Policy, Regulation, and Innovation in China’s Electricity and Telecom Industries
Bibliographic record
Abstract
China represents a new chapter in ongoing controversy over the use of state intervention to promote innovation and upgrading. This book analyzes the nature and outcome of recent Chinese efforts to accelerate progress toward global technology frontiers in electricity, telecommunications and semiconductors. Our contributors document impressive absorption and upgrading of a wide array of technologies as well as individual instances of path-breaking innovation. Notable advances include achievement and even extension of global technical standards in particular segments of electricity transmission and thermal power generation along with rising competitive strength in global markets for conventional and nuclear electricity, telecom equipment and software. We also observe instances – notably semiconductors and wind turbine equipment - in which government efforts to promote innovation have encountered difficulty in penetrating overseas (wind equipment) and even domestic (semiconductors) markets. In addition, we observe consistently high levels of excess capacity and operational inefficiency – for example, the average cost of generating and delivering each unit of electricity is at least 30 percent higher in China than in the United States Recent initiatives center on the Made in China 2025 plan to develop an extensive array of advanced manufacturing industries. Current strategy emphasizes instruments – self-reliance, channeling resources toward state enterprises, top-down selection of strategic products and technologies and Party control – that have retarded productivity growth in the past, while moving away from openness, competition, private-sector expansion and other measures strongly associated with past increases in productivity. Telecommunication equipment, a sector that has thrived in an environment of openness and competition, offers a strong contrast to the recent decline in productivity outcomes visible at the industry-wide level and in several electricity-related product categories. Current policies reinforce distorted incentives that can only expand the massive costs associated with excess investment, misallocation and corruption. These costs will act as a powerful counterweight to the extraordinary human, financial and policy resources that China’s leaders are now directing toward innovation and upgrading across a broad spectrum of industries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".