2008 is a year of bumper harvest in summer grain across China. The failure of numerous state-owned grain depots to purchase grain in times of bumper harvest, however, directly threatens grain reserve security and stat...2008 is a year of bumper harvest in summer grain across China. The failure of numerous state-owned grain depots to purchase grain in times of bumper harvest, however, directly threatens grain reserve security and state control over grain prices in the upcoming year. An important factor underpinning the difficulty of state grain depots to purchase grain is the unwillingness of farmers to sell grain due to the excess of the current market price over the government "protected price" aimed at preventing cheap grain from harming farmers. When grassroots grain depots find themselves in trouble, foreign capital stealthily moves in by taking advantage of this situation. To fulfill grain storage tasks and receive various state subsidies, some state-owned grain depots have no alternative but to surreptitiously raise the purchase price. By contrast, some not so courageous state-owned grain depots can only borrow money to finance the purchase of commodity grain at market prices and subsequently figure out a way to pay back such loans. Behind such distorted grain purchase behavior lies a rough and rugged history of grain price reform in China.展开更多
ABSTRACT: China began to introduce market principles and establish price mechanism to better manage land and improve land use efficiency in the late 1980s. Since then, land markets begin to emerge. A benchmark land pr...ABSTRACT: China began to introduce market principles and establish price mechanism to better manage land and improve land use efficiency in the late 1980s. Since then, land markets begin to emerge. A benchmark land price system, providing guidelines for land use rights selling and transferring, was established in order to overcome lack of market data and experiences in land transaction. The benchmark prices of land use rights are determined by land use, land use density (floor-land ratio), land grades, land improvement, and tenant resettlement costs. This paper first conducts a formal analysis based on modern urban economic theory. The formal model provides a theoretical foundation in which the benchmark land price system is assessed and evaluated in terms of land use and urban development. The paper then concludes that the benchmark price system has two theoretical problems. One is associated with the fact that floor-land ratio plays an important role in land price determination whereas the theory suggests the other way around. That is, floor-land ratio depends on land prices. The other problem is that the benchmark land price system does not provide adequate room for the substitution between land and capital inputs. The substitution is a key in achieving land use efficiency in land markets and urban development process. It is concluded that the practice of the benchmark land price system is at odd with reforms that aim to introduce market principles and mechanism to guide resource uses. Therefore, it is recommended that further land policy reform should be taken.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
To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hyd...To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.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
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
This paper introduces Israeli agricultural water price sharing system. According to Israeli agricultural water cost composition,water price sharing by farmers as well as government subsidy and its forms,the financial ...This paper introduces Israeli agricultural water price sharing system. According to Israeli agricultural water cost composition,water price sharing by farmers as well as government subsidy and its forms,the financial subsidy-based agricultural water price system has been established on the basis of the farmers' income in our country and reasonable water price sharing,thus to promote the development of water-saving agriculture in China.展开更多
A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cos...A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.展开更多
Coal is essential to ensure China’s energy security.The sudden or gradual change of coal price reflects the degree of disequilibrium or expected disequilibrium of coal supply and demand,which will not be conducive to...Coal is essential to ensure China’s energy security.The sudden or gradual change of coal price reflects the degree of disequilibrium or expected disequilibrium of coal supply and demand,which will not be conducive to energy security.Therefore,it is important to analyze the change points of coal price and explore the reason of the price fluctuation.This paper analyses the coal price from January2008 to June 2019 as the perspective of the financial market.Firstly,the PPM-DBSCAN model is used to identify the mutation point of coal price fluctuation.Secondly,path analysis is used to extract the core driving factors that affect coal price.Thirdly,the authors construct a time-varying and time-lag effect analysis model for structural changes of coal price based on the TVP-VAR model.The results show that there are 11 mutation points of coal price fluctuation.Financial market factors,coal supply and demand and alternative factors are the reasons of coal price mutation.The authors find that the imbalance of coal supply and demand in traditional view cannot fully explain the fluctuation of coal price.The impact of the financial market and non-thermal power generation have more influence on the coal price.展开更多
In this paper, we present a real decision support system applied to China's macroeconomic management. The structure, design and functions of the system are discussed, and the problems occurring in designing and ma...In this paper, we present a real decision support system applied to China's macroeconomic management. The structure, design and functions of the system are discussed, and the problems occurring in designing and making the system are also studied. A case study for China's petroleum price reform is given at the end of the paper.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
Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger...Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger than or equal to 1, and are more general than the Banach contraction mapping theorem. Application to the proof of existence of solutions of cycling coupled nonlinear differential equations arising from prey-predator system and A&H stock prices are given.展开更多
