Mobilizing Firms for Climate Action: Paris Climate Agreement and Corporate Green Bond Issuance
Bibliographic record
Abstract
This study examines the effect of the Paris Climate Agreement, as a plausibly exogenous shock, on the issuance of corporate green bonds (CGBs), a new financial instrument to address climate change in line with the goals of the Agreement. We argue that firms in industries that contribute most to climate change are potentially more affected by the Agreement because they perceive themselves as the target of the upcoming national environmental regulations derived from the Agreement. So firms in the affected industries are more likely to issue CGBs after the Agreement than firms in the minimally affected (or unaffected) industries. This differential effect becomes stronger among firms in countries with more media attention on environmental issues, but weaker in countries with more stringent legal enforcement. Using a proprietary cross-national dataset of CGBs and difference-in-differences estimation, we find strong support for our hypotheses. This study advances our understanding of the intersection between firms’ green financing as a form of self-regulation and the broader institutional context, and responds to the recent call for more research on integrating multiple pressures that jointly shape firms’ environmental behaviors.
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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 | 0.016 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".