Federal Pipeline Rate Making: Alternative Approaches of the United States Federal Energy Regulatory Commission
Bibliographic record
Abstract
This article provides an overview of the alternative rate making methodologies adopted by the United States Federal Energy Regulatory Commission (FERC) in itsregulation of transportation rates for oil and natural gas pipelines. In 1997, authority over rate making for interstate oil and natural gas pipelines was transferred to the newly created FERC. This article describes the history of interstate pipeline rate making and the transfer of rate making authority to the FERC.The author looks at the innovative pipeline rate making methodologies implemented by the FERC in its regulation of transportation rates for both oil andnatural gas pipelines. The article describes the adoption by FERC of market based rates and a generally applicable indexed rate cap methodology for oil pipelinerate setting. In respect of natural gas pipelines, the legislative requirements and practical realities associated with cost-of-service rate making by FERC aredescribed and FERC’s policies permitting selective discounting, shipper-specific negotiated rates, and market based rates for natural gas pipelines arereviewed.The Commission’s adoption of the alternative rate making methodologies has taken the emphasis off of general rate case litigation as a means of establishingjust and reasonable rates for interstate oil and natural gas pipelines and related facilities. The alternative rate making methodologies also represent a significantdeparture from cost-of-service rate making, with increasing focus on rate flexibility and competition as a means of generating efficiencies for customers of interstate oil and natural gas pipelines.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".