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Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger. [ABSTRACT FROM AUTHOR]

In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks' performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost. [ABSTRACT FROM AUTHOR]

The early and accurate mapping of winter canola is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Although some methods have been proposed to map the winter canola at the flowering or later stages, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. The objective of this study was to evaluate the potential of using the combination of optical and synthetic aperture radar (SAR) data for mapping winter canola at the early stage. We assessed the contributions of spectral features, backscatter coefficients, and textural features, derived from Sentinel-2 and Sentinel-1 SAR images, for mapping winter canola at early stages. Random forest (RF) and support vector machine (SVM) classification models were built to map winter canola based on early-stage images and field samples in 2017 and then the best model was applied to corresponding satellite data in 2018–2022. The following results were obtained: (1) The red edge and near-infrared-related spectral features were most important for the mapping of early-stage winter canola, followed by VV (vertical transmission, vertical reception), DVI (Difference vegetation index), and GOSAVI (Green Optimized Soil Adjusted Vegetation Index); (2) based on Sentinel-1 and Sentinel-2 data, winter canola could be mapped as early as 130 days prior to ripening (i.e., early overwinter stage), with the F-score over 0.85 and the OA (Overall Accuracy) over 81%; (3) adding Sentinel-1 could improve the OA by about 2–4% and the F-score by about 1–2%; and (4) based on the classifier transfer approach, the F-scores of winter canola mapping in 2018–2022 varied between 0.75 and 0.97, and the OAs ranged from 79% to 86%. This study demonstrates the potential of early-stage winter canola mapping using the combination of Sentinel-2 and Sentinel-1 images, which could enable the large-scale early mapping of canola and provide valuable information for stakeholders and decision makers. [ABSTRACT FROM AUTHOR]

Word-of-mouth (WOM) can be considered one form of public opinion, reflecting consumers' views towards product or service quality. With the development of social media, WOM holds influence over investors and subsequently impacts the stock prices of companies. The purpose of this study is to uncover the dynamics encompassing the factors that influence WOM, its propagation mechanisms, and the consequential impact on reality illustrated through fluctuations in stock prices. The mathematical model to describe the whole process can be separated into two components: information propagation model and stock price fluctuation model. The cumulative abnormal return (CAR) is used to demonstrate fluctuations in stock prices. Utilizing a Lyapunov function, it is proven that the equilibrium point of the model is globally asymptotically stable. As for the real world, it suggests that WOM will eventually prevail online, leading to fluctuations in stock prices. Based on the event study method, the empirical examination of 43 real events shows that our model can effectively predict the CAR and reflect the influence on stock prices caused by WOM in real-world scenarios. [ABSTRACT FROM AUTHOR]

Power-to-Ammonia (P2A) is a promising technology that can provide a low-emission energy carrier for long-term storage. This study presents an optimization approach to a novel small-scale containerized P2A concept commissioned in 2024. A dynamic nonlinear optimization problem of the P2A concept is set up, employing the non-commercial MOSAIC® software V3.0.1 in combination with the NEOS® server. In total, seven optimization solvers, ANTIGONE®, CONOPT®, IPOPT®, KNITRO®, MINOS®, PATHNLP®, and SNOPT®, are used. The first and main part of this work optimizes several disturbance scenarios of the concept and aims to determine the optimal reactor temperature profile to counter the disturbances. The optimization results suggest, for example, lowering the reactor temperature profile if the hydrogen and nitrogen inlet streams into the system decrease. The second part of this work presents a crude dynamic optimal scheduling model. This part aims to determine the amount of ammonia to be produced and sold given a randomized price of electricity for three consecutive points in time. The optimization results recommend decreasing production when the price of electricity is high and vice versa. However, the dynamic model must be improved to include fluctuations in the price of ammonia. Then, it can be used as a real-time optimization tool. [ABSTRACT FROM AUTHOR]

As global climate change intensifies, nations around the world are implementing policies aimed at reducing emissions, with carbon-trading mechanisms emerging as a key market-based tool. China has launched carbon-trading markets in several cities, achieving significant trading volumes. Carbon-trading mechanisms encompass cap-and-trade markets and voluntary markets, influenced by various factors, including policy changes, economic conditions, energy prices, and climate fluctuations. The complexity of these factors, coupled with the nonlinear and non-stationary nature of carbon prices, makes forecasting a substantial challenge. This paper proposes a dynamic weight hybrid forecasting model based on a dual sliding window approach, effectively integrating multiple forecasting models such as LSTM, Random Forests, and LASSO. This model facilitates a thorough analysis of the influences of policy, market dynamics, technological advancements, and climatic conditions on carbon pricing. It serves as a potent tool for predicting carbon market price fluctuations and offers valuable decision support to stakeholders in the carbon market, ultimately aiding in the global efforts towards emission reduction and achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

A thorough understanding of the development process of grazing activities and an elucidation of their complex mechanisms are crucial for the formulation and adjustment of livestock management policies. In the Selinco region of the Qinghai–Tibet Plateau, we conducted a comprehensive analysis of year-end livestock numbers and densities over the past 30 years. The results indicate a gradual decline in overall livestock numbers in the Selinco region during this period, with a notable decrease between 2004 and 2014, followed by stabilization. Notably, the number of yaks has significantly increased, whereas the numbers of sheep, goats, and horses have markedly decreased. Regarding livestock density, there is a spatial pattern of decrease from northwest to southeast, with the density order being Seni District > Bange County > Anduo County > Shenzha County > Nima County > Shuanghu County. Between 2004 and 2014, most counties experienced a significant decline in livestock density, exhibiting a trough–peak pattern. However, after 2014, a complex spatiotemporal dynamic emerged. Concerning driving factors, from 1990 to 2004, rural population and economic development were the primary influences on livestock density. After 2004, forage–livestock balance management policies, snowstorms, and fluctuations in livestock prices likely became the main influencing factors. Further detailed analysis of these factors is essential for developing more effective management strategies. [ABSTRACT FROM AUTHOR]

In recent years, frequent external emergencies have continuously impacted China's pig industry chain. As the scale and standardization of pig farming in China have increasingly improved, pig futures have met the conditions for good operation and were listed for trading on the Dalian Commodity Exchange on 8 January 2021. To study the impact and influence of African swine fever, COVID-19, and the listing of pig futures on the price transmission mechanism at various stages of China's pig industry, weekly price data from the pig industry from January 2015 to June 2023 were selected to construct an SV-TVP-VAR model for analysis. The empirical results showed that the shocks of African swine fever and COVID-19 caused price fluctuations at various stages of the pig industry chain, while price fluctuations significantly decreased after the listing of pig futures. Therefore, the introduction of pig futures effectively alleviated the price fluctuations at various stages of the pig industry chain following the shocks of African swine fever and COVID-19, and relevant policy recommendations are proposed accordingly. [ABSTRACT FROM AUTHOR]

