From Data to Decarbonization: An AI-Driven Approach to Tracking Digital Carbon Footprints

Abstract

As the digital landscape rapidly evolves, the environmental cost of cloud computing, AI model training, and internet usage has become an urgent yet often overlooked concern. This study investigates digital carbon-footprints the quantifiable impact of digital activities on carbon emissions by leveraging AI and data science to track, analyze, and predict energy consumption patterns. The study employs advanced machine learning models, including LSTM, GRU, and transformers, to forecast cloud energy demands, while classification algorithms like XGBoost and Random Forest pinpoint high-impact digital services. Anomaly detection techniques, such as Isolation Forests and Autoencoders, identify unexpected energy spikes, and reinforcement learning strategies optimize server resource allocation to reduce emissions. A distinctive feature of this research is the development of an interactive, real-time dashboard built using Streamlit and Tableau, offering dynamic visualizations of CO2 emissions and energy usage trends. Beyond merely assessing current environmental impacts, this project proposes actionable insights and AI-driven optimizations, guiding businesses, cloud providers, and policymakers toward sustainable digital practices. By merging AI innovation with environmental accountability, this study not only raises awareness about the hidden carbon costs of the digital world but also empowers stakeholders to make data-informed, eco-conscious decisions paving the way for a more sustainable technological future.

Introduction

The rapid expansion of cloud computing has significantly increased global energy consumption and carbon emissions, with data centers alone using nearly 1% of worldwide electricity. Existing sustainability solutions largely rely on retrospective analysis and lack real-time monitoring, forecasting, and anomaly detection, limiting proactive carbon reduction efforts.

To address this gap, the paper proposes a Cloud Carbon Analytics (CCA) framework that integrates real-time monitoring, machine learning–based prediction, anomaly detection, and interactive visualization to improve cloud sustainability. The framework uses models such as Random Forest Regressor for emission prediction, Isolation Forest for anomaly detection, SARIMA for trend forecasting, along with clustering and simulation techniques for workload optimization and risk analysis. Key sustainability drivers—such as Power Usage Effectiveness (PUE), renewable energy integration, and AI workload balancing—are analyzed to optimize cloud operations.

The CCA platform collects real-time data from cloud APIs and sensors, applies preprocessing and feature engineering, and delivers insights through dynamic dashboards. Results show measurable improvements, including reductions in energy consumption (15%), carbon emissions (20%), and cooling costs, alongside increased renewable energy usage and improved PUE. Geographical and provider-based analyses further support efficient infrastructure planning and resource allocation.

Conclusion

In this study, extensive Cloud Carbon Analytics model with real-time data collection, predictive analysis, and machine learning techniques have been used to evaluate and reduce the carbon impact of cloud infrastructure. The system utilizes sophisticated technologies like Random Forest Regressor, Isolation Forest, and SARIMA to reduce energy consumption and ease environmental burdens. Key sustainability metrics like Power Usage Effectiveness (PUE), renewable energy integration, and workload distribution via AI provide quantifiable data for cloud infrastructure optimization. Utilization of an interactive visualization dashboard ensures enhanced transparency and decision-making as well as empowers stakeholders with data-driven models of sustainability. The paper lays down the capabilities of AI-powered carbon analytics in making cloud computing a sustainable process. Cloud Carbon Analytics is a stretchable solution to resolve faults in measuring, forecasting, and streamlining the carbon footprint of using cloud services. Using real-time reports and automated suggestions, the tool enables business companies to decide and make an optimum reduction of carbon usage with ease. With increasing cloud infrastructure, there is a need for action toward sustainability and thinking out of the box to decrease the carbon footprint and green the digital world.

References

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Copyright

Copyright © 2025 Priyansha Sachdev, Taruna Sharma, Supriya Kumari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.