Life cycle assessment of computers and electronic devices: A comprehensive review of environmental impacts
Bibliographic record
Abstract
The objective of this study was to evaluate Life Cycle Assessment studies applied to computers with respect to temporal and geographical distribution, research subjects, impact assessment methods, databases and software utilized, and principal findings. To achieve this, a bibliographical search was conducted using the Web of Science Core Collection database platform, using the keywords "Life cycle assessment" "AND" "computer" for title topic. The search yielded 12 publications selected from 28, for which a descriptive analysis was performed. It was determined that most of the studies (six in total) took place between 2011 and 2020. Geographically, the majority of these studies were conducted in Asia and the USA/Canada, with six studies in each region. The majority of studies (6) have aimed to evaluate the environmental impacts of computers (desktop/all-in-one). Regarding the impact assessment methods employed, there is considerable variation among ReciPe (2), Ecoindicator (2), IPCC (2), and CML (1), although some studies have utilized multiple methods. The primary findings indicate significant environmental benefits from adopting newer, energy-efficient technologies (APCs and LCDs), improving formal e-waste management, and focusing on recycling and sustainable manufacturing. Proper End-of-Life handling and the minimization of informal disposal also yield substantial environmental gains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".