Using economic indicators in a simple model to predict annual growth in the wind energy industry
Bibliographic record
Abstract
Climate change is going to be the main problem this and future generations will face. Wind energy is promising to be one of the industries that could help mitigate this impending crisis. In literature many different models and predictions can be found to describe the possible futures of wind energy. But there is still a lot of uncertainty in this field as to what factors play which role in its development.<br/>This research contributes to this challenge by comparing the economic attractiveness of windmills expressed as Net Present Value(NPV) with annual added wind capacity in five countries for 2008 till 2019. The countries used for this research are Germany, Denmark, Canada, Texas and Sweden. This research found an exponential relationship between the NPV and added wind power capacity. A 10% increase in NPV(AC/MWh) found an increase of 15% for the annual added wind capacity(MW/TWh) of added windmill capacity per TWh of electricity produced. The vast amount of data sources used could have lead to a higher uncertainty regarding their uniformity and trustworthiness. Doing a sensitivity analysis yielded no improvements in the results. The simple economic model used was able to describe the growth in wind energy in countries, though there is still a significant spread in the results. A likely explanation for this spread is the lack of several important aspects such as permits, company influences, cultural differences and social-economic challenges which were all not taken into account. This research also makes several recommendations regarding possible policies countries could in order for them to reach their wind or clean energy goals.
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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 | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".