Search Results (11,519)

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH₂ as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability. Full article

This paper develops an evaluation of profitability for firms in Greece and Cyprus from 2005 to 2020. More specifically, it contains an investigation of comparative relevance and dominance of accounting versus non-financial variables, which affect the daily operations of firms, on the firms’ level of profitability. Moreover, this research examines the impact of corruption, unemployment, part-time employment and Research and Development (R&D) on the performance of companies, in order to help managers by giving them more information and assisting in long-term strategic planning. The results indicate that these variables do not have a large effect on the firm-level profitability of these two countries, which is largely influenced by profit margin and other interaction variables, such as profit margin on asset turnover ratio and equity multiplier. The findings underline that internal operational efficiency acts as the primary driver of short-term profitability, whereas macro-level indicators display weaker immediate associations. However, managing these structural elements remains strategically relevant for long-term springiness. Full article

Promoting resilience, increasing productivity and sustainability, and profit maximization remain key challenges facing farmers globally. These are exacerbated by factors such as climate change, low to no access to technological advancement, financial constraints, poor technical and management skills, inadequate government support, and limited access to resources. However, there are diverse strategies that abound, including on-farm diversification, that farmers could leverage on to address these numerous and complex challenges. This study investigated the determinants of on-farm diversification strategies among smallholders in Mpumalanga Province. The study employed a quantitative approach using closed-ended survey questionnaires to elicit information from a total of 465 farmers who were randomly sampled from a total population of 14,411. The data gathered were analysed using descriptive statistics to determine the on-farm diversification strategies employed by farmers and the factors influencing the use of these strategies. A binary logistic regression model was employed to establish the relationship between on-farm diversification strategies and the determining factors. More than half of the farmers were female (51.8%), with only 48.2% male. The majority (59.1%) of the farmers were between the ages of 36 and 60, with only 20.2% youth participation in farming. Slightly more than half (50.8%) of the farmers practise mixed farming as their on-farm diversification strategy, while only 4.3% of the farmers practise mono-cropping. The study identified significant variables such as level of education (p = 0.001), secondary source of income (p = 0.057), farmland size (p = 0.022), number of farm assistants (p = 0.016), and on-farm diversification awareness as key determinants of on-farm diversification among smallholder farmers in Mpumalanga Province. Therefore, it is recommended that policies within the agricultural sector be revised to encourage on-farm diversification in order to motivate farmers to transition to agripreneurship for poverty alleviation, food security and rural economic development (RED). Full article

Driven by the global energy transition, the proportion of new and renewable sources of energy (NRSE) such as wind and solar power in the electricity systems of many countries continues to rise. However, this also exacerbates frequency fluctuations in the power system, giving rise to new issues such as curtailment of wind and solar power generation and a continuous decline in inertia levels. The hybrid energy storage system composed of a flywheel and a battery can fully utilize the advantages of their power and energy characteristics, respectively, becoming an effective solution to this problem. Firstly, the characteristics of NRSE and various energy storage technologies were introduced in the paper. Then, the frequency regulation requirements and process of NRSE were discussed, as well as the common architecture and control methods of flywheel–battery hybrid energy storage systems, and the application research and current development status of the flywheel–battery hybrid energy storage system on the power supply side and grid side of the power system were elaborated, including the control strategies for participating in NRSE and methods to reduce costs and increase profits. Finally, the future research directions of flywheel–battery hybrid energy storage systems were discussed and anticipated. Full article

Stock price prediction remains a prominent area of interest among investors due to its potential impact on financial decision making. We developed a deep learning-based system for stock market analysis, forecasting, and automated trading. Utilizing historical financial data, technical indicators, and sentiment information, long short-term memory (LSTM) networks were employed to model and predict stock price movements. The predicted outcomes were integrated into a rule-based automated trading system to simulate real-time buy and sell decisions. Experimental evaluations conducted on the Taiwan Stock Exchange (TWSE) indicate that the developed model surpasses baseline models in both prediction accuracy and trading profitability. The system presents the capability of deep learning to improve forecasting precision and facilitate intelligent, automated trading strategies within contemporary financial markets. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

