Corporate opposition to climate change disclosure regulation in the United States
Bibliographic record
Abstract
Extensive research shows that corporations tacitly resist climate change-related regulations even as they publicly espouse pro-climate strategies. In this study, we examine corporate responses to a major regulatory proposal by the U.S. Securities and Exchange Commission (SEC) to increase climate-related disclosures, which was made into law in March 2024. Corporate opposition to climate change disclosure regulation is measured by performing sentiment analysis, using GPT-3 from OpenAI, on comments and letters submitted by companies to the SEC on the proposed regulation. Analysis of data from 146 large corporations shows a positive average sentiment, indicating a statistically significant support for the proposed regulation. However, there are substantial variations across firms, with energy firms exhibiting the highest, and service firms the least, opposition. Opposition to climate change disclosure regulation was significantly greater in companies with higher Scope 1 greenhouse gas (GHG) emissions though no significant association was found for Scope 2 and 3 emissions. Companies with strong recent stock market performance, politically liberal boards, robust environmental disclosure practices, and sound sustainability governance were less opposed to the regulation. These results show that companies face mixed incentives that simultaneously increase the appeal and risk of climate change disclosures, reducing the efficacy of voluntary disclosure regimes.Key policy insights Sentiment analysis of letters submitted to the U.S. Securities and Exchange Commission reveals notable heterogeneities in corporate opposition to climate change disclosure regulation.Energy and service firms exhibit the highest and the lowest level of opposition, respectively, to climate change disclosure regulation.Corporate opposition is significantly greater in companies with higher Scope 1 GHG emissions but significantly lower in companies with higher stock market performance.Companies with politically liberal boards, robust environmental disclosure practices, and sound sustainability governance are less opposed to climate change disclosure regulation.Companies face conflicting pressures that confound the efficacy of voluntary corporate climate change disclosures.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".