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AI and Machine Learning Advances

ISSN: 3067-3216

The AI and Machine Learning Advances Journal works towards becoming a leading journal for AI/ ML research findings. In this way, it performs a function of connecting academic, industrial, top machine learning algorithms and governmental researchers to exchange know-how and innovations that are shaping the development of intelligent systems at the present time.

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Real-Time Carbon Footprint Tracking for Data Sovereignty Compliance in Telecom Data Centers: Architecture, Implementation, and Policy Implications

1Babar Tariq

1 Department of Complex Deals, Digital Data Centers for Data and Telecommunications Company, Riyadh, Kingdom of Saudi Arabia

Received: 10-Dec-2025 | Revised: 22-Dec-2025 | Accepted: 24-Dec-2025

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Doi

https://doi.org/10.64220/amla.v2i1.002

Abstract

Problem: Telecom operators were subjected to unprecedented pressure caused by data sovereignty legislation and global climate requirements. The migration to 5G networks made the issue of managing distributed edge data centres in these conflicting regulatory domains even harder. Most of the current systems treated energy efficiency and compliance as isolated issues.

Purpose: The purpose of this paper was to create a framework, Carbon-Aware Sovereignty Enforcement (CASE) to bring real-time carbontracking and geographical data residency in balance.

Procedure: The mixed method involved the use of a system of architectural design and simulation modelling of a programme that was based on tier-1 providers telecom. The framework incorporated real time grid APIs and code as policy in order to automate twelve different jurisdictions of compliance. 

Findings: The deployment had a 34 percent carbon reduction in smart urban settings and a 28 percent weighted average reduction in all the experimental deployment settings. The compliance with sovereignty was 100 and the latency effect of real-time services was less than 7% only. The observed automated audit trail generated reduced manual compliance reporting activities by 85 percent.

Conclusion: The CASE model revealed that the legal compliance of environmental and sustainability ensured by integrated data orchestration was functional. The next research studies should deal with the scaling of this model to emerging network 6G architecture and quantum secure protocols.

Keywords

Environmental; Carbon Footprint; regulatory domains; CASE; emissions; real-time

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