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In this paper, we select the China Carbon Market Price Index, which reflects the overall price changes in China's carbon market (CCM), and employs the TVP- VAR-BK model to examine the risk spillover effects between the carbon market and high-carbon-emission industries in China from a frequency domain viewpoint. Employing the nonparametric quantile Granger causality test, it delves further into the effects of economic policy uncertainty (EPU) in China on the degree of risk spillovers between the carbon market and high-carbon- emission industries. There are significant risk spillover effects between the carbon market and high-carbon-emission industries. During the short term, the carbon market affects the cement industry more than the electric power and steel industries. However, the carbon market is affected by the volatility of the high-carbon-emission industries over the long term. In addition, the effect of EPU on the magnitude of risk spillovers between the carbon market and high- carbon-emission industries is nonsignificant at extreme quartiles and significant at the middle quartile level, which is typically asymmetric. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021. Design/methodology/approach: Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices. Findings: The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities. Originality/value: This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities. [ABSTRACT FROM AUTHOR]

This paper compiles a market-based price index by accounting for the impact of price regulation. We then use the parallel market exchange rate and market-based price index to test Purchasing Power Parity (PPP) over a more extended period (1952–2019) in China. We find weak evidence support PPP when official data are used. However, we fail to find evidence against PPP when the parallel market exchange rate and the market-based price index are used. Moreover, the half-life of the RMB real exchange rate is approximately one year, substantially lower than the consensus estimates of 3–5 years. [ABSTRACT FROM AUTHOR]

In this study, we aim at investigating dynamic relations among steel price indices of all seven regional markets in China, spanning the period of 2010M1–2021M4. With daily data, we apply techniques of vector error correction modeling and the directed acyclic graph to analyze the issue of contemporaneous causality among the seven indices, the latter of which is facilitated through the LiNGAM algorithm. We find via vector error correction modeling that each of the seven indices is part of cointegration relations and responses to long-run equilibrium disturbances. We arrive at causal paths via the LiNGAM algorithm for enabling innovation accounting analysis. With this analysis, we find sophisticated dynamics in price adjustment processes following shocks, for which east and northeast regions have the most significant impacts on other regions, followed by south and north regions, followed by the southwest region. Central south and northwest regions are not found to have significant impacts on others. These results suggest that policies on regional steel prices in the long run might be designed with most attention paid to five of the seven regions, particularly east and northeast regions. [ABSTRACT FROM AUTHOR]

This study aims to predict financial distress in an emerging country using data on ST listed companies in China from 2001 to 2021. A new Aalen hazard model with mixed data sampling (MIDAS) is adopted to investigate the impact of monthly macroeconomic variables and quarterly financial variables on financial distress. The empirical results show that the current ratio, operating profit ratio, current capital ratio, retention ratio, profit ratio and income ratio of listed companies have a significant impact on the time-varying intensity of financial distress. The consumer price index has a negative relation with the intensity of financial distress, while the production price index and credit spreads have a positive influence. Finally, the results of the robustness tests are consistent with those with different lag orders. [ABSTRACT FROM AUTHOR]

Pork price fluctuations are closely related to the national economy and people's livelihoods in China. Based on the monthly pork price fluctuations in China from January 2011 to August 2022, this study uses ARCH family models to assess the characteristics and laws of these fluctuations in China. The pork price fluctuations show obvious clustering, with external shock information from the previous month affecting the pork price in the following period; the pork market price is characterized by risk compensation, with the high risk of pork supply driving the pork price up. In addition, the pork price fluctuations are characterized by asymmetry, with a greater impact of good than of bad news on the pork price. Due to the pork industry' low entry threshold and the existence of sunk costs, positive information on the pork market has a stronger impact on price fluctuations than negative information. To guide pork supply, we recommend improving monitoring and earlywarning mechanisms in the pork market to identify the pork price volatility threshold and measure the price volatility. In addition, price index insurance products should be constantly strengthened, with different types of insurance products being offered to meeting the insurance demand of various sectors in the pig meat supply chain. [ABSTRACT FROM AUTHOR]

Efficient budget management is crucial for the successful execution of construction projects. To achieve this, the design of a Construction Project Audit Budget Management System based on combinatorial optimization offers significant advantages. Combinatorial optimization techniques focus on finding optimal solutions from a vast number of possible combinations, making them well -suited for complex budget management scenarios. This system employs advanced algorithms to analyze and optimize various factors such as resource allocation, cost estimation, risk assessment, and scheduling. This paper focused on the construction of the Project Fuzzy Audit System in China. The developed model computes total investment decision, preliminary design, design, contracting, construction, completion acceptance and other stages of the investment/cost, namely investment estimation, budget estimate, control price, contract price and settlement price. According to the project total investment/cost control needs in China Project. The constructed model uses the combinatorial Optimization model for the estimation of the investment estimation of the Construction Project for the cost index. Through the Fuzzy Audit System new method abolishes the "maximum price" in the bidding document with the limit as the unit, and gradually terminates the preparation of the budget quota, highlighting the market price, so there is an urgent need for a set of cost index in line with the actual needs. The results demonstrated that with the fuzzy Audit system new method abolishes the "maximum price" in the bidding document with the limit as the unit, and gradually terminates the preparation of the budget quota, highlighting the market price, Through analysis it is concluded that with fuzzy based optimization model effectively accumulate fast and accurate calculation of the Project cost. [ABSTRACT FROM AUTHOR]

