The Influence of Firms’ Emissions Management Strategy Disclosures on Investors’ Valuation Judgments
Bibliographic record
Abstract
Recent accounting research indicates that capital markets price firms’ greenhouse gas (GHG) emissions and that disclosed emission levels are negatively associated with firms’ market values (Griffin et al. 2017; Matsumura et al. 2014). 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 non-professional investors perceive the firm to be more valuable when it primarily uses an operational change strategy versus an offsets strategy. However, consistent with theory (Flammer 2013), 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 non-professional 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 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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".