On the corporate performance of issuers of various green assets
Bibliographic record
Abstract
• Green bonds and ABSs have a positive impact on corporate performance • Having a higher ESG rating and being a green issuer help improve profitability • Renewable energy and new energy vehicles companies outperform their peers • Government support for renewable energy is beneficial to the asset premium This study examines the corporate performance of green and brown asset issuers in China and the driving forces for the asset premium of green bonds, asset-backed securities (ABSs) and real estate investment trusts (REITs). Using quarterly data from 2016 to 2023 under a two-layered model structure, a significant positive impact is found on the corporate profitability of companies issuing green bonds and ABSs compared to brown securities, but insignificant results for green REITs. While green bonds help improve corporate performance, having green bonds and ABSs simultaneously is more conducive. A higher environmental, social, and governance (ESG) rating, an environmentally friendly issuer and an involvement in renewable energy or new energy vehicle businesses all have beneficial effects on the issuer's profitability regardless of the type of assets launched. Moreover, credit enhancement leads to a higher premium especially for corporate assets without a guarantee clause or frequent transactions. In summary, to boost the influence of green assets, companies should improve their ESG rating and green reputation, and the government should advance research and promotion of green financial instruments, improve information disclosure and risk evaluation, and advocate the renewable energy and new energy vehicle sectors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".