Price System Research Articles

Financial Reform of Public Policy on Ageing in France and the Persistent Invisibilization of Caregivers

This article aims to examine the financing of public policy on ageing in France with a focus on gender. Our research draws from an analysis of the pricing system applied to nursing homes for dependent elderly people (établissement hébergement pour personnes âgées dépendantes – EHPADs), last reformed in 2015 by the Adaptation of Society to Ageing Act. In this paper, we question the roles reserved to women as employees of the welfare state, caregivers, and citizens, in light of the representations driven by the pricing system. Our work draws mainly from a survey conducted in 2023 in four care homes in Brittany, France. The study highlights that the invisibilization of women takes place in various ways. Firstly, the pricing system applied to nursing homes drives a familialist conception of this policy, which relies mainly on the free contribution of carers. Secondly, this tool tends to misrepresent the work carried out by care professionals by undervaluing relational care. Finally, our survey shows that the system underestimates the return on social and policy investment that this institution represents at the local level.

Carbon Trading in Developing Economies: Challenges, Opportunities, and Best Practices

Globally, carbon trading, a market-driven approach to cut greenhouse gas emissions, has become increasingly popular. Developing economies are increasingly looking at carbon trading as a strategy to balance economic progress with environmental sustainability, even though wealthier nations have historically been the main players in carbon markets. This assessment looks at the main obstacles, chances, and recommended procedures related to carbon trading in developing nations. The obstacles encompass feeble regulatory structures, insufficient knowledge, unstable markets, and giving precedence to commercial expansion above ecological issues. On the other hand, carbon trading offers chances for technological transfer, international financial access, climate risk mitigation, and sustainable development. These regions' best practices for carbon markets place a premium on robust regulatory frameworks, capacity building, equitable pricing systems, and public-private partnerships. By addressing the challenges and embracing these best practices, developing economies can leverage carbon trading to meet their development goals while contributing to global climate action.

Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid.

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.

Boosting Electric Vehicle Adoption with PCT Policy: Insights from China's Private Transportation

Personal Carbon Trading (PCT) motivates low-carbon consumption and travel by rewarding sustainable behaviors. This study examines the impact of PCT policies on electric vehicle (EV) adoption in China's private transportation sector. Using a Stackelberg game model involving the government, EV manufacturers, and consumers, it finds that PCT policies can boost EV adoption rates by 2.29% to 14.3%. Each addition of 100,000 new EVs could reduce carbon emissions by 30,000 tons. Effective PCT policies require an independent carbon pricing system and should be implemented where infrastructure and public willingness to pay are high. Both allocation methods are conducive to the promotion of EVs: one facilitates the government in setting regional emission reduction plans, while the other ensures that the government does not incur policy-related expenditures, and different allocation methods should be chosen according to the time and circumstances in different periods of EV market development.

Assessing Key Constraints in Cashew Production: Insights from Kerala and Tamil Nadu, India

India ranks among the world’s top cashew producers, with Kerala and Tamil Nadu serving as key contributors to the country's cashew production. Among the Plantation sector, specifically cashew cultivation plays a pivotal role in boosting farm economy in India. This particular study explores field constraints experienced by cashew growers in Kerala and Tamil Nadu, which significantly impacts cashew cultivation. The key objective is to compare field challenges experienced by cashew growers among major producers in southern regions of India. The methodology involves a purposive multi-stage sampling approach to select regions, with an equal proportion of 80 respondents from each state selected through simple random sampling. Totally, 160 cashew growers were investigated for data collection through pre-tested structured interview schedule. The data sets were analysed using SPSS software, with frequency and percentage analysis employed to rank the identified constraints. The significant findings were pest and disease infestations, labour scarcity, unstable market price, lack of technical know-how and inadequate processing facilities. Notably, 100 per cent of growers reported that, Cashew Stem and Root Borer (CSRB) infestation as a most critical issue in both states. This paper highlights, region-specific and timely technical interventions, commodity-specific assured markets, proper pricing system and infrastructure development. The study's interpretation highlights the need to address high-priority and specialized constraints through targeted policy reforms and technical support systems, aiming to enhance the productivity and sustainability of cashew farming in India. These findings also provide valuable insights that can be applied to other cashew-producing regions and contribute to shaping national agricultural policies.

