Price System Research Articles

State regulation of agricultural sector based on the example of cultivation of industrial crops in the Zhetisu region of the Republic of Kazakhstan

The goal is to study the mechanisms of state support for agricultural sector (using the example of industrial crops in the Zhetisu region), analyze the current state of agro-industrial production in the Republic of Kazakhstan, consider problems and ways of their solution. Methods – theoretical basis of the work consists of the works of foreign and domestic scientists; the analysis method assesses the development of agro-industrial complex of the Zhetisu region, regulation of the price level for industrial crops and food. Using the statistical method, conclusions on the current situation and prospects for their production. The modeling method made it possible to form effective model for managing agricultural sector. Results – problems associated with raw materials orientation of agricultural sector, incomplete utilization of production capacity, low level of introduction of innovative technologies and technical equipment slow down the pace of development of agricultural industry. Ways to solve these problems have been outlined: proposals aimed at improving the availability of financing for small businesses through investing in agricultural infrastructure and expanding the network of production and service cooperatives, increasing the efficiency of state support for agricultural economy of the Zhetisu region have been prepared. Conclusions – the main scientific principles obtained during the study can be used as information and analytical material for the development and implementation of effective policies for cultivation, processing and sale of industrial crops in the region. Natural and labor resources will allow the region to become self-sufficient in food supply. The main directions of modern state policy for regulating agricultural production are indicated: creating conditions for maintaining sustainable growth in demand for food products and raw materials; creation of infrastructure, system of wholesale and retail markets; support of price system that satisfies the population's demands for food.

Factors Influencing People’s Willingness to Shift Their Electricity Consumption

As the share of renewable energy sources, which are weather dependent and consequently volatile, continues to grow, it becomes increasingly important to explore strategies for organising both electricity production and consumption to ensure system stability. People’s flexibility in their energy consumption is one option to regulate the system. To better understand people’s willingness to align their electricity-consuming activities with a flexible pricing system, an online survey with 962 respondents was conducted. The analysis focused on the factors influencing their willingness to shift electricity-consuming activities away from peak hours, as well as the maximum shift duration of using certain devices. The results indicate that people with more flexible lifestyles and those who perceive shifting activities as taking less effort are more willing to shift their activities and indicate longer shift durations. The data also show that attitudes towards the environment, as well as financial, ecological, and motivational factors, play a role in explaining the variance in the willingness to shift and the shift duration. To conclude, increasing flexibility in everyday life could make a valuable contribution to the optimal use of electricity resources.

Pricing Chinese Convertible Bonds with Learning-Based Monte Carlo Simulation Model

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner.

Discrimination among Fresh, Frozen-Stored and Frozen-Thawed Beef Cuts by Hyperspectral Imaging.

The detection of the storage state of frozen meat, especially meat frozen-thawed several times, has always been important for food safety inspections. Hyperspectral imaging (HSI) is widely applied to detect the freshness and quality of meat or meat products. This study investigated the feasibility of the low-cost HSI system, combined with the chemometrics method, to classify beef cuts among fresh (F), frozen-stored (F-S), frozen-thawed three times (F-T-3) and frozen-thawed five times (F-T-5). A compact, low-cost HSI system was designed and calibrated for beef sample measurement. The classification model was developed for meat analysis with a method to distinguish fat and muscle, a CARS algorithm to extract the optimal wavelength subset and three classifiers to identify each beef cut among different freezing processes. The results demonstrated that classification models based on feature variables extracted from differentiated tissue spectra achieved better performances, with ACCs of 92.75% for PLS-DA, 97.83% for SVM and 95.03% for BP-ANN. A visualization map was proposed to provide detailed information about the changes in freshness of beef cuts after freeze-thawing. Furthermore, this study demonstrated the potential of implementing a reasonably priced HSI system in the food industry.

Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis

The emergence of new suppliers and energy resources has reshaped the energy market in terms of contractual structures and pricing systems. The market shifts were accelerated in response to the latest Russian-Ukraine crisis, impacting natural gas supply chains from financing projects to contracting volumes. The increased demand for liquified natural gas volumes intensified the need to switch from long-term oil-indexed contracts to short-term gas-indexed contracts. Those shifts were anticipated to influence the selling strategies for the expected added 49 MTPA of Qatari LNG, wherein increasing the share of spot selling would be reflected in higher economic performance. This study used forecasted prices to investigate potential Qatari LNG selling strategies. Initially, projections of the most dominant pricing systems used for pricing Qatari LNG (i.e., brent, Henry Hub, Title Transfer Facility, and Japan Korea Marker) were estimated between 2023 and 2040. While Qatar has been relying on long-term oil-indexed contracts, the second step estimated annual LNG revenues under different combinations of selling strategies (i.e., long-term and spot sales). Finally, the influence of varying brent slopes on the estimated revenues was measured. Due to data limitations and non-stationarity, the double exponential smoothing model was selected among the different tested models. Considering current market dynamics, forecasts of the double exponential smoothing model showed an upward price trend until 2040. An annual average increase of 1.24% for the studied pricing systems was reported. Reducing the share of long-term brent-indexed contracts to 70% and dedicating the remaining 30% of volumes to spot sales yielded the highest premiums for revenue estimates. An average annual revenue of $62 bn was reported for the 70/30 strategy, around 6% higher than the 100% brent-indexed contracts strategy. The findings revealed that diversifying the selling approach and introducing spot sales can enhance revenues. From the buyers’ perspective, the outcomes support policymakers in understanding the implications of escalated prices driven by a lack of liquidity investments.

A deep reinforcement learning approach based energy management strategy for home energy system considering the time-of-use price and real-time control of energy storage system

To enhance the flexibility of the home load optimization dispatching strategy and ensure the safe operation of the energy storage system, an optimization dispatching strategy for home energy management system (HEMS) based on time-of-use pricing and real-time energy storage system control is proposed. Firstly, a HEMS dispatching model is established with the constraints of dispatchable load and energy storage system working status, aiming to minimize the total cost for home users. This dispatching strategy controls the energy storage system charging and discharging behavior based on time-of-use pricing and real-time battery state of charge, which helps to reduce the power cost for home users and ensure the safe operation of the battery. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called soft actor-critic (SAC). The example results verify the effectiveness and superiority of the proposed method compared with other benchmark methods, which the system cost can be reduced by 15.87% at least.

Implementation of the Global Minimum Tax in Italy and EU Directive Proposals: Does This Approach Effectively Pave the Way for the Establishment of a More Sustainable Tax System?

This contribution describes the implementation of a global minimum tax in Italy and provides an initial assessment of its anticipated impact, both financially and in respect of the sustainable evolution of the tax system in economic, social and environmental terms, considering the latest directive proposals, i.e. the directives on Business in Europe: Framework for Income Taxation, transfer pricing and Head Office Tax system for micro, small and medium-sized enterprises.

Implementation of the Global Minimum Tax in Italy and EU Directive Proposals: Does this Approach Effectively Pave the Way for the Establishment of a More Sustainable Tax System?

This contribution describes the implementation of a global minimum tax in Italy and provides an initial assessment of its anticipated impact, both financially and in respect of the sustainable evolution of the tax system in economic, social and environmental terms, considering the latest directive proposals, i.e. the directives on Business in Europe: Framework for Income Taxation, transfer pricing and Head Office Tax system for micro, small and medium-sized enterprises.

Price Dynamics of Tomato, Onion and Potato (TOP) in India

The development of society depends on several core elements, including food, livelihood, nutritional security, and healthcare. To gain insight into the dynamics and volatility of prices of top crops, a study was conducted analysing wholesale monthly price data for selected states and all of India from January 2005 to December 2021. Various methodologies such as the Compound Annual Growth Rate (CAGR), seasonality index, Cuddy Della Valle Index, rescaled range analysis, and ARCH-GARCH model were utilized to achieve the research objectives. According to the study, West Bengal had major crop price fluctuations with tomatoes being costly from July to November, onions from August to January, and potatoes from July to December. Karnataka, Rajasthan, and Punjab experienced the highest monthly price instability for tomatoes, onions, and potatoes. Additionally, the prices of TOP crops were more unpredictable than cereals and pulses. The price series of potatoes had long-term memory detection, and onions had the highest price volatility among TOP crops. According to the study, in order to tackle the price variability of TOP crops, it is necessary to enhance price stabilization measures. By implementing integrated policies, a stable pricing system can be established for top crops, which will not only safeguard the interests of consumers but also ensure food and economic security for farmers.

