Rethinking complementarity: The <scp>co‐evolution</scp> of public and private governance in corporate climate disclosure
Bibliographic record
Abstract
Abstract In its 20 years of operation, the Carbon Disclosure Project (CDP) has been enormously successful as a private governor of corporate climate risk disclosure. Despite an influx of potentially competitive government‐led disclosure initiatives and interventions, the use of CDP's platform has nonetheless accelerated. To explain this outcome, we argue that public interventions augment the value of private governance for firms when the costs of compliance overlap, benefits of compliance with private rules are undiminished, and normalization helps kickstart positive feedback effects. These conditions of complementarity are made possible by private governors leveraging authority, access, and adaptability as public responses materialize. We illustrate our argument with two cases: the Non‐Financial Reporting Directive in the European Union and the G20's Task Force for Climate‐Related Financial Disclosures. In elaborating the conditions for complementarity beyond a functional division of governing labor, our study helps clarify how public and private governance co‐evolve in a mutually reinforcing manner.
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.
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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".