THE BENCHMARK LAND PRICE SYSTEM AND URBANLAND USE EFFICIENCY IN CHINA

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.

Implications of Israeli Agricultural Water Price Sharing System to China

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.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

A Decision Support System for Economic Development and Price Reform

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.

三色膏联合PRICE疗法治疗急性踝关节Ⅰ和Ⅱ度扭伤的临床效果

目的探讨三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤的临床效果。方法选取2022年2月至2023年5月上海市嘉定区中医医院骨伤科收治的52例Ⅰ、Ⅱ度急性踝关节扭伤患者作为研究对象,其中急诊患者35例,门诊患者17例,采用随机数字表法将其分为对照组和观察组,每组均失访1例,两组各25例纳入统计。对照组采用PRICE疗法治疗,观察组采用三色膏联合PRICE疗法,两组患者均治疗1周,治疗结束后,比较两组患者治疗前和治疗后第1、3周的视觉模拟评分法(VAS)评分及Mazur功能评分。结果52例患者中50例得到随访,随访时间不短于4周。治疗1、3周后,两组患者的VAS评分低于本组治疗前,Mazur功能评分高于本组治疗前,观察组的VAS评分低于对照组,Mazur功能评分高于对照组,差异有统计学意义(P<0.05)。结论三色膏联合PRICE疗法治疗急性踝关节Ⅰ、Ⅱ度扭伤可以获得更好的疗效,具有重要的临床价值和推广意义。

G-Contractive Sequential Composite Mapping Theorem in Banach or Probabilistic Banach Space and Application to Prey-Predator System and A &H Stock Prices

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.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

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.

Impacts of a Japan–South Korea power system interconnection on the competitiveness of electric power companies according to power exchange prices

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.

The Impact of Enterprise Resource Planning （ERP） System on the Cost and Price of Auditing Auditor＇s Perspective

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.

A Comprehensive Price Prediction System Based on Inverse Multiquadrics Radial Basis Function for Portfolio Selection

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.

China’s Price System Jeopardizes its Grain Reserves

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.

Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

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.

Climate Change and Food Price: A Systematic Review and Meta-Analysis of Observational Studies, 1990-2021

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.

Brent vs.West Texas Intermediate in the US petro derivatives price formation

In this paper,we apply the spatial panel model to explore the relationship between the dynamic of two types of crude oil prices(WTI and Brent crude oil)and their refined products over time.Considering the turbulent months of 2011,when Cushing Oklahoma had reached capacity and the crude oil export ban removal in 2015 as breakpoints,we apply this method both in the full sample and the three resultant regimes.First,results suggest our results show that both WTI and Brent display very similar behaviour with the refined products.Second,when attending to each regime,results derived from the first and third regimes are quite similar to the full sample results.Therefore,during the second regime,Brent crude oil became the benchmark in the petrol market,and it influenced the distillate products.Furthermore,our model can let us determine the price-setters and price-followers in the price formation mechanism through refined products.These results possess important considerations to policymakers and the market participants and the price formation.

Hadoop + Spark Platform Based on Big Data System Design of Agricultural Product Price Analysis and Prediction by HoltWinters

In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hot dry rock(HDR)is rich in reserve,widely distributed,green,low-carbon,and has broad development potential and prospects.In this paper,a distributionally robust optimization(DRO)scheduling model for a regionally integrated energy system(RIES)considering HDR co-generation is proposed.First,the HDR-enhanced geothermal system(HDR-EGS)is introduced into the RIES.HDR-EGS realizes the thermoelectric decoupling of combined heat and power(CHP)through coordinated operation with the regional power grid and the regional heat grid,which enhances the system wind power(WP)feed-in space.Secondly,peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing.Finally,the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS.By simulating a real small-scale RIES,the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Flexible Load Participation in Peaking Shaving and Valley Filling Based on Dynamic Price Incentives

Considering the widening of the peak-valley difference in the power grid and the difficulty of the existing fixed time-of-use electricity price mechanism in meeting the energy demand of heterogeneous users at various moments or motivating users,the design of a reasonable dynamic pricing mechanism to actively engage users in demand response becomes imperative for power grid companies.For this purpose,a power grid-flexible load bilevel model is constructed based on dynamic pricing,where the leader is the dispatching center and the lower-level flexible load acts as the follower.Initially,an upper-level day-ahead dispatching model for the power grid is established,considering the lowest power grid dispatching cost as the objective function and incorporating the power grid-side constraints.Then,the lower level comprehensively considers the load characteristics of industrial load,energy storage,and data centers,and then establishes a lower-level flexible load operation model with the lowest user power-consuming cost as the objective function.Finally,the proposed method is validated using the IEEE-118 system,and the findings indicate that the dynamic pricing mechanism for peaking shaving and valley filling can effectively guide users to respond actively,thereby reducing the peak-valley difference and decreasing users’purchasing costs.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.