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Net sustainability assessment of AI-driven data centers in the GCC: an MCDA approach framework
- Truvace record
- TRV-2026-2605raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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- a792fd4adbed…check it
The Gulf Cooperation Council (GCC) is witnessing a rapid growth in artificial intelligence (AI)-enabled data center development, thanks to national digital transformation strategies and the region’s strategic location bridging global markets. Yet, no evaluation framework holistically compares the direct impacts of data center operations on the environment with the dispersed sustainability impacts of AI applications across major business sectors. To address this, this study proposes and applies a Multi-Criteria D…
51Gain side
How the two sides scoreClose call
54Problem side
The gain
A scenario model of GCC data centers suggests AI applications in buildings, industry and utilities could offset part of the footprint, and net-positive outcomes appear only when renewable procurement, best-practice cooling and verified AI benefits combine.
The problem
A scenario model of GCC data centers suggests that under business-as-usual high growth, emissions rise substantially by 2035, with AI sustainability applications offering only partial offsets even under optimistic adoption assumptions.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
The score is a scenario-conditional indicator based on secondary data and assumed weights, not an absolute causal prediction; AI-benefit attribution, scaling and the capacity figures depend on assumptions and limited operator data.
Sources
1 on file- Frontiers in Environmental Sciencedoi:10.3389/fenvs.2026.1852462
Cite this record
v1Truvace Impact Record TRV-2026-2605, v1: “Net sustainability assessment of AI-driven data centers in the GCC: an MCDA approach framework.” Truvace, 2026-11-12. /record/TRV-2026-2605 (accessed on the day you copy it). sha256 a792fd4adbedbffa…
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1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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