Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of ...Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In re...In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.展开更多
This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of ...This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of the cash dividend ratio on stock pricing was analyzed.The empirical results indicated a significant positive relationship between the cash dividend ratio and stock price.Furthermore,stocks with high dividend payouts demonstrated greater resilience during macroeconomic downturns,while notable differences were observed across industries.These findings provide a theoretical foundation for investors in making informed decisions and offer practical guidance for listed companies in formulating effective dividend policies.展开更多
In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs a...In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs and enhance benefits.This study deeply analyzes the characteristics of engineering quantity list pricing,and elaborates on the dilemmas faced by construction enterprises in project cost control,such as lagging concepts,imperfect mechanisms,weak risk management and control,and lack of construction-stage management.Based on this,from the dimensions of strengthening management and control concepts,improving supervision mechanisms,enhancing risk management and control capabilities,and attaching importance to construction-stage cost management,this study proposes project cost management and control strategies that are in line with the actual situation of construction enterprises,aiming to promote construction enterprises to achieve scientific management and optimization of project costs under the engineering quantity list pricing model.展开更多
Amid the global shift toward climate governance and low-carbon transformation,accurately quantifying environmental risk factors within green bond pricing mechanisms has emerged as a critical issue.Drawing on data from...Amid the global shift toward climate governance and low-carbon transformation,accurately quantifying environmental risk factors within green bond pricing mechanisms has emerged as a critical issue.Drawing on data from China’s green bond market between 2018 and 2023,this study develops a multifactor pricing model that integrates environmental risk premiums.Through regression analysis,it empirically investigates the effects of environmental reputation,transparency of information disclosure,and third-party certification on bond risk premiums.The results indicate that green-labeled bonds carry,on average,a 42.6 basis point lower risk premium compared to non-green bonds,while third-party certification further reduces this premium by an additional 54.1 basis points.Moreover,a one standard deviation improvement in the quality of environmental information disclosure leads to a reduction in bond financing costs by approximately 18 to 25 basis points.Issuers operating in high-energy-consuming industries bear significantly higher environmental risk premiums relative to those in low-energy-consuming sectors.By integrating an ESG scoring framework into bond pricing,this study reveals the transmission channels of environmental risks into market pricing and provides a theoretical foundation for enhancing pricing benchmarks in the green bond market.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which...Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.展开更多
As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Informatio...As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.展开更多
In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space...In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.展开更多
The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and ...The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.展开更多
Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the pos...Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.展开更多
This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,suppo...This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,support vector machines,and neural networks,the prediction of the default rate was evaluated.The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%.Unlike other studies on the same topic,this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions.For the first-time,default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics.The findings highlighted that gross domestic product,exports,trade type,and trade bands are significant factors that explain defaults.Furthermore,the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment.The results showed that price reflects a significantly higher implicit probability of default than observed default,which in turn suggests that underlying factors exist besides the borrowers’probability of default.展开更多
We derive methods for risk-neutral pricing of multi-asset options,when log-returns jointly follow a multivariate tempered stable distribution.These lead to processes that are more realistic than the better known Brown...We derive methods for risk-neutral pricing of multi-asset options,when log-returns jointly follow a multivariate tempered stable distribution.These lead to processes that are more realistic than the better known Brownian motion and stable processes.Further,we introduce the diagonal tempered stable model,which is parsimonious but allows for rich dependence between assets.Here,the number of parameters only grows linearly as the dimension increases,which makes it tractable in higher dimensions and avoids the so-called“curse of dimensionality.”As an illustration,we apply the model to price multi-asset options in two,three,and four dimensions.Detailed goodness-of-fit methods show that our model fits the data very well.展开更多
Extreme mortality bonds(EMBs),which can transfer the extreme mortality risks confronted by life insurance companies into the capital market,refer to the bonds whose nominal values or coupons are associated with mortal...Extreme mortality bonds(EMBs),which can transfer the extreme mortality risks confronted by life insurance companies into the capital market,refer to the bonds whose nominal values or coupons are associated with mortality index.This paper first provides the expected value of mortality index based on the double exponential jump diffusion(DEJD)model under the risk-neutral measure;then derives the pricing models of the EMBs with principal reimbursement non-cumulative and cumulative threshold respectively;finally simulates the bond prices and conducts a parameter sensitivity analysis.This paper finds that the jump and direction characteristics of mortality index have significant impacts on the accuracy of the EMB pricing.展开更多
Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of...Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of drug pricing in China.Methods Through the literature analysis and policy review,the pricing subject,pricing basis and price control system in the pricing process of medical-accessed medicines were analyzed from the perspective of binary equilibrium and harmonious management.Results and Conclusion It is found that four balances in the drug pricing process,two balances in pricing basis and three balances in price control system need to be considered,respectively.Drug pricing is the key content of national medical insurance access,which is also the hotspot of the policy in the pharmaceutical fields in recent years.Drug pricing not only reflects the value of drugs,but also reflects a lot of top-level designs of binary equilibriums in medical insurance policy.While the rational design of drug pricing requires the joint efforts of the government,pharmaceutical companies and relevant experts to comprehensively consider many equilibriums,so as to improve the relevant systems.展开更多
The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering w...The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.展开更多
Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot o...Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.展开更多
The maximum relative error between continuous-time American option pricing model and binomial tree model is very small. In order to improve the European and American options in trade course, the thesis tried to build ...The maximum relative error between continuous-time American option pricing model and binomial tree model is very small. In order to improve the European and American options in trade course, the thesis tried to build early exercise European option and early termination American option pricing models. Firstly, the authors reviewed the characteristics of American option and European option, then there was compares between them. Base on continuous-time American option pricing model, this research analyzed the value of these options.展开更多
We examine Africa's vaccine manufacturing potential,spurred by the coronavirus disease 2019(COVID-19)pan-demic,while critically analyzing vaccine price inequities and procurement strategies during the pandemic,wit...We examine Africa's vaccine manufacturing potential,spurred by the coronavirus disease 2019(COVID-19)pan-demic,while critically analyzing vaccine price inequities and procurement strategies during the pandemic,with anticipation of future outbreaks.Although Africa consumes approximately 25%of the global vaccine supply,over 99%of these vaccines are produced outside the continent,primarily due to insufficient local investment.Vaccine procurement strategies have relied heavily on pooled procurement mechanisms and tiered-pricing mod-els,predominantly controlled by external organizations.Significant disparities in vaccine pricing have resulted in vaccine price inequities,with evidence suggesting price discrimination,where different prices are charged for the same vaccine across countries and regions.While vaccine prices are only one component of vaccination cam-paign costs,the inequitable pricing of vaccines poses serious challenges to fair access,especially in low-income countries.Given the inevitability of future pandemics and other outbreaks,the central question remains:Does Africa possess the capacity to strengthen its vaccine production infrastructure and reduce dependency on ex-ternal suppliers?Our review reveals that,with robust political commitment,enhanced investment in Research and Development,and leveraging the heterogeneous nature of the regional bloc,Africa has made strides toward establishing vaccine manufacturing hubs with the potential for substantial capacity expansion.Furthermore,we argue for a regional campaign based on the principles of the fair priority model as an ethical framework for vaccine procurement,which prioritizes need and ensures equitable distribution,thereby complementing existing pooled procurement arrangements in times of future pandemics.This paper concludes with two key recommen-dations based on lessons learned from the COvID-19 crisis and future preparedness.First,Africa must push for a transparent and equitable tiered-pricing structure to ensure affordability for all Second,intentional and sustained investment in R&D is critical to addressing systemic inequities in vaccine supply,not only for cOVID-19 but for future outbreaks and routine immunization programs.展开更多
文摘Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
基金Hierarchical Identification and Cross-Layer Correlation of Key Core Technologies from the Perspective of Industrial Chain Structure (National Social Science Fund of China, 24BTQ067)Chongqing Education Commission (CEC) Funding:Research on the Co-governance Mechanism of Patent Quality Based on the Dual-Filter Perspective(24SKGH213)Chongqing Graduate Education and Teaching Funding:Research on the Interdisciplinary Law of Intellectual Property and Optimization of Graduate Talent Training Mode(yjg213122)。
文摘In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.
文摘This study selected 45 A-share listed companies that have paid dividends for five consecutive years from 2019 to 2024,with an average dividend yield of at least 3%,as the sample.Using a panel data model,the effect of the cash dividend ratio on stock pricing was analyzed.The empirical results indicated a significant positive relationship between the cash dividend ratio and stock price.Furthermore,stocks with high dividend payouts demonstrated greater resilience during macroeconomic downturns,while notable differences were observed across industries.These findings provide a theoretical foundation for investors in making informed decisions and offer practical guidance for listed companies in formulating effective dividend policies.
