Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries

Global warming due to the rapid increase in greenhouse gas emissions, mainly carbon dioxide (CO2), is a worldwide issue that leads to escalating pollutions and emerging diseases. This study applies regression analysis to examine interrelationship among the determinants of CO2 emissions. The compa...

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Main Author:	Tay, Sze Hui
Format:	Thesis
Language:	English
Published:	2015
Subjects:	GE Environmental Sciences Q Science (General)
Online Access:	http://ir.unimas.my/id/eprint/13969/1/Tay%20SH.pdf
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spelling	my-unimas-ir.139692023-08-24T04:37:25Z Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries 2015 Tay, Sze Hui GE Environmental Sciences Q Science (General) Global warming due to the rapid increase in greenhouse gas emissions, mainly carbon dioxide (CO2), is a worldwide issue that leads to escalating pollutions and emerging diseases. This study applies regression analysis to examine interrelationship among the determinants of CO2 emissions. The comparative performances of multiple regression (MR) and multivariate adaptive regression splines (MARS) for modelling CO2 emissions in ASEAN countries over the period of 1980-2007 are discussed. The regression models are fitted individually for every potential variable investigated so as to find the best-fit parametric or non-parametric regression model. The results show a significant difference between the performance of MR and MARS models with the inclusion of interaction terms. The MARS model is computationally feasible and has better predictive ability than the MR model in predicting CO2 emissions. Overall, MARS can be viewed as a modification of stepwise regression that improves the latter’s performance in the regression setting. MARS is better apt to model situations that involve a large number of variables or a high degree of interaction among the independent variables. In this study, two scenarios are considered for the forecasting of CO2 emissions for the year 2008-2020. The forecasts of CO2 emissions are computed based on MARS model using the lagged values of the influential variables obtained from vector autoregression in the first scenario and the average percentage changes of each influential variable in the second scenario. The mean absolute percentage error (MAPE) for Scenario 1 and 2 are 1.24% and 3.60% respectively. Scenario 1 has the smaller value of MAPE, indicating that it reflects the actual data better than Scenario 2. It is projected that there is an overall increasing trend and the total CO2 emissions, on average, will vary between 51.57 and 62.63 metric tons per capita in the ASEAN Ten based on a 95% confidence interval. The estimated figures show that the amount of CO2 released in ASEAN countries by the year 2020 will be more than double as compared with the 2007 level. Universiti Malaysia Sarawak, (UNIMAS) 2015 Thesis http://ir.unimas.my/id/eprint/13969/ http://ir.unimas.my/id/eprint/13969/1/Tay%20SH.pdf text en validuser masters Universiti Malaysia Sarawak, (UNIMAS) Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	GE Environmental Sciences Q Science (General)
spellingShingle	GE Environmental Sciences Q Science (General) Tay, Sze Hui Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
description	Global warming due to the rapid increase in greenhouse gas emissions, mainly carbon dioxide (CO2), is a worldwide issue that leads to escalating pollutions and emerging diseases. This study applies regression analysis to examine interrelationship among the determinants of CO2 emissions. The comparative performances of multiple regression (MR) and multivariate adaptive regression splines (MARS) for modelling CO2 emissions in ASEAN countries over the period of 1980-2007 are discussed. The regression models are fitted individually for every potential variable investigated so as to find the best-fit parametric or non-parametric regression model. The results show a significant difference between the performance of MR and MARS models with the inclusion of interaction terms. The MARS model is computationally feasible and has better predictive ability than the MR model in predicting CO2 emissions. Overall, MARS can be viewed as a modification of stepwise regression that improves the latter’s performance in the regression setting. MARS is better apt to model situations that involve a large number of variables or a high degree of interaction among the independent variables. In this study, two scenarios are considered for the forecasting of CO2 emissions for the year 2008-2020. The forecasts of CO2 emissions are computed based on MARS model using the lagged values of the influential variables obtained from vector autoregression in the first scenario and the average percentage changes of each influential variable in the second scenario. The mean absolute percentage error (MAPE) for Scenario 1 and 2 are 1.24% and 3.60% respectively. Scenario 1 has the smaller value of MAPE, indicating that it reflects the actual data better than Scenario 2. It is projected that there is an overall increasing trend and the total CO2 emissions, on average, will vary between 51.57 and 62.63 metric tons per capita in the ASEAN Ten based on a 95% confidence interval. The estimated figures show that the amount of CO2 released in ASEAN countries by the year 2020 will be more than double as compared with the 2007 level.
format	Thesis
qualification_level	Master's degree
author	Tay, Sze Hui
author_facet	Tay, Sze Hui
author_sort	Tay, Sze Hui
title	Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
title_short	Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
title_full	Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
title_fullStr	Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
title_full_unstemmed	Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries
title_sort	comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in asean countries
granting_institution	Universiti Malaysia Sarawak, (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology
publishDate	2015
url	http://ir.unimas.my/id/eprint/13969/1/Tay%20SH.pdf
_version_	1783728144486760448

Comparison between multiple regression and multivariate adaptive regression splines for modelling and forecasting co2 emmissions in Asean countries

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