Search Results (60)

Agrivoltaic greenhouses in southern Spain offer a sustainable way to manage excessive irradiance levels by generating renewable energy. This study presents a shading experiment on tomato cultivation in a raspa-y-amagado greenhouse in Almeria, southern Spain, during the 2023–2024 growing season. Photovoltaic modules were mimicked by opaque plastic sheets that were arranged in a checkerboard pattern on the roof of the greenhouse. Two shading zones (30% and 50% roof cover ratio) were compared against an unshaded control zone. Microclimate, plant physiology, yield and quality were monitored during the study. The results show that shading influenced the microclimate, which directly impacted crop yield. The 30% and 50% shading zones resulted in 15% and 26% crop yield reductions, respectively. A preliminary, theoretical analysis of potential revenues of the photovoltaic yield showed that reductions in crop yield can be overcompensated by the energy generated by the PV system. For the summer crop cycle, a higher PV production and lower crop yield reductions can be expected. The economic advantage demonstrates the potential of agrivoltaic greenhouses in southern Spain. Full article

This study investigates whether government subsidies promote R&D cost stickiness in the new energy vehicle (NEV) industry in China—that is, whether public funding encourages firms to retain R&D resources even during periods of declining sales. While prior literature primarily explores the relationship between subsidies and R&D investment levels, it often overlooks firms’ financial position and dynamic cost behaviors. Given that R&D investment has high adjustment costs and is sensitive to cash flows, reductions in R&D spending during downturns may reflect managerial cost asymmetry rather than a crowding-out effect of subsidies. Moreover, government subsidies may serve as a signal of long-term market optimism, motivating managers to retain R&D resources during economic downturns. Using a panel dataset of 573 listed new energy vehicle (NEV) firms in China’s A-share market from 2007 to 2021, we construct a model based on the asymmetric cost behavior framework to empirically assess the impact of government subsidies on R&D cost stickiness. The results show that government subsidies significantly increase the degree of R&D cost stickiness. Serving as a signal of future market optimism, subsidies raise managerial expectations and incentivize decisions to retain R&D-related costs during economic downturns. This positive relationship is more pronounced in firms with high levels of green innovation, large-scale enterprises, and non-state-owned firms. These findings suggest that public funding alleviates managerial pressure to cut R&D expenses amid revenue declines, thereby supporting firms’ long-term innovation strategies. Our study contributes to the cost management literature by highlighting a novel channel through which subsidies influence managerial discretion under uncertainty. It also provides policy implications for the future phase-out of subsidies, emphasizing the need for complementary market mechanisms to sustain innovation investment, particularly for small, young, and financially constrained firms. Full article

Background: Investment decisions in stocks are one of the most complex tasks due to the uncertainty of which stocks will increase or decrease in their values. A diversified portfolio statistically reduces the risk; however, stock choice still substantially influences the profitability. Methods: This work proposes a methodology to automate investment decision recommendations with clear explanations. It utilizes generative AI, guided by prompt engineering, to interpret price predictions derived from neural networks. The methodology also includes the Artificial Intelligence Trust, Risk, and Security Management (AI TRiSM) model to provide robust security recommendations for the system. The proposed system provides long-term investment recommendations based on the financial fundamentals of companies, such as the price-to-earnings ratio (PER) and the net margin of profits over the total revenue. The proposed explainable artificial intelligence (XAI) system uses DeepSeek for describing recommendations and suggested companies, as well as several charts based on Shapley additive explanation (SHAP) values and local-interpretable model-agnostic explanations (LIMEs) for showing feature importance. Results: In the experiments, we compared the profitability of the proposed portfolios, ranging from 8 to 28 stock values, with the maximum expected price increases for 4 years in the NASDAQ-100 and S&P-500, where both bull and bear markets were, respectively, considered before and after the custom duties increases in international trade by the USA in April 2025. The proposed system achieved an average profitability of 56.62% while considering 120 different portfolio recommendations. Conclusions: A t-Student test confirmed that the difference in profitability compared to the index was statistically significant. A user study revealed that the participants agreed that the portfolio explanations were useful for trusting the system, with an average score of 6.14 in a 7-point Likert scale. Full article

