Bibliographic record
Abstract
The wind power development policy community faces a conundrum. On the one hand, as the most commercially viable form of utility-scale renewable energy, the wind power industry has experienced in excess of ten-fold growth in total installed capacity over the past decade. On the other hand, installed wind power capacity still accounts for less than 2% of global electricity-generation capacity, despite the prevalence of studies indicating that, in certain situations, wind power can be a cheaper form of electricity than most fossil fuel alternatives. Accordingly, the most puzzling aspect of wind power development policy can be summed up in the following manner: given the global imperative to facilitate an expedient transition away from CO2-intensive energy technologies and the commercial viability of wind power, what is stopping the wind power industry from capturing higher market shares around the world? In Wind Power Politics and Policy, Scott Valentine examines this question from two angles. First, it presents an analysis of social, technical, economic and political (STEP) barriers which research shows tends to stymie wind power development. Case studies which examine phlegmatic wind power development in Japan, Taiwan, Australia and Canada are presented in order to demonstrate to the reader how these barriers manifest themselves in practice. Second, the book presents an analysis of STEP catalysts which have been linked to successful growth of wind power capacity in select nations. Four more case studies that examine the successful development of wind power in Denmark, Germany, the USA and China are put forth as practical examples of how supportive factors conflate to produce conditions that are conducive to growth of wind power markets. By examining its impediments and catalysts, the book will provide policymakers with insight into the types of factors that must be effectively managed in order to maximize wind power development.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".