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Bitcoin has received a great deal of attention as a highly volatile asset with investors attempting to profit from its dramatic price fluctuations. We develop a hybrid deep learning model based on feature selection in different frequency domains to enrich the literature of Bitcoin price prediction. Indicators such as Technology, Economy, Green Finance and Media Attention are considered. We first decompose all the data into different frequencies through CEEMDAN approach, and then the data at the same frequency are integrated into a Random Forest model to reduce the subset of potential predictors by measuring the importance of different factors. Finally, the selected factors are put into the LSTM/GRU to make the prediction of different components of Bitcoin prices at the same frequency, and aggregate together to obtain the predicted Bitcoin prices. The empirical results show that our proposed model outperforms the benchmark models, which is verified by MCS test. The proposed hybrid method obtains much higher return on investment in simulated trading than other benchmark models. Our study inspired the investors to accurately predict Bitcoin price and dig possible relationships between different assets and its determinants in frequency domain. [ABSTRACT FROM AUTHOR]

In the current economic downturn, consumers are showing increased price sensitivity. With mobile technology enabling anytime connection, understanding daily price sensitivity fluctuations is crucial. This study analyzes transaction data from two companies, covering both offline and online scenarios. Additionally, an online experiment was conducted to further reveal the causal relationship between time-of-day and consumers' price sensitivity. The studies indicate that price sensitivity is highest in the morning, decreasing by approximately 0.5% per hour. These findings are interpreted using the circadian rhythms theory. This research deepens our understanding of how price sensitivity changes over time, offering invaluable insights for businesses to tailor their pricing and promotional strategies throughout the day. [ABSTRACT FROM AUTHOR]

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions. [ABSTRACT FROM AUTHOR]

To address marketing challenges in the agricultural sector, provide financial support for small-scale farmers over marketing seasons, manage price risks of agricultural products, and enhance the functioning of agricultural mercantile exchanges, it is attainable to implement an efficient and compliant warehouse receipt system (WRS) that aligns with the legal, institutional, social, and economic-financial conditions of a country. The aim of this study is to design, simulate, and assess the feasibility of an innovative WRS in the agricultural sector. To achieve this, a WRS was designed and evaluated for maize crop in Iran. The research methodology of this study is divided into three main parts: design, simulation, and feasibility assessment of the WRS. The design process incorporated the FAO-provided (2009) warehouse receipt system development, considering the experiences of various countries and the institutional and financial regulations specific to Iran. Additionally, a dynamic programming model was used to simulate the system, and an agent-based model was utilized for system feasibility assessment. The study results demonstrated that it is possible to design an innovative and efficient WRS by involving five key actors, including farmers, buyers (maize traders), banks, mercantile exchange market, and the warehouse (governmental institution), and establishing clear communications among them. Based on simulation results using dynamic programming model, it was evident that four parameters, namely the annualized loan interest rate, the valuation coefficient for loan collateral, price volatility of the product over the marketing season, and the warehouse cost-to-product value ratio, significantly impact the adoption of the WRS by farmers. In conclusion, the findings from the agent-based model revealed that setting the annualized loan interest rate at 8%, a collateral valuation coefficient of 85%, price fluctuations over the non-harvest time at 60%, and a warehouse cost-to-product value ratio of 2% can result in the participation of nearly 100% of farmers in the proposed WRS. [ABSTRACT FROM AUTHOR]

As Hosseini and Gills argue, value must be re-examined and liberated from its subservient ties to the logic of markets, price fluctuations, and the circuit of capital. The question is, what is the right way to open up debate on value and what should be valued? Here I focus on revising the Marxian analysis of value and price relations. I develop the concept of commodity in terms of social theories developed a good century after Marx, utilizing the scheme of circuit of capital though in a manner that avoids the infamous transformation problem. The point is to open up possibilities for a realist, qualitative, and causal analysis of the processes of production and exchange. By unifying production, valuation, and consumption within an overall conceptual model, new questions emerge, and they have to do with value in a more general sense, including the value of nature, rights, and justice. [ABSTRACT FROM AUTHOR]

Copyright of Panorama Económico is the property of Universidad de Cartagena and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: This data note contains updated characterization factors including the corresponding datasets for the integrated method to assess resource efficiency (abbreviated as ESSENZ) as well as its successor (ESSENZ+) utilized in assessing the criticality of 48 abiotic resources. The updated characterization factors enable life cycle-based case studies of product systems to determine criticality aspects as well as aspects related to compliance with social and environmental impacts. Underlying data is provided as it is needed for efficiently interpreting the results. Data description: Data was collected from publicly available source, including i.e. the United States Geological Survey for abiotic resource production and reserves, the Fraser Institute for the Policy Perception Index, the World Economic Forum for the Enabling Trade Index, the World Bank for the Worldwide Governance Indicators and the German Mineral Resources Agency for price fluctuations. The compiled data has been reorganized and recalculated in accordance with the principles outlined in the published ESSENZ and ESSENZ + method, serving as a foundation for the calculation of the characterization factors for assessing criticality and compliance with social and environmental impacts. [ABSTRACT FROM AUTHOR]