Agricultural insurance is by far the most popular risk management tool used in Iran. Despite many years of experience, Iran’s current insurance policy has not managed to protect all producers in the sector. The basic principle of whole-farm insurance consists of pooling all the insurable risks of a farm into a single policy and overcoming most of the major impediments to existing policies. This study aimed to evaluate the benefits of whole-farm insurance (WFI) in Zanjan province of Iran. This study employed historical farm-level and county-level data from 1982 to 2021 to estimate yield and price density functions and predict future values. Parametric and non-parametric approaches were utilized to calculate farmers’ expected compensation and guaranteed and simulated revenues. The premium rates were then calculated using the PQH simulation and Cholesky decomposition and compared under three scenarios: the single-crop, double-crop, and triple-crop options. Finally, farmers’ welfare benefits were compared under the three scenarios with the no-insurance case. The results demonstrate that WFI provides lower loss ratios compared to yield insurance and crop-specific insurance. Furthermore, producer welfare can be improved when they insure at least one crop compared to no-insurance. For example, the welfare benefits of insuring wheat, barley, alfalfa, wheat–barley, wheat–alfalfa, barley–alfalfa, and barley–alfalfa in terms of cost reduction to producers at 75% coverage are 8.8, 1.8, 2.9, 1.2, 0.9, and 1.8, respectively. Therefore, we recommend that the Iranian Agricultural Insurance Fund adopts WFI as a new risk management tool. This policy has the potential to decrease insurance premiums and administrative costs while improving the certainty equivalents and benefits to farmers through crop insurance. Full article

Taxation serves as a vital lifeline for government revenue, directly contributing to national development and the welfare of its citizens. Ensuring the efficiency and effectiveness of the tax collection process is essential for maintaining a sustainable economic framework. This study investigates (a) trends and patterns of direct tax collection, (b) the cost of tax collection, (c) the proportion of direct tax in total tax collection, and (d) the tax-to-GDP ratio in India. By utilizing a novel grey forecasting model (GM (1,1)), this study attempted to predict the future trends of India’s direct tax collections, through which it aims to provide a concurrent and accurate future outlook on tax revenue, ensuring resources are optimally allocated for the country’s growth. Results revealed that direct tax collection has consistently increased in the past two decades, and the proportion of direct tax in total tax has also improved significantly. On the contrary, the cost of tax collection has decreased regularly, indicating the efficiency of tax collection. Forecasting shows that the collection from direct tax is expected to reach INR 30.67 trillion in 2029–30, constituting around 54.41% of the total tax, leaving behind collections from indirect tax at a total of INR 25.70 trillion. Such findings offer insights that could enhance revenue management strategies with policy decisions relevant to economists, government, and other stakeholders to understand trends and the efficiency of direct tax collection in India. Full article

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment. Full article

Over the past two decades, rapid climate change has severely impacted people’s lives globally, affecting their safety and sustainability. Water, a vital human resource, has been severely affected, with drought and high temperatures leading to desertification, the drying up of rivers and lakes, spontaneous fires in forests, and massive floods and torrents due to melting ice and rising sea and ocean surface water levels. The expected impacts of climate change on the Nile, Egypt’s primary water source, are significant. These impacts can vary across regions, depending on factors like local climate, socio-economic dynamics, topography, and environmental nature. Upper Egypt, characterized by arid and semi-arid regions, faces water scarcity and socio-economic development challenges. Climate change exacerbates these issues, posing significant threats to the region’s ecological sustainability and socio-economic development. Therefore, it is crucial to address these impacts to ensure the Nile’s continued vitality and sustainability. The study aims to analyze the climate change data over the past few decades, analyze its characteristics, and model its effects on Upper Egypt’s water sources. The study expected a big decrease in the water resources of the Nile. While what is currently occurring in terms of fluctuating rainfall rates between scarcity and severity contradicts the results of those studies, that is the best evidence of the need for further research and studies to obtain more reliable and consistent results with the reality that it may help decision-makers to develop scenarios to manage climate change effectively, preventing or reducing negative effects, and finding suitable alternatives. Studies predict a 10% decrease in Nile revenue at Aswan High Dam Lake by 2095, with some predicting a 30% increase. This lack of credibility underscores the need for more comprehensive studies. Full article

