Search Results (9,755)

The harvesting of energy from movements is one of the purposes of triboelectric nanogenerators (TENGs). Among the various devices designed to perform this function, floors are one of the primary ones, as they do not need to be individually fitted to each subject and can be manufactured and installed on a large scale. This work classifies previously published TENG-based floors based on their materials, electrical performance in terms of the voltage, current, and power they produce, and their application in different fields. The materials used have been correlated with other important aspects for floors, such as weather or flame resistance, sustainability, recyclability or biodegradability of materials, and price. The synthesis of the variety of TENG-based floor models, which incorporate novel materials, hybrid technologies, or various functionalities, among other characteristics, can enrich and inspire the reader to enhance the performance of future floor designs based on the triboelectric effect. In addition, a novel triboelectric floor design made of nitrile butadiene rubber (NBR) and fluorine kautschuk material is presented, along with the electrical power generated when tribolayers are in contact. For the three floor strips measuring 40 cm long × 4 cm wide and 1 mm thick, electrical current and voltage output was measured, achieving nearly 0.1 W (20 V & 4.5 mA) of electrical power generation. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

Developing far-offshore wind power integrated with hydrogen production represents a critical pathway for China’s energy decarbonization. However, the investment prospects of off-grid offshore wind-to-hydrogen projects remain highly uncertain due to volatile technology costs and hydrogen prices, complicating the evaluation of project value and optimal timing. To address the oversimplified treatment of electrolyzer operation and the limited consideration of alkaline electrolyzers in the existing studies, this paper proposes an integrated assessment framework that combines time-series operational simulation with real options analysis. A detailed dynamic model of an alkaline (ALK)–proton exchange membrane (PEM) hybrid configuration is developed to simulate the coordinated hydrogen production under fluctuating wind power. Technical learning effects and stochastic hydrogen price processes are incorporated, and the least-squares Monte Carlo method is applied to determine the optimal investment strategies. A case study of a planned far-offshore wind farm in Guangdong indicates that, compared with a standalone PEM configuration, the hybrid configuration reduces the levelized hydrogen cost by about 15%, increases the investment value by up to 17 times under slow technological progress, and brings forward the optimal investment year by five years, from 2039 to 2034. Sensitivity analysis shows that expected hydrogen prices and discount rates dominate the investment outcomes. Full article

Traditional cloud pricing models are allocation-centric, where users are charged based on reserved resources rather than workload energy consumption. However, modern AI workloads exhibit substantial and heterogeneous power behavior, limiting the effectiveness of such allocation-centric pricing. This paper presents a comprehensive experimental study of nine widely used workloads across 50 controlled configurations, including standalone and concurrent executions under varying resource partitions. Our results show that total system power is largely unaffected by how resources are divided among co-located workloads, except in cases of explicit resource under-provisioning or severe resource contention. Across 45 workload–core groups, 41 exhibit a coefficient of variation below 3% across different co-located workloads, demonstrating structural stability of workload-level power profiles under heterogeneous execution environments. In contrast, deployment choice (e.g., CPU versus GPU execution) can shift the same model into distinct power regimes. Based on measured power decomposition and scaling behavior, we derive an empirical categorization framework distinguishing GPU-dominant and CPU-dominant workloads, further characterized by utilization and memory dimensions. From an energy perspective, CPU utilization (for CPU-dominant workloads) and SM utilization (for GPU-dominant workloads) emerge as the primary determinants of power magnitude, while memory-related parameters contribute marginally to overall power. These findings provide empirical evidence that allocation-based pricing is a weak proxy for actual energy cost and motivate energy-aligned cloud management strategies grounded in workload power profiles. As our findings are derived from a controlled single-node experiment, evaluations under more realistic data center environments will be required for further generalization. Full article