Using panel data from 30 provinces in China from 2005 to 2020, this paper uses a spatial double difference model to evaluate the policy impact of the "grain-to-feed" policy on feed grain production in pilot areas and adjacent spatial areas. Research has found that the "grain-to-feed" policy has a significant impact on the feed grain production in pilot areas and can significantly increase the feed grain production in pilot areas by about 2.71 million tons. The "grain-to-feed" policy has strengthened the positive connection between pilot areas and adjacent pilot areas, increased feed grain production, and has a significant spatial spillover effect. Robustness analysis shows that whether using different methods to measure spatial adjacency or using different standards to distribute subsidies, the "grain-to-feed" policy can significantly increase feed grain production, narrow the supply and demand gap of feed grain, and ensure feed grain security. Further analysis shows that the "grain-to-feed" policy can not only ensure the security of feed grain for the current and next periods but also promote the increase in farmers' income, which is long-term and sustainable. Compared with non-pilot areas, the "grain-to-feed" policy can mitigate the negative impact of wage–price signals on feed grain production in pilot areas. It is recommended that government departments accelerate the transformation of food security concepts, establish a "Big Food Perspective", gradually promote the pilot of the "grain-to-feed" policy nationwide, increase the subsidy amount of the "grain-to-feed" policy, increase financial support for scientific and technological research and achievement transformation in the field of feed grain, prevent the impact of economic price signal fluctuations on feed grain production, and effectively ensure the security of feed grain in China. [ABSTRACT FROM AUTHOR]

Residential energy storage systems offer significant potential for price arbitrage by capitalizing on fluctuations in electricity prices throughout the day. This study investigates the potential revenue from optimal battery scheduling for residential storage in different north-western European electricity price zones during 2023. Using Nord Pool day-ahead prices, we applied an optimization model to determine the revenue for two types of batteries: 5 kW/10 kWh and 10 kW/10 kWh. The analysis considered battery capacity, charging and discharging efficiency, and maximum charge/discharge rates. Our results show notable variations in potential revenue across different regions, with the Baltic states demonstrating the highest revenue potential. The findings indicate that while 10 kW batteries can generate higher total revenue, 5 kW batteries are more efficient in terms of revenue per cycle. These regional disparities underscore the need for targeted incentives and policies to enhance the economic viability of residential energy storage. The research results provide valuable insights into optimizing residential battery storage for price arbitrage, offering guidance for consumers, policymakers, and energy providers to maximize economic benefits in various electricity markets. [ABSTRACT FROM AUTHOR]

As large-scale infrastructure construction projects conclude, the overall civil construction market shrinks, leading to increased competition among construction companies. Accordingly, various construction companies are gradually emphasizing the issue of project costs. Numerous studies have shown the impact of material costs on the overall project cost. However, sharp fluctuations in material prices have been observed in recent years due to various unstable factors in the market. Thus, accurate prediction of material prices facilitates the development of appropriate material procurement strategies to deal with market risks. This paper collects the rebar prices announced in the first half of 2023 in China's Guangdong Province, selects one type of rebar price as a representative, and analyzes the time series characteristics of the rebar price composition. Then, it judges whether the time series passes the white noise detection, grey correlation detection, and level ratio detection. The prediction model is established based on the seasonal auto-regressive integrated moving average (SARIMA) model and the grey model (GM) (1.1) to predict future rebar prices. According to the characteristics of the rebar price data in June 2023, the residual inverse method combines the results predicted by the two models. The price in the early and middle of July 2023 is then predicted using the newly constructed combined model. The results indicate that the combined model is more accurate than the single prediction model. [ABSTRACT FROM AUTHOR]

The need to transition from fossil fuels to renewables arises from factors such as depletion, price fluctuations, and environmental considerations. Lignocellulosic biomass, being abundant, and quickly renewable, and not interfering with food supplies, offers a standout alternative for chemical production. This paper explores the energetic characteristics of two derivatives of furfural—a versatile chemical obtained from biomass with great potential for commercial sustainable chemical and fuel production. The standard (p° = 0.1 MPa) molar enthalpies of formation of the liquids furfurylamine and 5-methylfurfurylamine were derived from the standard molar energies of combustion, determined in oxygen and at T = 298.15 K, by static bomb combustion calorimetry. Their standard molar enthalpies of vaporization were also determined at the same temperature using high-temperature Calvet microcalorimetry. By combining these data, the gas-phase enthalpies of formation at T = 298.15 K were calculated as −(43.5 ± 1.4) kJ·mol−1 for furfurylamine, and −(81.2 ± 1.7) kJ·mol−1 for 5-methylfurfurylamine. Furthermore, a theoretical analysis using G3 level calculations was performed, comparing the calculated enthalpies of formation with the experimental values to validate both results. This method has been successfully applied to similar molecules. The discussion looks into substituent effects in terms of stability and compares them with similar compounds. [ABSTRACT FROM AUTHOR]

In Colombia, coffee futures contracts represent essential financial agreements that allow producers and buyers to establish prices, quality, and conditions for future transactions in the coffee market. Despite the evident benefits of stability and predictability, this practice faces significant sustainability challenges that threaten its long-term viability. One of the reasons is the significant lack of transparency in the supply chain. Farmers, affected by abrupt price fluctuations and adverse weather conditions such as the El Niño phenomenon, experience an increase in market prices, leading to the non-delivery of the final product, and contract breaches as they find better prices in the local market. In this context, smart contracts emerge as a promising technological solution to address these problems. These contracts enable the verification of each step in the process, from harvest to final sale, within a blockchain. Therefore, this research designs a smart contract managed through a platform called SmartBeanFutures, which records the clauses of futures contracts using the IERC721 framework, allowing the generation of a unique and non-repeatable asset. It aims to sell, promote, and manage coffee sale prices during the agreement's signing, creating a transparent environment for chain actors. This proposal undergoes evaluation in a test environment, providing farmers access to the designed platform. Following the validation of the proposal, it was identified that over 74% would use this type of contract in their agricultural processes, highlighting that implementing this technology contributes to eliminating intermediaries in the chain and gives farmers more control over their participation in the market. [ABSTRACT FROM AUTHOR]

