Search Results (725)

We investigate whether a belief in trend continuation, often associated with the so-called “hot-hand effect,” can be endogenously triggered by personal performance feedback in a controlled financial experiment. Participants allocated funds across assets with randomly generated prices, under conditions of known probabilities and varying levels of risk. In a two-stage setup, participants were first exposed to random price sequences to learn the task and potentially develop perceptions of personal success. They then faced additional price paths under incentivized conditions. Our findings show that participants initially increased purchases following gains—consistent with a feedback-driven belief in momentum—but this pattern faded over time. When facing sustained losses, loss aversion dominated decision-making, overriding early optimism. These results highlight how cognitive heuristics and emotional biases interact dynamically, suggesting that belief in trend continuation is context-sensitive and constrained by the reluctance to realize losses. Full article

Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts. Full article

Silvopastoral systems, though ecologically beneficial, remain underrepresented in the European Union’s Common Agricultural Policy and are seldom studied in Mediterranean contexts. The current study assesses both the environmental and economic aspects of five typical silvopastoral systems in central Greece, encompassing cattle, sheep, and goat farming. A Life Cycle Assessment approach was implemented to quantify greenhouse gas emissions using economic allocation, distributing impacts between milk and meat outputs. Enteric fermentation was the major emission source, accounting for up to 65.14% of total emissions in beef-based systems, while feeding and soil emissions were more prominent in mixed and small ruminant systems. Total farm-level emissions ranged from 60,609 to 273,579 kg CO₂eq per year. Economically, only beef-integrated systems achieved an average annual profitability above EUR 20,000 per farm, based on financial data averaged over the last five years (2020–2024) from selected case studies in central Greece, while the remaining systems fell below the national poverty threshold for an average household, underscoring concerns about their economic viability. The findings underline the dual challenges of economic viability and policy neglect, stressing the need for targeted support if these multifunctional systems are to add value to EU climate goals and rural sustainability. Full article

Promoting energy efficiency in the Italian production sector is significantly hampered by the lack of knowledge, the scarcity and the limited distribution of tools for supporting energy audits in small and medium-sized enterprises (SMEs) in a wide range of Italian economic sectors (industry, tertiary sector, transport). The Advanced Tool for ENErgy Audit for SMEs, ATENEA4SME, is intended to help SMEs promote energy-efficiency projects, supports energy audits and self-evaluation of energy consumption. The tool uses an original mathematical model that takes into account the results of questionnaires and a multi-criteria analysis to generate recommendations for energy efficiency investments. This article will give a thorough explanation of the tool, emphasizing and outlining the sections as well as the procedures to get the ultimate summary of the energy usage of the enterprises under investigation and the potential for energy saving. From a technological and financial perspective, the tool helps to remove obstacles to the development of energy-efficiency measures. In this article, the IT and methodological structure of the tool will therefore be extensively described, and its operation for the context of SMEs will be illustrated, with application cases. Ample space will be allocated to the dissemination campaign and the replicability of the tool for all economic sectors of the industrial and tertiary sectors. Full article

As a key engine driving China’s green financial transformation, the Green Financial Reform and Innovation Pilot Zones have demonstrated significant achievements in enhancing the capacity of financial services to support green real economies, preventing and mitigating green financial risks, and bolstering national and urban economic resilience. On this basis, a spatial Markov chain model is applied to further analyze the economic toughness of prefecture-level cities. This study treats the establishment of these pilot zones as a quasi-natural experiment, using panel data from 269 prefecture-level cities in China from 2013 to 2023 and employing a multi-period difference-in-differences (DID) model to empirically examine the impact of green financial reform on urban economic resilience and its underlying mechanisms. The results reveal that the establishment of these pilot zones significantly enhances urban economic resilience. Specifically, green financial reforms primarily improve urban economic resilience by increasing credit accessibility and capital allocation efficiency in the pilot cities. Furthermore, the policy effects are more pronounced in large cities and resource-dependent cities compared to small and medium-sized cities and non-resource-dependent cities, with stronger impacts observed in southern and coastal regions than in northern inland areas. Additionally, the policy effects are significantly greater in environmentally prioritized cities than in non-prioritized cities. By integrating green financial reforms and urban economic resilience into a unified analytical framework, this study provides valuable insights for policymakers to refine green financial strategies and design resilience-enhancing policies. Full article

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article

China’s national strategies emphasize both achieving carbon peaking and neutrality (“dual carbon” objectives) and fostering high-quality economic development. This dual focus highlights the critical importance of the Green and Low-Carbon Transition (GLCT) of the economy and the development of New Quality Productive Forces (NQPF). Firms are central actors in this transformation, prompting the core research question: How does corporate engagement in GLCT contribute to the formation of NQPF? We investigate this relationship using panel data comprising 33,768 firm-year observations for A-share listed companies across diverse industries in China from 2012 to 2022. Corporate GLCT is measured via textual analysis of annual reports, while an NQPF index, incorporating both tangible and intangible dimensions, is constructed using the entropy method. Our empirical analysis relies primarily on fixed-effects regressions, supplemented by various robustness checks and alternative econometric specifications. The results demonstrate a significantly positive relationship: corporate GLCT robustly promotes the development of NQPF, with dynamic lag structures suggesting delayed productivity realization. Mechanism analysis reveals that this effect operates through three primary channels: improved access to financing, stimulated collaborative innovation and enhanced resource-allocation efficiency. Heterogeneity analysis indicates that the positive impact of GLCT on NQPF is more pronounced for state-owned enterprises (SOEs), firms operating in high-emission sectors, those in energy-efficient or environmentally friendly industries, technology-intensive sectors, non-heavily polluting industries and companies situated in China’s eastern regions. Overall, our findings suggest that corporate GLCT enhances NQPF by improving resource-utilization efficiency and fostering innovation, with these effects amplified by specific regional advantages and firm characteristics. This study offers implications for corporate strategy, highlighting how aligning GLCT initiatives with core business objectives can drive NQPF, and provides evidence relevant for policymakers aiming to optimize environmental governance and foster sustainable economic pathways. Full article

