Farm size, spatial externalities, and wind energy development
Bibliographic record
Abstract
Abstract The global push for renewable energy must overcome the local challenge of convincing neighboring landowners to lease their properties for wind power. Is this challenge more or less pronounced in rural landscapes with small landholdings? Our theoretical model combines ideas from literatures on the commons, anticommons, and spatial externalities to explain conditions when small landholdings could promote or inhibit voluntary leasing. Empirically, we estimate the effects of landholding size and landscape fragmentation on wind farm uptake across rural areas of the United States over the past 20 years. Evidence from three spatial levels of analysis (counties, square‐mile sections, and individual parcels) indicates that areas with more landowners have less installed wind capacity after controlling for windiness, access to transmission lines, and other relevant factors that vary across and within counties. The findings imply that fragmented ownership, which is an overlooked factor in studies of the feasibility of decarbonization, will pose an impediment to future wind expansion on private land as remaining areas without wind development become disproportionately fragmented.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".