The proposed mandatory swing pricing regime and the hard close requirement : Practical considerations

In November 2022, the U.S. Securities and Exchange Commission (the ‘Commission’) proposed rule amendments that would require, among other things, open-end mutual funds to implement a mandatory swing pricing regime under certain circumstances (the ‘Proposals’). To ensure that funds receive order information with sufficient time to make a swing pricing determination, the Proposals would require funds to set a hard cut-off time for the receipt of purchase and redemption orders. Currently, the vast majority of investments in open-end mutual funds are made through intermediaries and retirement plans, such that investors who submit orders to intermediaries and retirement plans before the time the fund has established for calculating its net asset value (NAV) generally receive the same day’s NAV, even if the fund receives the order information from the intermediary or retirement plan after NAV is calculated. This is how mutual fund pricing has operated in the US for over 50 years. The Proposals would upend current operations. Under the Proposals, an investor’s order would have to be received by the fund — not a financial intermediary or a retirement plan — before the cut-off time set by the fund for calculating NAV in order to be eligible for the same day’s NAV. If adopted as proposed, the Proposals would require extensive changes to mutual fund pricing systems and infrastructure in the US. In this paper, we acknowledge the arguments against the Proposals by an overwhelming majority of industry commenters and, without undercutting those arguments, we explore practical, regulatory and compliance considerations for funds seeking to better understand how the Proposals might affect their day-to-day operations.

Evaluation of Anatolian Water Buffalo Carcass Weights Based on a Slaughterhouse Data Collection.

This study analyzed data collected on a slaughterhouse from 2017 to 2021, belonging to five hundred and twenty one Anatolian water buffalos from different farms located in Edirne, Istanbul, and Kirklareli. Specifically, it aimed to determine the factors affecting the carcass weights and slaughter ages of the Anatolian water buffalos. The results of the study showed that the slaughter age of the buffalos was a significant determinant of their carcass weights. Meanwhile, the sex, slaughter year, and slaughter season affected the carcass weight. Differences were observed for the slaughter age regarding the sex and farm origin. Since the pricing system in local markets is based on the buffalo carcass weight, the findings of this study could be essential for farmers when determining their fattening strategies.

Towards a greener future: examining carbon emission dynamics in Asia amid gross domestic product, energy consumption, and trade openness.

The purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specific contributions of each country to the study variables and their impact on carbon emissions. The findings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly significant impact (p < 0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development, and international cooperation within Asia to reach the goal of being carbon neutral by 2050.

The dependence structure and causality detection in crude oil markets

ABSTRACT It is essential to capture the dependence structure among the world’s dominant crude oil markets for understanding the world’s crude oil pricing mechanism. The dependence structure and interaction between Shanghai International Energy Exchange (INE) market and other major crude oil markets in the world are investigated to determine whether the world crude oil market is efficient. Using the vine copula model based on GARCH, we analyze the dependence structure and interactions among these markets. The highest dependence is found between West Texas Intermediate (WTI) and BRENT crude oil markets, displaying strong symmetric tail dependence. DUBAI market exhibits unidirectional Granger causality to WTI and INE markets. Empirical results suggest that the world crude oil markets are not efficient and are far from One Great Pool. DUBAI and Brent markets emerge as leaders in the current international crude oil pricing system, while INE markets do not significantly influence DUBAI and BRENT markets.

