{"title":"Research on GGDP Evaluation System Based MRA-GPR Analysis","authors":"","doi":"10.25236/ajcis.2023.061011","DOIUrl":null,"url":null,"abstract":"This paper focuses on demonstrating that GGDP is a better indicator of a country's economic health than GDP. In this study, seven variables were selected as secondary indicators, and then CO<sub>2</sub> emissions were used as dependent variables, and the identified seven variables were used as independent variables for multiple regression. In this regression, significant and robust results are obtained, which can prove that using GGDP as a macroeconomic variable is more environmentally friendly. This paper also uses the cross validation method to train the Gaussian process regression model, and obtains better regression results (R<sup>2</sup>= 99.9 %, RMSE = 1.364e + 5).","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Computing & Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25236/ajcis.2023.061011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper focuses on demonstrating that GGDP is a better indicator of a country's economic health than GDP. In this study, seven variables were selected as secondary indicators, and then CO2 emissions were used as dependent variables, and the identified seven variables were used as independent variables for multiple regression. In this regression, significant and robust results are obtained, which can prove that using GGDP as a macroeconomic variable is more environmentally friendly. This paper also uses the cross validation method to train the Gaussian process regression model, and obtains better regression results (R2= 99.9 %, RMSE = 1.364e + 5).