The electricity market in Spain holds significant importance in the nation's economy and sustainability efforts due to its diverse energy mix that encompasses renewables, fossil fuels, and nuclear power. Accurate energy price prediction is crucial in Spain, influencing the country's ability to meet its climate goals and ensure energy security and affecting economic stakeholders. We have explored how leveraging advanced GPT tools like OpenAI's ChatGPT to analyze energy news and expert reports can extract valuable insights and generate additional variables for electricity price trend prediction in the Spanish market. Our research proposes two different training and modelling approaches of generative pre-trained transformers (GPT) with specialized news feeds specific to the Spanish market: in-context example prompts and fine-tuned GPT models. We aim to shed light on the capabilities of GPT solutions and demonstrate how they can augment prediction models by introducing additional variables. Our findings suggest that insights derived from GPT analysis of electricity news and specialized reports align closely with price fluctuations post-publication, indicating their potential to improve predictions and offer deeper insights into market dynamics. This endeavor can support informed decision-making for stakeholders in the Spanish electricity market and companies reliant on electricity costs and price volatility for their margins. [ABSTRACT FROM AUTHOR]

China is a major player in the marine fish trade. The price prediction of marine fish is of great significance to socio-economic development and the fisheries industry. However, due to the complexity and uncertainty of the marine fish market, traditional forecasting methods often struggle to accurately predict price fluctuations. Therefore, this study adopts an intelligent combination model to enhance the accuracy of food product price prediction. Firstly, three decomposition methods, namely empirical wavelet transform, singular spectrum analysis, and variational mode decomposition, are applied to decompose complex original price series. Secondly, a combination of bidirectional long short-term memory artificial neural network, extreme learning machine, and exponential smoothing prediction methods are applied to the decomposed results for cross-prediction. Subsequently, the predicted results are input into the PSO–CS intelligence algorithm for weight allocation and to generate combined prediction results. Empirical analysis is conducted using data illustrating the daily sea purchase price of larimichthys crocea in Ningde City, Fujian Province, China. The combination prediction accuracy with PSO–CS weight allocation is found to be higher than that of single model predictions, yielding superior results. With the implementation of weight allocation intelligent combinatorial modelling, the prediction of marine fish prices demonstrates higher accuracy and stability, enabling better adaptation to market changes and price fluctuations. [ABSTRACT FROM AUTHOR]

Long Short-Term Memory (LSTM) is an effective method for stock price prediction. However, due to the nonlinear and highly random nature of stock price fluctuations over time, LSTM exhibits poor stability and is prone to overfitting, resulting in low prediction accuracy. To address this issue, this paper proposes a novel variant of LSTM that couples the forget gate and input gate in the LSTM structure, and adds a "simple" forget gate to the long-term cell state. In order to enhance the generalization ability and robustness of the variant LSTM, the paper introduces an attention mechanism and combines it with the variant LSTM, presenting the Attention Mechanism Variant LSTM (AMV-LSTM) model along with the corresponding backpropagation algorithm. The parameters in AMV-LSTM are updated using the Adam gradient descent method. Experimental results demonstrate that the variant LSTM alleviates the instability and overfitting issues of LSTM, effectively improving prediction accuracy. AMV-LSTM further enhances accuracy compared to the variant LSTM, and compared to AM-LSTM, it exhibits superior generalization ability, accuracy, and convergence capability. [ABSTRACT FROM AUTHOR]

Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset. [ABSTRACT FROM AUTHOR]

Interior permanent magnet motors are widely used in applications requiring high power density and high efficiency due to their high torque-generating capabilities. Recently, given the price fluctuations and unstable supply of rare earth permanent magnets, alternative configurations with reduced use of permanent magnets are being sought. Among the various candidates related to this, the consequent-pole type rotor structure can halve the number of permanent magnets used compared with conventional structures. However, in a no-load analysis, the waveform of the back electromotive force becomes asymmetric, generating a harmonic component. As a result, there is a disadvantage that the torque ripple increases. To overcome these shortcomings, we propose a novel rotor structure that applies a consequent-pole structure to an embedded permanent-magnet motor structure, wherein a number of permanent magnets are arranged in a flared structure to constitute a single polarity. In the proposed flared-structured magnet arrangement, it is possible to adjust the angle of the permanent magnet and the polar angle to mitigate the asymmetry of the back-EMF waveform. The proposed structure was optimized with a genetic algorithm and a prototype of the optimal model was constructed and experimentally evaluated to verify its validity. Finally, the performance improvement and validity of the proposed structure were verified by comparing the analysis results of the optimal model with the experimental results. [ABSTRACT FROM AUTHOR]

The pioneering work in finance by Black, Scholes and Merton during the 1970s led to the emergence of the Black-Scholes (B-S) equation, which offers a concise and transparent formula for determining the theoretical price of an option. The establishment of the B-S equation, however, relies on a set of rigorous assumptions that give rise to several limitations. The non-local property of the fractional derivative (FD) and the identification of fractal characteristics in financial markets have paved the way for the introduction and rapid development of fractional calculus in finance. In comparison to the classical B-S equation, the fractional B-S equations (FBSEs) offer a more flexible representation of market behavior by incorporating long-range dependence, heavy-tailed and leptokurtic distributions, as well as multifractality. This enables better modeling of extreme events and complex market phenomena, The fractional B-S equations can more accurately depict the price fluctuations in actual financial markets, thereby providing a more reliable basis for derivative pricing and risk management. This paper aims to offer a comprehensive review of various FBSEs for pricing European options, including associated solution techniques. It contributes to a deeper understanding of financial model development and its practical implications, thereby assisting researchers in making informed decisions about the most suitable approach for their needs. [ABSTRACT FROM AUTHOR]

The Multiple Microgrid System (MMG) facilitates synergistic complementarity among various energy sources, reduces carbon emissions, and promotes the integration of renewable energy generation. In this context, we propose a two-stage robust cooperative scheduling model for MMGs based on non-cooperative game theory and a benefit allocation mechanism. In the first stage, considering electricity price fluctuations and uncertainties in wind and solar power outputs, a robust optimization approach is applied to establish an electric energy management model for MMGs. This model enables point-to-point energy sharing among microgrids. In the second stage, addressing the benefit allocation problem for shared electric energy, we introduce a Cost Reduction Ratio Distribution (CRRD) model based on non-cooperative game theory. The generalized Nash equilibrium is utilized to determine the benefit distribution for shared electric energy. Finally, through case studies, the proposed model is validated, ensuring fair returns for each microgrid. The results indicate that the proposed model optimizes the operational states of individual microgrids, reduces operational costs for each microgrid, and lowers the overall total operational costs of the MMG system. Additionally, an investigation is conducted into the impact of electricity price uncertainty coefficients and confidence levels of wind and solar uncertainties on the operational costs of microgrids. [ABSTRACT FROM AUTHOR]