External shocks have a notable influence on the macroeconomic fluctuations of emerging economies. However, there is limited theoretical and empirical evidence on how dependence on a single commodity export affects the transmission of external shocks in these economies. This study uses data from 1998 to 2019 to estimate a Bayesian Vector Autoregressive (BVAR) model for the economies of Ethiopia, Morocco, Nigeria, and South Africa, focusing on the price of their leading export commodities. The study examines the relationship between fluctuations in export commodity prices and economic activity over time. Most of the findings are consistent with the theoretical framework and empirical evidence on emerging economies, which typically react to external shocks, although the responses vary across economies. The study shows that a shock to export commodity prices induces a more pronounced output response in low-income economies with lower shock-absorbing capacity. Export commodity prices are identified as a crucial transmission channel of external shocks to emerging market economies, highlighting the importance of paying distinct attention to this sector in macroeconomic modeling. The results are robust to different prior specifications and choices of lag lengths for exogenous variables. [ABSTRACT FROM AUTHOR]

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]

Transmission of prices, profits, and more generally, economic well‐being across vertically connected sectors of agriculture have a long history of interest—arguably of most current interest in meat and livestock markets. Disruptions in live animal harvesting, especially from COVID‐19, have corresponded with substantial market adjustment and hence elevated interest in inner‐industry relationships, including from policymakers. This paper's main contribution is assessing how price changes in the U.S. feedlot industry manifest in feeder cattle markets. We use Ricardian rent theory as a framework to quantify price transmission by testing how price fluctuations actually pass through the supply chain versus theoretical expectations. We posit that the capacity utilization of feedlots changes because of market shocks, impacting price relationships. In the empirical model, when feedlot capacity utilization rates are below the 65% critical point, we find that both fed to feeder cattle and corn to feeder cattle pass‐through rates are higher than hypothesized. When feedlot capacity utilization rates are high (>65%), estimated pass‐through rates are lower and not statistically different from Ricardian rent theory. Understanding how prices pass through in the beef industry can help inform policy discussions about beef market competitiveness and promote efficient resource allocation. [ABSTRACT FROM AUTHOR]

The dual-carbon strategy is a basic national policy to ensure high-quality and sustainable development of China's economy. Scientific understanding of carbon price fluctuations aids in mitigating investment risks, fostering a steady development of the carbon market, and enhancing the domestic carbon market's pricing competence in the global market. Notably, the carbon trading prices in China exhibit significant volatility and periodic variations, underscoring the imperative to enhance a unified carbon market nationwide. This article selects Shenzhen's monthly carbon emissions trading price data from August 2013 to February 2023 to represent the national carbon market price. Based on the analysis of massive data, multivariate analysis methods such as H-P filtering method and ARCH clustering model were used to study the fluctuation patterns and cyclical characteristics of Shenzhen's carbon emissions trading prices. The results show that: The domestic carbon price has the remarkable feature of "falling in fluctuation", showing four complete cycles, with each cycle time range of 10-26 months; The peak and valley values show a downward trend of varying degrees, with the peak and valley values changing from positive to negative, and the cycle types show a steep downward trend; The carbon price yield series has ARCH effect. The research results reveal the basic laws of my country's carbon price fluctuations and provide theoretical basis and data support for improving the global pricing capability of the domestic carbon market. [ABSTRACT FROM AUTHOR]

