Bibliographic record
Abstract
Engaging green investors is essential for firms seeking a green transformation. A considerable amount of research has investigated the factors influencing green investor entry (GIE) into a firm. However, the impact of financial technology (Fintech) on GIE has been inadequately studied. This study empirically examines the influence of Fintech on GIE and its underlying mechanisms, using data from Chinese A-share listed firms from 2011 to 2020. The findings indicate that the advancement of Fintech in a city significantly enhances the ability of firms within its jurisdiction to recruit GIE. This conclusion remains strong despite the implementation of robustness checks and the consideration of endogeneity issues. Further analysis reveals that the positive effect of Fintech on GIE is more pronounced in non-state-owned enterprises, mature enterprises, and heavily polluting enterprises. Mechanism studies demonstrate that Fintech enhances GIE by mitigating financing constraints and improving enterprises' environmental governance performance and the quality of corporate information disclosure. Finally, our findings offer data and a reference for governments to entice green investors via the advancement of Fintech. • We examine the impact of Fintech on green investors entry (GIE) using Chinese A-share listed firms’ data between 2011 and 2020. • Fintech in the cities where enterprises headquartered can significantly promote the GIE. • The positive effect of Fintech on GIE is more pronounced in non-state-owned enterprises, mature enterprises, and heavily polluting enterprises. • Fintech can attract GIE by easing financing constraints, reducing information asymmetry, and improving firms risk-taking. • Our results provide evidence and a reference for governments to attract green investors through the development of Fintech.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".