Search Results (2,681)

To analyze the effects of remanufacturing subsidies and emission reduction investments on pricing and quality decisions under cap-and-trade regulation, four profit-maximization Stackelberg game models for a remanufacturing supply chain (RSC), i.e., without remanufacturing subsidies and emission reduction investments, with remanufacturing subsidies only, with emission reduction investments only, and with both remanufacturing subsidies and emission reduction investments, are constructed, derived, compared, and analyzed. Results show that government subsidies and emission reduction investments can improve profits for the RSC members, while possibly leading to more total carbon emissions. Furthermore, it is worth noting that profit growth and emission reduction can be achieved even though reducing remanufacturing subsidies in some scenarios. Moreover, increasing emission reduction targets will reduce profits of the RSC members but does not necessarily contribute to emission reduction. Therefore, to help the RSC improve profits and reduce emission, the policymaker should formulate differentiated policies based on the types of manufacturers. For the non-abating manufacturer, the government should set higher emission reduction targets and cut down subsidies; for the low-efficiency abating manufacturer, higher emission reduction targets and subsidies are more suitable. However, for the high-efficiency abating manufacturer, lower emission reduction targets and subsidies are more effective. Full article

This study investigates the factors influencing green purchase behavior in emerging economies, focusing on Indonesian consumers’ preferences for organic food products. While sustainability awareness is growing globally, limited research has examined how environmental knowledge and trust interact with marketing efforts to shape green purchasing decisions in developing market contexts like Indonesia. The research model incorporates five constructs: environmental knowledge (awareness of ecological issues), green trust (confidence in environmental claims), green marketing mix (eco-oriented strategies for product, price, place, and promotion), green purchase intention (likelihood of buying eco-friendly products), and green purchase behavior (actual sustainable buying decisions). Data from 211 valid respondents were analyzed using structural equation modeling. The results indicate that environmental knowledge directly influences green trust and the green marketing mix but not green purchase intention or behavior. Instead, it affects behavior indirectly through trust and intention. Contrary to expectations, green trust does not influence the green marketing mix, suggesting it may operate independently of marketing strategies. Similarly, the green marketing mix does not significantly influence green purchase intention or behavior, suggesting that marketing strategies alone may be insufficient in driving sustainable consumer choices. These findings highlight the important role of environmental knowledge in fostering consumer trust and indirectly guiding green purchasing behavior in emerging markets. By promoting sustainable consumption through knowledge and trust, this study offers insights into consumer behavior as a pathway to advancing planetary health. This study advances the Theory of Planned Behavior by integrating green trust and the green marketing mix to explain how trust and economic factors shape green purchasing behavior. Practical implications suggest that businesses should adopt targeted green marketing strategies, such as educational campaigns, eco-labeling, or certifications, to enhance environmental awareness, build consumer trust, and encourage sustainable purchasing decisions. This study contributes to the literature by examining how environmental knowledge indirectly influences green purchase behavior through the mediation of trust and intention within the context of an emerging market. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. Full article

With the progress of green transformation, government subsidies have become an important incentive for enterprises to invest in green technologies. However, their effectiveness differs markedly under alternative decision-making structures. This study develops a two-tier green supply chain game model comprising manufacturers and e-commerce platform self-operators. Six game structures are examined, covering both scenarios without subsidies and those in which manufacturers receive subsidies. The analysis focuses on product greenness, service levels, retail prices, and the profits of supply chain members. The results show that government subsidies substantially enhance manufacturers’ green investments and motivate platform self-operators to provide higher levels of green services, thereby improving market performance and overall supply chain profitability. Among the different structures, centralized decision-making demonstrates the strongest coordination effect and maximizes the subsidy impact. In contrast, within decentralized structures, subsidies help alleviate double marginalization, but their effectiveness is constrained by the distribution of power. These findings highlight the heterogeneous impacts of subsidies on green supply chain performance, offering theoretical support for targeted government policy design and practical guidance for enterprises to optimize green collaborative strategies. Full article

