The Influence of Firms' Emissions Management Strategy Disclosures on Investors' Valuation Judgments
Bibliographic record
Abstract
ABSTRACT Recent accounting research indicates that capital markets price firms' greenhouse gas (GHG) emissions and that disclosed emissions levels are negatively associated with firms' market values. The departure point for this study is to investigate whether investors value firms differently based on the strategies firms use to mitigate GHG emissions. These strategies include making operational changes, which reduces emissions attributable to the firm, and purchasing offsets, which reduces emissions unattributable to the firm. Using an experiment, we hold constant a firm's financial performance, investment in emissions mitigation, and net emissions, and find evidence that nonprofessional investors perceive the firm to be more valuable when it primarily uses an operational change strategy versus an offsets strategy. However, consistent with theory, this result only occurs when the firm's prior sustainability performance is below the industry average and not when it is above the industry average. This difference in firm value is consistent with the notion that nonprofessional investors believe information about a firm's emissions management strategy is material. Supplemental exploratory analyses reveal that our results are mediated by investors' perception that an operational change strategy is more socially and environmentally responsible than an offsets strategy for below industry average firms. Implications for our findings on theory and practice are discussed.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".