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TRUVACE RECORD VERSION
record: TRV-2026-2498
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:43:37.392767Z
status: published
lens: p_space
sector: climate
headline: Beyond PUE: An integrated scenario assessment of electricity, cooling, polymer–elastomer demand, carbon, and circularity in AI data centers
dek: Artificial intelligence is transforming data centers into coupled energy–cooling–material systems that cannot be assessed adequately using electricity demand or power usage effectiveness (PUE) alone. This study develops a scenario-based screening framework for 2024–2035 that links institutional electricity pathways, H100 node-level calibration, facility-load decomposition, air/direct-to-chip/immersion cooling transitions, polymer–elastomer requirements, operational carbon, circularity, and uncertainty. Global el…
gain_title: (none)
problem_title: A scenario study projects global AI data-center electricity demand rising from 416 TWh in 2024 to 707–1,719 TWh in 2035, with efficiency gains (PUE of 1.13–1.22) outweighed by infrastructure growth.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A scenario study projects global AI data-center electricity demand rising from 416 TWh in 2024 to 707–1,719 TWh in 2035, with efficiency gains (PUE of 1.13–1.22) outweighed by infrastructure growth.
problem_evidence: Global electricity demand increases from 416 TWh in 2024 to 707–1,719 TWh in 2035 | infrastructure growth can dominate facility-efficiency gains
quick_read: A scenario-based screening study covering 2024–2035 projects that global AI data-center electricity demand will climb from 416 TWh in 2024 to between 707 and 1,719 TWh in 2035. Over the same period, power usage effectiveness is projected to improve to 1.13–1.22, yet the authors conclude that infrastructure growth can dominate facility-efficiency gains.

The work also looks at cooling and materials. In the Base pathway, cooling electricity reaches 123.1 TWh in 2035 and the central plastic requirement proxy is 344.8 kt; the Lift-Off pathway reaches 174.4 TWh and 525.3 kt. Stronger liquid-cooling adoption lowers cooling electricity but raises material intensity.

These are modelled projections, not measured outcomes. Material-coefficient bounds widen the plastic estimates to 235.1–470.2 kt (Base) and 358.2–716.3 kt (Lift-Off), and a 5,000-sample Latin Hypercube test gives a 2030 global range of 169.6–482.8 kt.
limitation: The figures are scenario-based screening estimates for 2024–2035, and uncertainty in material coefficients widens the plastic projections considerably.
tag: Evidence-backed problem
key_points: Projected global AI data-center electricity demand rises from 416 TWh in 2024 to 707–1,719 TWh in 2035 depending on pathway. | PUE is projected to improve to 1.13–1.22, but infrastructure growth can dominate facility-efficiency gains. | Base pathway: cooling electricity of 123.1 TWh and a central plastic requirement proxy of 344.8 kt in 2035. | Lift-Off pathway: cooling electricity of 174.4 TWh and 525.3 kt of plastic in 2035. | Material-coefficient bounds widen plastic estimates to 235.1–470.2 kt (Base) and 358.2–716.3 kt (Lift-Off). | A 5,000-sample Latin Hypercube analysis gives a P5–P95 plastic range of 169.6–482.8 kt for 2030 globally. | Stronger liquid-cooling adoption cuts cooling electricity but increases technology-weighted material intensity.
rundown: The framework links institutional electricity pathways, H100 node-level calibration, facility-load decomposition, and transitions between air, direct-to-chip and immersion cooling. It also covers polymer–elastomer requirements, operational carbon, circularity and uncertainty, rather than relying on electricity demand or PUE alone.

Results are given for Base and Lift-Off pathways. Cooling electricity reaches 123.1 TWh in the Base case and 174.4 TWh in Lift-Off by 2035, while the plastic requirement proxy reaches 344.8 kt and 525.3 kt respectively.

The authors identify a cooling energy–material trade-off: liquid cooling reduces cooling electricity while increasing material intensity. They conclude that sustainable AI infrastructure requires joint management of compute growth, cooling architecture, electricity decarbonization, material demand and circular supply.
sources:
- peer_reviewed | Results in Engineering | https://doi.org/10.1016/j.rineng.2026.113927 | 2026-11-02
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