In the context of rural sustainability and poverty alleviation within the developing world, a key dilemma facing the international community is to identify suitable strategies and mechanisms to bring multiple stakeholders together to work in efficient and sustainable ways. This paper focuses on the Public–Private–Producer Partnership (4P), a model that involves cooperation between government agencies, business firms, and small-scale producers to foster mutual trust and enhance collaboration through infrastructure development and capacity building in the agricultural value chain. Drawing on evidence from China, Indonesia, Rwanda, Ghana, and Nigeria, this study examines the impact of 4P on crop productivity, agricultural infrastructure, market access, stakeholder empowerment, employment, the land tenure system, and household income. This paper combines value chain analysis, Theory of Change mapping, and both qualitative and quantitative evaluation techniques to assess how the 4P model functions in different institutional and ecological contexts. While the model promotes inclusive growth, it also faces challenges such as price volatility, insufficient long-term sustainability, and limited integration of smallholder farmers into formal value chains. The paper discusses policy implications for improving the 4P model’s effectiveness in poverty alleviation and local economic development, highlighting the importance of better governance structures, financial mechanisms, and market stability. This paper sheds new light on inclusive, justified, and sustainable collaboration mechanisms for participatory agencies and individuals. Full article

The operation of the gas–steam–electricity multi-energy coupling system in iron and steel enterprises faces critical challenges: conflicts between energy efficiency and economic objectives, insufficient scheduling accuracy, and low energy utilization caused by source–load fluctuations. To address these issues, this paper proposes a hybrid scheduling model based on condition awareness and multi-objective optimization. The model integrates three key components. First, an energy fluctuation prediction technology based on working condition changes is developed. By acquiring real-time production signals and gas flow data, combined with a condition definition management module, it enables automatic identification and tracking of equipment operation status. A working condition sample curve superposition method is used to calculate energy medium imbalances, generating visual prediction curves for key parameters such as blast furnace, coke oven, and converter gas holder levels, achieving an average prediction accuracy of ≥95%. Second, a peak-shifting and valley-filling scheduling model for gas holders is designed, leveraging time-of-use electricity prices. During valley price periods, power purchases are increased and surplus gas is stored; during peak price periods, gas power generation is increased to reduce purchased electricity. A nonlinear model capturing the load–efficiency relationship of boilers and generators is established to dynamically optimize scheduling strategies. This reduces the proportion of peak hour power purchases by 10.3%, energy costs by 3.12%, and system energy consumption by 2.16%. Third, a multi-period and multi-medium energy optimization scheduling model is formulated as a mixed-integer nonlinear programming (MINLP) problem, with dual objectives of minimizing operating cost and energy consumption. Constraints include energy supply–demand balance, equipment operating limits, gas holder capacity, and generator ramp rates. The Pareto optimal solution set is obtained using the AUGMECON2 method and efficiently computed with the IPOPT solver. Application results demonstrate that the model achieves zero gas emissions, a dispatching instruction accuracy of 95%, and a 0.8% increase in the proportion of peak–valley-level self-generated power, outperforming comparable technologies. It provides technical support for the safe, efficient, and economic operation of multi-energy systems in iron and steel enterprises. Full article

The volatility of oil prices has a considerable impact on the economies of oil-exporting countries, making it critical to understand how price variations affect labour markets and unemployment. This study investigates the distinct role of labour market institutions in moderating the effects of oil price volatility on unemployment. Using the Cross-Sectionally Augmented Autoregressive Distributed Lag Model (CS-ARDL) on a panel dataset of nine African oil-exporting countries from 1994 to 2024, the study establishes a strong negative link between oil price changes and unemployment. Furthermore, the results show that real GDP growth leads to a reduction in unemployment in the long run, while the labour market institutional index has a negative impact on unemployment. Interacting the oil price with the labour market institutional index causes a further reduction in unemployment. These results suggest that good labour market institutions and macroeconomic stability are essential for reducing unemployment. While increases in oil prices directly stimulate a reduction in unemployment in African oil-exporting countries, this impact is reinforced by the presence of good labour market institutions in an economy. Therefore, the results suggest that countries with strong labour market institutions are more resilient in reducing the negative impact of oil price volatility on employment. As such, policymakers must prioritise labour market institutional reforms to enhance countries’ capacity to absorb oil price shocks and reduce unemployment during periods of oil prosperity and shield against employment declines when oil prices drop. Furthermore, the creation of oil stabilisation funds in these countries may serve a similar purpose. Contribution/originality: Against a background of inconclusive empirical evidence in the literature and a dearth of research on African countries, this study investigates the role of labour market institutions (LMIs) in the oil price–unemployment nexus in African oil-exporting countries. While highly dependent on oil revenue, these countries record persistent structural unemployment. Therefore, the study provides critical evidence to guide the formulation of policies necessary to deal with external shocks and facilitate structural shifts required for employment growth. Existing studies consider general institutional variables such as democratic accountability and the rule of law and do not assess the effect of labour market institutions. The current study fills in this gap by assessing the distinct role of labour market institutions that are specifically designed to regulate only work-related activities, such as quality of labour regulations, adequacy of social protection and unemployment benefits. Furthermore, this study employed the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) for econometric estimations. Compared to previous studies, this is a more appropriate method that accounts for unobserved common factors such as oil price shocks affecting all oil-exporting countries simultaneously. Full article

