Search Results (286)

The study presents a developed process for producing monocalcium phosphate from hydroxyapatite ash, a by-product of meat-bone meal incineration. The process integrates technological and environmental synergies, enabling efficient recycling of both materials and energy. Waste hydroxyapatite ash, obtained as an intermediate by-product of the meat-bone meal process, is converted into high-quality monocalcium phosphate. Furthermore, waste heat from incineration is recovered, improving energy efficiency and reducing costs. Preliminary economic analysis indicates that the process is highly profitable, with an annual production capacity of 21,700 tons at a cost of $924 per ton, compared to a market price of $1400 per ton. The total production cost is estimated at $20,058,947, while total sales are projected to reach $30,380,000, yielding a profit of $10,321,053 (34% profit margin). The proposed method is consistent with the principles of the Circular Economy and Cleaner Production, promoting sustainability by reducing waste, lowering resource consumption, and enhancing energy efficiency. The developed technology is both environmentally friendly and economically viable, offering a promising pathway for efficient monocalcium phosphate production and a blueprint for industrial-scale implementation. Full article

The distributed renewable energy sources have been rapidly increasing in distribution networks, and some of them are configured with energy storage devices. Indeed, sharing surplus energy storage capacities for subsidizing the investment costs is economically attractive. Although such willingness is emerging, targeted trading mechanisms are less explored. Inspired by the electricity markets, this paper innovates a peer-to-peer energy storage flexibility market within distribution networks, which involves multiple vendors and customers, accompanied by a marginal pricing mechanism to enable the economic reallocation of surplus energy storage capacities in distribution systems. A small-scale market is first studied to show the proposed market mechanism and a larger-scale case is used to further demonstrate the scalability and effectiveness of the mechanism. Case studies set three distinct scenarios: markets with or without deficits and with carryover energy constraints. The numerical simulation validates its ability in reflecting the capacity supply–demand relationship, ensuring revenue adequacy and effectively improving economic efficiency. Full article

With the integration of high-penetration renewable energy, existing uniform marginal pricing mechanisms face critical challenges, including difficulty in recovering flexibility resource capacity costs and free-riding phenomena caused by renewable energy’s variability. To address these issues, this paper proposes a differentiated pricing mechanism for renewable energy based on analytical uncertainty representation to avoid marginal price distortion and promote the rational allocation of ancillary service costs. Firstly, a joint clearing model for energy and reserve ancillary service is developed, incorporating a distributional robust chance constraint based on moment information to model the uncertainty of renewable energy. Then, the composition structure of the nodal marginal price for ancillary service demand is redefined, offering clearer and more explicit price signals compared with traditional uniform marginal pricing. After that, quantification of the impact of energy storage on renewable energy forecast errors and ancillary service pricing is conducted, with a systematic analysis of its role in reducing ancillary service costs and optimizing generation revenue. Simulation results on the modified IEEE 30-bus system demonstrate significant advantages over traditional uniform pricing: the proposed mechanism ensures fair cost allocation, effectively mitigates free-riding problems, and provides clear economic signals. With energy storage units regulating renewable power output, it could lead to a 12.9% reduction in ancillary service costs while increasing total generation revenue by 6.73%. Full article

The rapid rise of live streaming e-commerce has transformed retail dynamics; however, the allocation of after-sales service costs between live streaming salespeople and manufacturers remains a critical, unresolved issue, exacerbated by fairness concerns among stakeholders. Utilizing a Stackelberg game model where manufacturers act as leaders and live streaming salespeople as followers, this study examines the impact of cost allocation on profit distribution and supply chain efficiency. The framework incorporates a coefficient for fairness concerns and an after-sales effort to develop nine decision-making scenarios. Analysis demonstrates that perceptions of fairness significantly reshape cost-sharing strategies: when manufacturers assume after-sales responsibilities, their scale effects reduce marginal costs, maximizing overall supply chain profit. Conversely, when a live streaming salesperson bears costs, excessive focus on fairness reduces total supply chain efficiency, even if short-term profits are gained through premium pricing. These results validate that the Stackelberg game model combined with fairness concerns and after-sales efforts balances efficiency–profit dual objectives, providing a sustainable governance framework for live streaming e-commerce ecosystems. Full article

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

Reducing emissions through the invisible hand of the market has become an important way to promote sustainable environmental development. The shadow price of carbon dioxide ($C{O}_2$) is the core element of the carbon market, and its accuracy depends on the micro level of the measurement data. In view of this, this paper innovatively uses enterprise level input-output data and combines the stochastic frontier method to obtain $C{O}_2$ shadow prices in China’s industrial sector. On this basis, the impacts of research and development (R&D) intensity, opening up level, traffic development level, population density, industrial structure, urbanization level, human resources level, degree of education, and environmental governance intensity on shadow price are discussed. In further analysis, this study introduces a Spatial Durbin Model (SDM) to evaluate the spatial spillover effects of $C{O}_2$ shadow price itself and its influencing factors. The research results indicate that market-oriented emission abatement measures across industries and regions can reduce total costs, and it is necessary to consider incorporating carbon tax into low-carbon policies to compensate for the shortcomings of the carbon Emission Trading Scheme (ETS). In addition, neighboring regions should coordinate emission abatement tasks in a unified manner to realize a sustainable reduction in $C{O}_2$ emissions. 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

