Investments in environmental preservation: is the government crowding in green enterprises? Evidence from a-listed companies in China
Bibliographic record
Abstract
Government investment in environmental protection (Govin) plays a key role in stimulating private green investment (Prinv) to preserve the ecological environment with economic profits.To examine the effect of Govin on Prinv, this study uses data from 2010 to 2020 on green A-listed companies in China and estimates a dynamic panel model by using both the difference generalized method of moments and system generalized method of moments.The results indicate that Govin has a crowding-in effect on Prinv, and this conclusion is confirmed by several robustness tests.Furthermore, we identify revenues as a potential mechanism variable to explain how Govin affects Prinv.In addition, this study finds regional and enterprise ownership differences in the crowding in effect of Chinese Govin.Finally, based on these outcomes, the study suggests that the government should rationally and dynamically adjust the contents of public environmental investment and optimise its structure to effectively promote the development of green, low-carbon, and circular economies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".