Against the backdrop of increasing global emphasis on sustainable development and ecological conservation, green seafood has emerged as a key component of sustainable marine food consumption. However, the discrepancy between consumers’ intention to consume and their consumption behavior remains a critical issue requiring in-depth investigation. Herein, based on survey data collected from 415 consumers in China in 2025, this study employs structural equation modeling (SEM) to analyze the determinants and mechanisms influencing green seafood consumption intention and behavior. The findings indicate that heightened concerns regarding dietary health, food safety, and nutrition significantly enhance consumer intention, driven primarily by ecological awareness and the pursuit of a higher quality of life. Individual and household characteristics, along with consumers’ cognitive status of green seafood, exert significant positive effects on consumption intention, with cognitive status demonstrating the strongest influence. Nevertheless, a notable gap exists between consumption intention and actual behavior. Among respondents with consumption intention, only 48.7% had ever purchased green seafood, and the consumption frequency remained generally low. SEM path coefficients further reveal that marketing factors play a dominant role in actualizing consumption behavior. Compared to marketing factors, consumption intention shows a relatively weaker effect in facilitating consumption behavior. This finding further confirms the intention–behavior gap in green seafood consumption. The intention–behavior gap in green seafood consumption is jointly driven by asymmetric information on product quality, an underdeveloped certification system, a relatively undiversified supply structure, and elevated prices. Accordingly, this study proposes an integrated strategy that includes establishing a unified certification and traceability system, optimizing supply structures and pricing mechanisms, and strengthening science communication and targeted marketing. These measures aim to bridge the intention–behavior gap and promote the transition toward sustainable consumption patterns. Full article

The reconstruction of nodal prices and generation patterns in electricity markets with network constraints constitutes a challenging inverse analysis problem due to congestion-induced non-uniqueness and limited observability. This study introduces node symmetry analysis as a novel early indicator of locational marginal price (LMP) growth in power systems with high renewable energy penetration. Symmetric nodes, defined as nodes with identical generation cost structures and comparable network topology, exhibit near-identical price signals under uncongested conditions. In this study, the term “price” refers to the LMP obtained from the DC-OPF market-clearing model under scenarios with high renewable energy penetration. Deviations from this symmetry, quantified through price differences between symmetric node pairs (ΔLMP), serve as sensitive indicators of emerging network stress and congestion, providing early warning of peak-price events. Using DC power flow sensitivities and congestion indicators, LMPs are reconstructed in a simplified five-node test system under three scenarios: baseline operation, severe transmission congestion, and high renewable generation variability. Results show strong correlations between symmetry violations and system-wide price increases. In congested scenarios, ΔLMP exceeding €2/MWh consistently precedes peak prices by 1–2 h, demonstrating the metric’s predictive capability. Integration of storage further highlights the operational value of symmetry-based analysis, showing reductions in curtailed renewable generation and peak prices. The proposed framework offers a computationally efficient and interpretable tool for congestion diagnosis, price trend forecasting, and inverse market analysis, with potential scalability to larger AC networks and stochastic scenarios. These findings provide actionable insights for system operators, market participants, and regulators seeking to enhance flexibility, reliability, and economic efficiency in high-renewable electricity markets. Full article