文摘In the increasingly competitive construction market,the engineering quantity list pricing model,as an important way of project cost management,is of crucial significance for construction enterprises to control costs and enhance benefits.This study deeply analyzes the characteristics of engineering quantity list pricing,and elaborates on the dilemmas faced by construction enterprises in project cost control,such as lagging concepts,imperfect mechanisms,weak risk management and control,and lack of construction-stage management.Based on this,from the dimensions of strengthening management and control concepts,improving supervision mechanisms,enhancing risk management and control capabilities,and attaching importance to construction-stage cost management,this study proposes project cost management and control strategies that are in line with the actual situation of construction enterprises,aiming to promote construction enterprises to achieve scientific management and optimization of project costs under the engineering quantity list pricing model.
文摘Amid the global shift toward climate governance and low-carbon transformation,accurately quantifying environmental risk factors within green bond pricing mechanisms has emerged as a critical issue.Drawing on data from China’s green bond market between 2018 and 2023,this study develops a multifactor pricing model that integrates environmental risk premiums.Through regression analysis,it empirically investigates the effects of environmental reputation,transparency of information disclosure,and third-party certification on bond risk premiums.The results indicate that green-labeled bonds carry,on average,a 42.6 basis point lower risk premium compared to non-green bonds,while third-party certification further reduces this premium by an additional 54.1 basis points.Moreover,a one standard deviation improvement in the quality of environmental information disclosure leads to a reduction in bond financing costs by approximately 18 to 25 basis points.Issuers operating in high-energy-consuming industries bear significantly higher environmental risk premiums relative to those in low-energy-consuming sectors.By integrating an ESG scoring framework into bond pricing,this study reveals the transmission channels of environmental risks into market pricing and provides a theoretical foundation for enhancing pricing benchmarks in the green bond market.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
基金Research on Innovative Method of Drug Rational Use Supervision Decision Based on Big Data of Medical Insurance(Grant No.82273899)。
文摘Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.
基金supported by the Key R&D Program of Anhui Province in 2020 under Grant No.202004a05020078China Environment for Network Innovations(CENI)under Grant No.2016-000052-73-01-000515.
文摘As users’access to the network has evolved into the acquisition of mass contents instead of IP addresses,the IP network architecture based on end-to-end communication cannot meet users’needs.Therefore,the Information-Centric Networking(ICN)came into being.From a technical point of view,ICN is a promising future network architecture.Researching and customizing a reasonable pricing mechanism plays a positive role in promoting the deployment of ICN.The current research on ICN pricing mechanism is focused on paid content.Therefore,we study an ICN pricing model for free content,which uses game theory based on Nash equilibrium to analysis.In this work,advertisers are considered,and an advertiser model is established to describe the economic interaction between advertisers and ICN entities.This solution can formulate the best pricing strategy for all ICN entities and maximize the benefits of each entity.Our extensive analysis and numerical results show that the proposed pricing framework is significantly better than existing solutions when it comes to free content.
基金supported by the Jiangsu University Philosophy and Social Science Research Project(Grant No.2019SJA1326).
文摘In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.
文摘The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.
文摘Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.
基金the funding provided by the Galician Regional Government[ED431C 2020/18]co-funded by the European Regional Development Fund(ERDF/FEDER)within the period 2020-2023.
文摘This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business(P2B)market.Using techniques such as logistic regression,conditional inference trees,random forests,support vector machines,and neural networks,the prediction of the default rate was evaluated.The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%.Unlike other studies on the same topic,this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions.For the first-time,default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics.The findings highlighted that gross domestic product,exports,trade type,and trade bands are significant factors that explain defaults.Furthermore,the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment.The results showed that price reflects a significantly higher implicit probability of default than observed default,which in turn suggests that underlying factors exist besides the borrowers’probability of default.
文摘We derive methods for risk-neutral pricing of multi-asset options,when log-returns jointly follow a multivariate tempered stable distribution.These lead to processes that are more realistic than the better known Brownian motion and stable processes.Further,we introduce the diagonal tempered stable model,which is parsimonious but allows for rich dependence between assets.Here,the number of parameters only grows linearly as the dimension increases,which makes it tractable in higher dimensions and avoids the so-called“curse of dimensionality.”As an illustration,we apply the model to price multi-asset options in two,three,and four dimensions.Detailed goodness-of-fit methods show that our model fits the data very well.
文摘Extreme mortality bonds(EMBs),which can transfer the extreme mortality risks confronted by life insurance companies into the capital market,refer to the bonds whose nominal values or coupons are associated with mortality index.This paper first provides the expected value of mortality index based on the double exponential jump diffusion(DEJD)model under the risk-neutral measure;then derives the pricing models of the EMBs with principal reimbursement non-cumulative and cumulative threshold respectively;finally simulates the bond prices and conducts a parameter sensitivity analysis.This paper finds that the jump and direction characteristics of mortality index have significant impacts on the accuracy of the EMB pricing.