Optimising ride-sharing efficiency: innovative shareability-focused pricing strategies

Ride-sharing is increasingly recognised as a potential solution to transportation issues such as traffic congestion and parking problems. The success of ride-sharing systems depends on the willingness of individuals to share rides, and effective pricing strategies are among the crucial factors. However, creating a fair pricing system for riders and providers remains challenging. This research introduces a new pricing scheme based on the concept of shareability. This concept considers all the possible sharing situations among trips. It is designed to incentivize riders and accommodate providers' criteria. This scheme calculates the trip fare for passengers, considering various ride-sharing scenarios derived from the shareability function, thus ensuring fairness and transparency for passengers. Using the macroscopic fundamental diagram, our model incorporates traffic dynamic conditions. We have applied the method to the real case of Lyon city in France to assess the effectiveness of our approach. Through a comprehensive analysis of various configurations of the proposed pricing scheme and benchmarking with the existing approaches, we demonstrate its superior performance in reducing ride rejections compared to traditional methods. This result showed the proposed method's potential for enhancing ride-sharing systems' effectiveness.

Pricing and Replenishment Decision System Based on LSTM

Fresh supermarket vegetables have a very short shelf life, and the quality of vegetables will also be affected by the storage time. If most of the goods are not sold on that day, they will not be sold on the next day. From the demand side, the sales volume of vegetables is related to the supply side of vegetables from April to October. At the same time, the sales space of the supermarket is limited, which makes a reasonable sales portfolio extremely important. The commercial overpricing replenishment decision system based on LSTM analyzes the relationship between daily sales volume and cost-plus pricing for each vegetable category and single product over three years. It predicts the sales volume and wholesale price of each category for the next week, determining replenishment volume and pricing strategy based on forecasted sales volume and average loss rate. The system considers limited sales space and uses the entropy method to calculate weights for key indicators like total profit. It employs the TOPSIS method to sort items, considering forecasted wholesale prices, to select items for pricing decisions. The system aims to maximize total profit while ensuring the supermarket meets minimum display conditions. By assisting supermarkets in formulating more reasonable pricing strategies, it aims to reduce overstock, backlog, or stock shortages, ultimately improving sales volume and operational efficiency.

Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Higher order expectations, learning, and sentiment pricing dynamics

We present a dynamic asset pricing model combining individual investor sentiment, higher order expectations with learning. In the basic model, the forward-looking expectation of individual investors is distorted by individual sentiment and higher order expectations, so prices react more sluggishly to changes in fundamentals of the asset. We find that investor sentiment plays a significant role in the effect of higher order expectations on asset pricing. Investor sentiment not only makes the price tightly anchor to the initial price, but also increases the sentiment drift of the price. Higher order expectations exhibit inertia, therefore aggravating the anchor to the initial price. With the increase of the order, more and more investor sentiment is integrated into the prices, amplifying the bias of pubic signal relative to fundamentals. When individual sentiment investors learn valuable public information through price system in the long term, the information component of the equilibrium price increases, thus drawing the asset price back toward the rational expected value. The model could offer a partial explanation to the inertia and drift in the price path

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Present Situation and Challenge of BlM Development in Construction Cost Industry

This paper analyzes the construction cost situation, BIM technology development, cost measurement methods, cost pricing system, cost control practice and cost management experience of our country and the United Kingdom, the United States and Japan. Finally, the outlook for the future development of engineering cost industry in our country is concluded. and the relevant suggestion is given, which provides a useful reference for the development of engineering cost industry in our country.