The establishment of the China-ASEAN Free Trade Area has prompted closer cooperation in aquatic products trade between China and Vietnam. Studying the aquatic trade situation and export trade growth dynamics of China and Vietnam can help clarify the aquatic trade relationship between China and Vietnam and promote the sustainable development of bilateral aquatic trade cooperation. This paper analyzes the trade growth dynamics of aquatic products exported from China and Vietnam from 2002 to 2020 using the ternary marginal method. The trade growth pattern of aquatic products exported from China to Vietnam is "both quantity and price growth," and the trade growth pattern of aquatic products exported from Vietnam to China is "quantity growth mainly, extensive growth supplemented." The two countries have apparent differences in the growth dynamics of aquatic products export trade. Moreover, there is a higher complementarity when exporting Vietnam's aquatic products to China than China's aquatic products to Vietnam. Based on these, analyze the factors affecting the growth dynamics of Vietnam's aquatic products export to China: Vietnam's economic development level negatively affects the price index and quantity index of Vietnam's aquatic products exported to China, and Vietnam's aquatic product output affects the price index. China's trade freedom positively affects the price index and negatively affects the quantity index. Finally, this paper put forward suggestions to promote the sustainable development of bilateral aquatic products trade between China and Vietnam to facilitate the formulation of relevant policies between the two countries. [ABSTRACT FROM AUTHOR]

For policy makers and a diverse spectrum of market participants, understandings of commodity price forecasts are considered as an important matter. In this work, we focus on the metal business sector and examine forecast problems for seven daily regional steel prices in the Chinese market over an eleven-year period spanning 2010–2021. Specifically, our analysis covers steel prices of east, south, north, central south, northeast, southwest, and northwest China through the use of non-linear auto-regressive neural networks. For each price series, we test forecast accuracy based upon one hundred and twenty settings over training algorithms for model estimations, numbers of hidden neurons, numbers of delays, and ratios used for segmenting the data into training, validation, and testing phases. Our analysis leads to the construction of a rather simple model that generates high forecast accuracy and stabilities for each of the seven price series. Correspondingly, forecast accuracy measured by the relative root mean square error is lower than 0.60% for all considered series based upon the overall sample. Forecast results here could be utilized on a standalone basis or combined with forecasts from other models by different forecast users as part of evaluating price trends and performing policy analysis. [ABSTRACT FROM AUTHOR]

Rapidly changing relative energy prices (R.E.P.) have put pressure on global markets all over the world. Even economic factors must have a high degree of self-sufficiency related to the R.E.P. and increasing consumption and its production costs in consumer sectors of China. This research is based on R.E.P. impact on the industrial energy consumption (E.C.) of the Chinese economy. The key dependent and independent variables employed for this purpose were R.E.P.s, consumption price index (C.P.I.), inflation expenses, and so on. The R-square test between R.E.P.s and industrial consumption sectors (C.S.), f-statistical analysis, graphical analysis, and a summary of the Model and its analysis were utilised for this aim. Results run from the P.L.S. software and data collected from the World Bank, and world indicators also collect data from different websites. Data form is secondary and is based-on frequency panel data for 1990 to 2019. Results concluded that the impact of R.E.P.s is fast growing on the E.C. of China and is positively related with the consumer prices index. The contribution of this research is the comprehensive review of the existing and potential markets and services. The assistance can be mainly found in the study on the distribution network reliability service. The change in energy policies considering the different countries must be highly changing on imported energy which occurs high self-sufficiently. This examination of R.E.P.s and index relates to China. The results indicate a significant impact between industry E.C. and R.E.P.s. Therefore, this research is more reliable due to the literature and enhances the knowledge related to the study of relative consumption energy prices in energy industries. [ABSTRACT FROM AUTHOR]

