Displacing uncomfortable carbon accounting knowledge: how avoided emission models justify the status quo
Bibliographic record
Abstract
Purpose This paper answers calls for an in-depth, critical evaluation of carbon accounting practices by examining the missing link between knowing about and acting on carbon emissions. It explores how managers may decide to ignore uncomfortable absolute carbon emission calculations and instead develop home-made carbon accounting models of avoided emissions in order to support the status quo. Design/methodology/approach This in-depth single case study based on 23 interviews and substantial non-participant observation (28 days) builds on the notion of displacement as a discursive mode of ignorance to better understand why knowledge generated by Carbon Accounting Tools (CAT) can be considered uncomfortable and may in fact encourage the status quo. Findings The case study shows that the voluntary production of carbon accounting calculations is not always synonymous with improved carbon emission performance. Focusing on home-made carbon accounting models, instead of on uncomfortable absolute carbon accounting calculations, can have a negative, rather than a positive, effect on environment-friendly decisions. Furthermore, in the case examined, this decision was not viewed favorably throughout the company, with some employees expressing their unease that the company had merely replaced an uncomfortable metric with a more favorable one. Originality/value The study builds on the concepts of displacement and uncomfortable knowledge to argue that CAT that avoid creating tensions with a company’s economic growth objectives have little impact on promoting sustainable practices. By using carbon accounting models to focus on avoided emissions, managers can deliberately move attention away from uncomfortable absolute carbon accounting calculations, thereby legitimating the status quo.
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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 | 0.057 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".