False Stability? How Greenwashing Shapes Firm Risk in the Short and Long Run
Bibliographic record
Abstract
This study examines the relationship between greenwashing and firm risk among listed Australian firms from 2014 to 2023. We construct a firm-level greenwashing score as the residual based on regressions of composite ESG on Scope 1–2 CO2 emissions; positive residuals indicate overstated sustainability relative to emissions. Using realized volatility as a measure of firm risk and applying the Generalized Method of Moments (GMM) regression framework, we uncover three key findings. First, contemporaneous greenwashing significantly lowers volatility, which is consistent with legitimacy and signalling theory, as overstated ESG credentials create a temporary perception of stability. Second, the risk-reducing effect is strongest with a one-period lag, likely reflecting delayed ESG and emissions reporting cycles and investor reaction times. Third, by the two-period lag, the effect reduces in magnitude, suggesting that markets eventually recognize the misalignment between ESG claims and environmental performance. Robustness checks with the E-pillar confirm these dynamics. Additional tests excluding the COVID-19 period (2020 and 2021) reveal that the risk-mitigating effects of greenwashing are even stronger during normal market conditions, implying that pandemic-related volatility may have muted the signalling power of ESG narratives. While firm fundamentals (e.g., book-to-market) explain part of risk variation, greenwashing-driven effects are economically meaningful yet short-lived. The findings underscore that greenwashing offers only temporary risk mitigation; as transparency improves and regulatory enforcement strengthens, firms relying on inflated ESG narratives face diminishing benefits and potential long-term risk penalties.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".