Globally, floods are a prevalent type of natural disaster. Simulating floods is a critical component in the successful implementation of flood management and mitigation strategies within a river basin or catchment area. Selecting appropriate calibration data to establish a reliable hydrological model is of great importance for flood simulation. Usually, hydrologists select the number of flood events used for calibration depending on the catchment size. Currently, there is no numerical index to help hydrologists quantitatively select flood events for calibrating the hydrological models. The question is, what is the necessary and sufficient amount (e.g., 10 events) of calibration flood events that must be selected? This study analyses the spectral characteristics of flood data in Sequences before model calibration. The absolute best set of calibration data is selected using an entropy-like function called the information cost function (ICF), which is calculated from the discrete wavelet transform (DWT) decomposition results. Given that the validation flood events have already been identified, we presume that the greater the similarity between the calibration dataset and the validation dataset, the higher the performance of the hydrological model should be after calibration. The calibration datasets for the Tunxi catchment in southeast China were derived from 21 hourly flood events, and the calibration datasets were generated by arranging 14 flood events in sequences from 3 to 14 (i.e., a Sequence of 3 with 12 sets (set 1 = flood events 1, 2, 3; set 2 = flood events 2, 3, 4, ..., and so on)), resulting in a total of 12 sequences and 78 sets. With a predetermined validation set of 7 flood events and the hydrological model chosen as the Hydrologic Engineering Center (HEC–HMS) model, the absolute best calibration flood set was selected. The best set from the Sequence of 10 (set 4 = S10′) was found to be the absolute best calibration set of flood events. The potential of the percentile energy entropy was also analyzed for the best calibration sets, but the ICF was the most consistent index to reveal the ranking based on similarity with model performance. The proposed ICF index in this study is helpful for hydrologists to use data efficiently with more hydrological data obtained in the new era of big data. This study also demonstrates the possibility of improving the effectiveness of utilizing calibration data, particularly in catchments with limited data. [ABSTRACT FROM AUTHOR]

This study aims to investigate the effects and mechanisms of biased technological progress on China's industrial overcapacity. Using a time-varying stochastic frontier model, standardized supply-side system method, and the production function method, we measure the capacity utilization rate, biased technological progress index, and factor price distortion index in China's industrial sector. Additionally, we employ a mediation effect model to analyze the impact of biased technological progress on overcapacity through factor price distortion. The findings suggest that biased technological progress can alleviate overcapacity, while factor price distortion exacerbates the problem. Biased technological progress alleviates overcapacity through capital price distortion, with a more significant effect in heavy industries than in light industries. Based on the results, we propose a series of policy recommendations, including optimizing the bias towards technological progress, improving the factor market, and enhancing the original innovation capability. [ABSTRACT FROM AUTHOR]

We revisit the purchasing power parity (PPP) between China and its five major trading partners, namely European Union, United States, Brazil, Japan, and Korea. Conventional unit root tests with structural breaks have mostly failed to validate the PPP. Apart from using CPI as the price index, the tradable‐goods price index is also used to test the PPP hypothesis (Balassa–Samuelson effect). Using the Fourier quantile unit root test for the potential structural breaks and non‐Gaussian distribution reveals strong evidence that the PPP holds between China and its five major trading partners. [ABSTRACT FROM AUTHOR]

This paper highlights the impact of variable demand on the evaluation of welfare gains from import variety. We use a nested demand structure and construct a model to estimate the gains from import variety, which allows us to relax the assumption of time-invariant demand in previous studies. Our modified approach shows that welfare gains from import variety depends on the demand shifts, in which the gains could be either positive or negative. In particular, China' s gains from imported varieties between 1995 and 2010 are underestimated 0.94% of 2010 GDP when assuming constant demand. The sources of variety gains also change significantly under the framework of variable demand. [ABSTRACT FROM AUTHOR]

Hydrogen will be an important part of China's energy system in the future and an important carrier for energy-using terminals to realize green and low-carbon transformation. It is important to establish a nationwide hydrogen market to promote the healthy and orderly development of the hydrogen industry chain. The core is to form a complete hydrogen price mechanism and play a decisive role in the process of resource allocation by the market. In this paper, we have developed the framework of the 'China Hydrogen Price Index' system by establishing the 'Assessment + Collection' model, which covers four types of hydrogen: hydrogen, clean hydrogen, renewable hydrogen and high-purity hydrogen. The model considers the raw materials required for hydrogen production, fixed equipment, engineering construction costs and carbon prices, and conducts sensitivity analysis on the trends and influencing factors of national and regional hydrogen prices of multiple categories since 2018. The results show that, with respect to the level of hydrogen prices, fossil-energy-rich and renewable-energy-rich areas have more advantages than other regions. The price of raw materials is the main factor of the hydrogen price change, and the utilization hours of renewable energy and hydrogen production equipment have a key influence on the price of renewable hydrogen. Next, by establishing an index update mechanism, improving the standard system and building a trading platform, we can further exert the role of price signals and continue to promote the efficient and smooth expansion of the domestic hydrogen market. [ABSTRACT FROM AUTHOR]

The frequent price volatility in China's agricultural futures market has aroused concern about price bubbles in the agricultural futures market. This paper analyzes the characteristics of price bubbles in China's agricultural futures market by using data from nine active agricultural commodities futures and the Nanhua agricultural price index. The empirical results show that only soybean futures, sugar futures, cotton futures, and the Nanhua agricultural price index exist price bubbles. The duration of price bubble accounts for 2.5–6.2% of the whole sample interval. Moreover, combined with China's agricultural policy, this paper finds that after 2014, China's policy reform from the "yellow box" policy to the "green box" policy and the "blue box" policy can inhibit price bubbles of agricultural futures market to a certain extent. Additionally, the higher the dependence on the international market, the higher the risk of price bubbles in the agricultural futures market. Besides, agricultural futures are more prone to price bubbles with long duration and large price change during the agricultural crisis (soybean and cotton experienced price bubbles from 2007 to 2008 and 2010 to 2011, respectively). The findings of this paper have important policy implications for policy-makers. [ABSTRACT FROM AUTHOR]