Objectives: Hepatitis E virus (HEV) infection is associated with severe hepatitis and high mortality rates, yet vaccination coverage remains suboptimal. Investigating public preferences for HEV vaccination is critical for developing targeted prevention strategies. This study employed a discrete choice experiment (DCE) to quantify attribute preferences and willingness-to-pay (WTP) for HEV vaccination among Chinese residents (in Shaanxi Province, for example), aiming to inform evidence-based immunization policy optimization. Methods: A cross-sectional survey recruited 3300 participants using stratified random sampling. The vaccine attributes—protective efficacy, duration of protection, and out-of-pocket cost—were identified using a systematic literature review and expert consultation. A comparative analysis of preference characteristics was conducted using conditional logit (Model 1) and mixed logit (Model 2) regression models. Population heterogeneity in vaccination preferences was further analyzed using the conditional logit framework, with marginal WTP estimated using parameter coefficients. Results: Among 3199 valid responses, duration of protection (Model 2: 10-years; β = 0.456, p < 0.001) and out-of-pocket cost (Model 2: 2000–3000 CNY; β = −0.179, p < 0.001) significantly influenced vaccination decisions. Preference heterogeneity was observed: women of childbearing age prioritized longer protection (10 years; β = 0.677, p < 0.001) and were sensitive to the cost of 1000–2000 CNY (β = 0.169, p = 0.011), while urban residents valued extended protection more than rural counterparts. Conclusions: Protection duration emerged as the primary determinant of HEV vaccination preference. Policy recommendations include implementing tiered pricing strategies and targeted health education campaigns emphasizing long-term protection benefits to enhance vaccine uptake and affordability. Full article

This paper proposes a novel fractional-order asset flow model based on the Atangana–Baleanu–Caputo (ABC) derivative to analyze asset price dynamics in financial markets. Compared to classical models, the proposed model incorporates a nonlocal and non-singular fractional operator, allowing for a more accurate representation of investor behavior and market adjustment processes. The model captures both short-term trend-driven responses and long-term valuation-based decisions. We establish key theoretical properties of the system, including the existence and uniqueness of solutions, positivity, boundedness, and both local and global stability using Lyapunov functions. Numerical simulations under varying fractional orders demonstrate how the ABC derivative governs the convergence speed and equilibrium behavior of the system. Compared to classical integer-order models, the ABC-based approach provides smoother dynamics, greater flexibility in modeling behavioral heterogeneity, and better alignment with observed long-term financial phenomena. Full article

Stock price forecasting remains a central challenge in financial modeling due to the non-stationarity, noise, and high dimensionality of market dynamics, as well as the growing importance of unstructured textual information. In this work, we propose a multimodal prediction framework that combines FinBERT-based financial sentiment extraction with technical and statistical indicators to forecast short-term stock price movement. Contextual sentiment signals are derived from financial news headlines using FinBERT, a domain-specific transformer model fine-tuned on annotated financial text. These signals are aggregated and fused with price- and volatility-based features, forming the input to a gradient-boosted decision tree classifier (XGBoost). To ensure interpretability, we employ SHAP (SHapley Additive exPlanations), which decomposes each prediction into additive feature attributions while satisfying game-theoretic fairness axioms. In addition, we integrate differential privacy into the training pipeline to ensure robustness against membership inference attacks and protect proprietary or client-sensitive data. Empirical evaluations across multiple S&P 500 equities from 2018–2023 demonstrate that our FinBERT-enhanced model consistently outperforms both technical-only and lexicon-based sentiment baselines in terms of AUC, F1-score, and simulated trading profitability. SHAP analysis confirms that FinBERT-derived features rank among the most influential predictors. Our findings highlight the complementary value of domain-specific NLP and privacy-preserving machine learning in financial forecasting, offering a principled, interpretable, and deployable solution for real-world quantitative finance applications. Full article

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences. Full article

Soils are critical natural resources, yet their abiotic contributions to ecosystem services remain largely unexplored in valuation studies. This pilot study represents, to the best of our knowledge, the first attempt to assess the perceived value of geosystem services (GSs) from a consumer perspective. Using a discrete choice experiment with 200 respondents, we evaluated preferences for Rotella apples and Moscatello wine through mixed multinomial logit and latent class models. Results show that attributes related to soil use and soil control were consistently significant drivers of consumer utility (e.g., odds ratios of 9.38 and 5.78 for Moscatello wine and 8.46 and 5.56 for Rotella apples, respectively; p < 0.01). These attributes align more closely with the concept of a “geological fingerprint” than with existing geographical labeling schemes such as the Protected Designation of Origin. Price effects were statistically insignificant, indicating virtually no influence on choices. Both estimated models revealed preference heterogeneity and a substantial number of no-buy responses. This suggests both limited consumer familiarity with GS concepts and a limitation of our attribute descriptions, which likely failed to convey information needed for effective purchasing decisions. This study is exploratory and limited by its convenience sample, imperfect price specification, and inability to estimate willingness-to-pay measures. Nevertheless, it provides empirical support for introducing geological footprint labeling and highlights the need for improved consumer information, policy tools, and public campaigns to promote recognition and sustainable management of geodiversity in agriculture. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

