Artificial Intelligence in Carbon Emissions Monitoring and Reduction: A Systematic Review
DOI:
https://doi.org/10.61424/dtmkwk09Keywords:
Artificial intelligence, carbon emissions, emissions monitoring, carbon reduction, machine learning;, climate change mitigationAbstract
Artificial intelligence (AI) is increasingly recognized as a transformative technology for monitoring carbon emissions, improving emissions accounting, and supporting strategies for climate change mitigation. This systematic review examines the applications of AI in carbon emissions monitoring and reduction, focusing on its principal technologies, implementation approaches, potential benefits, and associated challenges. The review synthesizes existing literature on machine learning, deep learning, computer vision, natural language processing, and AI-enabled remote sensing in carbon monitoring across energy systems, transportation, industrial production, buildings, and land-use activities. The findings indicate that AI can enhance the accuracy and timeliness of emissions estimation, identify emission patterns, forecast future carbon outputs, optimize energy consumption, and support data-driven decisions for reducing greenhouse gas emissions. AI-powered smart grids, intelligent transportation systems, predictive industrial maintenance, satellite-based emissions detection, and energy-efficient building management demonstrate the potential of these technologies to contribute to decarbonization. However, their effectiveness depends on data quality, model transparency, computational requirements, interoperability, financial accessibility, and the availability of reliable emissions data. Furthermore, the energy consumption of AI infrastructure and the risk of inaccurate or biased predictions highlight the need to evaluate its net environmental benefits. The review emphasizes the importance of integrating AI with robust carbon accounting frameworks, transparent governance mechanisms, and established climate policies. It concludes that AI offers significant opportunities to strengthen carbon emissions monitoring and reduction, but its contribution to climate mitigation depends on responsible implementation, continuous model validation, and coordinated action among governments, industries, researchers, and technology developers.
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