Thermodynamic and economic analyses of modified ejector enhanced solar-air composite dual-source heat pump system in residential buildings

This research utilizes an ejector enhanced solar-air composite dual-source heat pump (EDHP) system for water heating in residential buildings. The main advantage of the EDHP system is the use of an ejector and dual-heat source to efficiently utilize solar energy, thereby enhancing heating performance. This ejector operates by utilizing a refrigerant stream from the solar collector line with higher pressure to elevate the suction pressure of the compressor. This feature enables the EDHP system to maintain stable heating performance even in adverse weather scenarios with low temperatures or limited solar radiation. Thermodynamic modeling using energetic and exergetic analysis methods is employed to theoretically evaluate the conventional air-source heat pump and EDHP systems. The energy analysis of the EDHP system using R134a refrigerant has shown significant improvements in both heating coefficient of performance (COP) and exergy performance (ECOP). Under specific conditions, such as an ambient temperature of −5 °C and an irradiance level of 250 W∙m2, and utilizing a 20 m2 PVT (Photovoltaic and Thermal) collector for a heating supply of 5 kW, the EDHP system exhibits a 23.64 % increase in COP and a 25.83 % increase in ECOP. Furthermore, when the electricity generated by the PV system is incorporated into the EDHP system, the demand for electricity from the grid can be reduced by up to 51.96 % under the same conditions. In the Beijing area and considering the current energy price system, the heating operating cost of the EDHP system is 0.206 Yuan/kWh. Compared to a standard air-source heat pump system, the payback period for EDHP system ranges approximately from 10 to 12 years, taking into account an annual heating period of 5 months. Among the alternative refrigerants to R134a in the EDHP system, R152a is found to be the most suitable. It shows COP improvements of 24.92 %–30.0 % and ECOP improvements of 24.89 %–29.96 %. This innovative approach opens up new possibilities for the application of heat pump technology under adverse weather conditions in residential and commercial buildings.

Dynamic Parking Pricing: Implementation in North America

&#x0D; &#x0D; &#x0D; Dynamic parking prices, which adjust in response to changes in demand, can be a useful tool for managing parking availability and encouraging turnover. However, there are some limitations to this approach. First, it can be difficult to implement and administer, requiring complex pricing algorithms and systems to collect and process data in real-time. Additionally, dynamic pricing may not be well-received by drivers, who may perceive it as unfair or unpredictable. Parking as it stands is a relatively ‘dumb’ system, and introducing any smartness into the system will include a number of added complications that can limit the uptake and the success of the system. This paper will look into the parking problem overall, introduce dynamic parking as a potential solution, and then delve into the example of SF Park. Finally further considerations and alternate solutions will be considered in the context of dynamic parking pricing to provide context and consider further research. &#x0D; &#x0D; &#x0D;

Factors Affecting Acreage Response of Pulses in the State of Rajasthan

Background: Pulses are rich in protein, fiber and vital vitamins and minerals however, since the start of the so-called “green revolution” in 1966-1967, their cultivation has experienced a notable decrease. This declining trend in the area under pulses shows that farmers have been changing their crop selections in response to changing techno-economic circumstances. Thus, there is a strong need to study acreage reactions, which will provide important insights for developing efficient policy directives for pulse development. Methods: The objective of this research is to examine the factors that impact the production of pulse crops in the state of Rajasthan. The study is totally dependent on secondary data that was obtained from state government publications for the years 2000–01 and 2019-20. In order to achieve its goals, the study uses backward an elimination regression method. Result: The findings indicate that current-year irrigation significantly improved chickpea farming, while positive effects on pigeon pea acreage were linked to current-year rainfall, lagged irrigated area and pigeon pea yield variability. However, pigeon pea acreage was negatively affected by current-year irrigation and lagged production. Additionally, positive correlations were found between the lagged price of a rival crop (Bajra), current-year rainfall and moong bean acreage response. Urd bean acreage benefited from one-year lagged rainfall, lagged area and yield of pulse crops and lagged pricing of a rival crop (linseed). Therefore, investing in research and development, promoting rainfed techniques and establishing supportive pricing systems are crucial for boosting pulse production and meeting future demand efficiently.

Automated valuation of used sailboat prices based on random forest regression modeling

This study presents a machine learning method for regression prediction of used sailboat prices. The dataset contains attributes such as brand, length, year, and listing price of the sailboat, and the dataset is preprocessed by removing irrelevant fields and normalizing the data. A random forest model is constructed and evaluated against several models such as gradient boosting and neural networks through k-fold cross-validation. Random Forest performs well compared to other models. The ensemble approach of the algorithm effectively modeled the complex nonlinear relationships in the data. Rigorous validation ensures the generalizability of the model. The Random Forest model outperforms traditional manual assessments in terms of the accuracy of price assessments. This data-driven solution allows customers to value sailboats on their own and avoid paying excessive fees. It also allows sailboat companies to develop automated pricing systems to speed up operations. This research provides a powerful machine-learning approach for accurately predicting used sailboat prices. These techniques can be extended to other regression tasks. Further work includes refining the model and deploying real-world applications.

