Environmental policy uncertainty and green innovation: A TVP-VAR-SV model approach
Bibliographic record
Abstract
This paper aims to measure the impacts of environmental policy uncertainty on green innovation and explore the transmission channel that is less understood in past scientific works. In this paper, we use a newspaper-based sentiment mining approach to establish an index of environmental policy uncertainty in China and implement web crawlers and text analysis techniques to construct a network public opinion index of the Chinese financial market. Then, we explore the relationships between environmental policy uncertainty, network public opinion, and green innovation through the time-varying parameter structural vector autoregressive with stochastic volatility (TVP-VAR-SV) model. The transmission channels of environmental policy uncertainty to green innovation are depicted by selecting different timing of policy release. Our empirical study results show that the fluctuations of environmental policy uncertainty, network public opinion, and green innovation have time-varying characteristics. Furthermore, the findings reveal interactions among the three variables: 1) The environmental policy uncertainty can influence green innovation through network public opinion. 2) The environmental policy uncertainty has both inhibited and promoted effects on network public opinion and green innovation. 3) There are differences in the direction and the degree of impulse responses among the above three variables in the context of uncertainty shocks. Besides, managerial relevance and policy implications are also provided for decision-makers facing sustainable development challenges.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".