Supply chain finance, green innovation, and productivity: Evidence from China
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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it