Escalating costs of innovative medicines: perspective and proposals.

Public healthcare systems are challenged by the soaring costs of medications that require increasing resources, often at the expense of other investments. The increasing pharmaceutical budget poses a threat to the allocation of funds for essential preventive and primary healthcare services while also raising concerns about equitable access, particularly in models where patients bear part of the costs out of their own pockets. Proposals on how to ensure ongoing and long-term accessibility, efficiency, and financial stability are required. The escalating costs of medicines may be explained in part by the mismatch between the traditional value-based pricing and reimbursement frameworks and the type of clinical development of targeted therapies and precision medicine in clinical practice. New appraisal methods and managed access strategies should be adapted to therapies targeting small populations and addressing increased uncertainty. Fair pricing strategies, transparent healthcare investments based on problems and outcomes, regulatory reforms, international cooperation, and critically examining the drug acquisition model are potential solutions. Transitioning from an industry-driven pricing approach to a health-driven payment model can help align the cost of treatments with actual health outcomes, establishing a foundation for a healthcare system that addresses immediate challenges and fosters long-term well-being. Acknowledging the lack of a universally applicable solution, the practical implementation of interventions requires a reframing of the pricing and access system and adaption to the targeted therapeutic approaches. Balancing innovation with financial sustainability necessitates a collaborative, adaptive, and transparent approach, as well as transitioning toward health-driven payment models, moving the focus from the cost of medications to the well-being of populations worldwide.

An iterative price-based combinatorial double auction for additive manufacturing markets

The increasing adoption of additive manufacturing (AM) in the industrial sector is leading to an imbalance between supply and demand of additively manufactured subcomponents: companies demanding AM services require very specific products and AM suppliers differ widely in their capabilities. Some existing proposals aim to help match supply and demand by merely making customer–supplier allocations. Only a few recent works go beyond allocation issues and propose market mechanisms to also address pricing aspects. However, we observe that these mechanisms do not fully exploit the potential of additive manufacturing techniques. The aim of this paper is to design a market mechanism that considers the particularity of AM techniques, wherein suppliers can benefit from manufacturing multiple heterogeneous parts from multiple customers in the same build area to increase production throughput. This market mechanism has been implemented as an iterative combinatorial double auction that adapts to this feature of the AM market: customers will bid to get their orders produced and suppliers will submit asking quotes to win the production of combinations of those orders. The mechanism solves the allocation and pricing of AM orders while seeking the maximization of social welfare. The procedure is simulated in a theoretical environment to evaluate its performance and to identify the most appropriate conditions for its implementation in a real environment. Unlike other existing proposals for client-supplier allocation mechanisms in additive manufacturing, the proposed mechanism allows a single supplier to produce a combination of orders from different clients, leading to a pricing system that maximizes social welfare without participants disclosing sensitive information.

El impacto de las energías renovables sobre el precio mayorista de la electricidad

Rationale Renewable electricity generation capacity is rising significantly in Spain. Given the merit order pricing system used in the wholesale electricity market, this could have profound implications for electricity price dynamics. Takeaways •The share of wind and solar energy in total electricity generation increased from 26% in 2019 to over 40% in 2024 H1. In the first half of this year, wholesale electricity prices were more than 40% lower than they would have been had wind and solar generation remained at 2019 levels. •The model estimated in this article suggests that under the renewable generation deployment scenarios envisaged in the update of the National Energy and Climate Plan, wholesale electricity prices could drop by an additional 50% by 2030. •In any event, the future behaviour of these prices is subject to considerable uncertainty, influenced by various supply-side factors and how demand responds to the new levels of renewable generation.