Costs from switching suppliers can affect prices by discouraging buyer movements from high- to low-cost sellers. This paper uses confidential data on U.S. importers and their Chinese exporters to investigate these costs. I find barriers to supplier adjustments: nearly half of importers keep their partner over time. Importers switch less if their supplier offers higher quality or provides lower prices. I propose and structurally estimate a dynamic discrete choice model to compute switching costs. Cost estimates are large, heterogeneous across products, and matter for trade prices: halving switching costs reduces the U.S.-China Import Price Index by 7.6%. [ABSTRACT FROM AUTHOR]

This study is an attempt to explore the effect of the COVID-19 pandemic on selected sectoral indices of the national stock exchange and the relationship among themselves also examined. COVID-19 pandemic was first identified in China during December 2019. To prevent the spread of pandemics various measures were taken by countries around the world. All commercial activities were restricted and people had to remain in their houses which affected the economy of countries. The stock market was also affected by this pandemic. For exploring the relationship and effect of pandemic various statistical tools have been applied. Secondary data of all sectoral indices price from 24th Jan to 24th July 2020 have been taken for the analysis. findings suggest that there is a significant impact of the Lockdown period on the performance of the stock indices. [ABSTRACT FROM AUTHOR]

During the past decade, the housing market in China has witnessed rapid growth and the significance of forecasting related to housing prices has undoubtedly elevated, which has become an important issue to the people in investment and policymakers in regulations. In this study, we explore neural networks for residential housing price index forecasts from ten major Chinese cities for July 2005–April 2021. We aim at constructing simple and accurate neural networks as a contribution to pure technical forecasts of the Chinese residential housing market. To facilitate the analysis, we investigate different model settings across algorithms, delays, hidden neurons, and data spitting ratios, and arrive at a simple neural network with three delays and three hidden neurons, which produces stable performance of about 0.75% average relative root mean square error across the ten cities for the training, validation, and testing phases. Our results can be used on a standalone basis or combined with fundamental forecasts to form perspectives of residential housing price trends and carry out policy analysis. [ABSTRACT FROM AUTHOR]

The house market in China has been growing rapidly for the past decade and price forecasting has become a significant issue to the people and policymakers. We approach this problem by examining neural networks for second-hand house price index forecasting from 10 major cities for March 2012–May 2020. Our purpose is to build simple and accurate neural networks to contribute to pure technical house price forecasting of the Chinese market. We explore various model settings across the algorithm, delay, hidden neuron, and data spitting ratio, and arrive at a rather simple neural network with three delays and eight hidden neurons, which leads to stable performance of 0.8% average relative root-mean-square error across the 10 cities for the training, validation, and testing phases. Results here can be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis. [ABSTRACT FROM AUTHOR]

The Malmquist index (MI) has demonstrated its usefulness in comparing the performances of Decision Making Units (DMUs) performances. The global MI (GMI) has been suggested as a means to overcome three drawbacks of the MI: non-circularity, disparate measurements, and infeasibility. Recently, it has appeared that the MI can also be used to compare groups of DMUs. While this new function of the index has also increased its usefulness, it presents the same drawbacks as the MI. In this paper, we define the global counterpart of the MI for group contexts. We also consider the case where DMUs have an economic optimization behavior by proposing a global cost MI (GCMI). The GCMI requires the observation of the input prices. As it may represent a strong assumption, we propose solutions. These two novel indexes equip the practitioners with a new toolkit. We illustrate the usefulness of our new indexes with the Chinese energy sector. [ABSTRACT FROM AUTHOR]

This paper uses Python programming to improve the existing price index method and calculate the integration level of the forest products market in China. Based on the model, the influencing factors of domestic forest product market integration were examined empirically. The results show that the integration degree of the forest product market in recent years lags behind the average level in China. In this paper, the improvement of the existing price index method can calculate the level of market integration more quickly and accurately, and this improvement has obvious advantages in the case of a large amount of data. In terms of development, the overall pattern shows an increasing trend, and the eastern, central, and western provinces are gradually converging, although the differences between provinces continue to expand. In terms of influencing factors, the coefficient of the local government's emphasis on forestry, the prosperity of international trade in forest products, and highway density were significantly positive factors, while the number of foreign investors and the railway density had no significant effects. In this paper, the optimization of the measurement method of market integration makes it easier for scholars to obtain the data on the level of market integration and promote in-depth research in the field of market integration. [ABSTRACT FROM AUTHOR]