For political and economic reasons, renewable sources of energy have gained much importance in establishing a sustainable energy economy. By their very nature, however, their benefits depend on changeable weather conditions, and are unrelated to the generation and consumption patterns in industrial or home environments. This generation–dissipation disparity induces price fluctuations and threatens the stability of the supply system, yet can be alleviated by installing energy depots. While the classic methods of energy storage are hardly cost-effective, they may be supplemented, or replaced, by a distributed system of small-scale hydropower plants with ponds used as energy reservoirs. In this paper, following a rigorous mathematical argument, a dynamic model of a multi-cascade of hydropower plants is constructed, and a cost-optimal controller, with formally proven properties, is designed. On the one hand, it allows for an increase in the owners' revenue by as much as 30% (compared to a free-flow state); on the other hand, it reduces the load fluctuation imposed on the grid and the legacy supply system. Moreover, the risk of floods and droughts downstream resulting from inappropriate use of the plants is averted. [ABSTRACT FROM AUTHOR]

Platinum group metals (PGMs), including palladium, play a pivotal role in various industries due to their unique properties. Palladium is frequently employed in technologies aimed at environmental preservation, such as catalytic converters that reduce harmful emissions from vehicles, and in the production of clean energy, notably in the hydrogen evolution process. Regrettably, the production of this vital metal for our environment is predominantly centered in two countries—Russia and South Africa. This centralization has led to palladium being classified as a critical raw material, emphasizing the importance of establishing a secure and sustainable supply chain, as well as employing the most efficient methods for processing materials containing palladium. This review explores techniques for palladium production from primary sources and innovative recycling methods, providing insights into current technologies and emerging approaches. Furthermore, it investigates the economic aspects of palladium production, including price fluctuations influenced by emission regulations and electric vehicle sales, and establishes connections between palladium prices, imports from major producers, as well as copper and nickel prices, considering their often co-occurrence in ores. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs' dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the response of the South African stock market returns to oil price volatility, based on the daily South African stock market index, using the GARCH-Copula modelling technique. The results of the analysis show evidence of an asymmetric impact of fluctuations in oil prices on South African stock market returns, using a copula model specification, particularly the bivariate symmetrized Joe-Clayton (SJC) copula. The results also revealed that the EGARCH process is the best univariate model to capture oil price volatility. Interestingly, this study also revealed that the tourism industry is most dependent on oil price fluctuations, due to its heavy reliance on transportation costs. The economic implications of this study also suggest that sectors affected by oil price fluctuations need specific long-term and short-term monetary policy strategies. It is recommended that in the short term, expansionary monetary policy could assist in mitigating the impact of higher oil prices, while in the long-term, policies aimed at reducing the volatility in oil prices would be of great help in alleviating its harmful effect on stock market returns. [ABSTRACT FROM AUTHOR]

In today's environmentally conscious world, carbon trading has emerged as a widely accepted economic instrument to mitigate the externalities resulting from deteriorating environmental problems. Consequently, the use of market-based mechanisms to address environmental issues has reached a global consensus. Many countries are implementing progressive steps by establishing carbon markets to promote low-carbon development and meet their carbon reduction targets. However, the inherent risks in carbon trading markets may hamper the formation of a reasonable carbon price signal, leading to inadequate stimulation of low-carbon technology investments and potential failure to achieve national emission reduction goals. Therefore, managing the risks associated with carbon trading markets is crucial. This study focuses on measuring the risk of China's carbon market, with the primary aim of exploring carbon price fluctuation patterns and precisely measuring market risks. The risks associated with China's carbon market are quantified and analyzed using the exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model, extreme value theory (EVT), and the value at risk (VaR) method. Results show that (1) the effect of external shocks on each carbon market is asymmetrical, and positive shocks exert considerable leverage effects on carbon price fluctuations. (2) EVT can be used to effectively fit the risks in the carbon markets. The risks of each carbon market show different characteristics. The risk of Hubei and Guangdong carbon markets is relatively small, and the dynamic VaR is nearly ±0.2. (3) Compared with the performance of the Chinese carbon market, the performance of the European Union Emission Trading Scheme is more stable, and its dynamic VaR for most of the period is within ±0.1, which is considerably lower than the VaR of other Chinese carbon markets. This study also proposes suitable policy implications to ensure the healthy and sustainable development of China's carbon market. [ABSTRACT FROM AUTHOR]

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system. The proposed model simultaneously focuses on green and regular production methods with an optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, cycles do not depend on each other, and consequently, each model parameter can be adjusted to be responsive to the dynamic nature of demand rate and/or price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. This paper accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production, the lower the total amount of emissions generated by the system, i.e., the system becomes more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the minimum total cost per unit time when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower in the first cycle (subsequent cycles) than that of the existing literature, i.e., 33.59% (16.13%) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. In addition, the system earns further revenue by utilizing a mixed transportation policy that combines the Truck Load (TL) and Less than Truck Load (LTL) services. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights. [ABSTRACT FROM AUTHOR]

Economic benefits and risk premiums significantly affect the production system decision making of farmers and government departments. This study evaluated the economic feasibility and estimated the risk premium of 12 rainfed soybean production systems with various planting densities, fertilization rates and planting patterns by considering the impact of soybean price fluctuation. There were two planting densities (D1: 160,000 plants ha−1 and D2: 320,000 plants ha−1), two fertilization rates (F1: 20 kg ha−1 N, 30 kg ha−1 P, 30 kg ha−1 K; F2: 40 kg ha−1 N, 60 kg ha−1 P, 60 kg ha−1 K) and three planting patterns (F+W0: flat cultivation with no irrigation; R+W0: plastic-mulched ridge-furrow cultivation (PMRF) with no irrigation; R+W1: PMRF with supplemental irrigation of 30 mm at the pod-filling stage). Based on the two-year (2019–2020) field data in a semi-humid drought-prone region of northwest China and soybean price fluctuation from January 2014 to June 2021, the net income (NI) was calculated by considering the impact of soybean price fluctuation and assuming constant soybean production costs. The net present value (NPV) method and the stochastic efficiency with respect to a function (SERF) method were used to evaluate the profitability of protective alternatives and the risk of these alternatives. The results showed that the 12 proposed soybean production systems were economically feasible. Reducing the fertilization rate reduced the input costs, but it did not necessarily result in a decrease in soybean yield and NI. The payback period of all production systems was within two years for farmers investing through loans. High-fertilizer and high-density production systems made personal investment obtain the highest economic benefit in this study, which was not the best investment strategy from the perspective of production-to-investment ratio and environmental protection departments. The preferences of farmers with various risk aversion and environmental protection departments in terms of risk premium were also proposed. The economic and risk assessment framework of this study can enhance the understanding of the adjustment of production systems from different perspectives, and provide strategies for promoting the protection of economic, environmental and socially sustainable agricultural systems. [ABSTRACT FROM AUTHOR]

