Bibliographic record
Abstract
From modest beginnings, China’s renewable energy sector is today the world’s largest. We contrast the evolution of China’s solar and wind sectors, with an eye to the effect of differences in technology, government policies, and markets. In solar, relatively modest barriers to entry and returning Chinese with industry experience, combined with rapid growth in overseas demand and high quality standards to propel the sector forward. Localization of the supply chain lowered costs and conferred important advantages on the largely private sector, much as it had done in other successful export sectors in the electronic industry. In sharp contrast, the growth of China’s wind turbine sector has been tied to a “government-made” domestic market and highly protectionist measures favoring local firms. Although there are signs of upgrading, the sector remains SOE-dominated throughout the value chain, and uncompetitive internationally. Moving forward fortunes of solar and wind turbine manufacturers will be tied to an increasingly crowded domestic electricity market in which excess capacity, lackluster demand growth and regulatory issues are the new normal. In this setting, political connections rather than firm capability will determine winners and losers. The experience of the renewable sector reveals both the strengths and weaknesses of China’s industrial policy.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.011 | 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".