In order to explore the sustainable growth of Chinese agriculture, this paper assesses how the heterogeneous SPS measures affect China's growth margins and quality upgrading in the agricultural sector. We estimate improved gravity-model that exploit the cross-country differences in SPS measures over the period from 2000 to 2014. Our findings show that the heterogeneous SPS measures restrict the intensive margin and extensive margin, but significantly promote the product quality-even with no significant effect on price index. Conditional on quality upgrading, the heterogeneous SPS measures decrease the extent of quality upgrading. On one hand, because of cross-country differences in SPS standards, standards from developed countries have less effective trade effect on China's agri-products export quality upgrading; On the other hand, because of cross-firm differences in the ability to deal with SPS standards, the laggards in China have the higher probability to switch to lower entrance barrier countries. Therefore, it is possible for China to trap in the low-quality agricultural growth in the long term. [ABSTRACT FROM AUTHOR]

This paper uses complex network theory and Granger causality method to construct Granger Causality Network among price indices (PIGCN), specifically based on crude oil price and China's domestic price index from a systematic perspective and provides a statistical physical method to analyze conduction fluctuations. This paper selects four typical price indices in China, including 36 sub price indices as samples for empirical research. Through the analysis of the PIGCN structure, we have analyzed the volatility transmission path of the international crude oil price and Chinese domestic price index, and further revealed the dynamic relationship among the price indices. Also, we have put forward some policy recommendations to better monitor the spread of price fluctuation risks. [ABSTRACT FROM AUTHOR]

The disruption of natural disasters in agriculture is often seen to have influenced price indices to some extent over a certain period, especially in a relatively isolationist, predominantly agricultural economy such as ancient China. Here we use the information about historical disasters kept in Qing History - Treatise on Famine and Relief and the price indices for each year from 1644 to 1911, exploring the correlation between natural disasters and price indices. We next discuss the other factors that have an impact on the price index to reveal the dynamics of the influence of natural disasters on the price index. Results show that there is a weak correlation between natural disasters and price indices in general, but we find a strong correlation over a long time, and the correlation coefficient rises somewhat in the early Qing, and then decreases with time. We further show that factors such as production capacity, foreign trade and silver flows also had an impact on the price indices no less than natural disasters, and it is clear that the impact of these factors caused the correlation coefficient to change over time. [ABSTRACT FROM AUTHOR]

Approximately 95% of international trade in steam coal is concentrated in two areas: Asia-Pacific and Atlantic. Prices on the international market depend on the largest exporters and users of coal. The aim of the article is to characterize the price trends that took place in the international trade of energy coal in the years 2000-2020 and to distinguish price indices which, in the opinion of the authors, currently play an important role in this trade. The analysis of steam coal prices in international markets in 2000-2020 made it possible to highlight five periods of rising prices, four periods of falling prices, and one period of the stabilisation of prices. A detailed analysis of the highlighted periods of steam coal price fluctuations in 2000-2020 made it possible to identify groups of factors that significantly affect the level of prices of the analyzed coal in the long term. International steam coal markets are interlinked despite periodic volatility. A very important factor influencing world steam coal prices is the situation in China as it is the largest producer, user and importer of steam coal. A small change in coal production in China significantly affects the volume of trade on the international market. Therefore, the level of freight prices is an important factor influencing the price level for the customer. FOB Australia prices are also correlated with coal suppliers to the European market and Asia-Pacific market in this paper. The very high correlation coefficients obtained confirm the close relationship between the prices of these coals. For many years, the European market has no longer been a trendsetter in international coal markets but has instead been affected by general trends. [ABSTRACT FROM AUTHOR]

Though it is believed that public opinion helps to predict vegetable prices, an intelligent scheme for predicting vegetable prices by online public opinion is missing. This research proposes an intelligent vegetable price prediction model, TPCDR, which includes five modules, i.e., topic matching, public opinion computing, causing index selection, de-noise, and regression of price prediction. To investigate the correlation between vegetable price and public opinion in a fine-grained manner, this research considers four types of economic causing indexes, i.e., natural environment, supply, demand, and economic policy. TPCDR model integrates Granger causal testing and linear regression and discovers the significant causal factors raised from the natural environment and supply. Meanwhile, through vegetable price forecasting experiments, our model outperforms other baseline methods. This result suggests that the TPCDR pipeline is reasonable and reliable to model the association between public opinion and vegetable price. • An intelligent model for the association between public opinion and vegetable price. • Four types of economic causing indexes considered. Causal relation obtained. • Constructing a corpus for online public opinion with vegetable prices in China. [ABSTRACT FROM AUTHOR]

