The COVID-19 pandemic: opportunity or challenge for climate change risk disclosure?
Bibliographic record
Abstract
Purpose The purpose of this paper is to reflect on how climate change risk reporting might evolve in various world regions in the post COVID-19 pandemic era. Design/methodology/approach Using a multiple-case study approach and adopting an institutional theory lens, we assess whether the pandemic is likely to strengthen or weaken institutional pressures for climate change risk disclosures and predict how climate-related risk reporting will evolve post-pandemic. Findings The authors find that climate change risk reporting is likely to evolve differently according to geographical location. The authors predict that disclosure levels will increase in regions with ambitious climate policy and where economic stimulus packages support sustainable economic recovery. Where there has been a weakening of environmental commitments and economic stimulus packages support resource intensive business, climate change risk reporting will stagnate or even decline. The authors discuss the scenarios for climate change risk reporting expected to play out in different parts of the world. Originality/value The authors contribute to the nascent literature on climate change risk disclosure and identify future directions in the wake of the COVID-19 pandemic.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".