify supplier diversification as an important micro-level supply-side determinant of firms’ export product scope and provide implications for supplier portfolio design, supply chain resilience, and sustainable export strategies. Full article

Carbon emissions trading market is an important institutional innovation to promote green and low-carbon transformation of economic development and sustainable economic and social development. With the quasi-natural experiment of China’s carbon emissions trading pilot policy since 2013, this paper constructs a multi-period double-difference model based on the panel data of 30 provinces and systematically evaluates the effectiveness as well as the heterogeneous performance of the carbon emissions trading policy on carbon emissions. The results show that the implementation of carbon emissions trading policy significantly reduces regional carbon emissions, with a significant impact coefficient of −0.1701 at the 1% level, and the finding passes a series of robustness tests. Heterogeneity analysis shows that the impact effect of carbon emissions trading policies is more significant in the eastern and central regions and more significant in regions with high levels of human capital. Mechanism analysis indicates that the carbon emissions trading policies achieve carbon emission reduction through the market mechanism and government intervention mechanism, and promote regional investment in scientific and technological innovation, reduce the total amount of energy consumption, and optimize the structure of energy consumption. Further analysis indicates that the carbon trading policy exerts a significant spatial spillover effect on carbon emission reduction. Full article

Artificial intelligence (AI) is reshaping financial markets—accelerating execution, automating allocation, and concentrating analytical capacity within a shrinking set of foundational models. Prior work has documented AI’s contribution to instability through algorithmic herding and high-frequency volatility, but the literature lacks a coherent classification of the mechanisms through which AI triggers catastrophic, self-reinforcing market dislocations, or “flash crashes.” This prospective review proposes a three-category taxonomy: (1) endogenous algorithmic herding crashes, driven by correlated model behavior; (2) exogenous model error cascade crashes, in which AI system failures propagate across interconnected venues; and (3) adversarial generative AI (GenAI) disinformation crashes, in which fabricated narratives trigger automated trading responses. The taxonomy is further motivated by the structural parallel between contemporary AI model homogeneity and the homogenization of Value-at-Risk (VaR) models before the 2008 crisis—a link recently formalized in the modeling literature. The analysis is extended to the emerging frontier of agentic AI—autonomous systems capable of multi-step planning and inter-agent interaction—which introduces qualitatively new systemic risks that existing regulatory frameworks are unprepared to address. The October 2025 cryptocurrency liquidation cascade, which liquidated over $19 billion within 24 h, serves as the primary empirical case study. The article concludes with policy recommendations on model-diversity mandates, real-time AI trading surveillance, adaptive circuit-breaker design, and cross-regulatory coordination on GenAI financial disinformation. Full article

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain. Full article