Supply chain finance, green innovation, and productivity: Evidence from China
Bibliographic record
Abstract
This study empirically examines the impact of Chinese A-share-listed companies' application of supply chain finance (SCF) on green innovation by collecting, sorting, and textually analyzing SCF keywords from listed companies' 2.92 million announcements from 2010 to 2019. The results show that applying SCF can significantly increase green innovation output . Alleviating financial constraints, strengthening the supply chain network, satisfying the local government's green enforcement, and building a green image are critical mechanisms through which SCF enhances green innovation . Additionally, accounts-receivable-based and advance-payment SCF could have a more significant effect on green innovation. Furthermore, utilizing SCF can significantly increase firms' productivity, and green innovation has a significant mediating effect. Non-state-owned enterprises have a more significant growth effect on green innovation when using SCF. After using the dynamic DID test, DDD analysis, Heckman selection model, PSM test, placebo test, and other methods to control for potential endogeneity problems , we find that the results of this study remain valid.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".