Set & Done? Trade‐offs between Stakeholder Expectation and Attainment Pressures in Corporate Carbon Target Management
Bibliographic record
Abstract
Abstract Investors increasingly pressure firms for action on climate change and carbon emissions, in particular, by setting carbon targets. Whereas investors largely rely on quantitative information in evaluating this important aspect of non‐financial performance, scarce research has explored how firms may exploit the numerical magnitudes of carbon targets. We examine this possibility by analysing deceptive parameter changes in carbon targets after initial adoption, wherein firms create the perception of strengthening carbon targets while in reality loosening them, a novel form of decoupling. We theorize a U‐shaped relationship between the most conspicuous target parameter, target size (percentage emissions reduction), and the propensity for deceptive target change based on countervailing pressures – stakeholder expectation management and target attainment – that create tension. We further propose greater investor pressure over policy, through long‐term ownership and shareholder voice, exacerbates these pressures whereas media controversy that may prompt investor scrutiny over practice deters deceptive target changes. We find empirical support for our hypotheses and provide additional analyses that support our theorized latent, countervailing pressures and our characterization of deceptive change as decoupling.
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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 | 0.007 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".