Against the growing global focus on green development, government subsidies are widely recognized as a crucial policy tool to promote firms’ green transformation. In competitive markets, green investment decisions are jointly shaped by supply chain power structures, and different research and development (R&D) leadership can yield distinct policy outcomes. This study develops a Bertrand competition model of a green supply chain with one manufacturer and two competing retailers, comparing two structures: manufacturer-led R&D (SM) and retailer-led R&D (SR). We examine how these policies affect pricing decisions, product greenness, and revenues. Under the retailer-led R&D, a green cost-sharing ratio is introduced to capture the interaction between internal coordination and government support. The results show that subsidy effects depend on consumer green awareness. When green awareness is low, subsidies mainly raise prices through cost pass-through. When green awareness is high, subsidies can lower prices by stimulating demand. In addition, the interaction between subsidy intensity and cost sharing leads to non-monotonic changes in retailers’ revenues. By comparing different market structures and parameter settings, we identify the conditions under which SM or SR dominates in terms of prices, product greenness, and revenues, providing guidance for more flexible green subsidy design. Full article

As modern power markets integrate more renewable generation, day-ahead energy clearing remains the central procurement step, while flexibility products are procured to ensure that the cleared energy schedule can be operated securely. This paper proposes a closed-loop framework linking the Power Exchange (PX) and the Independent System Operator (ISO) to bridge energy-market settlement and network-feasible operation. The PX performs staged day-ahead clearing with energy settled first, followed by aAutomatic generation control (AGC) and spinning reserve (SR) procured from the residual headroom of committed (energy-awarded) units. The ISO then validates the cleared schedule using an equivalent current injection (ECI)-based screening. This paper uses a single-period (single-hour) IEEE 30-bus case setting; multi-period scheduling and intertemporal constraints are not modeled. When congestion is detected, power-flow tracing identifies the main contributors and guides a minimal-change redispatch. The ISO-feasible dispatch is then sent back to the PX for re-clearing, aligning prices and welfare with an executable operating point. The resulting nonconvex clearing problems with valve-point effects and prohibited operating zones are solved by Artificial Protozoa Optimizer with Social Learning (APO–SL) and evaluated against representative metaheuristic baselines. IEEE 30-bus studies show that off-peak and average-load cases pass ISO screening directly, whereas the peak case tightens reserve headroom (SR capped at 39.08 MW) and triggers congestion. After ISO feedback and energy re-clearing, line loadings return within limits. The ISO-feasible dispatch changes the marginal accepted offer and lifts the MCP (3.73 → 4.38 $/MWh). The welfare value reported here follows the paper’s settlement-based definition (purchase total minus accepted offer cost), and it increases accordingly (113.77 → 190.17 $/h). Full article

Many countries rely on coal for energy security during renewable transitions. This study conducts a technical, economic, and environmental analysis of hybridizing a supercritical coal-fired power unit with photovoltaics (PV) to create a sustainable hybrid system at a plant in Silesian Voivodeship, Poland. The goal is to assess costs and optimal operating conditions for a coal–PV hybrid under varying scenarios, using a decision-support model that integrates fuel prices, CO₂ emission charges (EUA), and technical parameters. Two main scenarios are modeled. In auxiliary-only PV (112 MW system), real-time power supplies pumps and fans, cutting coal consumption without storage; LCOE decreases with annual hours (2800–7000), outperforming conventional coal across EUA prices (20–50 EUR/t). In PV surplus export, excess generation (1300 h/year) is grid-fed for revenue, amplifying LCOE reductions—hybrid superiority emerges above 34 EUR/t EUA, per equivalence thresholds. Results show coal electricity exceeds low-emission costs above 34 EUR/t CO₂, with maximum disparity at 50 EUR/Mg. The hybrid leverages existing infrastructure, mitigates solar intermittency via auxiliary supply, ensures baseload continuity, boosts flexibility, and prolongs asset life—reducing >123,000 EUA/year at 145,000 MWh PV output. This sustainable hybrid promotes energy transition, reduces fossil fuel dependence, and aligns with global sustainability goals. Full article

