Analysis of Shale Gas Exploration and Production Regulations - Lessons from Poland, Canada and the US
Bibliographic record
Abstract
The aim of this paper is to review the theoretical and practical aspects of regulations concerning the exploitation of natural resources. Raw material policy is a public project, and therefore a decision tool known as Cost Benefit Analysis (CBA) should be applied. A nation's ownership of minerals is in practice frequently contested. Non-democratic governments have very often sought to collect revenues from natural resource exports in order to build their private fortunes. Such abuses are observed even today. The article begins with a short theoretical discussion about CBA. It then presents the issue of ownership of natural resources from the perspective of international law as well as the phenomenon of resource nationalism. The following section looks at the policy of granting concessions, a common tool used in regulating access to raw materials for exploration and production (E&P) companies. The empirical part of the paper analyses shale gas regulations in Poland, while also examining both the restrictiveness of rules adopted in this country and the legal preparedness of the Polish authorities to begin to use shale gas on a mass scale. Key findings of this part are enhanced with lessons from shale gas production in the US and Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".