CO2 Emissions Projections of the North American Cement Industry

Forecasting carbon dioxide (CO2) emissions has become a relevant issue. International organizations have emphasized the necessity of generating a plan to gradually reduce the concentrations of this pollutant to combat climate change. Cement industries represent one of the key sectors expected to solve this problematic. The objective of this study is to predict CO2 emissions for North American cement industries. To achieve this, a multi-objective mathematical model is developed, integrating various machine learning algorithms. The results demonstrate a considerable improvement in accuracy metrics, with a 48.13% reduction in Mean Absolute Error achieved using the Generalized Reduced Gradient method (GRG). The forecasts reveal an increment in emissions from about 0.58 MtCO2 every year between 2020 and 2050. The proposed framework can help decision makers and policy makers focus on the technical and logistics requirements to meet net-zero emissions targets.

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Published
2024-10-17
DOI
https://doi.org/10.3390/engproc2024076019
Primary Topic
Environmental Impact and Sustainability
Type
article
Field-Weighted Citation Impact
0.00

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article

CO2 Emissions Projections of the North American Cement Industry

Golam Kabir, Seyedmehdi Mirmohammadsadeghi, Ángel Francisco Galaviz Román
Environmental Impact and Sustainability
article

CO2 Emissions Projections of the North American Cement Industry

Golam Kabir, Seyedmehdi Mirmohammadsadeghi, Ángel Francisco Galaviz Román
article en

Abstract

Forecasting carbon dioxide (CO2) emissions has become a relevant issue. International organizations have emphasized the necessity of generating a plan to gradually reduce the concentrations of this pollutant to combat climate change. Cement industries represent one of the key sectors expected to solve this problematic. The objective of this study is to predict CO2 emissions for North American cement industries. To achieve this, a multi-objective mathematical model is developed, integrating various machine learning algorithms. The results demonstrate a considerable improvement in accuracy metrics, with a 48.13% reduction in Mean Absolute Error achieved using the Generalized Reduced Gradient method (GRG). The forecasts reveal an increment in emissions from about 0.58 MtCO2 every year between 2020 and 2050. The proposed framework can help decision makers and policy makers focus on the technical and logistics requirements to meet net-zero emissions targets.

University of Regina (CA)
University of Regina, Mitacs
Openalex Percentile: Top 84%
Environmental Impact and Sustainability
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