Known as the "King of Spice", pepper (Piper nigrum) is the most widely used and traded agricultural product in the world. In Sarawak, pepper is listed as the few industrial crops in producing and contributing the most to the gross domestic product (GDP) of the country, Malaysia. However, the recent price fluctuation in the global pepper market has caused some farmers to abandon pepper farms due to the lower pepper prices against the high cost of input materials and farm maintenance, in addition to limited marketing choices and bargaining power when dealing with buyers due to their geographic remoteness, small production quantity, and variable product. To improve the circumstances, the use of blockchain technology with Byzantine-based consensus algorithm is proposed in this paper to minimize some of the constraints faced by the sniallholder farmers. With the PBFT consensus algorithm on-board, the blockcliain network (BCN) operates without miners as it relies on the message-transfer mechanism to achieve the total consensus. Besides, the PBFT consensus algorithm with varying network structures were evaluated. Upon comparison between network structures, the group network structure dominated the entirety of the experiment and presented as the proposed network structure, with the addition of specific nodes such as relay, storage, administer and brackets: bench and penalty to facilitate and maintain the longevity of the BCN. The implementation of blockchain technology in Sarawak's pepper industry has the potential to improve the linkages and cohesion between pepper stakeholders but its overall integration would have to be further explored. [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]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We use a panel smooth transition regression model to investigate the influence of Conservation Reserve Program (CRP) rental rates on land enrollment under different crop price regimes. We employ a flexible estimation technique, allowing the CRP rental rate incentives to vary with crop prices, and construct a Hausman‐type instrumental variable to address endogeneity. Simulations are conducted to assess the impact of varying crop prices on CRP acreage and its associated environmental effects. Results reveal a nonlinear relationship between rental rates and enrollment, with the impact diminishing as crop prices increase. A 10% rise in CRP rental rates corresponds to a 5.1% expansion in enrollment, reducing to 4.2% when crop prices reach a specific threshold. High crop prices lead to fewer acres enrolled, decreased carbon sequestration, increased phosphorus and nitrogen runoff, and increased sediment loss. Our findings highlight the importance of considering price fluctuations in conservation planning as it has significant implications for environmental conservation policies and programs. [ABSTRACT FROM AUTHOR]

Petrochemical solvents are widely used for the extraction and fractionation of biomolecules from edible oils and fats at an industrial scale. However, owing to its safety concerns, toxicity, price fluctuations, and sustainability, alternative solvents and technologies have been actively explored in recent years. Technologies, such as ultrasound and microwave‐assisted extraction, supercritical carbon dioxide extraction, supercritical fluid fractionation, and sub‐critical water extraction, and solvents, like ionic liquids and deep eutectic solvents, are reported for extraction and fractionation of biomolecules. Among them, supercritical carbon dioxide extraction and fractionation are some of the most promising green technologies with the potential to replace petrochemical‐based conventional techniques. The addition of cosolvents, such as water, ethanol, and acetone, improves the extraction of amphiphilic and polar compounds from edible oils and fats. Supercritical fluid processing has diverse applications, including concentration of solutes, selective separation of desired molecules, and separation of undesirable compounds from the feed material. Temperature, pressure, particle size, porosity, flow rate, solvent‐to‐feed ratio, density, viscosity, diffusivity, solubility, partition coefficient, and separation factor are the fundamental factors governing the extraction and fractionation of desired biomolecules from lipids. Supercritical fluids stand alone compared to conventional fluids, because of their tunable solvent properties. Overall, it is to be noted that supercritical fluid‐based methods have lots of scope to replace conventional solvent‐based methods and progress toward the creation of sustainable food‐processing techniques. This review critically evaluates the parameters responsible for the extraction and fractionation of biomolecules from edible oils and fats under supercritical conditions. [ABSTRACT FROM AUTHOR]

This research incorporates crude oil price series with different time scales, China's dirty energy, new energy and green bond index into a system, so as to investigate the spillover effects during some major periods. The results indicate that the spillover effects of short-term oil price fluctuations are mild, and the green bond market is the least affected. While the long-term oil price fluctuations have profound effects on the aforementioned three markets, the spillovers are especially enhanced by extreme events. Our results are verified by appropriate robustness tests. [ABSTRACT FROM AUTHOR]

Carbon emissions trading is pivotal for advancing China's low-carbon goals. As the primary tradable asset in the carbon market, carbon emission allowances inevitably experience price fluctuations. However, numerous empirical studies show that the frequency of real-world data is highly unstable, which results in the failure of probabilistic modeling. Therefore, this paper aims to model the dynamics of carbon emission allowance prices in China using four mainstream uncertain differential equations. The optimal model is chosen through rolling window cross-validation using the criterion of minimizing average testing errors. Parameters of the optimal model are determined by moment estimation based on residuals, and the model's effectiveness is also assessed through uncertain two-sided hypothesis testing. Additionally, we forecast carbon emission allowance prices and their 95% confidence intervals for the next 14 business days. To manage trading risks, we propose a customized carbon option contract for pricing European carbon options and conduct sensitivity analysis on key parameters. Finally, we present a paradox of stochastic differential equations for modeling carbon emission allowance prices. [ABSTRACT FROM AUTHOR]

The major coconut products produced in Fiji are copra and coconut oil. As a consequence, coconut oil is the only product now exported, and price fluctuations have a substantial impact. This study examines the changes in copra and coconut oil prices for Fiji from 2009 to 2019. The present study used secondary data for analysis. Copra pricing information was gathered from Fiji Copra Millers, and coconut oil price information was gathered from the website for palm oil analytics' crude coconut oil price. The price changes for trend, cyclical, seasonal, and irregular fluctuations were computed using a multiplicative model. Both the price of copra and the price of coconut oil displayed poor connections, R2= 0.39 and R2= 0.18, respectively, despite the years' considerable price volatility. The price of copra and coconut oil fluctuated significantly throughout the year and in distinctly diverse ways. Seasonality and erratic price fluctuations were particularly pronounced for coconut oil, which directly affected the price of copra and discouraged farmers from investing in coconut plants, lowering productivity, production, and copra supply.

