Using life cycle assessment to drive innovation for sustainable cool clouds
Bibliographic record
Abstract
Addressing climate change requires accelerating the development of sustainable alternatives to energy- and water-intensive technologies, particularly for rapidly growing infrastructure such as data centres and cloud1. Here we present a life cycle assessment study examining the impacts of advanced cooling technologies on cloud infrastructure, from virtual machines to server architecture, data centre buildings and the grid. Life cycle assessment is important for early-stage design decisions, enhancing sustainability outcomes alongside feasibility and cost analysis2. We discuss constructing a life cycle assessment for a complex cloud ecosystem (including software, chips, servers and data centre buildings), analysing how different advanced cooling technologies interact with this ecosystem and evaluating each technology from a sustainability perspective to provide adoption guidelines. Life cycle assessment quantifies the benefits of advanced cooling methods, such as cold plates and immersion cooling, in reducing greenhouse gas emissions (15–21%), energy demand (15–20%) and blue water consumption (31–52%) in data centres. This comprehensive approach demonstrates the transformative potential of life cycle assessment in driving sustainable innovation across resource-intensive technologies. A life cycle assessment study is used to examine the impacts of advanced cooling technologies on cloud infrastructure, from virtual machines to server architecture, data centre buildings and the grid.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".