This study analyzes how sustainability practices and digitalization jointly influence the financial performance of European industrial firms, emphasizing differences between Western and Eastern Europe. The empirical analysis relies on a large multi-country panel dataset and employs fixed effects regression models with robust standard errors to account for unobserved firm-specific heterogeneity and common time shocks. Environmental sustainability is captured by the environmental component of ESG scores, digitalization is measured by digital investment intensity, and financial performance is proxied by return on equity (ROE). The findings indicate that stronger environmental practices are positively associated with profitability across the full sample. Digital investment intensity also has a positive and statistically significant effect on ROE. Importantly, the interaction term between environmental performance and digitalization is positive and significant for Western European firms but not for the full sample, suggesting that the relationship between environmental practices and financial performance may vary with the level of digital investment under specific regional conditions. However, the results reveal substantial regional heterogeneity. The positive effects of environmental practices, digitalization, and their interaction are primarily driven by firms in Western Europe, whereas the relationships are weaker and statistically insignificant in Eastern Europe. These findings underline the complementary role of digital transformation and the importance of institutional and technological readiness. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

Produced-water (PW) management in the Permian Basin faces tightening injection constraints, induced seismicity concerns, and volatile saltwater disposal (SWD) costs. At the same time, chemistry-rich PW contains dissolved constituents (e.g., Li, B, and Sr) that may be valorized if SWD recovery performance and market conditions support favorable techno-economics. Here, we develop an integrated decision-support framework that couples (i) chemistry-informed surrogate models for unit process performance (recovery, effluent quality, and energy/chemical intensity) with (ii) a network-based allocation model that routes PW from sources through pretreatment, optional treatment and mineral-recovery modules (e.g., desalination and direct lithium extraction), and end-use nodes (beneficial reuse, hydraulic fracturing reuse, mineral recovery/valorization, or Class II disposal). This is a screening-level demonstration using publicly available chemistry percentiles and representative pilot-reported performance windows; it is not a site-specific facility design or a bankable TEA for a particular operator. The optimization is posed as a tri-objective problem—to maximize expected net present value, minimize SWD, and minimize an injection-risk indicator R—subject to mass balance, capacity, quality, and regulatory constraints. Uncertainty in commodity prices, recovery fractions, and operating costs is propagated via Monte Carlo scenario sampling, yielding PARETO-efficient portfolios that quantify trade-offs between profitability and risk mitigation. Using the PW chemistry percentiles reported by the Texas Produced Water Consortium for the Delaware and Midland Basins, we derive screening-level break-even lithium concentrations and illustrate how lithium-carbonate-equivalent price and recovery govern the extent to which mineral revenue can offset SWD expenditures. Comparative brine benchmarks (Smackover Formation and Salton Sea geothermal systems) contextualize the Permian’s generally lower-Li PW and highlight transferability of the workflow across brine types. The proposed framework provides a transparent, extensible basis for design matrix planning under evolving injection limits, enabling risk-aware PW management strategies that reduce disposal dependence while improving water resilience. Full article

In pursuit of energy decarbonization and supply security, the integrated energy production unit (IEPU) is regarded as a notable multi-technology energy production model integrating coal-fired power, carbon capture, and renewable energy. As a core component of the IEPU, Power-to-X (P2X) technology encompasses various technical routes with distinct economic performance and technological maturity at different development stages. Thus, selecting the most techno-economically optimal route is critical. In view of this, this paper proposes an integrated decision-making framework for the selection of P2X technology routes in IEPU, which combines “technology selection—economic analysis—risk assessment”. Firstly, a decision model for key P2X processes is established, with the levelized cost of hydrogen and unit hydrogen conversion revenue as core performance metrics to identify the optimal technology combination for hydrogen production and utilization. Secondly, integrating the aforementioned optimized technical route, a life-cycle economic benefit evaluation model is constructed for IEPU retrofit projects to systematically assess the overall economic feasibility of the IEPU project. Thirdly, an investment risk assessment model for P2X-integrated IEPU retrofits is established based on interval number theory, which can quantify project risks under fluctuations of critical parameters such as electricity and carbon prices. Finally, a case study of a 600 MW coal-fired unit retrofit demonstrates that “alkaline electrolysis + methane synthesis” constitutes the optimal P2X technology combination. However, its profitability is relatively sensitive to fluctuations in external market parameters, necessitating the implementation of corresponding risk management strategies. Full article

The electric vehicle revolution has created an urgent need for lithium-ion battery (LIB) recycling, with projections exceeding 11 million tons of end-of-life batteries annually by 2030. However, progress toward a circular economy remains fragmented. This perspective article introduces the concept of a ‘Recycling Trilemma,’ arguing that technological advancements in material separation are systematically undermined by economic volatility and regulatory fragmentation. While current literature focuses on isolated domains—chemistry, business models, or policy—this work provides a systems-level synthesis. By analyzing the friction points between material science (e.g., binder removal, impurity sensitivity), economic realities (e.g., logistics costs, LFP profitability), and regulatory frameworks (e.g., EU vs. US divergence), we propose that true circularity requires synchronized design-for-recycling, market stabilization mechanisms, and harmonized digital product passports. The paper concludes that overcoming the trilemma demands a shift from isolated fixes to integrated, cross-sectoral coordination. Full article