Introduction: Emergent crises can have a catastrophic impact on regional economic systems. The concurrent outbreaks of African Swine Fever (ASF) and the COVID-19 pandemic have severely affected Macau's pork industry, making it an interesting case study for analyzing the effects of simultaneous emergent crises on food prices. Methods: The study utilized a comprehensive dataset spanning from March 30, 2018, to April 15, 2022. The analysis employed the Ensemble Empirical Mode Decomposition (EEMD) method to decompose the pork price data into high-frequency, low-frequency, and trend components, while the Bai-Perron test pinpointed key events and regulatory interventions affecting pork prices. Results: The findings revealed that short-term market fluctuations had a limited impact on pork price dynamics, with primary drivers being longterm and low-frequency emergencies. The Bai-Perron test identified four significant emergencies or regulatory policies as critical turning points, segmenting the price series into five distinct phases. The results highlight the importance of considering long-cycle emergencies when assessing their impact on food prices and underline the need for more resilient agricultural supply chains. Discussion: The study provides insights into how compounded emergent crisis threats impact market behavior and offers policy recommendations for enhancing market stability. Recommendations include strengthening cross-regional epidemic control, investing in vaccine research, optimizing cold chain logistics, updating legal frameworks, and improving emergency preparedness. The implications of these findings are relevant for similar regions facing concurrent health and economic crises, contributing to a better understanding of crisis management and market stability maintenance. [ABSTRACT FROM AUTHOR]

The price fluctuation of feed additives has a direct impact on the development of feed industry and the quality of feed products of farmers. Additionally, it plays a vital role in enhancing the income of breeders and in propelling the stable development of the economy and society. This article selects 168 monthly data from 2010 to 2023 and uses a vector autoregressive model to explore the main factors affecting the market price fluctuation of feed additives from the price of feed additives themselves and the internal and external levels. The results showed that the price of feed additives is most affected by itself, and the contribution rate is still 64.19% in the 9th period. In terms of external factors, the most significant influences on the price of feed additives are feed demand and related policies of breeding industry. Among them, the demand for feed in the 9th period has 7.54% impact on the price of feed additives in the month. In terms of internal factors, the two factors that have the most obvious impact on the price fluctuation of feed additives are the production price of raw materials and the wholesale price of raw materials. Among them, the production price of raw materials in the 9th period has a 9.04% impact on the price of feed additives. The study shows that the market price fluctuations of feed additives in China is most affected by itself, and the other influencing factors are in order of production prices of raw materials, feed demand, wholesale prices of raw materials, policies related to the breeding industry, transportation prices of raw materials, seasonal factors, and domestic and foreign futures. [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]

The emerging threat of climate change underscores the necessity for increased investments in renewable energy sources and hydrogen energy to enhance the transition towards a more sustainable and resilient future. This work presents a simple Linear Programming (LP) mathematical framework to ensure the optimal portfolio management and participation of an aggregator owning a Polymer Electrolyte Membrane (PEM) electrolyzer and renewable energy assets in the electricity, hydrogen and green certificates markets. The objective aims to maximize the aggregator's overall profits by optimally managing green certificates issuance and diminishing the curtailment of the renewable energy production. The proposed approach is applied to an illustrative case study, while additionally a number of scenarios are considered to account for fluctuations in electricity price and wind power production. Furthermore, the impact of expanding electrolyzer's power capacity from the aggregator's side is also investigated. The results obtained illustrate the impact of the above-mentioned scenarios on the optimal operation and profitability of the aggregator. • Optimal scheduling of a RES-electrolyser aggregator in energy markets. • A novel concept for hourly management of available green certificates. • Wind power generation uncertainty greatly affects aggregator's scheduling decisions. • Aggregator optimally manages green certificates according to hourly prices. • Electrolyser power capacity expansion enhances aggregator's profitability. [ABSTRACT FROM AUTHOR]

