Comparing Embodied Greenhouse Gas Emissions of Modern Computing and Electronics Products
Bibliographic record
Abstract
Information and communications technology (ICT) contributes substantially to global greenhouse gas (GHG) pollutant emissions, but it is time-consuming to estimate the environmental impacts caused by the production of ICT devices, and the literature lacks coverage for newer products. Using a process-sum life cycle assessment (LCA) approach, we estimate and compare the embodied GHG emissions of 11 ICT products, including large- and small-form-factor desktop and laptop personal computers, a thin client device, an LCD monitor, newer mobile devices (an Apple iPad, an iPod Touch, and an Amazon Kindle), a rack server, and a network switch. Full bills of materials are provided via hand disassembly and weighing and are mapped to processes in the ecoinvent v2.2 database to produce impact estimates. Results are analyzed to develop simplified impact estimation models using linear regressions based on product characteristics. A simple and robust linear relationship between mass and embodied emissions is identified; a more sophisticated linear model using display mass, battery mass, and circuit board mass as inputs is slightly more accurate. Embodied GHG emissions for newer products are 50-60% lower than corresponding older products with similar functionality, largely due to decreased material usage, especially reductions in integrated circuit content.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".