Wind power distribution across subalpine, boreal, and temperate landscapes
Bibliographic record
Abstract
Onshore wind power is increasingly expanding to meet global and national goals to increase renewable, clean, and fossil-free energy production. In many countries and regions, however, historical and current land use is extensive, and the expansion of wind power has to be well-tuned to avoid risking irreversible legacy losses of existing and traditional land uses, landscape values, and cultures. Hence, assessments of the siting premises of current and forecasted expansion of wind power are strongly needed as a basis for sustainable planning. We present a study from alpine to temperate biomes in Sweden, where an ambitious onshore wind power expansion strategy has been put in place and will result in Swedish landscapes that are typified by wind power. We explored the existing legal framework—i.e., the national interest for wind power according to the Swedish Environmental Code—concerning the spatial interaction with other national interests for nature conservation, landscape values, and other land uses, and the land cover, landowner, and formally protected areas distribution within wind power sites and in their proximity. We found that the national interest framework does not provide sufficient guidance for locating wind power to avoid spatial overlap with conflicting interests and values. Furthermore, our analysis revealed that wind power is located mainly in forest-dominated landscapes, and on lands where private forest companies are the dominant owners but where the proportion of public and non-industrial private ownership increases in the near surroundings. Finally, we found that large areas of formally protected areas are within the proximate areas influenced by wind power. As an extensive onshore wind power expansion is already going on, and an even more extensive expansion is projected, the ways forward toward a sustainable wind power expansion calls for integrated landscape planning approaches that are based on comprehensive assessments of existing interests and values.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".