Towards a Systematic Survey for Carbon Neutral Data Centers
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Abstract (arXiv)
Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8% of global carbon emissions by 2030. However, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Oct 2021.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 2.
7 papers.
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applicationscited by 3
- 2024Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Modelscited by 3
- 2025Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenterscited by 3
- 2024Towards Green AI: Current status and future researchcited by 2
- 2025Energy Management for Renewable-Colocated Artificial Intelligence Data Centerscited by 0
- 2026GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Searchcited by 0
- 2026Concentrated siting of AI data centers drives regional power-system stress under rising global compute demandcited by 0
- 2026Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand13 Mar 2026
- 2026GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Search17 Feb 2026
- 2025Energy Management for Renewable-Colocated Artificial Intelligence Data Centers4 Jul 2025
- 2025Sustainable Carbon-Aware and Water-Efficient LLM Scheduling in Geo-Distributed Cloud Datacenters29 May 2025
- 2024Towards Green AI: Current status and future research1 May 2024
- 2024Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models28 Apr 2024
- 2023Approximate Computing Survey, Part II: Application-Specific & Architectural Approximation Techniques and Applications20 Jul 2023