Prediction models for CO2 emission in Malaysia using best subsets regression and multi-linear regression

C. H. Tan; M. Z. Matjafri; H. S. Lim

doi:10.1117/12.2195442

14 October 2015 Prediction models for CO₂ emission in Malaysia using best subsets regression and multi-linear regression

C. H. Tan, M. Z. Matjafri, H. S. Lim

Author Affiliations +

Proceedings Volume 9638, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2015; 963812 (2015) https://doi.org/10.1117/12.2195442
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

This paper presents the prediction models which analyze and compute the CO₂ emission in Malaysia. Each prediction model for CO₂ emission will be analyzed based on three main groups which is transportation, electricity and heat production as well as residential buildings and commercial and public services. The prediction models were generated using data obtained from World Bank Open Data. Best subset method will be used to remove irrelevant data and followed by multi linear regression to produce the prediction models. From the results, high R-square (prediction) value was obtained and this implies that the models are reliable to predict the CO₂ emission by using specific data. In addition, the CO₂ emissions from these three groups are forecasted using trend analysis plots for observation purpose.

Citation Download Citation

C. H. Tan, M. Z. Matjafri, and H. S. Lim "Prediction models for CO₂ emission in Malaysia using best subsets regression and multi-linear regression", Proc. SPIE 9638, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2015, 963812 (14 October 2015); https://doi.org/10.1117/12.2195442

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