A comparative analysis to forecast carbon dioxide emissions
<p dir="ltr">Despite the growing knowledge and commitment to climate change, carbon dioxide (CO<sub>2</sub>) emissions continue to rise dramatically throughout the planet. In recent years, the consequences of climate change have become more catastrophic and have attracted...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2022
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513536512753664 |
|---|---|
| author | Md. Omer Faruque (17545671) |
| author2 | Md. Afser Jani Rabby (17545674) Md. Alamgir Hossain (1371456) Md. Rashidul Islam (11636491) Md Mamun Ur Rashid (11587099) S.M. Muyeen (15746160) |
| author2_role | author author author author author |
| author_facet | Md. Omer Faruque (17545671) Md. Afser Jani Rabby (17545674) Md. Alamgir Hossain (1371456) Md. Rashidul Islam (11636491) Md Mamun Ur Rashid (11587099) S.M. Muyeen (15746160) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Omer Faruque (17545671) Md. Afser Jani Rabby (17545674) Md. Alamgir Hossain (1371456) Md. Rashidul Islam (11636491) Md Mamun Ur Rashid (11587099) S.M. Muyeen (15746160) |
| dc.date.none.fl_str_mv | 2022-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.egyr.2022.06.025 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_comparative_analysis_to_forecast_carbon_dioxide_emissions/24720357 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning CO2 emissions Forecasting Deep learning FMOLS CNN–LSTM |
| dc.title.none.fl_str_mv | A comparative analysis to forecast carbon dioxide emissions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Despite the growing knowledge and commitment to climate change, carbon dioxide (CO<sub>2</sub>) emissions continue to rise dramatically throughout the planet. In recent years, the consequences of climate change have become more catastrophic and have attracted widespread attention globally. CO<sub>2</sub> emissions from the energy industry have lately been highlighted as one of the world’s most pressing concerns for all countries. This paper examines the relationships between CO<sub>2</sub> emissions, electrical energy consumption, and gross domestic product (GDP) in Bangladesh from 1972 to 2019 in the first section. In this purpose, we applied the fully modified ordinary least squares (FMOLS) approach. The findings indicate that CO<sub>2</sub> emissions, electrical energy consumption, and GDP have a statistically significant long-term cointegrating relationship. Developing an accurate CO<sub>2</sub> emissions forecasting model is crucial for tackling it safely. This leads to the second step, which involves formulating the multivariate time series CO<sub>2</sub> emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to analyze and compare the performances of the predictive models. The prediction errors in MAPE of the CNN, CNN–LSTM, LSTM, and DNN are 15.043, 5.065, 5.377, and 3.678, respectively. After evaluating those deep learning models, a multivariate polynomial regression has also been employed to forecast CO<sub>2 </sub>emissions. It seems to have nearly similar accuracy as the LSTM model, having a MAPE of 5.541.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Reports<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.06.025" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.06.025</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fbc1760c2c0ecf09198bdb62f54ae90c |
| identifier_str_mv | 10.1016/j.egyr.2022.06.025 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24720357 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A comparative analysis to forecast carbon dioxide emissionsMd. Omer Faruque (17545671)Md. Afser Jani Rabby (17545674)Md. Alamgir Hossain (1371456)Md. Rashidul Islam (11636491)Md Mamun Ur Rashid (11587099)S.M. Muyeen (15746160)EngineeringElectrical engineeringEnvironmental engineeringInformation and computing sciencesMachine learningCO2emissionsForecastingDeep learningFMOLSCNN–LSTM<p dir="ltr">Despite the growing knowledge and commitment to climate change, carbon dioxide (CO<sub>2</sub>) emissions continue to rise dramatically throughout the planet. In recent years, the consequences of climate change have become more catastrophic and have attracted widespread attention globally. CO<sub>2</sub> emissions from the energy industry have lately been highlighted as one of the world’s most pressing concerns for all countries. This paper examines the relationships between CO<sub>2</sub> emissions, electrical energy consumption, and gross domestic product (GDP) in Bangladesh from 1972 to 2019 in the first section. In this purpose, we applied the fully modified ordinary least squares (FMOLS) approach. The findings indicate that CO<sub>2</sub> emissions, electrical energy consumption, and GDP have a statistically significant long-term cointegrating relationship. Developing an accurate CO<sub>2</sub> emissions forecasting model is crucial for tackling it safely. This leads to the second step, which involves formulating the multivariate time series CO<sub>2</sub> emissions forecasting challenges considering its influential factors. Based on multivariate time series prediction, four deep learning algorithms are analyzed in this work, those are convolution neural network (CNN), CNN long short-term memory (CNN–LSTM), long short-term memory (LSTM), and dense neural network (DNN). The root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to analyze and compare the performances of the predictive models. The prediction errors in MAPE of the CNN, CNN–LSTM, LSTM, and DNN are 15.043, 5.065, 5.377, and 3.678, respectively. After evaluating those deep learning models, a multivariate polynomial regression has also been employed to forecast CO<sub>2 </sub>emissions. It seems to have nearly similar accuracy as the LSTM model, having a MAPE of 5.541.</p><h2>Other Information</h2><p dir="ltr">Published in: Energy Reports<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.06.025" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.06.025</a></p>2022-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.egyr.2022.06.025https://figshare.com/articles/journal_contribution/A_comparative_analysis_to_forecast_carbon_dioxide_emissions/24720357CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247203572022-11-01T00:00:00Z |
| spellingShingle | A comparative analysis to forecast carbon dioxide emissions Md. Omer Faruque (17545671) Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning CO2 emissions Forecasting Deep learning FMOLS CNN–LSTM |
| status_str | publishedVersion |
| title | A comparative analysis to forecast carbon dioxide emissions |
| title_full | A comparative analysis to forecast carbon dioxide emissions |
| title_fullStr | A comparative analysis to forecast carbon dioxide emissions |
| title_full_unstemmed | A comparative analysis to forecast carbon dioxide emissions |
| title_short | A comparative analysis to forecast carbon dioxide emissions |
| title_sort | A comparative analysis to forecast carbon dioxide emissions |
| topic | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning CO2 emissions Forecasting Deep learning FMOLS CNN–LSTM |