The inclusion of A shares into the MSCI indicates the gradual recognition by international investors for China's efforts in the reforms and liberalization of its capital markets. It is expected that more overseas investors and institutions will enter the Chinese capital market. The observations of the paper are the A shares that were included into MSCI indexes and the event study method is applied to explore whether there are abnormal returns and abnormal volume turnovers on announcement dates and implementation dates of events. Quantile regressions are conducted to examine the factors that influence cumulative abnormal returns after the outbreak of the China-US trade war. The empirical evidence indicates significant abnormal returns and abnormal volume turnovers both on announcement day and implementation day of events. The quantile regression analysis suggests that imposition of tariffs and sales from overseas are the two factors that influence cumulative abnormal returns after the eruption of the China-US trade war. The findings of this paper can serve as a reference to investors in buying and selling stocks in response to any change to index constituents. It is suggested that the government should provide timely information regarding economic policies and consider the impact of import duties in the policy making process. [ABSTRACT FROM AUTHOR]

Under the development strategy of traffic power, Chinese highway engineering has been in the critical stage, and controlling investment has become a top priority. The budgetary estimate is the main basis for the Chinese government to verify the highway project investment, which is estimated mainly based on the quota and price information issued by government but still lacks systematic cost indices to measure its rationality. The highway engineering cost of different regions in China varies greatly, and the accumulated cost data are not insufficient. This paper chooses the gray relation analysis-least absolute shrinkage and selection operator (GRA-LASSO) hybrid method, which has significant advantages in dealing with high-dimensional small sample data and multiple correlation variables, to establish the highway engineering budgetary estimate prediction model so as to examine the rationality of estimated investment at the initial stage of construction. Using budgetary estimate data on 121 highway projects that have been approved by the Chinese government and various macroeconomic data, the key cost drivers are identified through gray relation analysis (GRA) quantitatively, and 10 high-correlation indexes are selected as input variables. Then, the relationship among engineering characteristic variables, economic factors, and highway engineering budget are established by least absolute shrinkage and selection operator (LASSO) regression. The empirical test results show that eight indexes have significant impact on the highway cost, including earthwork quantities, route length, number of lanes, bridge-tunnel ratio, protection works quantities, highway construction investment, producer price index (PPI) and purchasing manager's index (PMI) of construction industry. Compared with ordinary least-squares (OLS) regression and LASSO method alone, the GRA-LASSO combined method has better performance in error evaluation index and stronger model accuracy and generalization ability. The study result can be mainly used for government to rapidly audit, forecast, forewarn, monitor, and manage the highway engineering cost, and also help investors and designers to pay close attention to the influence of design parameters and external environment on cost. Moreover, it provides a reference method or model for establishing a highway engineering cost index. [ABSTRACT FROM AUTHOR]

Today, China is the second-largest, fastest-growing economy in the world. This study analyzes asymmetric and time-varying impact of world energy prices (including world energy prices index, world coal prices, world crude oil prices and world natural gas prices) on China's CO2 emissions. We used a non-linear ARDL (NARDL) model and wavelet analysis using monthly data from 1992 to 2017. The results based on the NARDL estimate show that world energy prices have an asymmetric impact on CO2 emissions. However, the results of wavelet pairwise correlation and wavelet-transform coherence suggest that the relationship between world energy prices and CO2 emissions differs over time and across sectors (i.e. short-term, medium-term, long-term and very long-term). Evidence suggests that ignoring fundamental non-linearities can lead to misleading outcomes. Such empirical results are expected to have a high importance for the efficient design and implementation of world energy prices and Chinese environmental policies. [ABSTRACT FROM AUTHOR]

Energy price is a key factor in reducing carbon emissions. This paper assesses the relationship between energy prices and carbon emissions from the industrial sector in China. Using panel data covering 31 industrial sectors for the period 2003 to 2015, we calculate the industrial energy price index and then estimate the effects of sector-level energy prices on carbon emissions based on a panel smooth transition regression model. The results show that the nexus between industrial energy prices and carbon emissions is nonlinear overall, and energy prices have a significantly negative effect on carbon emissions, while this negative effect gradually weakens with an increase in energy prices. Moreover, the negative effect of energy prices on carbon emissions is more significant in energy-intensive sectors when energy prices exceed the threshold value. The findings emphasize the importance of energy prices for energy conservation and emission reduction and imply that energy prices can be used as a regulation tool for government industrial energy saving and emission reduction. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to identify the impact of economic integration on trade competitiveness and demonstrate its effects on trade and investment performance of member nations. Design/methodology/approach: The study compiles some price indices to provide a systematic assessment of competitiveness in the BRICS region. The panel regression framework estimates the impact of integration on trade competitiveness and the external sector performance of BRICS nations. Findings: The findings of the research highlight the prospects for strong, closer and sustained integration in BRICS and, more importantly, the contribution of competitiveness to FDI receipts and export growth. Research limitations/implications: The assessment of exports and investment experiences of BRICS nations, particularly China and India, provides further evidence in support of the logical design and strategic use of their foreign trade policies. Originality/value: The economic partnership that wants to sustain this high road to global economic space needs strategic orientations to promote their partnership in other interest areas to make the cooperation more competitive in price terms. [ABSTRACT FROM AUTHOR]

