Financial return for government support of large-scale thin-film solar photovoltaic manufacturing in Canada
Bibliographic record
Abstract
As the Ontario government has recognized that solar photovoltaic (PV) energy conversion is a solution to satisfying energy demands while reducing the adverse anthropogenic impacts on the global environment that compromise social welfare, it has begun to generate policy to support financial incentives for PV. This paper provides a financial analysis for investment in a 1. GW per year turnkey amorphous silicon PV manufacturing plant. The financial benefits for both the provincial and federal governments were quantified for: (i) full construction subsidy, (ii) construction subsidy and sale, (iii) partially subsidize construction, (iv) a publicly owned plant, (v) loan guarantee for construction, and (vi) an income tax holiday. Revenues for the governments are derived from: taxation (personal, corporate, and sales), sales of panels in Ontario, and saved health, environmental and economic costs associated with offsetting coal-fired electricity. Both governments enjoyed positive cash flows from these investments in less than 12 years and in many of the scenarios both governments earned well over 8% on investments from 100. s of millions to $2.4 billion. The results showed that it is in the financial best interest of both the Ontario and Canadian federal governments to implement aggressive fiscal policy to support large-scale PV manufacturing. © 2010 Elsevier Ltd.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".