Climate change disclosure ratings: the ideological play
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it