• Regional and provincial level decomposition is estimated. • Three principles of decomposition and their nine combinations are considered. • Eight indexes are used to identify sub-regions and decompose emission reduction task. • Non-radial DDF is used to calculate the shadow price index. China has promised to reduce 60–65% of its 2005 carbon emission per GDP unit in 2030. This study aims to decompose China's emission reduction task to regional and provincial levels according to efficiency, equity, and synthesizing principles, respectively, during 1996–2015. Results show the following: (1) Through the cluster analysis of eight indexes including shadow price, China's provinces can be divided into three sub-regions; (2) In regional level, Sub-region 2 should take the largest carbon reduction proportion, accounting for about 60%; Sub-region 3 and Sub-region 1 should take 30% and 10% respectively;(3) On provincial level, the provinces that account for more than 5% of carbon dioxide emission reduction task of China are Shandong (9.33%), Shanxi (8.83%), Hebei (7.56%), Jiangsu (6.90%), Sichuan (6.40%), Guangdong (5.23%), and Inner Mongolia (5.20%);(4) Considering the emission reduction costs, it is best to decompose tasks according to the equity principle in the provincial level. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to investigate the extent to which Chinese equity investors can benefit from diversifying their portfolio into Shariah-compliant (Islamic) indices. It examines three Islamic stock indices (FTSE Shariah China price index, MSCI China Islamic IMI price index and the DJ Islamic Greater China price index) and ten sectoral indices in Shanghai Stock Exchange as a sample. Design/methodology/approach: The multivariate GARCH dynamic conditional correlations (MGARCH-DCC) is deployed to estimate the time-varying linkages of returns of the selected indices, covering approximately eight years daily data starting from 28 August 2009 to 29 September 2017. Findings: In general, in terms of volatility, the results indicate that all Islamic Indices are less volatile than the conventional indices. From the correlation analysis, the results imply that Chinese conventional equity investors would benefit from Islamic stock indices, especially when they include DJ Islamic Greater China in their portfolio. Originality/value: The findings of this paper may have several significant implications for the Chinese equity investors and fund managers for better understanding about co-movements of the Chinese conventional sectoral indices with the Shariah-compliant stock indices with the purpose of gaining higher risk-adjusted returns through portfolio diversification. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine farmer preference for swine price index insurance in China focusing on whether Chinese farmers are willing to consider purchasing swine price index insurance, the premium they would like to pay, as well as the extend of heterogeneity in their preferences. Design/methodology/approach: A sample of 443 swine farmers in Jiangsu and Henan provinces is collected and analyzed. An Ordered Probit model is used to analyze farmers' willingness to buy swine price index insurance and a Tobit model is used to analyze farmers' willingness to pay (WTP) for insurance premium. Findings: Results show that some farmers are not willing to purchase swine price index insurance. However, WTP of majority of farmers is higher than what is prescribed in the current insurance policy. Factors affecting farmers' willingness to buy varied between two provinces. Experience in purchasing traditional swine insurance and risk perception affect farmers' willingness to buy in Jiangsu province, while joining agricultural cooperatives, experience in purchasing traditional swine insurance and understanding of swine price index insurance affect farmers' willingness to buy in Henan province. Farmers with non-agricultural income, longer years of swine breeding, higher degree of specialization, experience in purchasing traditional insurance, higher understanding of swine price index insurance and trust in local governments, stronger risk perception and risk preference, and not being a member of agricultural cooperatives have higher WTP. Originality/value: Few studies have been conducted on swine price index insurance in China. Even less information, to the authors' knowledge, is available on farmer preferences. The research provides a timely contribution to understand the Chinese swine price index insurance market from the perspectives of farmers. [ABSTRACT FROM AUTHOR]

Since the Chinese national carbon trading market was launched in 2017, the carbon trading price has become an important research topic. This study constructs the 'China carbon trading price index', and then a SVAR model with the China carbon trading price index, the EU carbon trading price index, an industrial index, the China Securities Index energy index (CSI), an air quality index (AQI) and the HS300 to study carbon trading prices in China. The result shows that the EU carbon trading price and AQI have a direct effect on China carbon trading price. Meanwhile, the CSI energy index, industrial index and HS300 have an indirect effect on the carbon trading price, and the effect is slightly positive. In addition, the volatility of China's carbon trading price is mainly internally driven, while the volatility of the other economic variables examined is mostly driven by the EU carbon trading price index and the industrial index. [ABSTRACT FROM AUTHOR]

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Under the background of increasing the price index of China's stock market, the revised EMD denoising method and BEKK-GARCH model are used to study the fluctuation spoillover effect before, during and after the financial crisis. For the high synchronization features of stock price and huge energy of trading noise, the improved EMD denoising method is used to denoise the industrial data, and then the BEKK-GARCH model is used to estimate the spoillover effect coefficients. The results show that the improved EMD denoising method has better denoising effect than the wavelet denoising method, the spoillover effect between the industries surges during the financial crisis and then falls, the factors of development of the traditional industries before and after the financial crisis have changed. [ABSTRACT FROM AUTHOR]

The article focuses on the use of price indices with standard estimation procedure to determine an efficable price level and money supply in China from 1954-1983. China has a centrally planned economy in which production levels and prices are controlled by the government. It is noted that since 1979 industries in China have been permitted by the government to keep a portion of their profits for their own use, including investments that they manage. The role of the People's Bank of China and its dual role as a commercial and central bank and as a credit provider is discussed.

