Assessment of the Financial Benefits from Wind Farms in US Rural Locations
Bibliographic record
Abstract
Rural locations provide limited economic opportunities, mainly relying on agricultural activities, with scarce industrial or commercial investment and employment. This has led to higher risks related to poverty, with a lack of opportunities for education, healthcare, and general services leading to rural migration. On the other hand, wind energy is growing exponentially in the United States (US). Rural locations offer very good siting options for wind farms considering the ever-growing size of their equipment and significant required area. Therefore, wind farms may provide financial opportunities to local rural communities, reducing poverty risks and improving living standards. These financial benefits include rental income for landowners, additional tax collection for public service funding, increased income for school districts, and overall larger local investments. However, the available data are too coarse, broad, and unconnected, not allowing local communities, wind developers, and stakeholders a clear panoramic of the benefits that each individual location, school district, or landowner may receive. This research compiled dispersed big data for its integration into a large Geographic Information System (GIS). This system is capable of performing analysis to provide a much better understanding of the actual benefits that the wind industry provides to each individual rural stakeholder. Data were converted to geospatial layers, when required, to allow for a fuller comprehension of all factors impacting financial benefits and risks from the wind industry. Analyses were expanded to evaluate the lease financial benefits for landowners in Texas, applying the data provided by local and state agencies. The approach developed in this research will allow for its application in diverse geographical locations to explore additional financial benefits that each individual rural stakeholder may receive from the wind industry. This will allow local authorities, landowners, wind developers, and communities to better negotiate for the future expansion of wind energy, providing all parties involved with significant benefits and allowing the continuous growth of renewable energy to overcome the damaging effects from climate change.
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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 | 0.001 |
| 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".