Corporate Governance and Climate Change: Smoothing Temporal Dissonance to a Phased Approach
Bibliographic record
Abstract
SUMMARY Projections for climate change extend decades into the future, and usually to the end of this century due to the long-lived nature of greenhouse gases (GHGs). Predominant normative frameworks for corporate governance are primarily short-term in nature, creating a temporal dissonance within the context of corporate governance and climate change. Adding to this complexity, the energy transition itself has temporal paradoxes and implications for the global economy – the transition away from fossil fuels cannot be too sudden and sharp, but an urgent yet stable, phased transition is required. Statutory interventions in the UK have imposed on directors the requirement to consider the long-term profitability of companies. New initiatives, such as the task force on climate related disclosures (TCFD), the Enterprise Principles, and the Oxford-Martin Principles, also advocate for directors to consider the risks from climate change, including emissions scenarios which take into account short-, medium- and long-term scenarios. It is by using a phased approach to climate risk that a smoothing of this temporal dissonance between corporate governance and climate change can be initiated by businesses. While many of these new governance initiatives do not yet provide the requisite level of specificity to demonstrate how a phased approach could be adopted by particular companies, the TCFD guidance does provide some tools which would allow companies to adopt a phased approach, however the types and levels of detail of these tools should be increased for a variety of types of industry.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".