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Record W4401809813 · doi:10.55016/ojs/sppp.v16i1.77468

A Made-in-Alberta Failure: Unfunded Oil and Gas Closure Liability

2023· article· en· W4401809813 on OpenAlexaboutno aff
Drew Yewchuk, Shaun Fluker, Martin Olszynski

Bibliographic record

VenueThe School of Public Policy Publications · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
Fundersnot available
KeywordsClosure (psychology)LiabilityEnvironmental scienceFossil fuelPetroleum engineeringForensic engineeringBusinessEngineeringPolitical scienceWaste managementAccountingLaw

Abstract

fetched live from OpenAlex

Alberta policy on inactive and orphan oil and gas wells is a massive regulatory failure characterized by a historical lack of transparency, excessive regulatory discretion, and regulatory capture — three deficiencies long since identified and understood in the scholarship as undermining the effectiveness of environmental laws and policies. The current policy to deal with the problem, the 2020 Liability Management Framework, fails to address these structural problems and is consequently unlikely to substantially reduce inventories of orphan and inactive assets. It is equally unlikely to uphold the polluter-pays principle, which states that the entity that pollutes the environment is responsible for cleaning it up. It is time for an independent and transparent public inquiry to examine Alberta’s mishandling of the inactive and orphan well problem and to recommend a regime that will effectively meet this challenge. The inactive and orphan oil and gas well problem is an immense environmental and financial crisis that has been unsuccessfully dealt with by various policies over several decades. Approximately 230,000 drilled wells in the non-oil sands sector need to be abandoned and reclaimed, while 90,000 others that have been abandoned still await reclamation. The industry has continually delayed this closure work, resulting in a current liability estimate of at least $60 billion—and quite possibly double that amount. This liability is largely unfunded as industry has not set aside enough (or any) money to pay for it, while successive governments over many decades have failed to require industry to post security in any meaningful amounts. In the absence of significant and immediate legal and policy reforms, the coming years and decades will see the enormous environmental, social, and economic costs of this regulatory failure fall on the province’s taxpayers. The new Liability Management Framework’s components include mandatory spending to reduce the inactive inventory, assessment of licensee risk and capacity, and an orphan program. On their face, these are steps in the right direction. However, persisting high levels of secrecy, discretion, and nearly exclusive industry influence put the framework’s goals in doubt. Under the new framework, the Alberta Energy Regulator (AER) will not disclose financial information on licensees or even the general state of the oil and gas industry. The new framework also still relies heavily on AER discretion to trigger closure obligations and fails to legislate timelines or quotas for closure work. Provisions for external scrutiny are minimal, impeding meaningful democratic oversight. Finally, the framework perpetuates historic industry influence in its design and implementation, which to date has resulted in a singular focus on minimizing industry’s costs at the expense of reducing environmental risks and protecting the public purse. Albertans have watched for decades as the problem of orphan and inactive assets has burgeoned into an environmental and financial crisis. They deserve a full accounting for the policies thathave led to this state of affairs and they need unimpeded access to all of the relevant facts and information so that they can better understand the policy choices facing them as residents and taxpayers in the province.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.117
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0160.011
Scholarly communication0.0110.004
Open science0.0040.004
Research integrity0.0090.012
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.039
GPT teacher head0.332
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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