Pipeline Accidents and Incidents, Environmental Consciousness, and Financial Performance in the Canadian Energy Industry
Bibliographic record
Abstract
This study employs a balanced panel of data which consists of 1281 firm-year pipeline accidents and incidents at a disaggregate level and 190 firm-year pipeline events at an aggregate level for 19 firms during the observation period between 2007 and 2016. This study examines the relationships among environmental accidents and incidents, environmental consciousness, and financial performance. Given that environmental consciousness acts as an overarching environmental context on the relationship between the accidents, incidents, and financial performance and could be relevant to shareholders to identify the weight of these accidents and incidents, this study carefully investigates how environmental consciousness moderates the relationship between pipeline accidents, incidents, and financial performance. This study applies the theoretical assumption of both corporate social responsibility (CSR) and corporate social irresponsibility, both of which explain the relationship between financial performance and the events that positively or negatively affect stakeholders. This study employs nested regression analyses with the fixed effects model to test the time-series panel data. The results show that environmental consciousness has an expected significant negative effect on financial performance, whereas pipeline accidents and incidents have no expected negative effect on financial performance. One surprising finding is that pipeline accidents and incidents weighted with environmental consciousness present a significant positive relationship with financial performance, suggesting that potential contextual factors should be considered to explain such an unexpected finding.
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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 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".