As investment portfolios become increasingly diversified and financial asset risks grow more complex, accurately forecasting the risk of multiple asset classes through mathematical modeling and identifying their heterogeneity has emerged as a critical topic in financial research. This study examines the volatility and tail risk of gold, crude oil, Bitcoin, and selected stock markets. Methodologically, we propose two improved Value at Risk (VaR) forecasting models that combine the autoregressive (AR) model, Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, Extreme Value Theory (EVT), skewed heavy-tailed distributions, and a rolling window estimation approach. The model’s performance is evaluated using the Kupiec test and the Christoffersen test, both of which indicate that traditional VaR models have become inadequate under current complex risk conditions. The proposed models demonstrate superior accuracy in predicting VaR and are applicable to a wide range of financial assets. Empirical results reveal that Bitcoin and the Chinese stock market exhibit no leverage effect, indicating distinct risk profiles. Among the assets analyzed, Bitcoin and crude oil are associated with the highest levels of risk, gold with the lowest, and stock markets occupy an intermediate position. The findings offer practical implications for asset allocation and policy design. Full article

Amidst the escalating global challenges of deforestation and climate change, effective forest governance has become a critical global imperative. As a key actor in this arena, China presents a crucial case for understanding state-led environmental governance. This study addresses the thematic evolution and governance structure of China’s forest policy planning from 1980 to 2024. Grounded in multi-level governance (MLG) theory, we apply the Non-negative Matrix Factorization (NMF) topic model to a corpus of 1265 policy documents sourced from the PKULaw database, spanning four administrative levels from central to county. An analysis of 13 core policy themes reveals a significant transition, shifting from early regulatory development and resource utilization to a modern emphasis on ecological protection, scientific monitoring, financial support, and governance innovation. The findings delineate a complex governance architecture: a vertical division of labor (central guidance, local implementation), a horizontal model of inter-departmental interaction where specialized management coexists with comprehensive coordination, and adaptive governance reflecting regional heterogeneity. These results illuminate the dynamic evolution of power allocation, central–local relations, and synergy within China’s forest sector. This study not only provides new empirical evidence and an analytical framework for understanding China’s natural resource policy transition but also offers scientific insights for optimizing multi-level forest governance systems and enhancing policy synergy and efficacy. Full article

The integration of passenger and freight transport by urban–rural buses is an effective approach to address two critical issues: the inefficiency of parcel delivery services and the financial struggles of public transport operators. This paper studies the county–township–village station location-routing problem for the integration of passenger and freight transport by urban–rural buses, aiming to develop an efficient transport network by establishing rational stations and designing optimal operation routes. A three-level county–township–village station network is proposed for the integration of passenger and freight transport, and a mixed-integer linear programming model is developed, including the constraints of location, allocation, capacity, and routing. A comprehensive series of numerical experiments is conducted on a randomly generated dataset to evaluate the feasibility and advantages of the proposed model. Lastly, key managerial insights are discussed. Full article

Background: Development Assistance for Health (DAH) plays a vital role in health financing across Sub-Saharan Africa, particularly in tackling communicable diseases such as HIV/AIDS, malaria, and tuberculosis. Despite its importance, the efficiency and equity of DAH allocation remain contested. Objectives: The study aims to evaluate the cost-effectiveness of DAH in Sub-Saharan Africa from 1995 to 2018, as well as to explore differences in efficiency across diseases and country contexts. Methods: Data were drawn from the Institute for Health Metrics and Evaluation and applied Generalized Cost-Effectiveness Analysis in conjunction with the Gross Domestic Product-based thresholds. Averted Disability-Adjusted Life Years were analyzed across countries and diseases, and countries were categorized by the Human Development Index (HDI) level to assess differential DAH performance. Results: DAH cost-effectiveness showed similar patterns across HDI groups, with roughly equal proportions of cost-effective and dominated outcomes in both low- and middle-HDI countries. Thirteen countries were identified as very cost-effective, nine as cost-effective, and two as non-cost-effective. Twenty-one countries were dominated, reflecting persistent inefficiencies in aid impact that transcends the various levels of development. Conclusions: Tailoring DAH allocation to specific disease burdens and development levels enhances its impact. The study underscores the need for targeted investment and a strategic shift toward integrated health system strengthening. Additionally, microinsurance is highlighted as a key mechanism for improving healthcare access and financial protection in low-income settings. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

Emerging economies such as China have benefited from rapid growth but now face acute carbon risk amid worsening environmental conditions. Carbon-intensive firms—major emitters—face rising carbon risk that pervades operations and threatens efficient capital allocation. To advance global climate-change mitigation, help China meet its dual-carbon goals, and enhance corporate financial sustainability, we analyze panel data on 575 Chinese carbon-intensive companies from 2012 to 2022 and estimate OLS models to assess how carbon risk influences capital mismatch. Results show that higher carbon risk significantly widens capital mismatch, whereas higher media attention and better corporate governance each weaken this effect. These findings suggest that regulators and the media should monitor carbon-intensive firms more closely to improve information transparency and guide capital to its most productive uses, while firms themselves need to strengthen governance to limit the damage carbon risk inflicts on capital allocation. Full article