An Association Study between Nation Carbon Dioxide Emissions and Changes in Stock Index of Taiwan

• Main Authors: Yi-Hui Wu, 吳怡慧
• Other Authors: Hua-Shan Tai
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/89364663106715529080

碩士 === 國立高雄第一科技大學 === 環境與安全衛生工程研究所 === 99 === Many countries are concerning the greenhouse effect and all types of greenhouse gas that carbon dioxide as the most, reduction of carbon dioxide has became the target all countries in the world. Previous studies found that most of the carbon dioxide emissions of the greenhouse effect have a certain relationship trend between the economic developments (Such as GDP, Gross Domestic Product, as the GDP). Between Carbon dioxide emissions and the stock index is less discussed, The stock index is most important indicator of economic development, therefore, relationship between carbon dioxide emissions and the stock index is really study value. In this study, both stock index and economic development (GDP) are the impact of variables, an appropriate model to predict the amount of carbon dioxide emissions The data period is 1989-2008, and analyzing by the linear regression model and ARIMA regression model. Assessment the results under in different combinations parameters conditions. To be used as an indicator of carbon dioxide emissions. The results are as below: 1. In four different combinations parameters conditions, GDP as independent variables, CO2 is best predicted by variables, only1.1585 ~ 1.1590%; while the GDP, twstock CO2as the independent variables as the dependent variable, twstock GDP as the dependent variable for the self-Second-best combination of variables to predict who is less than ideal results twstock as independent variables as the dependent variable CO2 2. GDP as the independent variables and CO2 as dependent variables, and to GDP, twstock as the independent variables as and CO2 the dependent variable, its predicted effect will increase as the change accurate estimation period; and twstock as the independent variables and GDP as the dependent variable, and twstock as the independent variables and CO2 as the dependent variable, the prediction effect is worse with the estimated increase in the period. But whether it is ARIMA regression model or linear regression model is predictive accuracy. 3. When prediction model parameters has a significant higher level , its efficiency will be better, and will increase during the period with the estimated will increase its efficiency. Conversely, if the prediction model has lower parameters significantly , its efficiency will be worse, and will increase with the estimated decrease during its efficiency. However, if the prediction model has lower parameters significantly is not always the worst forecast effect.