Do Environmental Management Policies Decrease Carbon Emissions? A Probabilistic Analysis of G7 Countries
Bibliographic record
Abstract
This paper examines the effect of an Environmental Management System (ENVMSB) on carbon dioxide emissions reduction, taking into account the role of Greenhouse Gas emissions taxes (TaxGHG) as well as a Sustainability Committee (CSRSC) in an international sample. To test our hypotheses, we use a sample of 9944 firm-year observations in the G7 countries (Canada, France, Germany, Italy, Japan, United Kingdom and United States) from 2013 to 2022. The econometric approach uses a logistic regression panel data model. The empirical results show that both ENVMSB and CSRSC significantly and positively reduce carbon dioxide emissions. However, we did not find evidence of a significant effect of TaxGHG on carbon dioxide emissions reduction. Moreover, the probabilistic analysis of carbon reduction shows heterogeneity behavior by countries and sectors related to various national policies. industrial practices. and alignment with international environmental initiatives. including United Nations initiatives. These findings have important implications for managers and regulators concerned with firm ecological performance.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".