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development. Full article

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R² of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production. Full article

Continuous-cover forestry (CCF), which maintains a relatively intact forest cover through selective harvesting, has emerged over the last few decades as a popular alternative to rotational forest management (RFM). Coast redwood, which is native to the western United States, grows rapidly in New Zealand and is well suited to CCF as it has high shade tolerance, an ability to coppice from the cut stem, and resistance to pests, diseases, wind and fire. A forest estate model was used to compare the carbon sequestration, timber production and profitability of redwood CCF, RFM and permanent carbon forestry (PCF) regimes at a regional level within New Zealand. Through linear programming, this model optimised carbon accumulation and harvesting decisions across a large forest to meet a series of constraints associated with each regime. All three regimes represented good investment decisions, but CCF had the highest soil expectation value (SEV) within most North Island regions while PCF had a slightly higher SEV within the South Island regions. Under the transitional CCF (CCFt), revenue from carbon initially increased before levelling out at 40 years, after which time a sustainable harvest of high-value timber commenced in perpetuity without additional revenue from carbon. The CCFt regime transitioned to a steady-state condition, with a uniform age class distribution from year 150 onwards (CCFs), after which time a very high SEV was attained that exceeded that of CCFt by four-fold in the North Island (NZD 136,126/ha vs. NZD 34,430/ha) and seven-fold (NZD 44,714 vs. NZD 6267/ha) in the South Island. This study highlights the profitability of managing redwood under CCF and how initial carbon revenue can be used to finance the transition of the forest to a steady-state condition that produces a stream of valuable timber with a very high rate of return. Full article

The rising concerns about electric and electronic equipment waste (WEEE) come from the rapid increase in demand for appliances and the decreasing lifetimes of equipment. Setting a sustainable WEEE management system that exploits this secondary resource is paramount to maximize resource efficiency, mitigate its environmental impact, and stimulate the circular economy. This paper aims, for the first time, to quantify the material flow expected from recycling the generated WEEE, propose the number of plants required to recycle this secondary resource, and outline the expected economic and environmental benefits that could be achieved from recycling operations. The findings of material flow calculations show that the amount of steel, copper, and aluminum is predominant in the WEEE composition. Also, the expected metal content in WEEE in 2022 is approximately 26 kt, 3.3 kt, and 2.5 kt, respectively. These are expected to substantially increase to approximately 109 kt, 11.9 kt, and 9 kt for the three metals in 2050, respectively. Other valuable metals are doubling their quantities between 2022 and 2050 to reach approximately 1133 kg silver and 475 kg gold. Approximately, four treatment plants are required to recover these materials in 2030 with relative installation costs of USD 100 million. The forecasted financial revenues of recovering materials included in WEEE and indicators for environmental impact based on life cycle assessment (LCA) are calculated. The results of this study can serve as a preliminary reference for future usage in guiding effective planning for WEEE recycling and sustainable management in the country. Full article

The adoption of electric vehicles (EVs) is increasing due to governmental policies focused on curbing climate change. EV batteries are retired when they are no longer suitable for energy-intensive EV operations. A large number of EV batteries are expected to be retired in the next 5–10 years. These retired batteries have 70–80% average capacity left. Second-life use of these battery packs has the potential to address the increasing energy storage system (ESS) demand for the grid and also to create a circular economy for EV batteries. The needs of modern grids for frequency regulation, power smoothing, and peak shaving can be met using retired batteries. Moreover, these batteries can also be employed for revenue generation for energy arbitrage (EA). While there are articles reviewing the general applications of retired batteries, this paper presents a comprehensive review of the research work on applications of the second-life batteries (SLBs) specific to the power grid and SLB degradation. The power electronics interface and battery management systems for the SLB are also thoroughly reviewed. Full article

