{"title":"Exploring carbon emissions, economic growth, energy and R&D investment in China by ARDL approach","authors":"Shiyan Zhai, Genxin Song","doi":"10.1109/Geoinformatics.2013.6626205","DOIUrl":null,"url":null,"abstract":"This paper examines the causal relationship among economic growth, energy structure, R&D investment and carbon emission in China by using autoregressive distributed lag bounds testing approach of cointegration during the period of 1990-2011. In order to examine this linkage, the authors use the two-step procedures. Firstly, the authors conducted the unit root tests to measure whether the single integrated of time series is not more than 1. Secondly, the authors explore the long-run relationships between the variables by using ARDL bounds testing approach complemented by Johansen-Juselius maximum likelihood procedure in a multivariate framework. The findings are as follows:when carbon emissions and economic growth, respectively, are the dependent variable, the other independent variables show the long-term stability cointegration relationship of the dependent variable. Whether in the short-run or long-run relationship, the impact of economic growth and R&D investment on carbon emission is not statistically significant. In the long term and short term relationships, carbon emissions have a positive impact on the economic growth. However, energy structure has a negative impact on the economic growth. The decrease in energy structure will cause carbon emissions reduction and boost economic growth in both the long-run and short-run period. Therefore, China's government should give more attention to the optimization of energy structure and make a reasonable and feasible energy saving policy.","PeriodicalId":286908,"journal":{"name":"2013 21st International Conference on Geoinformatics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 21st International Conference on Geoinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Geoinformatics.2013.6626205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
This paper examines the causal relationship among economic growth, energy structure, R&D investment and carbon emission in China by using autoregressive distributed lag bounds testing approach of cointegration during the period of 1990-2011. In order to examine this linkage, the authors use the two-step procedures. Firstly, the authors conducted the unit root tests to measure whether the single integrated of time series is not more than 1. Secondly, the authors explore the long-run relationships between the variables by using ARDL bounds testing approach complemented by Johansen-Juselius maximum likelihood procedure in a multivariate framework. The findings are as follows:when carbon emissions and economic growth, respectively, are the dependent variable, the other independent variables show the long-term stability cointegration relationship of the dependent variable. Whether in the short-run or long-run relationship, the impact of economic growth and R&D investment on carbon emission is not statistically significant. In the long term and short term relationships, carbon emissions have a positive impact on the economic growth. However, energy structure has a negative impact on the economic growth. The decrease in energy structure will cause carbon emissions reduction and boost economic growth in both the long-run and short-run period. Therefore, China's government should give more attention to the optimization of energy structure and make a reasonable and feasible energy saving policy.