Carbon and water conservation value of independent, place‐based repair in Lima, Peru
Bibliographic record
Abstract
Abstract To what extent do repair and maintenance of consumer electronics conserve the materials and energy they embody? In this paper we examine the conservation value of a cluster of independent third‐party electronics repair businesses in Lima, Peru. Drawing on a combination of methods that include fieldwork, digital methods for online sociology, and life cycle assessment (LCA) of phones and tablets we quantify the conservation value of typical repairs performed at businesses in this cluster in terms of CO 2 equivalent (CO 2 e) and water consumption relative to new manufactures of the same categories of electronics. We model typical repair scenarios and find that repair can offer substantial conservation benefits. However, these benefits vary by device sub‐unit repaired (e.g., replacing a camera vs. replacing a display). For example, while two screen repairs through replacement is nearly equivalent to replacement with a whole new device, repairing with components that are already in the market could save around 10% of total emissions in global warming potential (GWP) for both devices. Further, we discuss the politics of attributing the conservation value achieved by the third‐party repair cluster in Lima to either domestic (that is, Peruvian) or foreign CO 2 e and water consumption. Whose conservation of CO 2 e and water is this? How do the answers to that question shape understandings of the relevance of location for industrial ecology? Our work contributes to the emerging subfield of political industrial ecology and its incorporation of spatially explicit LCAs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".