In recent times, there has been a notable global phenomenon characterized by a double predicament arising from the concomitant rise in worldwide life expectancy and a significant decrease in birth rates. The emergence of this phenomenon has posed a significant challenge for governments worldwide. It not only poses a threat to the continued viability of state-funded welfare programs, such as social security, but also indicates a potential decline in the future workforce and tax revenue, including contributions to social benefits. Given the anticipated escalation of these issues in the forthcoming decades, it is crucial to comprehensively examine the extension of the human lifespan to evaluate the magnitude of this matter. Recent research has focused on utilizing stochastic differential equations as a helpful means of describing the dynamic nature of mortality rates, in order to tackle this intricate issue. The usage of these models proves to be superior to deterministic ones due to their capacity to incorporate stochastic variations within the environment. This enables individuals to gain a more comprehensive understanding of the inherent uncertainty associated with future forecasts. The most important aims of this study are to fit and compare stochastic differential equation models for mortality (the geometric Brownian motion and the stochastic Gompertz model), conducting separate analyses for each age group and sex, in order to generate forecasts of the central mortality rates in France up until the year 2030. Additionally, this study aims to compare the outcomes obtained from fitting these models to the central mortality rates in Portugal. The results obtained from this work are quite promising since both stochastic differential equation models manage to replicate the decreasing central mortality rate phenomenon and provide plausible forecasts for future time and for both populations. Moreover, we also deduce that the performances of the models differ when analyzing both populations under study due to the significant contrast between the mortality dynamics of the countries under study, a consequence of both external factors (such as the effect of historical events on Portuguese and French mortality) and internal factors (behavioral effect). Full article

The recycling of waste products can bring enormous economic and environmental benefits to supply chain participants. Under the government’s reward and punishment system, the manufacturing industry is facing unfolded pressure to minimize carbon emissions. However, various factors related to the design of closed-loop logistics networks are uncertain in nature, including demand, facility capacity, transportation cost per unit of product per kilometer, landfill cost, unit carbon penalty cost, and carbon reward amount. As such, this study proposes a new fuzzy programming model for closed-loop supply chain network design which directly relies on fuzzy methods based on the necessity measure. The objective of the proposed optimization model is to minimize the total cost of the network and the sum of carbon rewards and penalties when selecting facility locations and transportation routes between network nodes. Based on the characteristics of the problem, a genetic algorithm based on variant priority encoding is proposed as a solution. This new solution encoding method can make up for the shortcomings of the four traditional encoding methods (i.e., Prüfer number-based encoding, spanning tree-based encoding, forest data structure-based encoding, and priority-based encoding) to speed up the computational time of the solution algorithm. Several alternative solution approaches were considered to evaluate the proposed algorithm including the precision optimization method (CPLEX) and priority-based encoding genetic algorithm. The results of numerous experiments indicated that even for large-scale numerical examples, the proposed algorithm can create optimal and high-quality solutions within acceptable computational time. The applicability of the model was demonstrated through a sensitivity analysis which was conducted by changing the parameters of the model and providing some important management insights. When external parameters change, the solution of the model maintains a certain level of satisfaction conservatism. At the same time, the changes in the penalty cost and reward amount per unit of carbon emissions have a significant impact on the carbon penalty revenue and total cost. The results of this study are expected to provide scientific support to relevant supply chain enterprises and stakeholders. Full article

With the rapid development and technological innovation in the energy market, peer-to-peer (P2P) energy trading, as a decentralised and efficient trading model, has been widely studied and practically applied. However, in P2P energy transactions involving multiple prosumers, there are challenges such as information asymmetry, trust issues, and transaction transparency. To address these challenges, blockchain technology, as a distributed ledger technology, provides solutions. In this paper, we propose a blockchain technology-based prosumer–virtual power plant (VPP) two-tier interactive energy management framework to assist P2P energy transactions between multiple prosumers. In this framework, the virtual power plant acts as a leader and sets differentiated tariffs for different prosumers to equal the distribution of social welfare. The various prosumers act as followers and respond to the leader’s decisions in a cooperative manner. Blockchain’s immutability and transparency enable prosumers to participate in P2P energy trading with greater trust, share idle energy, and share revenues based on contribution. In addition, given the uncertainty of renewable energy, this paper employs a stochastic planning approach with conditional value at risk (CVaR) to describe the expected loss of VPP. Ultimately, as verified by the arithmetic simulation, the blockchain co-governance transaction model effectively supports energy coordination and optimization of complementarities while ensuring the utility of each transaction node. This model promotes the application of renewable energy in local consumption, while facilitating the innovation and sustainable development of the energy market. Full article