Analysis of the Threshold Effect of Renewable Energy Industry Subsidies Based on the Perspective of Industry Life Cycle
Bibliographic record
Abstract
Based on the industry life cycle theory, this study investigates the influence mechanism of renewable energy industry subsidies on industry development. Using data from 78 public Chinese electric power enterprises in the A-share market from 2011 through 2020, the threshold effect of government subsidies on the development of the renewable energy industry is empirically tested, and the heterogeneity of subsidy efficiency in the renewable energy industry is explored. The results reveal an inverted U-shaped nonlinear relationship between government subsidies and the enterprise output of the sample electric power enterprises. In addition, market investment has a positive and limited effect on enterprise output, and there is heterogeneity in subsidy efficiency and the signalling effect of subsidies on investment in the wind and photovoltaic power industries. Accordingly, we recommend that government departments use rational and flexible subsidy policy tools according to different industries, set reasonable subsidy ranges, and proactively design top-level policies that will actively broaden the application of renewable energy consumption to help industry development. By examining the overall threshold effect of electric power enterprises, this study fills a gap in the literature, as there has been scant research on the specific relationship between renewable energy industry subsidies and industry development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".