Journal of Modern Mathematics and Statistics
ISSN: OnlineISSN: Print 1994-5388
Abstract
The problems associated with global warming, ranging from increase in global temperature change in agricultural yields, glacier retreat, species extinction, increase in the ranges of diseases and disease vectors were reviewed. These underscore the need to reduce emission which causes global warming. The proposed method of emission reduction is by emission trading according to the Kyoto protocol. If this proposal holds for countries to participate actively, it is important to build a model for estimating their level of CO2 emission. The aim of this study is to develop an exploratory model of global warming, using CO2 emission as a surrogate. This was done using regression analysis and principal component analysis to explore some possible factors that could cause global warming and to know their actual contributions. The regression analysis result with a p<0.001 indicates that CO2 emission is related to some of the input variables used. However due to the effect of multicollinearity among the variables used, supervised principal component regression analysis was used and the result of the analysis shows that model built on this method gave a good fit.
How to cite this article:
Igwenagu Chinelo Mercy. Statistical Modeling of Global Warming.
DOI: https://doi.org/10.36478/jmmstat.2013.41.46
URL: https://www.makhillpublications.co/view-article/1994-5388/jmmstat.2013.41.46