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to 2023, we apply several established statistical techniques—including unit root tests, variance ratio analysis, Hurst exponent estimation, and GARCH modeling—to quantify the presence and strength of mean reversion in volatility. To assess the accuracy and practical usability of volatility metrics for option valuation, we compare realized volatility, GARCH-based forecasts, range-based estimators, and widely used implied volatility measures such as the VIX and daily implied volatility averages, benchmarking each against contract-specific implied volatility. The results indicate that more than 65% of the analyzed tickers exhibit statistically significant mean-reverting behavior, and that the 30-day average implied volatility consistently provides the most reliable predictive performance among the tested metrics, while range-based estimators perform poorly when applied to end-of-day data. Finally, backtests of six delta-neutral option strategies informed by these findings did not yield consistent profitability or statistically significant outperformance, suggesting that although volatility mean reversion is measurable, its direct application to systematic trading remains challenging. Full article

To address the mounting environmental burden caused by solid waste from the food supply chain—specifically agricultural residues and plastic packaging—this study systematically investigated the synergistic mechanisms and product regulation pathways in the co-pyrolysis of four representative food processing by-products—rice husk, pine wood, corn stover, and chestnut shell—with polypropylene, a common food packaging material. A comprehensive methodology integrating thermogravimetric analysis, kinetic modeling, and product characterization was employed. The results demonstrate that incorporating polypropylene into co-pyrolysis systems, such as those involving waste oil, significantly reduces the average activation energy, indicating a catalytic effect that enhances reaction kinetics. Notably, the co-catalytic interaction between corn stover and PP led to a substantial 54.90% reduction in oxygen content, underscoring PP’s role as an effective hydrogen donor that promotes deoxygenation and free radical reactions, thereby increasing hydrocarbon production. At an optimal pyrolysis temperature of 600 °C, product distribution was effectively regulated: the hydrocarbon yield in the CP (corn stover/PP) system increased from 39.8% to a maximum of 65.6%, reflecting a targeted conversion of oxygenated compounds into high-value hydrocarbons. Furthermore, greenhouse gas (GHG) emission calculation and techno-economic analyses indicate that a natural gas-assisted co-pyrolysis process (Scenario C) can generate a net daily profit of 1835 RMB while reducing annual CO₂ emissions by 6515 tons, demonstrating both economic feasibility and environmental benefits. This study provides a theoretical foundation for the circular economy in the food industry, offering a viable technical pathway for the simultaneous treatment of organic food waste and packaging plastics, thereby supporting the sustainable development of the agri-food sector. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

Given the rising demand for phosphate, a critical mineral for many countries due to its essential role in fertilizer production and global food security, reprocessing waste generated during phosphate mining has become increasingly important to mitigate demand pressures and reduce the environmental impact of the mining industry. This study aims to develop a sustainable hydrometallurgical process to recover residual phosphate from a lithology present in mining waste rock. To this end, a thermodynamic analysis was first performed to assess reaction feasibility during leaching and precipitation. A two-step process was then proposed: the first step involves leaching carbonates (mainly calcite) using acetic acid, optimized through response surface methodology based on a Box–Behnken design; the second step consists of precipitating calcium with phosphoric acid to produce a value-added by-product (brushite) while simultaneously regenerating the acetic acid. A preliminary economic assessment was conducted to evaluate process feasibility. The results show that acetic acid is highly selective for carbonates, yielding a phosphate concentrate containing 30% P₂O₅ with complete phosphate recovery under the following conditions: 3.4 molL⁻¹ acid concentration, 28 °C reaction temperature, a liquid-to-solid ratio of 6 mLg⁻¹ (14.2% solids), and a reaction time of 49 min. In the precipitation step, a calcium recovery of 97% was achieved under optimal conditions (20 °C, 15 min, 500 rpm stirring, and a P:Ca ratio of 1). Furthermore, the preliminary economic assessment indicates that the developed process, based on the use of an organic acid and its recycling, generates a net profit, confirming its economic viability and its contribution to environmentally sustainable phosphate processing. Full article