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Data Process of Net-Zero Revolution for Transforming Earth and Beyond Sustainably

Author

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  • Samuel O. Afolabi

    (Department of Engineering, DN Colleges Group, Doncaster DN1 2RF, UK)

  • Idowu O. Malachi

    (New Brunswick Community College, Moncton Campus, Moncton, NB E1C 8H9, Canada)

  • Adebukola O. Olawumi

    (Department of Agriculture Sustainability, University of Ibadan, Ibadan 200001, Nigeria)

  • B. I. Oladapo

    (Sustainable Development, De Montfort University, Leicester LE1 9BH, UK
    School of Science and Engineering, Afe Babalola University, Ado Ekiti 360102, Nigeria)

Abstract

This research examines the strategic integration of Artificial Intelligence (AI) into global net-zero emissions strategies, with a focus on both terrestrial and extraterrestrial sustainability. The objectives include quantifying AI’s impact on reducing greenhouse gas (GHG) emissions, improving energy efficiency, and optimizing resource utilization, a particularly critical but underexplored domain. A mixed-methods approach was employed, comprising a systematic literature review, a meta-analysis of quantitative data, and case study evaluations. Advanced mathematical models, including logistic growth and optimization equations, were applied to predict trends and assess the effectiveness of AI. The results reveal that AI-driven innovations achieve emissions reductions of 15–30% across energy, transportation, and manufacturing sectors, with predictive maintenance optimizing energy utilization by 20% and extending equipment lifespans. AI-enabled smart grids improved energy efficiency by 26.7%, surpassing the 20% benchmark in prior studies. Specific applications include optimized fuel usage and predictive modeling, which can cut emissions by up to 20%. Quantitative data demonstrated significant cost savings of 20% across sectors. Statistical tests confirmed results with p -values < 0.05, indicating strong significance. This study underscores AI’s transformative potential in achieving net-zero goals by extending sustainability frameworks. It provides actionable insights for policymakers, industry leaders, and researchers, advocating for the broader adoption of AI to address global environmental challenges.

Suggested Citation

  • Samuel O. Afolabi & Idowu O. Malachi & Adebukola O. Olawumi & B. I. Oladapo, 2025. "Data Process of Net-Zero Revolution for Transforming Earth and Beyond Sustainably," Sustainability, MDPI, vol. 17(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5367-:d:1676210
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    1. Bartosz Jóźwik & Siba Prasada Panda & Aruna Kumar Dash & Pritish Kumar Sahu & Robert Szwed, 2025. "Revisiting the Nexus Between Energy Consumption, Economic Growth, and CO 2 Emissions in India and China: Insights from the Long Short-Term Memory (LSTM) Model," Energies, MDPI, vol. 18(15), pages 1-23, August.

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