The Greening of Industry: Navigating the Nexus of Environmental Policies and Regulations, and Emission Abatement Strategies
Bibliographic record
Abstract
ABSTRACT Despite the prevailing discourses on the importance of environmental policy and regulation on emission control, related theoretical and empirical developments are lacking. Using institutional theory, we propose that environmental policy and regulation contribute to emission control by integrating environmental impact exposure into decision‐making processes. An empirical test of this theoretical framework was conducted using data from the World Bank Enterprise Survey on circular practices, which collected data from 18,734 firms. An analysis of emission control at the firm level indicates that environmental policy and regulation significantly influence emission control, and the degree of environmental exposure fully mediates their effects. This study presents a plausible theoretical account and empirical validation of a mechanism that enhances emission control strategies and decisions through environmental policy and regulation. It means that environmental policy and regulation do not solely affect the likelihood of carbon emissions from firms, but also the relationship is directly and indirectly influenced by the firm's environmental impact exposure.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".