Energy storage will play an important role in the new power system with a high penetration of renewable energy due to its flexibility. Large-scale energy storage can participate in electricity market clearing, and knowing how to make more profits through bidding strategies in various types of electricity markets is crucial for encouraging its market participation. This paper considers differentiated bidding parameters for energy storage in a two-stage market with wind power integration, and transforms the market clearing process, which is represented by a two-stage bi-level model, into a single-level model using Karush–Kuhn–Tucker conditions. Nonlinear terms are addressed using binary expansion and the big-M method to facilitate the model solution. Numerical verification is conducted on the modified IEEE RTS-24 and 118-bus systems. The results show that compared to bidding as a price-taker and with marginal cost, the proposed mothod can bring a 16.73% and 13.02% increase in total market revenue, respectively. The case studies of systems with different scales verify the effectiveness and scalability of the proposed method. Full article

With the rapid growth in the scale of high-percentage new energy generation, the structure of the new power system is changing. Influenced by the uncertainty and zero marginal cost characteristics of new energy, the security cost required by the power system under the high proportion of new energy access has increased dramatically. How to accurately measure the cost of the power system and assess the trend of the system cost changes and the impact on its end-user price has become critical. Accordingly, this paper creatively proposes a levelized cost of electricity (LCOE) prediction and end-user price deduction model for power systems with high renewable energy penetration. Firstly, the power system factor cost prediction model is constructed from the three dimensions of power-side, grid-side, and system operation cost. Secondly, a levelized cost of electricity prediction model is constructed based on the above model. Again, based on the analysis of the end-user price composition, the end-user price deduction model is proposed. Finally, the data of Gansu Province is selected for example analysis, and the results show that, in 2060, the power LCOE will be 0.064 USD/kWh, the system LCOE will be 0.103 USD/kWh, and the end-user price will rise to 0.1 USD/kWh. Full article

This paper seeks to provide a cost benefit analysis of the implementation of a vehicle-to-grid (V2G) charging strategy relative to a smart charging (V1G) strategy from the perspective of an individual electric vehicle (EV) owner with and without solar photovoltaics (PV) located on their roof. This work utilizes a novel AC optimized power flow model (ACOPF) to produce distributed location marginal prices (DLMP) on a modified IEEE-33 node network and uses a complete set of real-world costs and benefits to perform this analysis. Costs, in the form of the addition of a bi-directional charger and the increased vehicle depreciation incurred by a V2G strategy, are calculated using modern reference sources. This produces a more true-to-life comparison of the V1G and V2G strategies from the frame of reference of EV owners, rather than system operators, with parameterization of EV penetration levels performed to look at how the choice of strategy may change over time. Counter to much of the existing literature, when the analysis is performed in this manner it is found that the benefits of implementing a V2G strategy in the U.S.—given current compensation schemes—do not outweigh the incurred costs to the vehicle owner. This result helps explain the gap in findings between the existing literature—which typically finds that a V2G strategy should be favored—and the real world, where V2G is rarely employed by EV owners. Full article

This research focuses on analyzing the determinants of financial performance for the companies included in the Standard & Poor’s 500 index over the period from 2014 to 2023. To guide managerial decisions aimed at enhancing company performance, this study examines, as key drivers, the main financial indicators, core corporate governance characteristics, and U.S. public governance indicators. The investigation begins with a retrospective review of the specialized literature, highlighting the findings of previous studies in the field and providing the basis for selecting the variables used in the present empirical analysis. The research method employed is fixed-effects panel-data regression. The dependent variables are financial performance measures, such as the EBITDA margin, EBIT margin, net profit margin, and ROA. This study’s main results show that the price-to-book ratio, liquidity, sales growth, CEO duality, board gender diversity, ESG score, and U.S. regulatory quality exert a positive influence on financial performance. In contrast, the price-to-earnings ratio, net debt, capital intensity, R&D intensity, weighted average cost of capital, board independence, and the COVID-19 pandemic crisis have a negative impact on the financial performance of U.S. companies. The findings of this investigation could serve as benchmarks for supporting managerial decisions at the company level regarding the improvement of their financial performance. Full article

In a collaborative effort with private agents of the oilseed industry, INIA conducted a research study to determine the feasibility of framing soybean production in Uruguay into a sustainable development pathway. A spatial model based on land suitability analysis and the imposition of other soil restrictions (risk erosion, current regulations, and permanent soil uses) was adopted to estimate potential soybean yields and the most suitable cropping areas in the country. Assuming a national average production cost for soybeans, total costs were calculated by adding location-specific logistics and land rent costs. Crop economic margins were estimated using a combination of price, technology, and climate-change scenarios. Only areas exhibiting non-negative margins were considered suitable for sustainable cultivation. With all restrictions imposed, the potential soybean area on rotation with other crops and pastures in Uruguay would range between 2.1 and 2.9 million hectares, depending on the prevailing producer price level. Climate change effects did not show significant differences on their own. This ad-hoc approach can be useful for private and public decision-makers. It can be applied to any crop situation or region where the objective is to define how far it is possible to expand and intensify production sustainably, without compromising the environment. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

Energy Performance Contracts (EPC) are performance-based financing mechanisms designed to improve energy efficiency and support renewable energy adoption in the public sector. This study examines the economic efficiency of a 1710.72 kWp solar power plant (SPP), implemented under an EPC at Alanya Alaaddin Keykubat University, using a regression-based analysis. The model evaluates the effects of solar radiation, investment cost, and electricity sales price on unit production cost, and its predictions were compared with actual production data. Results show the system exceeded the EPC contract target by 16.2%, producing 2,423,472.28 kWh in its first year and preventing 1168.64 tons of CO₂ emissions. The developed multiple linear regression model achieved a predictive error margin of 14.7%, confirming its validity. This study highlights the technical, economic, and environmental benefits of EPC applications in Türkiye’s public institutions and offers a practical decision-support framework for policymakers. The novelty lies in integrating a regression model with operational data and providing a comparative assessment of planned, predicted, and actual outcomes. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article