Pricing in service industries: methods for justifying the cost of developing a design project

The subject of the study is the peculiarities of pricing in the service sector, in particular the process of justifying the standard cost of developing a design project in the domestic pricing system. The object of the study is methods for justifying the cost of developing a design project, used in domestic design practice both in the development of projects financed by public funds and projects financed by a private investor. The purpose of the work is a comparative analysis of domestic methods to justify the cost of developing a design project. The authors consider such aspects of domestic pricing methods as the mathematical apparatus, the price level, basic and current, the possibility of using the methodology to justify the value of the cost when replicating a project or its processing by making minor changes to the parameters, the level of qualifications of development specialists, etc. The research methods used are induction and deduction, analysis and synthesis, the method of analogy, logical and historical analysis, scientific abstraction, comparison and classification, theorization, idealization, formalization, positive and normative analysis, verification. The practical significance of the research results lies in the analysis of existing methods of regulatory pricing in the field of design, in the formulation, within the framework of the study, of their advantages and disadvantages, in the development of directions for their improvement. The authors’ special contribution to the study of the topic of pricing in the field of design is the substantiation of the main direction for improving existing domestic methods – the symbiosis of methods of state regulation of pricing in design and recommendations developed by the professional community, which will improve quality by increasing the scientific validity of calculations and taking into account the specifics of the development of design projects.

Coal crisis in China: development, mechanism, causes and possible consequences

This article analyzes the dynamics of coal prices and systemic changes in the coal market in the PRC, revealing the internal Chinese mechanism of the coal crisis that broke out in the PRC in 2020. The data studied and the conclusions obtained in the article refute the widely held view in expert circles, according to which the cause of the coal crisis in China was the rejection of Australian coal. The study was carried out by analyzing a data array on the dynamics of changes in the price of coal for different types of coal and for different regions and provinces of China. In particular, the article is based on data on the dynamics of coal prices in Shaanxi and Hubei.&#x0D; The results of this research provide useful insight into both the nature of the coal crisis and the patterns of the system of formation of coal prices in the PRC, partially touching on the issue of the integrity of the Chinese coal market. The statistical data referring to the coal prices is viewed through the prism of the concept of a “multi-track” pricing system put forward by M. V. Karpov and, in general, confirms fruitfulness and significance of his theory. Also, the article, based on data from the Hong Kong Stock Exchange, shows the commercial practical significance of the findings and the importance of having up-to-date information data on dynamics of coal prices in China.

Pricing of medicines as a factor influencing access to treatment

The article is devoted to the topic of access to treatment, and the issue of pricing as one of the factors that significantly affect the ability of the population to access treatment through the purchase of medicines, including expensive branded medicines that are not covered by government programs that provide such medicines free of charge. High medication costs can pose substantial barriers, particularly for those not covered by state programs, exacerbating health inequalities and hindering widespread access to necessary treatments.&#x0D; The article focuses on the existence of two pricing systems in the pharmaceutical market, namely, the state and market pricing. The aforementioned pricing systems are among the mechanisms for providing pharmaceutical care to the population in Ukraine. It is also argued that pricing is based on demand, cost, supply, and commodity and money circulation.&#x0D; The article presents the US experience of pricing and the problems of consumers who face the need to overpay for branded medicines by a significant number of times compared to European consumers.&#x0D; This dynamic reflects the delicate balance between the pharmaceutical industry’s need for fair returns on innovation investments and the imperative to make essential treatments financially accessible to the broader population.&#x0D; It is noted that the high price of medicines is due to the influence of numerous factors, including increased demand, insufficient supply of cheaper generic drugs on the pharmaceutical market, patent protection, and significant investment in the development and research of new medicines. At the same time, it is argued that the use of managed access agreements, along with both state and market pricing, can be a deterrent to the growth of prices for branded innovative medicines.