IS ONTARIO'S ELECTRICITY COST DISADVANTAGE IMPACTING IT'S MANUFACTURING SHIPMENTS?
Bibliographic record
Abstract
ABSTRACT Using monthly data, this research establishes that Ontario's electricity cost disadvantage versus Quebec has been large, somewhat variable, and causally linked to changes in the ratio of Ontario's manufacturing shipments to Quebec's manufacturing shipments over the 2002-2014 period. For nine of the 21 manufacturing industries analyzed, the ratio of Ontario to Quebec electricity prices Granger-caused the ratio of Ontario to Quebec manufacturing shipments. These nine industries were not randomly distributed across the set of 21 industries. Of the top eight industries when ranked by their electricity intensity of production, five were found to have the electricity price ratio Granger-causing the shipments ratio (NAIC 322, 331, 321, 327, and 326) while only three showed no impact from the electricity price ratio (NAIC 325, 324, 313). For the bottom 13 electricity intensive industries, only four had the electricity price ratio Granger-causing the shipments ratio (NAIC 337, 332, 335, and 333). The concentration of Granger-causality findings within the more electricity intensive manufacturing industries raises the likelihood that the observed Granger-causality is in fact reflecting a genuine causal impact from electricity prices upon manufacturing shipments in Ontario for these industries. While nine of 21 industries (42.9%) exhibit a causal impact from electricity prices upon manufacturing shipments, the share of Ontario's total manufacturing shipments from these industries is somewhat smaller at 31.1%, primarily because the large Transportation Equipment industry does not show electricity prices Granger-causing shipments. Keywords Ontario, electricity costs, manufacturing shipments, causality testing
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".