A Methodology for the Evaluation of the Economic Impacts of Wind Energy Projects
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a methodology for the evaluation of the economic impacts of wind energy projects is presented. With an application to the Canadian setting, the methodology is based on modified input-output techniques using national/provincial economic Input-Output tables and multiplier coefficients. The model makes use of a profile of investment for a generic 100 MW wind energy project based on publicly available financial data from current wind energy projects developed and operating in Canada. Subsequently, a case study of the economic impact assessment for both the construction and operations phases of a generic 100 MW wind farm project constructed and operated in the province of New Brunswick, Canada is performed. Results show that in total, including direct, indirect and induced jobs, the construction phase could create a total of 225 person-years of labour, while the operations and maintenance phase could create a total of 17 person-years of labour annually.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it