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
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 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 study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning.We adopted a datadriven approach to find a market-appropriate generator...This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning.We adopted a datadriven approach to find a market-appropriate generator of the backward stochastic differential equation,which is achieved by leveraging the universal approximation capabilities of neural networks.Option pricing,which is the solution to the equation,is approximated using a recursive procedure.The empirical results for the S&P 500 index options show that the proposed deep learning g-pricing model has lower absolute errors than the classical Black–Scholes–Merton model for the same forward stochastic differential equations.The g-pricing mechanism has potential applications in option pricing.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
This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power ...This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.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
The concept of the e-marketplace is introduced.Considering a supply chain with a single manufacturer who sells a single item in an e-marketplace,an analytical model for the use of the e-marketplace in a supply chain i...The concept of the e-marketplace is introduced.Considering a supply chain with a single manufacturer who sells a single item in an e-marketplace,an analytical model for the use of the e-marketplace in a supply chain is provided.Assuming the market demand is stochastic and price-dependent,the conditions under which the manufacturer and the e-marketplace owner share the market in equilibrium is developed.The existence and uniqueness of the optimal selling price,quantity and transaction percentage are proved.An integrated supply chain is put forward,and then the efficiency of supply chain coordination is studied by comparing the integrated supply chain with the decentralized supply chain.To gain further insights on the theoretical models,extensive simulations are then carried out.展开更多
The coordinating pricing strategies with asymmetric cost information under disruptions are investigated in a one-supplier-one-retailer supply chain system.While the retailer's cost structure is asymmetric informat...The coordinating pricing strategies with asymmetric cost information under disruptions are investigated in a one-supplier-one-retailer supply chain system.While the retailer's cost structure is asymmetric information,supply chain pricing contract models(a wholesale price contract and an all-unit quantity discount contract)under asymmetric information are proposed by employing the principal-agent principle in a regular scenario.When the retailer's cost distribution is fluctuated by disruptions,we obtain the optimal emergency strategies of the supply chain under asymmetric information by considering deviation costs and show how to effectively handle the cost uncertainty.Using numerical methods,impacts of cost disruptions on the optimal wholesale price,the retailer price,the order quantity and the expected profits of the retailer,the supplier,as well as the total system are analyzed.It is found that the all-unit quantity discount policy can obtain better performance than the wholesale pricing policy.展开更多
This paper first gives an explanation of moral hazard in the insurance field,and then offers a game theory model about insurance pricing according to the non zero sum game analysis between the insurer and the insured...This paper first gives an explanation of moral hazard in the insurance field,and then offers a game theory model about insurance pricing according to the non zero sum game analysis between the insurer and the insured when moral hazard exists.On the basis of the game analysis,this paper also presents a lowest pricing formula and studies the cost of moral hazard simultaneously.展开更多
This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combin...This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.展开更多
The collector managed inventory(CMI)management idea is introduced based on the comparison of manufacturer and third-party logistics(3PL)implementing vendor managed inventory(VMI)services.Considering recovery costs,the...The collector managed inventory(CMI)management idea is introduced based on the comparison of manufacturer and third-party logistics(3PL)implementing vendor managed inventory(VMI)services.Considering recovery costs,the service pricing strategies for 3PL corporations implementing VMI are studied to meet two conditions of participation constraints and incentive-compatibility constraints.The numerical simulation results indicate that the supply chain partners' profits change after considering recovery costs,and the 3PL corporation's profits and the total profits increase first,and then decrease.The retailers' and manufacturers' profits also increase.The total profits of the supply chain have a characteristic of increasing first and then decreasing with the increase of the callback ratio of unsold products.The concrete extremum point is codetermined by price flexibility,service pricing of the 3PL corporation,callback price and callback ratio.展开更多
将李群理论用于金融问题中出现的数学模型的微分方程,研究了Zero-Coupon bond pricing模型.求出了该模型的单参数李点对称及它相应的群伴随表达式,由此求得该模型允许的一维李群的子代数的最优系统并且利用最优系统构造该模型相应的微...将李群理论用于金融问题中出现的数学模型的微分方程,研究了Zero-Coupon bond pricing模型.求出了该模型的单参数李点对称及它相应的群伴随表达式,由此求得该模型允许的一维李群的子代数的最优系统并且利用最优系统构造该模型相应的微分方程的一些特殊的不同类的闭解.展开更多
In this paper,some methods of economics and management were used.Based on analysis of the house market structure,the factors influencing the house price,the house pricing methods and strategies were proposed.Then a ca...In this paper,some methods of economics and management were used.Based on analysis of the house market structure,the factors influencing the house price,the house pricing methods and strategies were proposed.Then a case in Nanjing was studied,and the results show:firstly,the house market is a monopolistic competitive market,and in some places it is even an oligopoly market;secondly,the cost-plus pricing method is reasonable and scientific,and the specificity is the base of pricing;thirdly,the average price of a building groups in Nanjing should be 8 906$/sq.m.Finally,aiming at the house pricing,some countermeasures and suggestions are put forward in this paper.展开更多
In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon syner...In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.展开更多
Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Genev...Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Geneva,Switzerland,on November 4,where the topic of cooperation on trade-related carbon standards aroused heated discussions.The Leaders'Summit of the 30th Conference of the Parties(COP)to the UN Framework Convention on Climate Change(UNFCCC)was held in Belém,Brazil,on November 7.At the meeting,the Open Coalition on Compliance Carbon Markets was officially launched with the initial membership of 11 economies including Brazil,China,and the EU.As the world's first transnational alliance on compliant carbon markets,the coalition aims to coordinate carbon pricing mechanisms,emission trading systems and related policies in various countries,and realize the interconnection of global compliance carbon market networks.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
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.