文摘Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of drug pricing in China.Methods Through the literature analysis and policy review,the pricing subject,pricing basis and price control system in the pricing process of medical-accessed medicines were analyzed from the perspective of binary equilibrium and harmonious management.Results and Conclusion It is found that four balances in the drug pricing process,two balances in pricing basis and three balances in price control system need to be considered,respectively.Drug pricing is the key content of national medical insurance access,which is also the hotspot of the policy in the pharmaceutical fields in recent years.Drug pricing not only reflects the value of drugs,but also reflects a lot of top-level designs of binary equilibriums in medical insurance policy.While the rational design of drug pricing requires the joint efforts of the government,pharmaceutical companies and relevant experts to comprehensively consider many equilibriums,so as to improve the relevant systems.
文摘The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.
文摘Dominant Finnish assortment pricing gives prices for sawlog and pulp wood volumes. Buyers buck stems to sawlogs using secret price matrices. Agreed dimensions allow wide range of sawlog volumes. Forest owners cannot objectively compare biddings: timber trade is a lottery game. Bucking is analyzed in terms of sawlog, pulp wood, log cylinder, sawn wood, value-weighted sawn wood, and chips. Sawn wood and its value are computed from top diameter of the sawlog. Profit maximization requires buyers to buck logs producing smaller than maximal value, causing dead weight loss. Nominal assortment prices have unpredictable relation to effective stumpage price. Assortment pricing does not meet requirements of market economy. If sawmills linked to pulp mills buck smaller sawlog percentages than independent sawmills, as generally believed, they use higher price for chips in their own harvests than they pay for independent sawmills, indicating imperfect competition for chips. Sawn wood potential pricing is suggested which gives prices for sawn wood and chips coming both from sawlogs and pulp wood in reference bucking which maximizes sawn wood for given minimum and maximum log length and minimum top diameter. Simple algorithm generates feasible bucking schedules from which optimum can be selected using any objective. Pricing produces unit price for all commercial wood utilizing ratio of theoretical sawn wood and commercial volume in stand. Unit price can be compared to stem pricing and could be compared to assortment pricing if assortment pricing would produce predictable sawlog percentages. Sawn wood potential pricing is concrete, transparent, easy to compute, considers stem size and tapering, reduces trading cost and is less risky to buyers than stem pricing. It meets requirements of market economy. Readers can repeat computations using open-source software Jlp22.
文摘The maximum relative error between continuous-time American option pricing model and binomial tree model is very small. In order to improve the European and American options in trade course, the thesis tried to build early exercise European option and early termination American option pricing models. Firstly, the authors reviewed the characteristics of American option and European option, then there was compares between them. Base on continuous-time American option pricing model, this research analyzed the value of these options.
文摘We examine Africa's vaccine manufacturing potential,spurred by the coronavirus disease 2019(COVID-19)pan-demic,while critically analyzing vaccine price inequities and procurement strategies during the pandemic,with anticipation of future outbreaks.Although Africa consumes approximately 25%of the global vaccine supply,over 99%of these vaccines are produced outside the continent,primarily due to insufficient local investment.Vaccine procurement strategies have relied heavily on pooled procurement mechanisms and tiered-pricing mod-els,predominantly controlled by external organizations.Significant disparities in vaccine pricing have resulted in vaccine price inequities,with evidence suggesting price discrimination,where different prices are charged for the same vaccine across countries and regions.While vaccine prices are only one component of vaccination cam-paign costs,the inequitable pricing of vaccines poses serious challenges to fair access,especially in low-income countries.Given the inevitability of future pandemics and other outbreaks,the central question remains:Does Africa possess the capacity to strengthen its vaccine production infrastructure and reduce dependency on ex-ternal suppliers?Our review reveals that,with robust political commitment,enhanced investment in Research and Development,and leveraging the heterogeneous nature of the regional bloc,Africa has made strides toward establishing vaccine manufacturing hubs with the potential for substantial capacity expansion.Furthermore,we argue for a regional campaign based on the principles of the fair priority model as an ethical framework for vaccine procurement,which prioritizes need and ensures equitable distribution,thereby complementing existing pooled procurement arrangements in times of future pandemics.This paper concludes with two key recommen-dations based on lessons learned from the COvID-19 crisis and future preparedness.First,Africa must push for a transparent and equitable tiered-pricing structure to ensure affordability for all Second,intentional and sustained investment in R&D is critical to addressing systemic inequities in vaccine supply,not only for cOVID-19 but for future outbreaks and routine immunization programs.
