Compliance, Not Enforcement: A Comparative Evaluation of Best Practice Regulation for Hydraulic Fracturing
Bibliographic record
Abstract
The following study seeks to define and identify regulatory excellence for hydraulic fracturing with a focus on issues of compliance and enforcement. The inspiration for this investigation developed as a result of intense criticism and scrutiny of oil and gas regulators and their enforcement practices, both in Alberta, and throughout North America. These critiques have appeared predominantly in news media, as well as independent studies produced by think tanks and environmental advocacy organizations. The overwhelming consensus is that regulatory compliance, and any subsequent enforcement, is critically low. Regulatory agencies, it is said, are therefore failing in their mandates to adequately protect the environment and the public from hydraulic fracturing’s numerous associated environmental and human health risks. I wanted to find out for myself, through a critical, comprehensive evaluation, to what extent these allegations might be true. My findings reveal that while they certainly contain some merit, and do offer some worthwhile contributions on how compliance and enforcement may be improved, the overall analyses are constrained through an inadequate understanding of the intricacies of modern environmental regulation. I begin my study by outlining the numerous informational gaps and inherent controversies associated with the hydraulic fracturing debate, as well as providing both an environmental and an economic justification for strong regulatory oversight, including enforcement. By incorporating the University of Pennsylvania Law School’s recent Best In Class Regulator Initiative, I establish a comprehensive framework for assessing regulatory excellence. This framework includes key areas such as a regulator’s level of general expertise and its organizational structure, as well as notions of transparency, approaches to risk management, and the degree to which it promotes flexibility and adaptability to changing circumstances.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".