In recent years, with the intensification of global warming and the greenhouse effect, the global consensus has focused on efficient, clean, low-carbon, and green development as a means of achieving new economic growth. China, as a major carbon emitter, has been at the forefront of efforts to reduce carbon emissions. The establishment of the carbon emissions trading market, commonly known as the "carbon market", provides an economic solution for reducing carbon emissions in both the carbon and energy markets. As China's carbon market continues to grow rapidly, fluctuations in the energy or carbon markets caused by information shocks can easily spread between the two markets, leading to increased interconnectedness. Moreover, the spillover effect of the volatility between China's carbon market and energy market is not constant, and the intensity and direction of this effect vary depending on different market volatility levels and periods. Therefore, it is crucial to conduct a comprehensive study on the characteristics of the volatility spillover effect between China's carbon market and energy market and to fully understand the mechanism of energy regulation on carbon prices. This research will have significant practical implications for promoting the establishment of a well-functioning internal price transmission mechanism between China's carbon market and energy market. This study took the risk spillover between the carbon market and energy market as the research object and systematically combed through its pricing mechanism and spillover impact. Through constructing the DY overflow index model based on a VAR model and generalized variance decomposition method, this study explored the linkage between China's carbon and energy markets, i.e., the linkage of price fluctuations between China's energy and carbon markets, as well as the time-varying nature of inter-market spillovers, and provides suggestions on the risk control of price fluctuations between the carbon and energy markets. [ABSTRACT FROM AUTHOR]

This article presents a study on forecasting silver prices using the extreme gradient boosting (XGBoost) machine learning method with hyperparameter tuning. Silver, a valuable precious metal used in various industries and medicine, experiences significant price fluctuations. XGBoost, known for its computational efficiency and parallel processing capabilities, proves suitable for predicting silver prices. The research focuses on identifying optimal hyperparameter combinations to improve model performance. The study forecasts silver prices for the next six days, evaluating models based on mean absolute percentage error (MAPE) and root mean square error (RMSE). Model A (the best model based on MAPE value) suggests silver prices decline on the first and second days, rise on the third, decline again on the fourth, and stabilize with an increase on the fifth and sixth days. Model A achieves a MAPE of 5.98% and an RMSE of 1.6998, utilizing specific hyperparameters. Conversely, model B (the best model based on RMSE value) indicates a price decrease until the third day, followed by an upward trend until the sixth day. Model B achieves a MAPE of 6.06% and an RMSE of 1.6967, employing distinct hyperparameters. The study also compared the proposed models with several other ensemble models (CatBoost and random forest). The model comparison was carried out by incorporating 2 additional metrics (MAE and SI), and it was found that the proposed models exhibited the best performance. These findings provide valuable insights for forecasting silver prices using XGBoost. [ABSTRACT FROM AUTHOR]

The European Union (EU) has agreed to gradually include shipping in the EU emissions trading scheme (EU ETS), which makes shipping companies vulnerable to carbon price fluctuations. The aim of this paper is to investigate the effectiveness of carbon and petroleum futures contracts in managing carbon and bunker risks. We examine the effectiveness of alternative hedging methods, including both static and dynamic approaches, to estimate optimal hedge ratios under single and composite cross-hedge settings. Our results show that carbon future contracts are important for hedging the carbon emission allowances price risk, and Brent oil futures are the most effective instrument for out-of-sample hedging of bunker prices. In addition, the hedging effectiveness indicates that conventional methods outperform the sophisticated models in terms of variance reduction. Our study offers new insights into how the carbon and bunker markets relate to a combination hedging in reducing the joint price risk, which can be used to promote risk management in the market. [ABSTRACT FROM AUTHOR]

Empirical studies suggest that asset price fluctuations exhibit "long memory", "volatility smile", "volatility clustering" and asset prices present "jump". To fit the above empirical characteristics of the market, this paper proposes a fractional stochastic volatility jump-diffusion model by combining two fractional stochastic volatilities with mixed-exponential jumps. The characteristic function of the log-return is expressed in terms of the solution of two-dimensional fractional Riccati equations of which closed-form solution does not exist. To obtain the explicit characteristic function, we approximate the pricing model by a semimartingale and convert fractional Riccati equations into a classic PDE. By the multi-dimensional Feynman-Kac theorem and the affine structure of the approximate model, we obtain the solution of the PDE with which the explicit characteristic function and its cumulants are derived. Based on the derived characteristic function and Fourier cosine series expansion, we obtain approximate European options prices. By differential evolution algorithm, we calibrate our approximate model and its two nested models to S&P 500 index options and obtain optimal parameter estimates of these models. Numerical results demonstrate the pricing method is fast and accurate. Empirical results demonstrate our approximate model fits the market best among the three models. [ABSTRACT FROM AUTHOR]

In the realm of foreign exchange (Forex) market predictions, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been commonly employed. However, these models often exhibit instability due to vulnerability to data perturbations attributed to their monolithic architecture. Hence, this study proposes a novel neuroscience-informed modular network that harnesses closing prices and sentiments from Yahoo Finance and Twitter APIs. Compared to monolithic methods, the objective is to advance the effectiveness of predicting price fluctuations in Euro to British Pound Sterling (EUR/GBP). The proposed model offers a unique methodology based on a reinvigorated modular CNN, replacing pooling layers with orthogonal kernel initialisation RNNs coupled with Monte Carlo Dropout (MCoRNNMCD). It integrates two pivotal modules: a convolutional simple RNN and a convolutional Gated Recurrent Unit (GRU). These modules incorporate orthogonal kernel initialisation and Monte Carlo Dropout techniques to mitigate overfitting, assessing each module's uncertainty. The synthesis of these parallel feature extraction modules culminates in a three-layer Artificial Neural Network (ANN) decision-making module. Established on objective metrics like the Mean Square Error (MSE), rigorous evaluation underscores the proposed MCoRNNMCD–ANN's exceptional performance. MCoRNNMCD–ANN surpasses single CNNs, LSTMs, GRUs, and the state-of-the-art hybrid BiCuDNNLSTM, CLSTM, CNN–LSTM, and LSTM–GRU in predicting hourly EUR/GBP closing price fluctuations. [ABSTRACT FROM AUTHOR]

