Environmental Certifications as Strategic Assets? Evidence from Italian Chemical and Pharmaceutical Firms
Bibliographic record
Abstract
Environmental sustainability reporting is increasingly adopted by firms, yet its actual impact on economic performance remains unclear, raising the question of whether such disclosures represent genuine strategic resources or merely symbolic practices. This study examines the relationship between environmental disclosure and economic performance, in the Italian chemical and pharmaceutical industries. Adopting the Resource-Based View (RBV), we evaluate the effectiveness of certified environmental practices as strategic assets that can enhance firm performance. We utilized an AI-based content analysis of financial reports from non-listed, non-SME Italian chemical and pharmaceutical companies between 2012 and 2020 to determine the level of firms’ generic environmental disclosures (without third-party verification) and on specific environmental certifications. We then examine the relationship between economic performance and the type of environmental disclosure observed. Using financial data at the firm level as moderators, we found that generic environmental disclosures have no significant impact on economic performance. In contrast, disclosures on environmental certifications are positively associated with higher economic performance in the chemical sector. Certifications may provide a competitive advantage in environmentally intensive sectors but appear to be less relevant in innovation-driven sectors such as the pharmaceutical industry. Our findings emphasize the strategic value of reliable, externally validated environmental practices, and highlight the limitations of symbolic disclosure.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".