Search Results (3,353)

In the context of the global response to climate change and the active promotion of energy transformation, a number of low-carbon policies coupled with the development of synergies to help power system transformation is an important initiative. However, the insufficient articulation of the green power market, tradable green certificate (TGC) market, and carbon emission trading (CET) mechanism, and the ambiguous policy boundaries affect the trading decisions made by its market participants. Therefore, this paper systematically analyses the composition of the main players in the electricity-CET-TGC markets and their relationship with each other, and designs the synergistic mechanism of the electricity-CET-TGC markets, based on which, it constructs the optimal profit model of the thermal power plant operators, renewable energy manufacturers, power grid enterprises, power users and load aggregators under the electricity-CET-TGC markets synergy, and analyses the behavioural decision-making of the main players in the electricity-CET-TGC markets as well as the electric power system to optimise the trading strategy of each player. The results of the study show that: (1) The synergistic mechanism of electricity-CET-TGC markets can increase the proportion of green power grid-connected in the new type of power system. (2) In the selection of different environmental rights and benefits products, the direct participation of green power in the market-oriented trading is the main way, followed by applying for conversion of green power into China certified emission reduction (CCER). (3) The development of independent energy storage technology can produce greater economic and environmental benefits. This study provides policy support to promote the synergistic development of the electricity-CET-TGC markets and assist the low-carbon transformation of the power industry. Full article

In marketing science, product categorization using cannibalization relationship data is an emerging but still underdeveloped area, where clustering using price effect information is a novel direction that is worth further exploration. In this study, by assuming a realistic modeling of the cross-price effect, we developed and experimentally validated with simulations an agglomerative clustering algorithm that outputs clustering results closer to the ground truth compared with other agglomerative algorithms based on traditional cluster linkages. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

Livestock, poultry, and fisheries play an important economic role in Pakistan’s animal industry. The pet industry is also emerging and contributing to the country’s economy and people’s emotional well-being. This review provides insight into the current challenges and future directions of the animal industry in Pakistan. Livestock, poultry, and fisheries provide an economically beneficial source of milk, meat, and eggs; however, they face challenges such as disease outbreaks, antimicrobial resistance, climate change, natural disasters, and a lack of proper policies. Likewise, humans benefit from companion animals that provide emotional attachment. Moreover, the pet food market has also shown potential growth, contributing to the country’s economy. Due to the close association between animals and humans, both are at risk for infectious disease transmission. Challenges such as the lack of strong animal welfare laws and the increasing number of stray dogs and cats threaten human safety and that of other animals. We highlight current problems and additional approaches to the management of livestock, poultry, fisheries, and pets, which need to be addressed to further advance the animal industry in Pakistan. Full article

The importance of non-performing loans (NPLs) for the stability of financial sectors is difficult to overestimate. The NPL level depends on numerous factors; this study’s goal is to determine the impact of macroeconomic factors on NPLs with the mediation effect of foreign, saving and social factors in Kazakhstan’s banking sector. To determine the affecting factors, the authors performed a systematic literature review. To determine the dependencies between constructs, the Partial Least Squares Structural Equation Modeling (PLS-SEM) method was used. Macroeconomic factors’ direct effect on non-performing loans (NPLs) was examined; a significant negative dependence was determined. The mediation effect of foreign, saving, and social factors was investigated. Foreign factors have a mediation effect, strengthening the dependence between macro factors and NPLs. Nevertheless, they do not have a mediating effect; moreover, they balance and make the effect of macro factors on NPLs statistically insignificant. These findings allow policy-makers to stabilize the situation on NPLs in the financial markets of developing countries like Kazakhstan by directly influencing not only the financial sector but also other sectors of the national economy. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article

