Long-run performance following corporate green bond issuance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to investigate the long-run financial and environmental performance of corporate green bond issuers, worldwide. Design/methodology/approach The data includes 259 corporate green bond issuers from 2013 to 2020. The authors adopt the matching approach, using the nearest neighbor method to select the control firms. The event-time approach is used to examine corporate green bond issuers’ long-run stock market performance, and robustness tests are conducted using the calendar-time method. The authors examine green bond issuers’ long-run environmental performance and carbon dioxide (CO 2) emissions using difference-in-differences estimations. Findings In contrast with the earlier long-run event studies, our results reveal that multiple-time issuers, and issuers operating in industries where the natural environment is financially material, perform financially in the long term relative to the control firms. The authors also document that corporate green bond issuers reduce their CO 2 emissions, and improve their resource use efficiency and environmental performance, in the long run. Originality/value To the authors’ knowledge, this is the first study that looks at the long-run effect of corporate green bond issuance on firms’ stock market performance. It has the particularity to document that corporate green bond issuance is beneficial for investors and positively affects the environment. Our findings help us understand that firms do not issue green bonds for greenwashing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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 it