Corporate ESG performance and artificial intelligence adoption: mediating role based on financing constraints
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
The adoption of artificial intelligence is of great significance in promoting sustainable development and developing new quality productivity. As people's attention to environmental protection and social welfare continues to increase, ESG performance is receiving attention from investors. In this study, we conducted empirical research using data from A-share listed companies from 2013 to 2022, examining the relationship and impact mechanism between corporate ESG performance and AI adoption from the perspective of signal theory. We found that corporate ESG performance plays a positive role in promoting AI adoption by alleviating financing constraints. After robustness and endogeneity tests, the results still hold true. In addition, heterogeneity testing found that this impact is stronger when the enterprise is state-owned or non-heavily polluting. The research results highlight the unexpected role that ESG performance plays in driving the process of enterprise intelligence and promoting high-quality social development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 it