This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in Chi...This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.展开更多
In many regions,international power system interconnections provide economic,energy-security,environmental,and technical benefits.In contrast,such interconnections remain scarce in Northeast Asia.In 2016,after approvi...In many regions,international power system interconnections provide economic,energy-security,environmental,and technical benefits.In contrast,such interconnections remain scarce in Northeast Asia.In 2016,after approving a joint memorandum of understanding between major electric power companies from China,Japan,South Korea,and Russia,related initiatives regained momentum in the region.Nevertheless,the corresponding developments in Japan remain limited,mainly owing to the lack of involvement of Japanese electric power companies.This study represents a pioneering attempt to provide an economic assessment based on power exchange prices of a power system interconnection between Japan and South Korea regarding the competitiveness of electric power companies in terms of competitive business segments and strategic consequences.We found that although the position of Japanese generators may slightly deteriorate,that of the supply segment would substantially improve,thus suggesting that more opportunities than threats are derived from the interconnection.This promising outcome may foster the adoption of an interconnection with South Korea considering the positive economic and business perspectives in Japan.Furthermore,realizing the interconnection may improve the energy security and air quality in the region.展开更多
Technology plays a key role in today＇s business environment. Many companies greatly rely on computers and software to provide accurate information to effectively manage their business processes. It is becoming increa...Technology plays a key role in today＇s business environment. Many companies greatly rely on computers and software to provide accurate information to effectively manage their business processes. It is becoming increasingly necessary for all businesses to incorporate information technology solutions to operate successfully. One way for many corporations to adopt information technology （IT） on a large scale is by installing enterprise resource planning （ERP） systems to accomplish their business transactions and data-processing needs. ERP systems are software packages that enable the integration of business processes throughout an organization. This study aims to determine the effect of the ERP system on the cost of auditing period compared with traditional computerized （non-ERP） systems. According to cost analysis, the study also points out the changes in audit price. The methodology used in this research is survey-based data collection. The questionnaires are sent to auditors who are working with companies with ERP systems. The answers are processed and analyzed using Statistical Package for Social Sciences （SPSS） 20. The data are performed using the statistical test to determine the effect of ERP usage on the cost of auditing process and pricing policy of auditors. The findings of this study are： （1） Companies with ERP systems are reducing their auditing costs; and （2） Auditing companies are not implying a low rate of price to their customers using ERP.展开更多
Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction....Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.展开更多
This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock p...This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.展开更多
Climate extreme events have threatened food security and the second Sustainable development goals (SDGs) “zero hunger” both directly via agricultural food loss and indirectly through rising food prices. We systemati...Climate extreme events have threatened food security and the second Sustainable development goals (SDGs) “zero hunger” both directly via agricultural food loss and indirectly through rising food prices. We systematically searched and used a combination of results from various models, which play a crucial role in predicting the potential impact of climate change on agricultural production and food price. Therefore, we searched online databases including EMBASE, Web of Science, Scopus, Google Scholar, and grey literature. Then observational studies were included from January 1990 to August 2021, which reported food price proportion under climate disturbances. Results showed that 22 out of 26 studies from 615 articles, identified in the meta-analysis predicted the food price ratio would be fluctuated up to 28% before 2020, while the ratio will be marked up at 31% from 2020 to 2049 and then will scale down during 2050-2100. The compiled ratio was estimated at 26% in the long period between 2000 until 2100 under climatic weather events. Drought was a significant weather disturbance with a 32% increase in food prices. Consequently, the Food price increase will significantly affect food accessibility in lower-income countries, primarily until 2050. Policymakers should prioritize and act through redesigning food security policies according to climatic extremes in their settings.展开更多
The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.Wh...The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.展开更多
文摘2008 is a year of bumper harvest in summer grain across China. The failure of numerous state-owned grain depots to purchase grain in times of bumper harvest, however, directly threatens grain reserve security and state control over grain prices in the upcoming year. An important factor underpinning the difficulty of state grain depots to purchase grain is the unwillingness of farmers to sell grain due to the excess of the current market price over the government "protected price" aimed at preventing cheap grain from harming farmers. When grassroots grain depots find themselves in trouble, foreign capital stealthily moves in by taking advantage of this situation. To fulfill grain storage tasks and receive various state subsidies, some state-owned grain depots have no alternative but to surreptitiously raise the purchase price. By contrast, some not so courageous state-owned grain depots can only borrow money to finance the purchase of commodity grain at market prices and subsequently figure out a way to pay back such loans. Behind such distorted grain purchase behavior lies a rough and rugged history of grain price reform in China.