International organizations have highlighted the importance of consistent and reliable environment, social and governance (ESG) disclosure and metrics to inform business strategy and investment decisions. Greater corporate disclosure is a positive signal to investors who prioritize sustainable investment. In this study, economic and climate sentiment are extracted from the integrated and sustainability reports of the top 40 corporates listed on the Johannesburg Stock Exchange, employing domain-specific natural language processing. The intention is to clarify the complex interactions between climate risk, corporate disclosures, financial performance and investor sentiment. The study provides valuable insights to regulators, accounting professionals and investors on the current state of disclosures and future actions required in South Africa. A time series analysis of the sentiment scores indicates a noticeable change in the corporates’ disclosures from climate-related risks in the earlier years to climate-related opportunities in recent years, specifically in the banking and mining sectors. The trends are less pronounced in sectors with good ESG ratings. An exploratory regression study reveals that climate and economic sentiments contain information that explain stock price movements over the longer term. The results have important implications for asset allocation and offer an interesting direction for future research. Monitoring the sentiment may provide early-warning signals of systemic risk, which is important to regulators given the impact on financial stability. Full article

Slope stability monitoring in open-pit mining remains a critical challenge for geological hazard prevention, where conventional qualitative methods often fail to address dynamic risks. This study proposes an integrated framework combining empirical modeling (slope classification, hazard assessment, and safety ratings) with multi-source real-time monitoring (synthetic aperture radar, machine vision, and Global Navigation Satellite System) to achieve quantitative stability analysis. The method establishes an initial stability baseline through mechanical modeling (Bishop/Morgenstern–Price methods, safety factors: 1.35–1.75 across five mine zones) and dynamically refines it via 3D terrain displacement tracking (0.02 m to 0.16 m average cumulative displacement, 1 h sampling). Key innovations include the following: (1) a convex hull-displacement dual-criterion algorithm for automated sensitive zone identification, reducing computational costs by ~40%; (2) Ku-band synthetic aperture radar subsurface imaging coupled with a Global Navigation Satellite System and vision for centimeter-scale 3D modeling; and (3) a closed-loop feedback mechanism between empirical and real-time data. Field validation at a 140 m high phosphate mine slope demonstrated robust performance under extreme conditions. The framework advances slope risk management by enabling proactive, data-driven decision-making while maintaining compliance with safety standards. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

This paper aims to explore Virtual Power Plants (VPPs) in combination with Demand Response (DR) concepts, integrating solar power generation, Electric Vehicle (EV) charging and discharging, and user loads to establish an optimal energy management scheduling system. Willingness curves for load curtailment are derived based on the consumption patterns of industrial, commercial, and residential users, enabling VPPs to design DR mechanisms under Time-of-Use (TOU), two-stage, and critical peak pricing periods. An energy management model for a VPP is developed by integrating DR, EV charging and discharging, and user loads. To solve this model and optimize economic benefits, this paper proposes an Improved Wolf Pack Search Algorithm (IWPSA). Based on the original Wolf Pack Search Algorithm (WPSA), the Improved Wolf Pack Search Algorithm (IWPSA) enhances the key behaviors of detection and encirclement. By reinforcing the attack strategy, the algorithm achieves better search performance and improved stability. IWPSA provides a parameter optimization mechanism with global search capability, enhancing searching efficiency and increasing the likelihood of finding optimal solutions. It is used to simulate and analyze the maximum profit of the VPP under various scenarios, such as different seasons, incentive prices, and DR periods. The verification analysis in this paper demonstrates that the proposed method can not only assist decision makers in improving the operation and scheduling of VPPs, but also serve as a valuable reference for system architecture planning and more effectively evaluating the performance of VPP operation management. Full article