Impacts of Emissions Trading Scheme Initiatives on Corporate Carbon Proactivity and Financial Performance
Bibliographic record
Abstract
This study introduces the concept of carbon proactivity and considers not only the quantity of emissions but also corporate carbon-reduction efforts and actions to explore the relationship between carbon proactivity, the emissions trading scheme (ETS) mechanism, and corporate financial performance. A matched-pair approach was adopted to explore the difference in carbon proactivity between ETS and non-ETS firms. The study aims to investigate the impacts of an ETS on corporate carbon proactivity and whether participating in an ETS can help a firm achieve a desired outcome in which it can improve both environmental and economic performance. Using manually collected data on carbon disclosure, it was found that carbon proactivity is higher among firms that participate in an ETS than among those that do not, and carbon proactivity is trending upward for the participating firms. In addition, evidence suggests that while investing more resources in carbon proactivity decreases current financial performance, it will boost future financial performance. This relationship is observed among firms that participate in an ETS. This study extends the understanding of the relationship between ETSs, corporate carbon proactivity, and corporate financial performance. It also provides evidence on how to improve the ETS mechanism.
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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 | 0.007 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".