Environment and Financial Performance: The Case of the Canadian Oil Industry
Bibliographic record
Abstract
Environmental and social responsibility has taken center stage in the corporate world. Companies operating in polluting industries are faced with stakeholders’ growing demand for environmental governance and are expected to account publicly for their environmental and social actions in annual financial statements and sustainability reports. ISO14001 and OHSAS18001: 18001 certification systems measure environmental and social performance and foster consensus-making between companies and their stakeholders. Researchers, however, are still divided as to the significance and scope of the relationship between environmental/social and financial performance. We measure this relationship in a cross-section of businesses in the Canadian oil industry.Working with a sample of 23 certified companies consisting of six organizations identified as socially responsible by Corporate Knights magazine, a publication that issues a list of the best 50 corporate citizens in Canada, and 17 companies not recognized by the magazine, we assess the effect of certification on financial performance as measured by the companies’ ROA over five-year pre- and post-certification periods. Regressions of post-certification on pre-certification ROA are performed. The results show that certification is economically beneficial for companies, and more so for those regarded as socially and environmentally responsible.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".