The Economic Impact of Environmentally Responsible Practices
Bibliographic record
Abstract
ABSTRACT The objective of this paper is to present a dynamic analysis of the relationship between environmental (EP) and financial performance (FP). More precisely, we have analysed this relationship by considering the measurement of EP lagged by one, two, and three periods. The introduction of lagged variables at both an aggregate and non‐aggregate level, aims to capture the effects of EP on FP over time. Our results show that the aggregate measure of the lagged EP has a persistent positive effect on FP, extended over three years. This effect appears to be more marked for large size companies, for companies with low risk levels and for those spending less on investment. Results for non‐aggregate measurements reveal an asymmetric relationship between FP and KLD's concerns score, of which the impact is negative and persistent, and between FP and KLD's strengths score, where the effect is positive and limited to one year. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".