During the 1990s and 2000s, many developing nations accepted fixed exchange rate regimes to sustain price stability. By pegging their currencies to an anchor currency (i.e. US dollar), developing countries could bring credibility and confidence into their economies. The study tries to analyse the impact of the pegged exchange rate regime on inflation in BRICS countries for the period spanning from 2003 to 2022. The result points to the loss of monetary policy autonomy due to the ‘impossible trinity’ coming into play with a steady increase in capital inflows. The country’s large trade surpluses and extensive trade relationships may buffer the impact of exchange rate fluctuations on domestic prices, as changes in the exchange rate may be absorbed by changes in trade volumes or pricing strategies. Other factors like demographic trends or government interventions in markets could overshadow the influence of the exchange rate regime on inflation. The impact of changes in the exchange rate regimes on inflation may operate with lags, meaning that any effects may not be immediately observable in the data. The analysis in this study proposes that exchange rate regimes choice and money supply influence the inflation dynamics in the BRICS countries. It merely reflects the success of the central bank in neutralizing its very effort of maintaining the pegged rate, which gives rise to inflationary growth of the money supply. [ABSTRACT FROM AUTHOR]

This paper takes the monthly prices of meat poultry in 30 provinces (city and municipality) of China as the research object, and empirically studies the asymmetric fluctuation characteristics of poultry market prices using the TGARCH model. The results show that the poultry prices in 12 provinces (city and municipality) have asymmetric fluctuation characteristics, indicating that the asymmetric fluctuation characteristics of poultry market prices in China are not universal. There are significant regional differences in the asymmetric fluctuation characteristics of meat poultry market prices in China. Hebei, Fujian, Hainan, Shanxi, Henan, Sichuan, and Gansu exhibit negative asymmetric fluctuation characteristics, while Jiangsu, Zhejiang, Anhui, Jiangxi, and Liaoning exhibit positive asymmetric fluctuation characteristics; the main factors causing the asymmetric fluctuation of meat poultry market prices include production costs, diseases, and market changes. Based on this, it is necessary to maintain the order of the poultry meat market, pay attention to the correlation of poultry market prices; build an information platform to improve the comprehensive benefits of the poultry industry, improve production and breeding supporting facilities, enhance the level of safe epidemic prevention, and achieve effective control of price fluctuations in the poultry market by multiple entities. [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]

This study applies the PSM-DID approach to investigate whether the energy price has been impacted by the Russia-Ukraine war among OECD countries during 1999–2022. Compared with previous studies, we obtain robust and heterogeneous results by considering endogeneity from the perspective of quantitative policy assessment. It concludes the Russia-Ukraine war has significantly raised energy prices, leading to a 9% increase in energy prices in OECD countries. The heterogeneity results show that the Russia-Ukraine war impacted the energy prices of EU and NATO members but not those of non-EU and non-NATO countries. Additionally, only the energy prices in Southern and Western European countries have been significantly increased by the Russia-Ukraine war, in which Southern Europe has been the most affected, rising by 22%. These findings have great practical significance for analyzing the degree of influence of war on fluctuations in energy prices and geopolitical risks in regions. [ABSTRACT FROM AUTHOR]

The irregular movement of cryptocurrency market makes effective price forecasting a challenging task. Price fluctuations in cryptocurrencies often appear to be arbitrary that has been a hot topic. Though various statistical and econometric forecasting models exists, still there is lack of advanced artificial intelligence models to explain behaviour of such price fluctuations. Artificial neural networks (ANNs) are data-driven models and can effectively handle complex nonlinear functions in presence of abundant data. However, optimal parameter tuning of such models with conventional back propagation-based learning entail domain expertise, higher computational cost, and yield inferior accuracy thus, makes its use tough. Contrast to this, metaheuristic-based ANN training has been emerging as an efficient learning paradigm. This article constructs few optimal ANNs through three efficient metaheuristics with less control parameters such as fireworks algorithm (FWA), chemical reaction optimization (CRO), and the teaching–learning based optimization (TLBO) separately. The role of a metaheuristic is to investigate the near-optimal weights and thresholds of an ANN of solitary hidden layer and thereby ensuring a higher degree of accuracy. The hybrid models are then used to simulate and predict the behaviour of four fast growing cryptocurrencies such as Bitcoin, Litecoin, Ethereum, and Ripple. Various experiments are carried out using real time cryptocurrency data and hybrid ANNs through four performance measures. We undertake a comparative performance analysis of forecasting models and Friedman tests to demonstrate the superiority and statistical significance. In particular, ANN trained with CRO, TLBO, and FWA obtained an average rank of 1, 2, and 2.75 respectively. [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]