THE STATE, PROBLEMS AND PROSPECTS OF PRICING DEVELOPMENT IN THE FIELD OF CONSTRUCTION OF ANTENNA-MAST STRUCTURES OF CELLULAR COMMUNICATIONS

The article examines issues related to the current state and problems of pricing in the field of construction of antenna-mast structures of cellular communications. The authors point out the peculiarity of the considered sphere of construction in terms of the mechanism for determining the estimated cost using tariffers, which differs from the generally accepted method, which implies the use of approved estimated standards, and is due to the lack of prices in the current regulatory framework that allow full marking of special types of work performed during the construction of antenna-mast structures. The disadvantages of this approach are given due to the establishment of prices in the tariff for enlarged work complexes and elements of the structure as a whole, which does not allow taking into account factors that have a significant impact on the design features of the support and, as a result, the cost of its construction and depend on the construction conditions and customer requirements. At the same time, the authors note and clearly confirm the absence of linking the rates of the tariff to the amount of work actually performed and the consumption of resources during the construction of a specific antenna-mast structure, which entails an unreliable estimate of the cost of construction. Separately, a comparison of the cost of materials for the construction of supports of the same height, depending on the projected payload, is provided, which also confirms the need to revise the existing pricing system. The authors provide recommendations aimed at improving the pricing mechanism used: options are proposed for the development and implementation of estimated standards for the construction area under consideration, as well as for improving the tariff rates in the interaction of construction organizations and customers.

Out-of-Pocket Expenditures for Health: A Case Study

Background of the Study: Health is a prime component in rural population who uses their muscle power for doing economic activities. Meanwhile according to the Economic Survey of India’s 2021 report, India was among the 10 worst performing countries in terms of prioritising health in government budgets, at both state and central levels. India was placed or ranked in 67th in the list of 189 countries in Out of Pocket Expenditure per capita in PPP (in $) for 2018. World Health Organisation report (March 2022) stated that high of Out-Of-Pocket Expenditure (OOPE) on health is impoverishing some 55 million Indians annually, with over 17 per cent households incurring catastrophic levels of health expenditures every year. The Economic Survey 2023 reported that the share of government expenditure in Total Health Expenditure (THE) has increased over time from 28.6% in financial year 2014 to 40.6% in Financial Year2019. However, OOPE is still higher at 48.2% of the total expenditure. Objectives: To explore the Out of Pocket Expenditure (OOPE) for health in India, Interstate and rural villages of Theni district, Tamil Nadu Methodology: Purposive non random sampling was used to reach the sample element. Sample elements or respondents were from rural Theni District. Sample size was 300. Data was collected using an interview schedule in the catchment area of Primary Health Centre, taluk hospital and district healthcare hospital. Interview was conducted in Tamil and then the interview output was transcribed and translated into English, followed by a descriptive analysis. Ordinary Least Square estimation was used to find the implications of individual income on pocket out expenditure for health. Findings of the Study: The secondary data from the website of World Health Organisation and from the reports of National Health Accounts reflects a difference in data information for every concerned period at least two to three per cent exist. The interstate data information for the period 2019 to 2020 revealed that the spending from government health care expenditure and out of pocket expenditure is not equal to total health care expenditure. Hence, the researcher made an attempt to explore the out of pocket expenditure among the rural population and their willingness to spend from their pocket. Furthermore, the impact of annual income and annual healthcare expenditure is examined by simple linear regression analysis, for to understand the role of income in shaping healthcare spending behaviour. The R Square value is very poor, and reflects the role of explanatory variable income is not impacting the out of pocket health care expenditure. The Keynesian theory of psychological law of consumption is invalid in out of pocket healthcare expenditure with respect to income of the respondents. 13 percentages of respondents were willing to do out of pocket expenditure for their health ailing. The 39.7percentages of respondents were disagreeing to do out of pocket expenditure for their health. Conclusion: Overall, this study emphasizes the importance of understanding out-of-pocket health expenditures is increasing in rural (feckless management of public healthcare) with attitude of unwilling to pay from their pocket because of existing zero priced healthcare system, which is available in their near locality. Policy makers needs to concentrate on enhancing the healthcare supply such as medical personals, drugs, lab facility, beds, and in general in all aspects for to cater the demand of rural inhabitants and protect them from pushing into impoverishment.