文摘ABSTRACT: China began to introduce market principles and establish price mechanism to better manage land and improve land use efficiency in the late 1980s. Since then, land markets begin to emerge. A benchmark land price system, providing guidelines for land use rights selling and transferring, was established in order to overcome lack of market data and experiences in land transaction. The benchmark prices of land use rights are determined by land use, land use density (floor-land ratio), land grades, land improvement, and tenant resettlement costs. This paper first conducts a formal analysis based on modern urban economic theory. The formal model provides a theoretical foundation in which the benchmark land price system is assessed and evaluated in terms of land use and urban development. The paper then concludes that the benchmark price system has two theoretical problems. One is associated with the fact that floor-land ratio plays an important role in land price determination whereas the theory suggests the other way around. That is, floor-land ratio depends on land prices. The other problem is that the benchmark land price system does not provide adequate room for the substitution between land and capital inputs. The substitution is a key in achieving land use efficiency in land markets and urban development process. It is concluded that the practice of the benchmark land price system is at odd with reforms that aim to introduce market principles and mechanism to guide resource uses. Therefore, it is recommended that further land policy reform should be taken.
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To address the issues of unclear carbon responsibility attribution,insufficient renewable energy absorption,and simplistic carbon trading mechanisms in integrated energy systems,this paper proposes an electricheat-hydrogen integrated energy system(EHH-IES)optimal scheduling model considering carbon emission stream(CES)and wind-solar accommodation.First,the CES theory is introduced to quantify the carbon emission intensity of each energy conversion device and transmission branch by defining carbon emission rate,branch carbon intensity,and node carbon potential,realizing accurate tracking of carbon flow in the process of multi-energy coupling.Second,a stepped carbon pricing mechanism is established to dynamically adjust carbon trading costs based on the deviation between actual carbon emissions and initial quotas,strengthening the emission reduction incentive.Finally,a lowcarbon economic dispatch model is constructed with the objectives of minimizing operation cost,carbon trading cost,wind-solar curtailment penalty cost,and energy loss.Simulation results show that compared with the traditional economic dispatch scheme 3,the proposed schemel reduces carbon emissions by 53.97%and wind-solar curtailment by 68.89%with a 16.10%increase in total cost.This verifies that the model can effectively improve clean energy utilization and reduce carbon emissions,achieving low-carbon economic operation of EHH-IES,with CES theory ensuring precise carbon flow tracking across multi-energy links.
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.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Supported by Provincial Water Conservancy Research and Technology Promotion Project:Research on Key Technical Problems of Farmland Water Conservancy Projects in Shandong Province(SDSLKY201401)
文摘This paper introduces Israeli agricultural water price sharing system. According to Israeli agricultural water cost composition,water price sharing by farmers as well as government subsidy and its forms,the financial subsidy-based agricultural water price system has been established on the basis of the farmers' income in our country and reasonable water price sharing,thus to promote the development of water-saving agriculture in China.
基金Sponsored by National Natural Science Foundation of China(51304053)International Science and Technology Cooperation Program of China(2013DFA10810)
文摘A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China under Grant No.71874133“Special Support Program for High-Level Talents”Youth Top Talent Program of Shaanxi Province,China。
文摘Coal is essential to ensure China’s energy security.The sudden or gradual change of coal price reflects the degree of disequilibrium or expected disequilibrium of coal supply and demand,which will not be conducive to energy security.Therefore,it is important to analyze the change points of coal price and explore the reason of the price fluctuation.This paper analyses the coal price from January2008 to June 2019 as the perspective of the financial market.Firstly,the PPM-DBSCAN model is used to identify the mutation point of coal price fluctuation.Secondly,path analysis is used to extract the core driving factors that affect coal price.Thirdly,the authors construct a time-varying and time-lag effect analysis model for structural changes of coal price based on the TVP-VAR model.The results show that there are 11 mutation points of coal price fluctuation.Financial market factors,coal supply and demand and alternative factors are the reasons of coal price mutation.The authors find that the imbalance of coal supply and demand in traditional view cannot fully explain the fluctuation of coal price.The impact of the financial market and non-thermal power generation have more influence on the coal price.
基金This Project is partly supported by World Bank and National Science Foundation of China.And this is a team work,Prof. Deng Shuhui and Dr.Wu jianzhong also play an important role in the project
文摘In this paper, we present a real decision support system applied to China's macroeconomic management. The structure, design and functions of the system are discussed, and the problems occurring in designing and making the system are also studied. A case study for China's petroleum price reform is given at the end of the paper.