Crowding is widely regarded as one of the most important risk factors in designing portfolio strategies. In this article, the authors analyze stock crowding using network analysis of fund holdings, which is used to compute crowding scores for stocks. These scores are used to construct costless long–short portfolios, computed in a distribution-free (model-free) way and without using any numerical optimization, with desirable properties of hedge portfolios. More specifically, these long–short portfolios provide protection for both small and large market price fluctuations because of their negative correlation with the market and positive convexity as a function of market returns. By adding their long–short portfolio to a baseline portfolio such as a traditional 60/40 portfolio, the authors show that their method provides an alternative way to hedge portfolio risk including tail risk, which does not require costly option-based strategies or complex numerical optimization. The total cost of such hedging amounts to the total cost of rebalancing the hedge portfolio. [ABSTRACT FROM AUTHOR]

Collection has become a significant form of consumption in consumers' lives. However, to date, our understanding of the downstream behaviors associated with collection consumption remains limited. Could collection have a more extensive impact on consumers' subsequent behaviors? Across four experiments and one field study, results indicate that adopting a collection mindset can shift consumers' perceptions of price changes, influencing their subsequent consumption. This effect arises, because when consumers possess a collection mindset, they lean towards hedonic (as opposed to utilitarian) evaluations of objects. Consequently, such consumers are less sensitive to price fluctuations, leading them to spend more in subsequent unrelated purchases. Furthermore, two studies demonstrate that the influence of collection consumption on price sensitivity is moderated by the nature of the collection and consumers' motivations for collecting. Given the pervasive role of collection consumption in consumers' lives, this research offers crucial theoretical and practical insights. [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]

This research delves into whether access to natural resources is a blessing or a curse, a matter of contention within contemporary literature. Lacking conclusive evidence, particularly within the G‐20 countries, this study endeavors to establish the intricate linkages between economic development, natural resources, and economic complexity. A subset of G‐20 economies from 1990 to 2021 was used to analyze the results of the second‐generation CS‐ARDL model. After accounting for variables like corruption, total labor force, gross capital formation, trade openness, and inflow of foreign direct investment, we find that natural resources, economic complexity, corruption and gross capital formation are significant and adverse predictors of economic growth. Natural resource dependence is susceptible to price fluctuations and sustainability concerns, economic complexity may introduce market volatility challenges, corruption erodes trust and transparency, and excessive gross capital formation can lead to economic imbalances. On the other hand, economic growth is positively impacted by the additional effects of the control variables, including foreign direct investment, trade openness and labor force. In crafting its policy recommendations, this study artfully weaves a tapestry that resonates with the core principles of the Sustainable Development Goals (SDGs). These strategic imperatives include the adept stewardship of resources, the cultivation of economic diversity, the seamless facilitation of trade, the fortification against corruption, and a harmonious symphony that underscores financial stability and the nurturing of human capital, ultimately painting a vivid portrait of sustainable economic growth. [ABSTRACT FROM AUTHOR]

Fractional Brownian motion has become a standard tool to address long-range dependence in financial time series. However, a constant memory parameter is too restrictive to address different market conditions. Here, we model the price fluctuations using a multifractional Brownian motion assuming that the Hurst exponent is a time-deterministic function. Through the multifractional Itô calculus, both the related transition density function and the analytical European Call option pricing formula are obtained. The empirical performance of the multifractional Black–Scholes model is tested by calibration of option market quotes for the SPX index and offers best fit than its counterparts based on standard and fractional Brownian motions. [ABSTRACT FROM AUTHOR]

This study aimed to explore big-time series data on agricultural commodities with an autocorrelation model comprising long-term processes, seasonality, and the impact of exogenous variables. Among the agricultural commodities with a large amount of data, chili prices exemplified criteria for long-term memory, seasonality, and the impact of various factors on production as an exogenous variable. These factors included the month preceding the new year and the week before the Eid al-Fitr celebration in Indonesia. To address the factors affecting price fluctuations, the Seasonal Autoregressive Fractionally Integrated Moving Average (SARFIMA) model was used to manage seasonality and long-term memory effects in the big data analysis. It improved with the addition of exogenous variables called SARFIMAX (SARFIMA with exogenous variables is known as SARFIMAX). After comparing the accuracy of both models, it was discovered that the SARFIMAX performed better, indicating the influence of seasonality and previous chili prices for an extended period in conjunction with exogenous variables. The SARFIMAX model gives an improvement in model accuracy by adding the effect of exogenous variables. Consequently, this observation concerning price dynamics established the cornerstone for maintaining the sustainability of chili supply even with the big data case. [ABSTRACT FROM AUTHOR]

