Industrial wastewater discharge in China is increasing with the country′s economic development and it is worthy of concern. The discharge is primarily relevant to the direct discharge coefficient of each sector of th...Industrial wastewater discharge in China is increasing with the country′s economic development and it is worthy of concern. The discharge is primarily relevant to the direct discharge coefficient of each sector of the economy, its direct input coefficient and the final demand in input-output models. In this study, we calculated the sensitivity of the reduction in the Chinese industrial wastewater discharge using the direct input coefficients based on the theory of error-transmission in an input-output framework. Using input-output models, we calculated the direct and total industrial wastewater discharge coefficients. Analysis of 2007 input-output data of 30 sectors of the Chinese economy and of 30 provincial regions of China indicates that by lowering their direct input coefficients, the manufacturers of textiles, paper and paper products, chemical products, smelting and metal pressing, telecommunication equipment, computers and other electronic equipment will significantly reduce their amounts of industrial wastewater discharge. By lowering intra-provincial direct input coefficients to industrial sectors themselves of Jiangsu, Shandong and Zhejiang, there will be a significant reduction in industrial wastewater discharge for the country as a whole. Investment in production technology and improvement in organizational efficiency in these sectors and in these provinces can help lessen the direct input coefficients, thereby effectively achieving a reduction in industrial wastewater discharge in China via industrial restructuring.展开更多
Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by t...Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by the National Bureau of Statistics of the People's Republic of China according to well-defined conventions.However,county-level 10tables are not provided as a rule by official statistics organizations.This paper conducts an overview of compiling EEIO tables for environmental and resources accounting at the county level and then answers several questions:First,what kind of data should be prepared for the compilation of county-level EEIO tables?Second,how can we set up comprehensive EEIO tables at the county level?Third,regarding the survey methods and the indirect modeling,which one should be chosen to build EEIO tables at the county level?Finally,what policy questions could such a table answer?EEIO tables at the county level can be used to predict the economic impacts of environmental policies and to perform trend and scenario analysis.展开更多
Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the au...Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.展开更多
基金Under the auspices of Key Program of Chinese Academy of Sciences(No.KZZD-EW-06-02)National Natural Science Foundation of China(No.41201129)Humanities and Social Science Research Planning Fund,Ministry of Education of China(No.13YJAZH042)
文摘Industrial wastewater discharge in China is increasing with the country′s economic development and it is worthy of concern. The discharge is primarily relevant to the direct discharge coefficient of each sector of the economy, its direct input coefficient and the final demand in input-output models. In this study, we calculated the sensitivity of the reduction in the Chinese industrial wastewater discharge using the direct input coefficients based on the theory of error-transmission in an input-output framework. Using input-output models, we calculated the direct and total industrial wastewater discharge coefficients. Analysis of 2007 input-output data of 30 sectors of the Chinese economy and of 30 provincial regions of China indicates that by lowering their direct input coefficients, the manufacturers of textiles, paper and paper products, chemical products, smelting and metal pressing, telecommunication equipment, computers and other electronic equipment will significantly reduce their amounts of industrial wastewater discharge. By lowering intra-provincial direct input coefficients to industrial sectors themselves of Jiangsu, Shandong and Zhejiang, there will be a significant reduction in industrial wastewater discharge for the country as a whole. Investment in production technology and improvement in organizational efficiency in these sectors and in these provinces can help lessen the direct input coefficients, thereby effectively achieving a reduction in industrial wastewater discharge in China via industrial restructuring.
基金supported by the Key Project of the Chinese Academy of Sciences[grant number KZZD-EW-08]the Exploratory Forefront Project for the Strategic Science Plan in IGSNRR,CAS
文摘Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by the National Bureau of Statistics of the People's Republic of China according to well-defined conventions.However,county-level 10tables are not provided as a rule by official statistics organizations.This paper conducts an overview of compiling EEIO tables for environmental and resources accounting at the county level and then answers several questions:First,what kind of data should be prepared for the compilation of county-level EEIO tables?Second,how can we set up comprehensive EEIO tables at the county level?Third,regarding the survey methods and the indirect modeling,which one should be chosen to build EEIO tables at the county level?Finally,what policy questions could such a table answer?EEIO tables at the county level can be used to predict the economic impacts of environmental policies and to perform trend and scenario analysis.
基金supported by the Natural Science Foundation of CQ CSTC under Grant No.cstc.2018jcyj A2073Chongqing Social Science Plan Project under Grant No.2019WT59+3 种基金Science and Technology Research Program of Chongqing Education Commission under Grant No.KJZD-M202100801Mathematic and Statistics Team from Chongqing Technology and Business University under Grant No.ZDPTTD201906Open Project from Chongqing Key Laboratory of Social Economy and Applied Statistics under Grant No.KFJJ2022056Chongqing Graduate Research Innovation Project under Grant No.CYS23568。
文摘Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.