Solving competitive facility location problems can optimize market share or operational efficiency in environments where multiple firms compete for customer attention. In such contexts, facility attractiveness is shaped not only by geographic proximity but also by customer preference characteristics. This study presents a novel heuristic framework that integrates multi-view K-means clustering with customer behavior modeling reinforced by a co-regularization mechanism to align clustering results across heterogeneous data views. By jointly exploiting spatial and behavioral information, the framework clusters customers and facilities into meaningful market segments. Within each segment, a bilevel optimization model is applied to represent the sequential decision-making of competing entities—where a leader first selects facility locations, followed by a reactive follower. An empirical evaluation on a real-world dataset from San Francisco demonstrates that the proposed approach, using optimal co-regularization parameters, achieves a total runtime of approximately 4.00 s—representing a 99.34% reduction compared to the full CFLBP-CB model (608.58 s) and a 99.32% reduction compared to a genetic algorithm (585.20 s). Concurrently, it yields an overall profit of 16,104.17, which is an approximate 0.72% increase over the Direct CFLBP-CB profit of 15,988.27 and is only 0.21% lower than the genetic algorithm’s highest profit of 16,137.75. Moreover, comparative analysis reveals that the proposed multi-view clustering with co-regularization outperforms all single-view baselines, including K-means, spectral, and hierarchical methods. This superiority is evidenced by an approximate 5.21% increase in overall profit and a simultaneous reduction in optimization time, thereby demonstrating its effectiveness in capturing complementary spatial and behavioral structures for competitive facility location. Notably, the proposed two-stage approach achieves high-quality solutions with significantly shorter computation times, making it suitable for large-scale or time-sensitive competitive facility planning tasks. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s², comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article

The increasing demand for plant-based foods has led to significant growth in the availability, at a retail level, of plant-based cheese analogue products. This study presents the first comprehensive benchmarking of commercially available plant-based cheese analogue (PBCA) products in the Irish market, comparing them against conventional cheddar and processed dairy cheeses. A total of 16 cheese products were selected from Irish retail outlets, comprising five block-style plant-based analogues, seven slice-style analogues, two cheddar samples, and two processed cheese samples. Results showed that plant-based cheese analogues had significantly lower protein content (0.1–1.7 g/100 g) than cheddar (25 g/100 g) and processed cheese (12.9–18.2 g/100 g) and lacked a continuous protein matrix, being instead stabilized largely by solid fats, starch, and hydrocolloids. While cheddar showed the highest hardness, some plant-based cheeses achieved comparable hardness using texturizing agents but still demonstrated lower tan δ_max values, indicating inferior melting behaviour. Thermograms of differential scanning calorimetry presented a consistent single peak at ~20 °C across most vegan-based variants, unlike the dual-phase melting transitions observed in dairy cheeses. Sensory analysis further highlighted strong negative associations between PBCAs and consumer-relevant attributes such as flavour, texture, and overall acceptability. By integrating structural, functional, and sensory findings, this study identifies key formulation and performance deficits across cheese formats and provides direction for targeted improvements in next-generation PBCA product development. Full article

Extracellular vesicle (EV)-based therapies have attracted considerable attention as a novel class of biologics with broad clinical potential. However, their clinical translation is impeded by the fragmented and rapidly evolving regulatory landscape, with significant disparities between the United States, European Union, and key Asian jurisdictions. In this review, we systematically analyze regional guidelines and strategic frameworks governing EV therapeutics, emphasizing critical hurdles in quality control, safety evaluation, and efficacy demonstration. We further explore the implications of EVs’ heterogeneity on product characterization and the emerging direct-to-consumer market for EVs and secretome preparations. Drawing on these insights, in this review, we aim to provide a roadmap for harmonizing regulatory requirements, advancing standardized analytical approaches, and fostering ongoing collaboration among regulatory authorities, industry stakeholders, and academic investigators. Such coordinated efforts are essential to safeguard patient welfare, ensure product consistency, and accelerate the responsible integration of EV-based interventions into clinical practice. Full article

This article examines the relationship between human capital accumulation, household income, and shared prosperity using 2005–2018 household surveys in Cameroon, Ethiopia, Kenya, Nigeria, and Uganda. Human capital is found to be positively and significantly correlated with household wellbeing in all five nations. Health’s indirect benefits in Cameroon, Ethiopia, and Kenya augment its direct benefits. Education has monotonic welfare benefits from primary to tertiary levels in all countries. Human capital and labour market participation are strongly associated with household wellbeing. The equalization of human capital endowments increases income for the 40% of the least well-off groups in three of the sample countries. All countries except Uganda record a decrease in human capital deprivation over the period studied. Redistribution is associated with a reduction in human capital deprivation, although less systematically than in the growth scenario. These results suggest that sizeable reductions in human capital deprivation are more likely to be accomplished by interventions that focus on boosting general human capital outcomes than those that redistribute the human capital formation inputs. In countries with declining human capital deprivation, the within-sector interventions seem to account for this success. Substantial heterogeneity in human capital poverty exists within and across countries and between rural and urban areas. Full article