Present and Future of Oil and Gas Exploration and Production Transition-Out: What Can Iran Learn from Quebec's Act?
Bibliographic record
Abstract
One approach to supply-side climate policy is phasing out the production of fossil fuels by enacting bans or moratoriums on new or active upstream projects. Some countries have enacted national ban legislation on new petroleum licensing rounds or extensions of current exploration and production licenses. Beyond that, some jurisdictions like Quebec have created an act that explicitly revoked issued or to be issued petroleum licenses for exploration and production operations. Quebec’s Act on ending petroleum exploration and production regulates some institutional, technical, environmental, and financial issues regarding oil and gas phaseout which can be localized and adopted by other countries.Hence, this paper investigates what can Iran learn from the Quebec’s Act. By analyzing the Quebec’s Act and applying it to the Iranian legal system, I conclude that winding down petroleum production in Iran requires major legal and institutional reforms. The legal regime on awarding the right of exploration and production of petroleum reserves in Iran is in a state of confusion and chaos and at first, this regime shall be modified or developed in consistency with Quebec’s Act. Then, the institutional capacity of the new regime shall be utilized along with other institutions to translate ending petroleum exploration and production policy into legislation. By so doing, the proposed legal regime on petroleum rights awarding has the potential to effectively implement and monitor the ban or revocation legislation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".