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
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
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.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
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,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.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
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
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
Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation ...Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.展开更多
Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,...Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.展开更多
Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit si...Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.展开更多
We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accom...We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accommodative monetary policy to mitigate the impact of the China-U.S.trade friction.Will this further easing of the monetary environment lead to an increase in natural gas prices?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
This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Acc...This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Accounting Research Database(CSMAR)for empirical analysis.By examining the impact of CSR performance on stock price crash risk,this study identifies key relationships and further investigates the moderating role of media promotion and communication as an intermediary to explore the transmission mechanisms and influence between the two.The empirical results indicate that CSR performance is significantly negatively correlated with stock price crash risk,suggesting that strong CSR performance can effectively reduce the likelihood of a stock price crash.Furthermore,additional analysis reveals that media plays a moderating role in the relationship between CSR performance and stock price crash risk.This study aims to contribute to the understanding of the formation mechanisms and analytical paradigms of factors influencing stock price crash risk while providing theoretical support and reference value for risk prevention strategies.展开更多
While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean e...While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.展开更多
On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge i...On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge in the benchmark oil prices of WTI and Brent over the subsequent three trading days.Despite the significant impact of the United States’“reciprocal tariffs”plan on the global political and economic landscape,the fundamental dynamics of supply and demand remain the decisive factors in the fluctuations of international oil prices.The current trend of international oil price fluctuations is still primarily driven by the supply side,with both supply and demand factors playing a role.Investment,costs,and resource constraints on the supply side do not allow for a significant increase in crude oil production,while“consumption rigidity”on the demand side does not permit a significant decrease in crude oil demand.As a result,International oil prices are expected to fluctuate in the short term,but a significant decline is unlikely to be sustained in the near to medium term.In this context,Chinese oil companies should focus on four key areas to ensure the security of national oil and gas supplies:first,promoting high-quality increases in domestic oil and gas reserves and production;second,steadily strengthening the acquisition of overseas oil and gas resources;third,continuously driving innovation in oil and gas exploration and development technologies;fourth,enhancing the capacity for domestic oil and gas reserves in an orderly manner.展开更多
This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology acros...This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.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
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.
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.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
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funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
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.
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Innovation Team Project of Liaoning Institute of Science and Technology:“Smart Economy Practice and Innovation Team”College Students’Innovation and Entrepreneurship Training Program Projects:“Research on the Application of Big Data Analysis Tools”and“Zhice Quantitative Investment Studio”+2 种基金Teaching and Research Project:“Research on the Path of AI-Enabled Undergraduate Education and Teaching Reform Based on the Needs of Liaoning’s Revitalization and Development(Project No.:LKJY202510)”Teaching Reform Project:“Research and Practice on the Evaluation of Digital Talents in Application-Oriented Universities(Project No.:LNKJ202412)”Project of Liaoning Federation of Social Sciences:“Research on the Key Elements and Practical Paths of Educational Digital Transformation(Project No.:2025lslybkt-050)”。
文摘Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.
文摘Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.
文摘Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.
文摘We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accommodative monetary policy to mitigate the impact of the China-U.S.trade friction.Will this further easing of the monetary environment lead to an increase in natural gas prices?
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.
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
基金R&D Program of Beijing Municipal Education Commission(Grant No.SM202210005007)。
文摘This paper selects the Corporate Social Responsibility(CSR)index from Hexun.com(2010–2020)and the stock price crash index of China’s Shanghai and Shenzhen A-share listed companies from the China Stock Market&Accounting Research Database(CSMAR)for empirical analysis.By examining the impact of CSR performance on stock price crash risk,this study identifies key relationships and further investigates the moderating role of media promotion and communication as an intermediary to explore the transmission mechanisms and influence between the two.The empirical results indicate that CSR performance is significantly negatively correlated with stock price crash risk,suggesting that strong CSR performance can effectively reduce the likelihood of a stock price crash.Furthermore,additional analysis reveals that media plays a moderating role in the relationship between CSR performance and stock price crash risk.This study aims to contribute to the understanding of the formation mechanisms and analytical paradigms of factors influencing stock price crash risk while providing theoretical support and reference value for risk prevention strategies.
文摘While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.
文摘On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge in the benchmark oil prices of WTI and Brent over the subsequent three trading days.Despite the significant impact of the United States’“reciprocal tariffs”plan on the global political and economic landscape,the fundamental dynamics of supply and demand remain the decisive factors in the fluctuations of international oil prices.The current trend of international oil price fluctuations is still primarily driven by the supply side,with both supply and demand factors playing a role.Investment,costs,and resource constraints on the supply side do not allow for a significant increase in crude oil production,while“consumption rigidity”on the demand side does not permit a significant decrease in crude oil demand.As a result,International oil prices are expected to fluctuate in the short term,but a significant decline is unlikely to be sustained in the near to medium term.In this context,Chinese oil companies should focus on four key areas to ensure the security of national oil and gas supplies:first,promoting high-quality increases in domestic oil and gas reserves and production;second,steadily strengthening the acquisition of overseas oil and gas resources;third,continuously driving innovation in oil and gas exploration and development technologies;fourth,enhancing the capacity for domestic oil and gas reserves in an orderly manner.
基金Project of National Social Science Fund of China(Project No.:23BGJ010)。
文摘This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.
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.
