Do higher financial returns lead to better environmental performance in North America’s forest products sector?
Bibliographic record
Abstract
This study examines the relation between corporate environmental performance and corporate financial (economic) performance in North America’s forest products industry to determine whether there is a firm-level environmental Kuznets curve (EKC). An unbalanced panel of firm-level observations is constructed using data from PricewaterhouseCoopers, the US Environmental Protection Agency, and Environment Canada. The analysis focuses on methanol and formaldehyde emissions because these are the only pollutants for which consistent firm-level data are available in forestry. We find strong evidence of a firm-level EKC. The evidence is considerably weaker if endogeneity related to the effect of past pollution on current pollution or endogeneity resulting from a possible circular relationship between rate of return and pollution is taken into account, although the available time horizon is too short to conclude that endogeneity is a problem. Even so, there remains evidence of a negative relationship between financial performance and environmental performance for formaldehyde.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".