Using weekly data from January 2020 to December 2021 on the prices of various links in the Chinese broiler industry chain and the COVID-19 epidemic, we employed a time-varying parametric vector auto-regressive (TVP-VAR) model to investigate the dynamic effects of public health events on price fluctuations of upstream, midstream, and downstream products in the Chinese broiler industry chain. Our findings showed that the COVID-19 epidemic had different effects on the prices of various broiler products, both in direction and magnitude, at different lags and time intervals. Chicken and live chicken prices were impacted the most, followed by broiler chick prices, while broiler feed prices were impacted the least. The epidemic constantly impacted broiler chick and chicken prices, while its effect on live chicken prices was initially negative but turned positive afterwards. Additionally, the impact of the COVID-19 epidemic on broiler product prices consistently increased with more extended lag periods. The impulse responses at different epidemic time points were heterogeneous. With the results of this study, policy recommendations can be suggested to relevant government departments to optimize the prevention and control measures for public health emergencies and ensure price stability in the broiler industry. [ABSTRACT FROM AUTHOR]

In the era of digital economy, the livestock industry, as a key driving force for the development of modern agriculture and the promotion of agricultural economic growth, plays an important role in pig prices in China. Based on pig price data from February 2011 to January 2023, spanning a total of 144 months, this paper employs the VAR model to empirically test the impact of changes in the livestock industry structure on pig prices. The results show that changes in the livestock industry structure are a Granger cause of pig price fluctuations. The pig prices are affected by the lagged impact of changes in the livestock industry structure. The pig prices were negatively impacted by the changes in the livestock industry structure in the first two periods, and and began to be positively affected by the changes in the livestock industry structure from the third period. The response reached its peak in the 12th period, and the impact tended to disappear in the 24th period. The shock response cycle of changes in the livestock industry structure to pig prices is approximately two years, with a contribution rate of 10.85% still present by the end of the second year. The study indicates that changes in the livestock industry structure have a positive impact on the pig prices. [ABSTRACT FROM AUTHOR]

With the opening of the power market and the development of the energy Internet, the optimal allocation of regional energy systems has become the key to achieving energy efficiency and economic balance. The article studies how to achieve this balance by optimizing the allocation of regional energy systems under the influence of price fluctuations and load demand uncertainty in the electricity market. This study introduces a real-time electricity price adjustment mechanism to stimulate user participation in energy adjustments and improve energy utilization efficiency. By constructing an optimization model based on multiple uncertainties and comprehensive demand response, uncertainty factors such as energy market price fluctuations and climate change were considered, and user demand response was integrated. The research results indicate that electricity price fluctuations have a significant impact on system operation, while CSP power plant thermal storage fluctuations have a relatively small impact. After the introduction of demand response, the electricity load can be reduced to zero during specific periods, and the adjustment of electricity prices stimulates user participation and improves the consumption rate of renewable energy. The total revenue of the system increased by 54.147 million yuan, demonstrating the potential of optimized configuration in reducing costs and improving efficiency. This study provides important references for building more efficient and sustainable energy systems. [ABSTRACT FROM AUTHOR]