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 Taishan Scholar Project of Shandong Province of China(Grant tstp20240803)the National Key R&D Program of China(Grant No.2023YFA1008903)the Major Fundamental Research Project of Shandong Province of China(Grant No.ZR2023ZD33).
文摘This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning.We adopted a datadriven approach to find a market-appropriate generator of the backward stochastic differential equation,which is achieved by leveraging the universal approximation capabilities of neural networks.Option pricing,which is the solution to the equation,is approximated using a recursive procedure.The empirical results for the S&P 500 index options show that the proposed deep learning g-pricing model has lower absolute errors than the classical Black–Scholes–Merton model for the same forward stochastic differential equations.The g-pricing mechanism has potential applications in option pricing.
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financially supported by:National Natural Science Foundation of China(72261002,72141304)Youth Foundation for Humanities and Social Sciences Research of the Ministry of Education(22YJC790190)+1 种基金National Key Research and Development Program of China(2022YFC3303304)Student Research Program of Guizhou University of Finance and Economics(2022ZXS).
文摘This study examines a comprehensive set of 30 cross-sectional anomalies in the Chinese A-share market to investigate whether incorporating investor sentiment as conditioning information enhances the explanatory power of asset pricing models.Utilizing a long–short portfolio strategy and Fama–MacBeth cross-sectional regression,we find that trading-based anomalies outnumber accounting-based anomalies in the Chinese market.Our results demonstrate that conditional models significantly outperform their unconditional counterparts.Notably,investor sentiment is crucial for capturing the size anomaly when excluding observations from the COVID-19 pandemic period.Additionally,it substantially improves the ability of conditional Fama–French three-factor models to capture individual anomalies and enhances the return–prediction accuracy of conditional CAPMs.We suggest further investigating high-frequency investor sentiment-based conditional models to anticipate stock price fluctuations during extraordinary public health events.
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The National Key Technology R&D Program of China during the11th Five-Year Plan Period(No.2006BAH02A06)the Program Project of Humanity and Social Science of Ministry of Education in China(No.06JA630012)
文摘The concept of the e-marketplace is introduced.Considering a supply chain with a single manufacturer who sells a single item in an e-marketplace,an analytical model for the use of the e-marketplace in a supply chain is provided.Assuming the market demand is stochastic and price-dependent,the conditions under which the manufacturer and the e-marketplace owner share the market in equilibrium is developed.The existence and uniqueness of the optimal selling price,quantity and transaction percentage are proved.An integrated supply chain is put forward,and then the efficiency of supply chain coordination is studied by comparing the integrated supply chain with the decentralized supply chain.To gain further insights on the theoretical models,extensive simulations are then carried out.
基金The National Key Technology R&D Program of China during the11th Five-Year Plan Period(No.2006BAH02A06)Jiangsu Postdoctoral Foundation(No.0601015C)
文摘The coordinating pricing strategies with asymmetric cost information under disruptions are investigated in a one-supplier-one-retailer supply chain system.While the retailer's cost structure is asymmetric information,supply chain pricing contract models(a wholesale price contract and an all-unit quantity discount contract)under asymmetric information are proposed by employing the principal-agent principle in a regular scenario.When the retailer's cost distribution is fluctuated by disruptions,we obtain the optimal emergency strategies of the supply chain under asymmetric information by considering deviation costs and show how to effectively handle the cost uncertainty.Using numerical methods,impacts of cost disruptions on the optimal wholesale price,the retailer price,the order quantity and the expected profits of the retailer,the supplier,as well as the total system are analyzed.It is found that the all-unit quantity discount policy can obtain better performance than the wholesale pricing policy.