Addressing price forecasting problems is an important exercise to policymakers and market participants in the resource business sector. In this work, we build Gaussian process regression models through cross validation and Bayesian optimizations over different kernels and basis functions for daily price index forecasting of ten major steel products in the Chinese market during July 20, 2011–April 15, 2021. This study aims to be the first attempt of exploring potential of Gaussian process regressions for price forecasting exercises with the coverage of all of the ten most important steel products that carry enormous economic significance in China as the largest steel consumer and producer in the world. The models offer accurate out-of-sample forecasts for the two-year period of April 16, 2019–April 15, 2021 with relative root mean square errors ranging from 0.07404% to 0.22379% across the ten price indices and correlation coefficients above 99.9%. They also lead to better forecast performance than some traditional econometric models and some other machine learning models as benchmarks. The models constructed here could be utilized by policymakers as part of policy design and implementations and by market participants as part of market assessments and decision making. • Gaussian process regression models are built to forecast steel price indices. • The models are trained with Bayesian optimizations and cross validation. • Price indices of ten major steel products in China are examined. • The models generate relative root mean square errors of below 0.25% for all indices. [ABSTRACT FROM AUTHOR]

This paper studies the True Cost of Living Index (TCLI) of food consumption for all income groups of urban households in China from 1997 to 2012. Based on the Generalised Fechner-Thurstone direct utility function, this paper estimates the TCLI under variable preference assumptions, and also decomposes the total bias of the traditional Laspeyres Price Index (LPI) estimation of TCLI into substitution bias and preference variation bias. For all income group households, the estimated preference variation bias is found to be quite substantial. According to the finding, when it is necessary to apply the cost of living adjustment clause in any public welfare programmes, the TCLI calculation compatible with preference variation may be more appropriate than the usual Laspeyres Consumer Price Index estimation. [ABSTRACT FROM AUTHOR]

Big cities often witness land price outgrowing structure price. For such cities this paper derives two predictions regarding the dynamics between house prices, rent and structure age. First, older houses have a higher price growth rate than younger ones, even after controlling for location and other attributes; second, the age depreciation of house price, defined as the decline of house price with respect to house age, is slower than the similarly-defined age depreciation of rent. These hypotheses are supported by the micro-data on housing market in Beijing. These two inferences have implications for both real estate valuation and house price index construction. [ABSTRACT FROM AUTHOR]

The rapid growth of China's economy has increased China's demand for imports required to meet the increased demand for raw materials in its industries. In 2006, China became the largest export destination for South Africa. This paper seeks to examine the relationship between China's economic growth and South Africa's exports to China. The paper aims to find out whether there is any significant relationship between China's economic growth and South Africa's exports to China. Using an import demand function for China, we employ an ARDL framework to analyze the research problem based on quarterly data covering the period 1992 to 2015. We find no evidence that there is a significant relationship between China's economic growth and South Africa's exports to China. However, the results do show that there is a positive long-run relationship between South Africa's exports to China and the price of South Africa's exports. The positive coefficient of South Africa's export prices indicates lack of price sensitivity in terms of South Africa's exports to China. Based on the results, we recommend that South Africa should find ways to enable its exports sector to benefit more from the high economic growth in China. Among other ways, this could be achieved through negotiating a free trade agreement (FTA) between South Africa and China. [ABSTRACT FROM AUTHOR]

This bibliometric analysis provides the first overview of the literature on emerging contaminants in coastal waters. Bibexcel, Microsoft Excel, VOSviewer and the bibliometix R package software were used for bibliometric analysis and science mapping. 556 publications, published between 1986 and 2022, were retrieved from the Web of Science (WoS) database. The results showed that this field of research follows Price's law of exponential growth and has exploded since 2001. Authors, journals and institutions followed Lotka's law, and Price's index (43.4 %) showed that the field is far from being obsolete. The most influential institution is the Chinese Academy of Sciences, while authors from China, USA, Japan and Singapore have the most publications and international co-authorships. The leading journal is Marine Pollution Bulletin, with a focus on Environmental Science WoS category. Hot research areas focus on risk assessment of PCBs, organochlorine compounds and personal care products in surface water and wastewater. [Display omitted] • A bibliometric analysis on emerging contaminants in coastal waters was conducted. • The field has exploded since 2001, following Price's law of exponential growth. • China, USA, and Japan have the most documents. • The productive journal on this topic was Marine Pollution Bulletin. • Assessment of the risk of ECs in surface water and wastewater is urgently needed. [ABSTRACT FROM AUTHOR]