Lentil seed coat colour influences market value, whilst germination is associated with crop establishment and hydration capacity with optimal processing outcomes. Storing lentil grain assists growers in managing price fluctuations; however, exposure to oxygen at higher temperatures during extended storage degrades seed coat colour, germination, and hydration capacity. Depleting oxygen prevents such degradation in other crops; however, studies in lentil are limited. This study examined the effects of oxygen-depleted modified atmospheres and temperatures on seed coat colour, germination, and hydration capacity in two red lentil cultivars, PBA Hallmark and PBA Jumbo2, stored for 360 days. Small volumes of lentil grain were placed in aluminium laminated bags filled with nitrogen (N2), carbon dioxide (CO2), or air and stored at either 15 or 35 °C. At 35 °C in an air atmosphere, the lentil's seed coat significantly (p = 0.05) darkened after 30 days of storage, whereas germination and hydration capacities decreased after 60 days regardless of cultivar. In contrast, N2 and CO2 atmospheres maintained initial seed coat colour, germination, and hydration capacities in both cultivars throughout the study period regardless of temperature. Storing lentil grain in an oxygen-depleted modified atmosphere may assist to maximise returns to grower and maintain key quality traits. [ABSTRACT FROM AUTHOR]

This article constructs a behavioral financial model that provides feedback on both historical prices and company fundamentals without considering asset liquidation to discuss the long-term impact of investor sentiment and feedback trading on asset price fluctuations. The research conclusion shows that the abnormal volatility of asset prices is captured by the value effect, the cognitive bias effect, the sentiment shock effect, and the trading inductive effect. The value effect is the volatility of asset prices that is completely determined by fundamental factors; the higher the degree of cyclical fluctuations in fundamental factors, the higher the volatility of prices. The bias effect refers to investors' misreading of basic information and trends in asset prices; the greater the instability of emotional shocks, the greater the abnormal volatility of asset prices. The trading inductive effect is also called the Keynes effect, which reflects the role played by rational traders. [ABSTRACT FROM AUTHOR]

The whole world has entered the era of the Vuca. Some traditional methods of problem analysis begin to fail. Complexity science is needed to study and solve problems from the perspective of complex systems. As a complex system full of volatility and uncertainty, price fluctuations have attracted wide attention from researchers. Therefore, through a literature review, this paper analyzes the research on complex theories on price prediction. The following conclusions are drawn: (1) The price forecast receives widespread attention year by year, and the number of published articles also shows a rapid rising trend. (2) The hybrid model can achieve higher prediction accuracy than the single model. (3) The complexity of models is increasing. In the future, the more complex methods will be applied to price forecast, including AI technologies such as LLM. (4) Crude-oil prices and stock prices will continue to be the focus of research, with carbon prices, gold prices, Bitcoin, and others becoming new research hotspots. The innovation of this research mainly includes the following three aspects: (1) The whole analysis of all the articles on price prediction using mathematical models in the past 10 years rather than the analysis of a single field such as oil price or stock price. (2) Classify the research methods of price forecasting in different fields, and found the common problems of price forecasting in different fields (including data processing methods and model selection, etc.), which provide references for different researchers to select price forecasting models. (3) Use VOSviewer to analyze the hot words appearing in recent years according to the timeline, find the research trend, and provide references for researchers to choose the future research direction. [ABSTRACT FROM AUTHOR]

Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China's major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions. [ABSTRACT FROM AUTHOR]

Most adhesives used in the wood-based panel (WBP) industry are petroleum-based and are associated with environmental impact and price fluctuations. Furthermore, most have potential adverse health impacts, such as formaldehyde emissions. This has led to interest from the WBP industry in developing adhesives with bio-based and/or non-hazardous components. This research focuses on the replacement of phenol-formaldehyde resins by Kraft lignin for phenol substitution and 5-hydroxymethylfurfural (5-HMF) for formaldehyde substitution. Resin development and optimization was carried out regarding varying parameters such as molar ratio, temperature or pH. The adhesive properties were analyzed using a rheometer, gel timer and a differential scanning calorimeter (DSC). The bonding performances were evaluated using an Automated Bonding Evaluation System (ABES). Particleboards were produced using a hot press, and their internal bond strength (IB) was evaluated according to SN EN 319. Hardening of the adhesive could be achieved at low temperatures by increasing or decreasing the pH. The most promising results were obtained at pH 13.7. The adhesive performances were improved by adding filler and extender (up to 28.6% based on dry resin) and several boards were produced reaching P1 requirements. A particleboard achieved a mean IB of 0.29 N/mm2, almost reaching almost P2 requirements. However, adhesive reactivity and strength must be improved for industrial use. [ABSTRACT FROM AUTHOR]

In recent years, frequent linkage events of natural gas and electricity prices have seriously affected the stability of the energy supply market. In view of the increasingly close linkage relationship between gas and electricity prices, a gas–electricity price linkage analysis method considering the benefit–cost and price transmission of gas units is proposed. Firstly, a benefit–cost model of gas units based on risk premium theory is constructed to reflect the willingness of gas units to offer prices in different market environments. Secondly, a vector error correction model (VECM)-based gas–electricity price conduction analysis method was proposed to study the long-term co-integration relationship between gas–electricity prices and the short-term fluctuation conduction process. Then, aiming at the problem of delay in price transmission, a time-delay analysis method of gas–electricity price based on the attention mechanism is proposed. Finally, the gas price index and average gas power price data of China from August 2020 to November 2022 are used to verify the validity of the proposed model; the numerical results show that the proposed method can determine the long-term stable fitting relationship between gas and electricity prices, and can also analyze and judge the conduction direction of gas and electricity price fluctuations in different time periods. [ABSTRACT FROM AUTHOR]