Carbon Abatement and Leakage in China's Regional Carbon Emission Trading.

Emission trading schemes (ETS) are increasingly becoming a popular policy instrument to balance carbon abatement and economic growth. As a globally unified carbon pricing system has not yet been established, whether regionally operated ETSs cause carbon leakage remains a major concern. Taking China's regional pilot ETSs as a quasi-natural experiment, the study uses the spatial difference-in-differences method to examine how regional ETSs affect carbon emissions in and outside cities of policy implementation. Our analysis finds that China's regional ETS policy contributes to a 6.1% reduction in urban CO2 emissions and a 6.6% decline in emissions intensity in regulated cities, causing carbon leakages that increase CO2 emissions in neighboring cities by 1.7% on average. Our finding further suggests that regional ETSs mitigate local CO2 emissions through outsourcing production, improving energy efficiency and decarbonizing energy structure, whereas the outsourcing of industrial production drives up CO2 emissions in adjacent cities. Moreover, the performances of regional ETSs vary largely by socioeconomic context and mechanism design. China's regional ETSs reduce CO2 emissions more effectively in central and industrial cities but with more severe carbon leakage, while rigorous compliance mechanisms and active market trading help deepen carbon abatement and alleviate carbon leakage.

IMPLEMENTATION OF A DYNAMIC PRICING SYSTEM IN RETAIL: AN ANALYTICAL REVIEW OF THE PRACTICES OF RUSSIAN COMPANIES

Over the past few years, the conditions and nature of doing business for Russian companies have changed dramatically. The processes of procurement, logistics, product sales, accounting and other business processes are automated. The development of artificial intelligence technology and the growing volume of data are additional growth drivers for the company. Within the framework of this article, an analytical review of the practices of applying the pricing system in retail of Russian companies was conducted. The pricing process is key, which allows you to directly influence the main economic indicators of the company’s activities, since sales volume, profit, demand for products, inventories of inventory resources can be regulated by means of competent changes in prices for goods. Dynamic pricing is gaining great popularity among retailers, which leads to the expansion of patent activities in terms of software and models for automating pricing, including using machine learning. In this article, the authors reviewed some of the most popular offers on the market for the introduction of dynamic pricing systems and analyzed them, revealed the main features and identified common features of building automated dynamic pricing models. The practices of implementing dynamic pricing systems in Russian companies were also investigated. The results obtained allow us to draw conclusions about the importance of using automated pricing systems in retail in modern conditions, their capabilities and application problems.

Towards Equitable Compensation: Unraveling China’s Regional Comprehensive Land Price System from Legal Connotation to Practical Implementation

Towards Equitable Compensation: Unraveling China’s Regional Comprehensive Land Price System from Legal Connotation to Practical Implementation

The Influence of Car Rental Product Quality, Prices, and Complaint Handling System on Decisions to Use Car Rental Services at Botana Transport Rent Car & Tour Travel

The aim of this research is to determine the influence of product service quality, price, rental car complaint handling system on the decision to use car rental services at Botana Transport Rent Cars & Tours travel. This research was conducted at Botana Transport Rent Cars & Tours travel located on Jl. Raya Kemang, Cilegon City. The sample in this study consisted of 100 respondents using Accidental Sampling. The data analysis used is multiple linear regression. The results of the research are that product quality and the complaint handling system have a positive influence, while price has a negative influence on the decision to use a rental car at Botana Transport Rent. Cars & Tours travel. Simultaneously or at the same time, product quality, price and car rental handling system at Botana Transport Rent Cars & Tours travel. Meanwhile, the R Square value explains the decision to use car rental services at Botana Transport Rent Cars & Tours Travel is influenced by product quality, price and complaint handling system by 44.2% while the remaining 55.8% is influenced by other variables not used in this research.