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Theorems of iteration g-contractive sequential composite mapping and periodic mapping in Banach or probabilistic Bannach space are proved, which allow some contraction ratios of the sequence of mapping might be larger than or equal to 1, and are more general than the Banach contraction mapping theorem. Application to the proof of existence of solutions of cycling coupled nonlinear differential equations arising from prey-predator system and A&H stock prices are given.
基金supports from the National Natural Science Foundation of China(under Grants No.72073105,71903002,and 71774122)the Natural Science Foundation of Anhui Province,China(under Grant No.1908085QG309)are greatly acknowledged.
文摘This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.
文摘In many regions,international power system interconnections provide economic,energy-security,environmental,and technical benefits.In contrast,such interconnections remain scarce in Northeast Asia.In 2016,after approving a joint memorandum of understanding between major electric power companies from China,Japan,South Korea,and Russia,related initiatives regained momentum in the region.Nevertheless,the corresponding developments in Japan remain limited,mainly owing to the lack of involvement of Japanese electric power companies.This study represents a pioneering attempt to provide an economic assessment based on power exchange prices of a power system interconnection between Japan and South Korea regarding the competitiveness of electric power companies in terms of competitive business segments and strategic consequences.We found that although the position of Japanese generators may slightly deteriorate,that of the supply segment would substantially improve,thus suggesting that more opportunities than threats are derived from the interconnection.This promising outcome may foster the adoption of an interconnection with South Korea considering the positive economic and business perspectives in Japan.Furthermore,realizing the interconnection may improve the energy security and air quality in the region.
文摘Technology plays a key role in today＇s business environment. Many companies greatly rely on computers and software to provide accurate information to effectively manage their business processes. It is becoming increasingly necessary for all businesses to incorporate information technology solutions to operate successfully. One way for many corporations to adopt information technology （IT） on a large scale is by installing enterprise resource planning （ERP） systems to accomplish their business transactions and data-processing needs. ERP systems are software packages that enable the integration of business processes throughout an organization. This study aims to determine the effect of the ERP system on the cost of auditing period compared with traditional computerized （non-ERP） systems. According to cost analysis, the study also points out the changes in audit price. The methodology used in this research is survey-based data collection. The questionnaires are sent to auditors who are working with companies with ERP systems. The answers are processed and analyzed using Statistical Package for Social Sciences （SPSS） 20. The data are performed using the statistical test to determine the effect of ERP usage on the cost of auditing process and pricing policy of auditors. The findings of this study are： （1） Companies with ERP systems are reducing their auditing costs; and （2） Auditing companies are not implying a low rate of price to their customers using ERP.
文摘Price prediction plays a crucial role in portfolio selection (PS). However, most price prediction strategies only make a single prediction and do not have efficient mechanisms to make a comprehensive price prediction. Here, we propose a comprehensive price prediction (CPP) system based on inverse multiquadrics (IMQ) radial basis function. First, the novel radial basis function (RBF) system based on IMQ function rather than traditional Gaussian (GA) function is proposed and centers on multiple price prediction strategies, aiming at improving the efficiency and robustness of price prediction. Under the novel RBF system, we then create a portfolio update strategy based on kernel and trace operator. To assess the system performance, extensive experiments are performed based on 4 data sets from different real-world financial markets. Interestingly, the experimental results reveal that the novel RBF system effectively realizes the integration of different strategies and CPP system outperforms other systems in investing performance and risk control, even considering a certain degree of transaction costs. Besides, CPP can calculate quickly, making it applicable for large-scale and time-limited financial market.
文摘This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.
文摘Climate extreme events have threatened food security and the second Sustainable development goals (SDGs) “zero hunger” both directly via agricultural food loss and indirectly through rising food prices. We systematically searched and used a combination of results from various models, which play a crucial role in predicting the potential impact of climate change on agricultural production and food price. Therefore, we searched online databases including EMBASE, Web of Science, Scopus, Google Scholar, and grey literature. Then observational studies were included from January 1990 to August 2021, which reported food price proportion under climate disturbances. Results showed that 22 out of 26 studies from 615 articles, identified in the meta-analysis predicted the food price ratio would be fluctuated up to 28% before 2020, while the ratio will be marked up at 31% from 2020 to 2049 and then will scale down during 2050-2100. The compiled ratio was estimated at 26% in the long period between 2000 until 2100 under climatic weather events. Drought was a significant weather disturbance with a 32% increase in food prices. Consequently, the Food price increase will significantly affect food accessibility in lower-income countries, primarily until 2050. Policymakers should prioritize and act through redesigning food security policies according to climatic extremes in their settings.
基金supported by National Natural Science Foundation of China(52407126).
文摘The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
