Not So Fast: How Slower Utilities Regulation Can Reduce Prices and Increase Profits
Bibliographic record
Abstract
Energy consumers are facing cost pressures from multiple directions. Wholesale natural gas prices have been climbing substantially from their record lows. Oil prices have only recently cooled slightly after reaching nearly $100 a barrel (WTI) earlier this year. That makes it that much more important to minimize costs to wholesale consumers of energy, and ultimately, retail buyers, wherever possible. There is little room in the energy network for unnecessary costs. But in a regulated system, profits for utilities must remain healthy, too, if we expect them to stay active in the market. But the way that government agencies regulate oil and gas pipelines in Canada, and elsewhere, appears to be increasing costs beyond where they need to be in order to fairly serve both utilities and customers. By relying on traditional rate-of-return regulation models — which calculate price-rates based on the regulated firm’s cost of capital (that is, how much it costs the company to finance its operations) — regulators, including the National Energy Board and the Alberta Utilities Commission, reward firms for over-investing in their operations, rather than reducing costs. Utilities are motivated to prolong the period in which they can earn a return on their capital, since it is one of the few opportunities they have to increase profits under the widely used rateof-return regulatory model. That results in utilities keeping assets on the books — and paying for them — longer than they might otherwise need to be. The end result is a distortion of the decisions made by regulated firms and higher prices for consumers than might occur under a different regulatory model. Regulators that take a more passive role in setting the rate of return for their client industries, however, are likely to see their idleness pay off. Firms with a freer hand to do so will seek to accelerate the depreciation of capital assets, reducing costs more quickly. The result may see end-consumers pay more in the short term, but substantially less over the long term.
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".