文摘This paper first gives an explanation of moral hazard in the insurance field,and then offers a game theory model about insurance pricing according to the non zero sum game analysis between the insurer and the insured when moral hazard exists.On the basis of the game analysis,this paper also presents a lowest pricing formula and studies the cost of moral hazard simultaneously.
基金The National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z202)the National Key Technology R & D Program of China during the 11th Five-Year Plan Period(No. 2006BAJ18B03)the Fundamental Research Funds for the Central Universities (No. DUT10RC(3) 112)
文摘This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.
基金The National Key Technology R&D Program of China during the11th Five-Year Plan Period(No.2006BAH02A06).
文摘The collector managed inventory(CMI)management idea is introduced based on the comparison of manufacturer and third-party logistics(3PL)implementing vendor managed inventory(VMI)services.Considering recovery costs,the service pricing strategies for 3PL corporations implementing VMI are studied to meet two conditions of participation constraints and incentive-compatibility constraints.The numerical simulation results indicate that the supply chain partners' profits change after considering recovery costs,and the 3PL corporation's profits and the total profits increase first,and then decrease.The retailers' and manufacturers' profits also increase.The total profits of the supply chain have a characteristic of increasing first and then decreasing with the increase of the callback ratio of unsold products.The concrete extremum point is codetermined by price flexibility,service pricing of the 3PL corporation,callback price and callback ratio.
文摘In this paper,some methods of economics and management were used.Based on analysis of the house market structure,the factors influencing the house price,the house pricing methods and strategies were proposed.Then a case in Nanjing was studied,and the results show:firstly,the house market is a monopolistic competitive market,and in some places it is even an oligopoly market;secondly,the cost-plus pricing method is reasonable and scientific,and the specificity is the base of pricing;thirdly,the average price of a building groups in Nanjing should be 8 906$/sq.m.Finally,aiming at the house pricing,some countermeasures and suggestions are put forward in this paper.
基金supported by Technology Project of State Grid Tianjin Electric Power Company(2024-06)“Research on hierarchical partition dynamic calculation and panoramic monitoring technology of electric power carbon emission and its application”.
文摘In the pursuit of carbon peaking and neutrality goals,multi-energy parks,as major energy consumers and carbon emitters,urgently require low-carbon operational strategies.This paper proposes an electricity-carbon synergy-driven optimization method for the low-carbon operation ofmulti-energy parks.Themethod integratesmultienergy complementary scheduling with a tiered carbon trading mechanism to balance operational security,economic efficiency,and environmental objectives.A mixed-integer linear programming model is developed to characterize the coupling relationships and dynamic behaviors of key equipment,including photovoltaic systems,ground-source heat pumps,thermal storage electric boilers,combined heat and power units,and electrical energy storage systems.Furthermore,a tiered carbon trading model is established that incorporates carbon quota allocation and tiered carbon pricing to internalize carbon costs and discourage high-emission practices.Multi-scenario comparative analyses demonstrate that the electricity-carbon synergy scenario achieves a 42.64%reduction in carbon emissions compared to economy-oriented operation,while limiting the increase in operational costs to 20.85%.The carbon-prioritized scenario further reduces emissions by 9.7%,underscoring the inhibitory effect of the tiered carbon pricing mechanism on highcarbon activities.Sensitivity analyses confirm the model’s robustness against fluctuations in energy load,uncertainty in renewable generation,and variations in carbon price.This optimization method provides theoretical support for multi-energy coordinated scheduling and carbon responsibility allocation in industrial parks,offering valuable insights for promoting green transformation initiatives.
文摘Two international conferences in November 2025 jointly outlined a profound transformation of climate governance.The Committee on Trade and Environment(CTE)of the World Trade Organization(WTO)held a conference in Geneva,Switzerland,on November 4,where the topic of cooperation on trade-related carbon standards aroused heated discussions.The Leaders'Summit of the 30th Conference of the Parties(COP)to the UN Framework Convention on Climate Change(UNFCCC)was held in Belém,Brazil,on November 7.At the meeting,the Open Coalition on Compliance Carbon Markets was officially launched with the initial membership of 11 economies including Brazil,China,and the EU.As the world's first transnational alliance on compliant carbon markets,the coalition aims to coordinate carbon pricing mechanisms,emission trading systems and related policies in various countries,and realize the interconnection of global compliance carbon market networks.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
