Policy Risk and Private Investment in Ontario’s Wind Power Sector
Bibliographic record
Abstract
Even though governments may adopt favourable regulatory policies for renewable power generation, their ability to encourage private sector investment depends also on the presence of regulatory governance institutions that provide credible long-term commitments to potential investors. In the case of Ontario we contend that, despite large market potential and comparatively strong regulatory incentive policies, weak regulatory governance is one factor that has accounted for the challenges in attracting and implementing large-scale private investment in power generation at a reasonable cost. A unique survey of 63 wind power firms that assessed private sector opinions about the investment environment for renewable energy provides empirical support for our arguments. Firms rated the stability of regulatory policy, compared to a range of factors, among the weakest aspects of Ontario’s business environment. However, policy stability ranked among the most important factors in firms’ assessments of the attractiveness of alternative jurisdictions in their location decisions. Subsequent interviews revealed that firms have responded to this risk by explicitly pricing it into wind project financial models in Ontario—implying higher wind power prices for ratepayers— and by directing investment funds to other jurisdictions. We argue that policy stability in Ontario may be improved by devolving greater decision-making authority to regulatory agencies in the energy sector and by strengthening their institutional independence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".