Qualitative meta-analysis of the socioeconomic impacts of offshore wind farms
Bibliographic record
Abstract
Climate change and an increased interest in renewable energy have resulted in a burgeoning wind energy sector. However, in the recent past, wind farms have faced resistance in acquiring permits due to concerns about their long-term effects on the local community. To understand the extent of these externalities, this study qualitatively meta-analyses four socio-economic impacts of interest, namely: house prices, tourism, catalytic effects of supply chain clustering, and social change. Geographically, the analysed reports include Europe, Canada and the US, and deductions are made for the EU. In order to bridge the gap of unavailability of primary data on the wind sector, relevant conclusions are drawn from other comparable sectors. Based on a rigorous review of primary qualitative research, this study concludes that offshore wind farms should be located more than 40 km away from the coast to eliminate risks of housing price devaluation and tourist activity reduction, which would directly affect the economic value of the region. In addition, the study found limited evidence to acknowledge the employment benefits in the local economy and social change in the community due to offshore wind farms. Monitoring mechanisms should be set up to prove or disprove the creation of local employment, crime and substance abuse. Furthermore, the study finds that adequate planning and management can ensure better socioeconomic outcomes in the community. Further research is recommended for the specific impact of overhead transmission lines and substations on property values and tourism.
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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 | 0.078 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".