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Predicting the carbon dioxide emissions using machine learning

There are severe impacts and consequences to humans, societies, and the environment due to global warming. Though there are various activities that contributes to global warming, the major contributor is carbon dioxide (CO2) emissions. Human activities release large amounts of carbon dioxide from th...

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Main Authors: Geevaretnam, Jothi Letchumy, Megat Mohd. Zainuddin, Norziha, Kamaruddin, Norshaliza, Rusli, Hazlifah, Maarop, Nurazean, Wan Hassan, Wan Azlan
Format: Article
Language:English
Published: Penerbit UTM Press 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/108834/1/NorzihaMegat2022_PredictingtheCarbonDioxideEmissions.pdf
http://eprints.utm.my/108834/
http://dx.doi.org/10.11113/ijic.v12n2.369
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spelling my.utm.1088342024-12-11T09:30:07Z http://eprints.utm.my/108834/ Predicting the carbon dioxide emissions using machine learning Geevaretnam, Jothi Letchumy Megat Mohd. Zainuddin, Norziha Kamaruddin, Norshaliza Rusli, Hazlifah Maarop, Nurazean Wan Hassan, Wan Azlan T Technology (General) There are severe impacts and consequences to humans, societies, and the environment due to global warming. Though there are various activities that contributes to global warming, the major contributor is carbon dioxide (CO2) emissions. Human activities release large amounts of carbon dioxide from the burning of fossil fuels, such as oil, gas, or coal in producing energy. Net zero is the new ambition of industries in balancing the CO2 emissions in environment. Thus, this study finds the best predictive model for CO2 emissions using machine learning model with the dataset of CO2 emissions from 1991 until 2020. Machine Learning techniques is an efficient approach to study the CO2 emissions prediction and has been very appealing to few research. The dataset is split into a train-test (estimation-validation) set with 80% train set and 20% test set (80:20) proportion. The predictive model was developed using Random Forest, Support Vector Machine and Artificial Neural Network algorithms with different parameters to get the outcome. The predictive model's performance was evaluated based on the error measurement metric of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Its reveals that Support Vector Machine with linear kernel function is the best model among others which produces 65.7254 Mean Absolute Error (MAE), 112.2196 Root Mean Square Error (RMSE) and 0.2279% Mean Absolute Percentage Error (MAPE) from the train set. For industries committed to net zero carbon emissions, this analysis will be an advising factor on the prediction system to find the CO2 emissions and how much fossil fuels’ reduction is required in achieving net zero carbon emission by 2050. Penerbit UTM Press 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/108834/1/NorzihaMegat2022_PredictingtheCarbonDioxideEmissions.pdf Geevaretnam, Jothi Letchumy and Megat Mohd. Zainuddin, Norziha and Kamaruddin, Norshaliza and Rusli, Hazlifah and Maarop, Nurazean and Wan Hassan, Wan Azlan (2022) Predicting the carbon dioxide emissions using machine learning. International Journal of Innovative Computing, 12 (2). pp. 17-23. ISSN 2180-4370 http://dx.doi.org/10.11113/ijic.v12n2.369 DOI : 10.11113/ijic.v12n2.369
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Geevaretnam, Jothi Letchumy
Megat Mohd. Zainuddin, Norziha
Kamaruddin, Norshaliza
Rusli, Hazlifah
Maarop, Nurazean
Wan Hassan, Wan Azlan
Predicting the carbon dioxide emissions using machine learning
description There are severe impacts and consequences to humans, societies, and the environment due to global warming. Though there are various activities that contributes to global warming, the major contributor is carbon dioxide (CO2) emissions. Human activities release large amounts of carbon dioxide from the burning of fossil fuels, such as oil, gas, or coal in producing energy. Net zero is the new ambition of industries in balancing the CO2 emissions in environment. Thus, this study finds the best predictive model for CO2 emissions using machine learning model with the dataset of CO2 emissions from 1991 until 2020. Machine Learning techniques is an efficient approach to study the CO2 emissions prediction and has been very appealing to few research. The dataset is split into a train-test (estimation-validation) set with 80% train set and 20% test set (80:20) proportion. The predictive model was developed using Random Forest, Support Vector Machine and Artificial Neural Network algorithms with different parameters to get the outcome. The predictive model's performance was evaluated based on the error measurement metric of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Its reveals that Support Vector Machine with linear kernel function is the best model among others which produces 65.7254 Mean Absolute Error (MAE), 112.2196 Root Mean Square Error (RMSE) and 0.2279% Mean Absolute Percentage Error (MAPE) from the train set. For industries committed to net zero carbon emissions, this analysis will be an advising factor on the prediction system to find the CO2 emissions and how much fossil fuels’ reduction is required in achieving net zero carbon emission by 2050.
format Article
author Geevaretnam, Jothi Letchumy
Megat Mohd. Zainuddin, Norziha
Kamaruddin, Norshaliza
Rusli, Hazlifah
Maarop, Nurazean
Wan Hassan, Wan Azlan
author_facet Geevaretnam, Jothi Letchumy
Megat Mohd. Zainuddin, Norziha
Kamaruddin, Norshaliza
Rusli, Hazlifah
Maarop, Nurazean
Wan Hassan, Wan Azlan
author_sort Geevaretnam, Jothi Letchumy
title Predicting the carbon dioxide emissions using machine learning
title_short Predicting the carbon dioxide emissions using machine learning
title_full Predicting the carbon dioxide emissions using machine learning
title_fullStr Predicting the carbon dioxide emissions using machine learning
title_full_unstemmed Predicting the carbon dioxide emissions using machine learning
title_sort predicting the carbon dioxide emissions using machine learning
publisher Penerbit UTM Press
publishDate 2022
url http://eprints.utm.my/108834/1/NorzihaMegat2022_PredictingtheCarbonDioxideEmissions.pdf
http://eprints.utm.my/108834/
http://dx.doi.org/10.11113/ijic.v12n2.369
_version_ 1818834055728201728
score 13.223943

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