Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics

Climate change, primarily driven by CO2 emissions from energy and non-energy sectors, necessitates effective mitigation strategies. This study develops a stochastic diffusive model to capture the complex dynamics of CO2 concentration, human population growth, and energy production. The objectives ar...

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Main Authors: Muhammad Shoaib Arif, Kamaleldin Abodayeh, Hisham M. Al-Khawar, Yasir Nawaz
Format: Article
Language:English
Published: Ital Publication 2025-02-01
Series:Emerging Science Journal
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Online Access:https://ijournalse.org/index.php/ESJ/article/view/2786
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author Muhammad Shoaib Arif
Kamaleldin Abodayeh
Hisham M. Al-Khawar
Yasir Nawaz
author_facet Muhammad Shoaib Arif
Kamaleldin Abodayeh
Hisham M. Al-Khawar
Yasir Nawaz
author_sort Muhammad Shoaib Arif
collection DOAJ
description Climate change, primarily driven by CO2 emissions from energy and non-energy sectors, necessitates effective mitigation strategies. This study develops a stochastic diffusive model to capture the complex dynamics of CO2 concentration, human population growth, and energy production. The objectives are to enhance the predictive accuracy of existing models by incorporating diffusion effects and stochastic variability, offering insights for sustainable environmental policies. A novel numerical scheme, an extension of the Euler-Maruyama algorithm, is proposed to solve stochastic time-dependent partial differential equations governing the model. The scheme's consistency and stability are rigorously analyzed in the mean square sense. Findings reveal that increasing emission rate coefficients in energy and non-energy sectors exacerbates CO2 levels, emphasizing the need for stringent controls. The proposed scheme demonstrates superior accuracy to the non-standard finite difference method, establishing its efficacy in modeling complex environmental processes. This research contributes a robust computational tool to improve existing predictive models, aiding decision-making for long-term ecological sustainability. By addressing uncertainties in the environmental process, the work advances the understanding of interactions between population growth, energy production, and CO2 emissions, offering a significant improvement over the traditional modeling approach. The novelty lies in integrating stochastic dynamics with diffusion to better inform CO2reduction strategies.   Doi: 10.28991/ESJ-2025-09-01-012 Full Text: PDF
format Article
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issn 2610-9182
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series Emerging Science Journal
spelling doaj-art-51ca4621454d404bae4682f4cebea1ec2025-02-08T14:26:27ZengItal PublicationEmerging Science Journal2610-91822025-02-019121022810.28991/ESJ-2025-09-01-012772Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy DynamicsMuhammad Shoaib Arif0Kamaleldin Abodayeh1Hisham M. Al-Khawar2Yasir Nawaz31) Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia. 2) Department of Mathematics, Air University, PAF Complex E-9, Islamabad, 44000, Pakistan.Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586,Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh, 11586,Department of Mathematics, Air University, PAF Complex E-9, Islamabad, 44000,Climate change, primarily driven by CO2 emissions from energy and non-energy sectors, necessitates effective mitigation strategies. This study develops a stochastic diffusive model to capture the complex dynamics of CO2 concentration, human population growth, and energy production. The objectives are to enhance the predictive accuracy of existing models by incorporating diffusion effects and stochastic variability, offering insights for sustainable environmental policies. A novel numerical scheme, an extension of the Euler-Maruyama algorithm, is proposed to solve stochastic time-dependent partial differential equations governing the model. The scheme's consistency and stability are rigorously analyzed in the mean square sense. Findings reveal that increasing emission rate coefficients in energy and non-energy sectors exacerbates CO2 levels, emphasizing the need for stringent controls. The proposed scheme demonstrates superior accuracy to the non-standard finite difference method, establishing its efficacy in modeling complex environmental processes. This research contributes a robust computational tool to improve existing predictive models, aiding decision-making for long-term ecological sustainability. By addressing uncertainties in the environmental process, the work advances the understanding of interactions between population growth, energy production, and CO2 emissions, offering a significant improvement over the traditional modeling approach. The novelty lies in integrating stochastic dynamics with diffusion to better inform CO2reduction strategies.   Doi: 10.28991/ESJ-2025-09-01-012 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2786stochastic diffusive modelconsistencystabilityexistenceco₂ emissions reductiondiffusion processesenvironmental uncertainty population dynamics.
spellingShingle Muhammad Shoaib Arif
Kamaleldin Abodayeh
Hisham M. Al-Khawar
Yasir Nawaz
Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
Emerging Science Journal
stochastic diffusive model
consistency
stability
existence
co₂ emissions reduction
diffusion processes
environmental uncertainty population dynamics.
title Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
title_full Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
title_fullStr Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
title_full_unstemmed Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
title_short Stochastic Diffusive Modeling of CO₂ Emissions with Population and Energy Dynamics
title_sort stochastic diffusive modeling of co₂ emissions with population and energy dynamics
topic stochastic diffusive model
consistency
stability
existence
co₂ emissions reduction
diffusion processes
environmental uncertainty population dynamics.
url https://ijournalse.org/index.php/ESJ/article/view/2786
work_keys_str_mv AT muhammadshoaibarif stochasticdiffusivemodelingofco2emissionswithpopulationandenergydynamics
AT kamaleldinabodayeh stochasticdiffusivemodelingofco2emissionswithpopulationandenergydynamics
AT hishammalkhawar stochasticdiffusivemodelingofco2emissionswithpopulationandenergydynamics
AT yasirnawaz stochasticdiffusivemodelingofco2emissionswithpopulationandenergydynamics