The Impact of Corporate Biodiversity Information Disclosure on Green Investment Confidence and Willingness of Retail Investors in China: The Moderating Roles of Risk Aversion and Climate Risk Awareness
Bibliographic record
Abstract
The growing emphasis on environmental sustainability and green finance has intensified the need for effective corporate disclosures, particularly regarding biodiversity. Despite the increasing relevance of biodiversity in global investment strategies, there remains a significant research gap in understanding how corporate biodiversity information disclosure influences retail investors, particularly in emerging markets such as China. Based on this, in order to fill this research gap, this study conducts an empirical analysis using valid sample data from 464 retail investors in China and the structural equation modeling method. The results indicate that: (1) Corporate biodiversity information disclosure (CD) has a positive impact on investors’ investment confidence (IC) and investment willingness (IW). (2) Investors’ IC positively influences their IW. (3) Risk aversion (QA) weakens (negatively moderates) the effect of CD on enhancing investors’ IC. (4) QA also weakens (negatively moderates) the effect of CD on promoting investors’ IW. (5) Climate risk awareness (CA) positively moderates the effect of CD on enhancing investors’ IC. (6) CA also positively moderates the effect of CD on promoting investors’ IW. This study enriches relevant theories by emphasizing how psychological factors influence investment behavior and provides important insights for companies, policymakers, and financial intermediaries to promote sustainable investment practices.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".