Simple Summary: Over the last ten years, the UK (United Kingdom) puppy trade has moved almost entirely online. Breed demand and impulse buying have made puppies lucrative commodities in an industry driven by profitability. To compensate, production levels have risen, feasibly fuelled by unethical breeding, poor husbandry/handling practice, and reduced overheads. As a result, breeding stock and puppies may have suffered potentially fatal physiological and long-term psychological issues. Surveys of veterinary professionals report an increase in sick puppies purchased online, whilst the British Small Animal Veterinary Association (BSAVA) reports an epidemic of canine inherited disease. The rapid growth of the puppy trade leaves significant gaps in regulation, and, to date, the nature of online trade remains difficult to quantify; however, a wealth of information can be gleaned from online advertisements, voluntarily posted to the public domain. This includes litter size, breed, seller location, activity, and more. In this study, a buoyant, consumer-driven market was revealed over a two-year period, in which the top 20 most advertised breeds were in accordance with the Kennel Club (KC) registered breed statistics, signifying a significant overlap with real-world data. Of all puppies advertised online, 66% were of 20 breeds, and 46.8% of puppies were listed as breeds linked to conformational disorders. Regional and seasonal fluctuations in price were evident, suggesting a market influenced by consumer trends. The highest number of advertisements per capita were located within Wales, the reported 'puppy farm capital' of the UK. Mean price for all breeds was GBP 927.14, increasing by over GBP 150 per individual, over the two-year duration of the study. The UK online puppy trade has rapidly outgrown the current legislation, aided by the anonymity provided by classified advertisement platforms. In an effort to meet increased demand, some unregulated and regulated breeders may have employed practices that negatively impact canine welfare. A paucity of up-to-date empirical data, necessary to characterise the scale and nature of this industry, makes intervention challenging. This study quantifies the online puppy trade via web-scraped online classified advertisements, providing empirical data that reveal market trends, along with spatial and temporal patterns. A total of 17,389 unique dog advertisements were collated and analysed over a 2-year period (1 June 2018 to 31 May 2020). The second year included the COVID-19 Lockdown (23 March 2020 to 31 May 2020). Statistical comparisons between dependent and independent variables were performed by linear regression. In the case of a single continuous variable, a one sample t-test was used. Of these advertisements, 57.2% were sourced from a pet-specific classified advertisement website (Pets4Homes, n = 9948), and the remaining 42.8% from two general classified advertisement websites (Gumtree, n = 7149, 41.1%; Preloved, n = 292, 1.7%, respectively). England exhibited the greatest number of advertisements (n = 10,493), followed by Wales (n = 1566), Scotland (n = 975), and Northern Ireland (NI; n = 344). Scaled for estimated human population density, Wales possessed as many advertisements per million inhabitants (489.4) as the other three combined (England = 186.4, Scotland = 177.3, and NI = 181.1). Across both years, 559 unique breeds were advertised, yet 66% of all advertisements focused on 20 breeds, and 48% advertisements focused on only 10 breeds. Regional breed popularity was suggested, with French Bulldog as the most advertised breed in England (7.3%), Scotland (6.8%), and Wales (6.8%), but Schnauzers were most popular within Northern Ireland (6.83%). Within the 559 unique breeds advertised, only 3.4% had links to conformational disorders CD); however, these breeds were among the most commonly advertised, totalling 46.9% of all ads. Across all regions, price density peaked between GBP 300 and GBP 1000, with Bulldogs presenting the greatest cost (mean = GBP 1461.38, SD = GBP 940.56), followed closely by French Bulldog (mean = GBP 1279.44, SD = GBP 664.76) and Cavapoo (mean = GBP 1064.56, SD = GBP 509.17). CD breeds were found to be GBP 208.07 more expensive, on average, than non-CD breeds. Our results represent a buoyant online market with regional and seasonal fluctuations in price, advertised breed frequency and total counts. This suggests a market influenced by consumer trends, with a particular focus on breed preference, despite links to illness/disease associated with conformation. Our findings highlight the value of utilising online classified advertisement data for long-term monitoring, in order to assist with evidence-based regulatory reform, impact measurement of targeted campaigns, and legislative enforcement. [ABSTRACT FROM AUTHOR]

The aim of this paper was to provide a more scientific and effective analysis of the fluctuation pattern of the Chinese potato market by extracting the characteristics of the price fluctuation cycle to effectively grasp the characteristics of price changes in the potato market, thus promoting the stable and healthy development of the Chinese potato industry, and to expand the application scenarios of the EEMD model to provide a reference for the study of price fluctuation patterns in other agricultural markets. This study used an ensemble empirical modal decomposition (EEMD) model to examine time-series data on Chinese wholesale potato market prices from January 2005 to December 2021. The results showed that (1) Chinese wholesale potato market prices are characterized by some rigidity, with sharp changes in growth rates; (2) Chinese wholesale potato market prices are dominated by short- and medium-term fluctuations, and the decomposed components can better reflect the characteristics of the original series fluctuations; (3) Chinese wholesale potato market monthly prices have long- and short-term fluctuations with a 6- and 19-month cycle, and are dominated by short-term high-frequency fluctuations; (4) monthly price fluctuations in the Chinese wholesale potato market are more intense in high-frequency than low-frequency fluctuations, and there is a strong correlation between high- and low-frequency fluctuations in precipitation, temperature and potato prices. Finally, suggestions were made for creating and improving a national potato price information platform and strengthening the information early warning mechanism; improving the potato production interest linkage mechanism and enhancing potato farmers' ability to cope with market and natural risks; and improving the potato reserve system and potato storage facilities. [ABSTRACT FROM AUTHOR]

This study examines whether shifts in the stance of policymaking can account for the observed predictability in excess energy poverty (EP) or fuel poverty (FP) levels. Energy-related poverty is a subcategory of global poverty and can be categorized into accessibility problems related to EP and affordability issues associated with FP, which have a similar but not identical meaning. Furthermore, developed and developing countries have different energy issues, as the former deal with FP and the latter with EP. However, there are discrepancies in EP not only between countries but within counties as well; for instance, there are differences in urban and rural areas too. Difficulties in energy access can be devastating for people living at risk of poverty. Social welfare, although at stake due to the energy crisis sparked at the same time as the warfare in eastern Europe. Renewables and green fossil fuels have price fluctuations, and inflation is also a stress factor in EP. Generally, solutions to EP and FP could be, inter alia, the adoption of renewables, governmental regulation, and supranational support through the green deals and sustainable development goals (SDGs). In short, the inflationary trend disequilibrium and raging war have put Agenda 2030 at stake due to the provocation of sustainability via energy-related vulnerability, insecurity, and poverty phenomena. [ABSTRACT FROM AUTHOR]

