Using life cycle assessment to drive innovation for sustainable cool clouds
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it