Humans face environmental deterioration. Scholars have identified carbon dioxide as one of the culprits, and they emphasize carbon offset. Researchers are investigating carbon offset investments. Some researchers have encouragingly deployed multivariate variational mode decomposition methods, but they have not fully optimized them. Some researchers have opportunely assessed capital asset pricing models, but they have not fully justified them. We devise multiple-objective portfolio selection models, fully optimize them, and dominate carbon offset indexes. We extend the classical methodology of advancing from portfolio selection to capital asset pricing models into the methodology of advancing from multiple-objective portfolio selection to multiple-objective capital asset pricing models. Specifically, we explore multiple-objective capital asset pricing models by numerically verifying many tangent lines (instead of the traditionally singular tangent line) and suggesting a tangent plane (instead of tangent lines). For multiple-objective zero-covariance capital asset pricing models, we numerically compute a set of zero-covariance portfolios (instead of the traditionally singular zero-covariance portfolio) and suggest picking an advantageous zero-covariance portfolio. We consider the second-level indicators of carbon offset and generalize three-objective portfolio selection to k-objective portfolio selection. As for contributions, first, this paper’s methodology is to logically advance from multiple-objective portfolio selection to multiple-objective capital asset pricing models, whereas the literature typically covers multiple-objective portfolio selection alone and barely covers multiple-objective capital asset pricing models. Second, this paper numerically demonstrates some difficulties and proposes hypothetical solutions in the process of obtaining multiple-objective capital asset pricing models. Full article

National legislative frameworks governing prescribing, pricing, reimbursement, and dispensing play a decisive role in shaping access to medicines. This study examines the financial availability of medicines in Bulgaria and North Macedonia through a comparative review of national pharmaceutical legislation, pricing mechanisms, reimbursement models, and digitalisation policies, assessed in relation to European Union standards. The findings indicate that access to medicines in both countries is shaped by the combined effects of multiple regulatory and financial instruments rather than by individual policy measures. Both systems apply strict control of prescribing and dispensing, external reference pricing, and positive reimbursement lists, reflecting alignment with international recommendations. However, significant differences in policy design lead to divergent access outcomes. Bulgaria’s more advanced digitalisation of prescribing and reimbursement, including mandatory electronic prescribing for selected therapeutic groups, enhances regulatory oversight and expenditure control but is associated with higher patient out-of-pocket expenditure, partly due to the application of the standard value-added tax on medicines. In contrast, North Macedonia combines lower taxation with capped patient co-payments, higher regulated pharmacy margins, and fixed pharmacy remuneration per prescription, contributing to improved financial affordability for patients while supporting pharmacy sustainability. Additional instruments, such as the Generics without Co-Payment List, further strengthen patient financial protection. The study provides comparative evidence relevant to pharmaceutical policy reforms and highlights the importance of balanced regulatory approaches that promote affordability, system sustainability, and equitable access to medicines. Full article

This study examines the long-run relationship between the external terms of trade (TOTs) and real GDP of the services sector in a small open economy, focusing on the Peruvian economy. Although the services sector concentrates the largest share of employment and urban income, its exposure to persistent external price cycles remains relatively understudied in sector-specific empirical research. Building on the notion that TOTs operate as an external anchor shaping macroeconomic conditions beyond export activities, this paper evaluates whether sustained external shocks are structurally linked to services-sector performance. The analysis employs a Johansen cointegration framework and a bivariate Vector Error Correction Model (VECM) using quarterly data for the period 1996–2024. This approach allows for distinguishing long-run equilibrium relationships from short-run adjustments without imposing strong causal assumptions. The results indicate the presence of a stable long-run relationship between the TOTs and services-sector GDP, with adjustment dynamics consistent with a gradual absorption of external shocks. From a sustainability perspective, the findings suggest that the expansion of the services sector is not independent from external trade conditions, highlighting the relevance of structural resilience under recurrent international price volatility. This study contributes to the literature by providing sector-level empirical evidence for a resource-dependent economy and offers a replicable analytical framework for examining structural sustainability in other small open economies with similar productive characteristics. Full article