Climate change disclosure ratings: the ideological play
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the impact of climate change rating organisations on rated firms, to understand whether disclosure ratings can facilitate enhanced emissions performance. Design/methodology/approach This study uses 1,848 cross-country firm-year observations from organisations that responded to the carbon disclosure project (the rater) between 2011 and 2015 and, hence, were rated for their disclosure. Drawing on the ideology of numbers, this paper hypothesises that the disciplinary power of ratings will result in rated firms improving their subsequent disclosure scores. Following the environmentally-friendly ideology, this study hypothesises that poorly-rated firms will adopt decoupling behaviour, by improving their climate change disclosure scores without reducing the intensity of their greenhouse gas (GHG) emissions. Findings The results indicate that climate change disclosure ratings pressure poorly-rated firms to improve their disclosure scores in subsequent years, yet these firms are not inclined to lower their GHG emissions. Further, the direct publication of firms’ GHG emissions intensity can exert some restricted disciplinary impact on rated firms, as the more polluting firms tend to improve their subsequent climate change performance compared with those having lower emissions levels. Practical implications This paper argues that the ability of corporate sustainability rating schemes to influence corporate behaviour comprehensively is limited and should be used with caution. Originality/value This paper sheds new light on the ideological dynamics at play between the rater and the rated, while highlighting new aspects of the power-rating nexus in the climate change arena.
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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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".