With the development of the new energy vehicle market, the pricing of battery swapping stations (BSS) is becoming a concern. The pricing models of BSS usually only consider the interaction between the distribution system operator (DSO) and the BSS or between the BSS and electric vehicles (EVs). The impact of DSO and EVs on the pricing strategy of BSS has received less attention, which does not reflect the actual complex situation. Therefore, we propose a three-level BSS pricing method that includes market clearing and EV behaviors. Firstly, the distribution locational marginal price (DLMP) is modeled to determine the impact of the DSO on BSS. Secondly, the EV demand response is used to estimate the impact of EVs on BSS. Thirdly, to increase the adaptability of this model, an iteration algorithm with approximations and relaxations is used with mixed integer linear programming, effectively solving the pricing optimization. According to this optimization, it is evident that the BSS make decisions in the market environment by monitoring the quantity of batteries in various states and generate extra income by acting in response to price fluctuations in the electricity market. The model's viability and applicability are confirmed. [ABSTRACT FROM AUTHOR]

The Investor Sentiment Index (ISI) is widely regarded as a useful measure to gauge the overall mood of the market. Investor panic may result in contagion, causing failure in financial markets. Market participants widely use the ISI indicator to understand price fluctuations and related opportunities. As a result, it is imperative to systematically review the compiled literature on the subject. In addition to reviewing past studies on the ISI, this paper attempts a bibliometric analysis (BA) to understand any related publications. We systematically review over 100 articles and carry out a BA on a set of information based on the publication year, the journal, the countries/territories, the deployed statistical tools and techniques, a citation analysis, and a content analysis. This analysis further strengthens the study by establishing interesting findings. Most articles use the Baker and Wurgler index and text-based sentiment analysis. However, an Internet-search-based ISI was also used in a few of the studies. The results reveal the lack of direct measures or a robust qualitative approach in constructing the ISI. The findings further indicate a vast research gap in emerging economies, such as India's. This study had no limit on the period for inclusion and exclusion. We believe that our current work is a seminal study, jointly involving a systematic literature review and BA, that will enormously facilitate academicians and practitioners working on the ISI. [ABSTRACT FROM AUTHOR]

As the media have continued to pay increasing attention to pig epidemic events, some local pig epidemic events may have a large degree of negative impact on the pork market and the whole pig industry chain, leading to pork price fluctuations. Strengthening pig epidemic control, monitoring media reporting sentiment, and stabilizing pork price fluctuations are important measures to improve the economy and people's livelihood. This paper sets out to identify the relationship between the negative media sentiment about the pig epidemic and the market risk of pork prices within a setting with pig epidemic risk. Based on the provincial panel data of China from January 2011 to December 2022, this paper uses the spatial panel Durbin model to investigate the impact of negative media sentiment about the pig epidemic on pork price fluctuations from the perspective of local and spillover effects, and further discusses the mechanism of consumer sentiment. The empirical results show that: (1) The negative media sentiment about the pig epidemic significantly exacerbates pork price fluctuations, and there is a single threshold effect, which is weakened after crossing the threshold value. (2) The negative media sentiment about the pig epidemic has a significant positive spillover effect on pork price fluctuations, showing the characteristics of "being a neighbor". The spatial spillover effect shows a significant spatial attenuation feature and an inverted U-shaped change with the inflection point at 1400 km. (3) The effect is related to the heterogeneity of media reputation. The local aggravation effect of local media's negative sentiment on pork price fluctuations is greater than that of central media and information network platforms. In terms of the spatial spillover effect, the negative sentiment of the information network platforms has the strongest effect on the aggravation of pork price fluctuations in neighboring regions. (4) The mechanism study finds that the negative media sentiment about the pig epidemic positively affects pork price fluctuations through the path of "consumer sentiment". Therefore, this research recommends that the government department should strengthen the supervision of media sentiment about the pig epidemic and reasonably guide consumer sentiment to stabilize the pork market. [ABSTRACT FROM AUTHOR]

The fluctuation of vegetable prices in recent years underscores the need to identify contributing factors and develop effective policies. In order to examine the factors affecting the fluctuation of vegetable prices, this paper uses a structural model constructed by demand, supply, import, and export functions to decompose price variance, and also performs a numerical simulation to generalize the results. We studied the Korean vegetable market, and selected cabbage, radish, dried red pepper, garlic, and onion as research objects. The results indicates that variability of domestic production is the primary factor that influences price fluctuations in the Korean vegetable market. In contrast, our analysis revealed that demand, import, and export had a limited impact on price fluctuations in the Korean vegetable market, except for dried red pepper and onion. [ABSTRACT FROM AUTHOR]

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China's power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China's power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs' perception of "gain" and "loss" with market risks in China's two-tier market environment, the prospect theory and the marine predator's algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators' participation in electricity trading. [ABSTRACT FROM AUTHOR]

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility. [ABSTRACT FROM AUTHOR]

Machine learning has been proven to be very effective and it can help to boost the performance of stock price predictions. However, most researchers mainly focus on the historical data of stocks and predict the future trends of stock prices by designing prediction models. They believe that past data must hide useful information in the future. Due to the lack of human participation, the result of this practice must be accidental. To solve this problem, we propose a novel model called Convolutional Neural Network with Sentiment Check (CNN-SC) in this paper. The model recommended by the authors refers to and expands upon the ideas of experts, and then takes the sentiment value in expert comments as the basis for stock price prediction. This model reflects the humanization of stock price prediction and eliminates the problem of a lack of supervision in machine learning. To demonstrate the effectiveness of our novel method, we compare it with five other popular and excellent methods. Although the C-E-SVR&RF and GC-CNN models are also quite effective, our results indicate the superiority of CNN-SC and it is accurately used to calculate the short-term (seven days later) stock price fluctuation of a single stock. [ABSTRACT FROM AUTHOR]

Energy retailers that sell energy at fixed prices are at risk of bankruptcy due to instantaneous fluctuations in wholesale electricity prices. Energy derivatives, e.g., electricity options, can be purchased by energy retailers then sold to customers as one potential risk-mitigation tool. A class of energy retailers that trade energy derivatives, including the electricity option, the carbon option and the green certificate, is considered in this paper. In terms of energy retailers, a strategy that can maximize the value of the purchased energy derivatives over a period of time and minimize the risk due to the stochastic price fluctuations is developed. Firstly, the dynamic prices of the electricity option as well as the carbon option are described by stochastic differential equations, and the dynamic prices of the green certificate are described by ordinary differential equations. Historical price data are used to obtain the parameters of both stochastic and ordinary differential equations by maximum likelihood estimation. Next, an investment portfolio is established as a mean-variance portfolio selection problem where the retailer maintains the satisfactory asset value and minimizes the risk simultaneously. Then, the problem is transformed into a stochastic optimal control problem which can be solved analytically by using the linear-quadratic method. Finally, the numerical simulations illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]