This study investigates the predictive power of sentiment metrics concerning the volatility of tourism-related stocks listed on the Pakistan Stock Exchange (PSX) during recent crises. We employed univariate and multivariate GARCH-family econometric models to analyze daily data collected from March 2020 to April 2023. The data is categorized into two periods: A (COVID-19 until February 2022) and B (the vote of no-confidence motion until April 2023). The findings indicate that Market Sentiment (MS) plays a more significant role than individual sentiment in predicting stock price volatility and negatively impacts return volatility. During period A, the Fear Index (FI) and in B, economic policy uncertainty (EPU) were identified as factors contributing to increased fluctuation in tourism-related stocks. Additionally, MS exhibited a heightened level of bearishness compared to the COVID-19 era. The multivariate BEKK-GARCH analysis revealed that MS past volatility negatively spillover onto the current conditional volatility of PSX firms. This highlights the paramount importance of MS in understanding stock price fluctuations in the PSX. Interestingly, the technology sector exhibited lower sensitivity across the analysis. This study provides valuable insights for decision-makers by elucidating the principles of behavioral finance theory within the context of the PSX. They should prioritize long-term strategies to institutionalize investors, enhance their emotional intelligence, and mitigate psychological biases in the market. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the socio-economic aspects of crab fisheries in Maginti Island, West Muna Regency. Data collection was carried out from March 2021 to December 2023. The data collected were the socio-economic aspects of crab fisheries, including the level of community education, the number of fishermen, income from crab fishing, crab production 2017 - 2023, crab prices during 2017-2023, and the crab trade supply chain. Data were obtained through in-depth interviews with stakeholders, including 62-74 fishermen, three local collectors, and two district-level collectors. Interviews were directly conducted with an open-ended question system based on data needs. Data analysis was carried out with a descriptive-quantitative approach. The study results showed that in the social aspect, crab fishermen's education is dominated by elementary school (S.D.) graduates, namely 61.29% in 2017 and 60.81% in 2023. In the economic aspect, most fishermen have a net income ranging from 1,000,000 - 2,000,000 IDR/month, which was affected by fluctuations in BSC prices. Crab prices from fishermen to collectors tend to fluctuate throughout 2017-2023. The average price of crabs per kg was 32,083.33 IDR in 2017, 36,166.67 IDR in 2018, 30,750 IDR in 2019, 23,750 IDR in 2020, 64,666.67 IDR in 2021, 31,083.33 IDR in 2022, and 44,791.67 IDR in 2023. Price fluctuations occurred since BSC fisheries in Maginti Island are part of the global fisheries supply chain that reaches the international market to America, China, and others. Fluctuations in the global BSC prices not only affected the prices locally but could increase fishermen's income by up to 11.25% which is >2,000,000 IDR/month from 2017-2023. The increase in income indicates that BSC fisheries in Maginti Island have supported the implementation of the sustainable development goals (SDGs), specifically SDGs-1, namely no poverty. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work aims to contribute to a deeper understanding of cryptocurrencies, which have emerged as a unique form within the financial market. While there are numerous cryptocurrencies available, most individuals are only familiar with Bitcoin. This knowledge gap and the lack of literature on the subject motivated the present study to shed light on the key characteristics of cryptocurrencies, along with their advantages and disadvantages. Additionally, we seek to investigate the integration of cryptocurrencies within the financial market by applying a dynamic equicorrelation model. The analysis covers ten cryptocurrencies from June 2nd, 2016 to May 25th, 2021. Through the implementation of the dynamic equicorrelation model, we have reached the conclusion that the degree of integration among cryptocurrencies primarily depends on factors such as trading volume, global stock index performance, energy price fluctuations, gold price movements, financial stress index levels, and the index of US implied volatility. [ABSTRACT FROM AUTHOR]

This paper explores the impact of imposing various price limit bands on risk-return trade-off and asymmetric volatility on the Tunisian Stock Exchange (TSE). The study applies the EGARCH-M approach during the period spanning from 2 January 2019 to 31 January 2024, covering the periods before, during, and after the COVID-19 era. During the COVID-19 period, the TSE reduced the per-session price limit to protect investors from severe price fluctuations. Despite this protective measure, the results show that higher volatility is compensated by lower returns on all sectors' returns. After the crisis, as a first step, the TSE widened the price limits, but subsequently, it narrowed them. The results show that the shift from the wider price limit regime to the narrow price limits regime structurally modifies volatility for small and large cap sectors. [ABSTRACT FROM AUTHOR]

Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily returns of S&P BSE Sensex, S&P BSE Oil & Gas and S&P BSE-Auto sectoral indices from January 2000 to March 2020, we examined the impact of Brent crude oil price fluctuations on the two major sectorial stock indices and overall market index. The results of the Granger causality test show significant results on S&P BSE-Sensex and S&P BSE-Oil and Gas Index Returns except S&P BSE-Auto Index Returns. Further for contemporaneous association between the oil price changes and sectorial returns, we operationalised the simple regression analysis. The results from the regression analysis show statistically significant impact of crude oil price fluctuations on the sectorial returns. [ABSTRACT FROM AUTHOR]

As one of the leading oil producers in the world, Nigeria's economy is profoundly influenced by fluctuations in global oil prices, affecting the government capacity to allocate resources on national healthcare system and its ability to ensure healthy lives and promote wellbeing for all, as outlined in Sustainable Development Goal 3 (SDG-3). On this backdrop, this study has undertaken a comprehensive examination of both linear and non-linear relationship between oil price shocks and sustainable development in Nigeria, a country heavily reliant on oil revenue from 1970 to 2022 in Nigeria. Using linear Autoregressive Distributed Lag (ARDL) and nonlinear Autoregressive Distributed Lag (NARDL) models, the findings show that increasing crude oil price shock has a substantially large adverse impact on SDG-3 in the short run while decreasing crude oil price has unobserved effect in both short and longrun when asymmetries were considered. Based on the findings of this study, Nigerian policymakers should expedite efforts to diversify its economy away from oil dependency. This diversification should focus on promoting sectors with growth potentials, creating jobs, and generating stable revenues to support healthcare and sustainable development initiatives. The study highlights other actionable insights and policies to navigate the challenges posed by oil price volatility while advancing the noble aspirations of SDG 3 for the people of Nigeria. [ABSTRACT FROM AUTHOR]

Copyright of Agricultural Outlook (1673-3908) is the property of Institute of Agricultural Information, Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)