Winds of Change: Supply Chain Analysis of a Wind Turbine
Bibliographic record
Abstract
Skeptics of degrowth often place their hopes in clean energy technologies, such as wind turbines, to offset the consequences associated with a growing economy that demands equally high rates of energy.Rather than reduce economic production, as proponents of degrowth argue is necessary to mitigate social and environmental consequences, clean energy is held up as a perfect 'business as usual' solution that will allow economic growth to continue.Taking a degrowth perspective, this paper uses a supply chain analysis to de-fetishize the wind turbine as a source of immaterial clean energy.While affirming that renewable energy technologies such as wind turbines and solar panels are necessary, they are insufficient from a degrowth perspective to offset the consequences of continuous growth.Instead, technological improvements, including wind power, must be complimented by policy that reduces economic impact overall.For many, wind turbines are the most visible symbols of clean, renewable energy.For others, wind turbines "are themselves embodiments of fossil fuels" (Smil, 2016, p.27).A single wind turbine is the culmination of enormous production processes that include the transportation of tons of steel and raw materials by truck, the preparation of sites by cranes and excavators, and the coordination of freight trains and cargo ships (Bai et al., 2023;
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".