MétaCan
Menu
Back to cohort
Record W2795333917 · doi:10.11575/prism/34303

Oil Sands, Carbon Sinks and Emissions Offsets: Towards a Legal and Policy Framework

2003· article· en· W2795333917 on OpenAlexfundaboutno aff
Steven A. Kennett

Bibliographic record

VenueOpen MIND · 2003
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
FundersBIOCAP Canada
KeywordsOil sandsCarbon sinkNatural resource economicsGreenhouse gasEnvironmental scienceFossil fuelCarbon fibersClimate changeBusinessEnvironmental protectionEarth scienceEnvironmental resource managementEconomicsGeologyGeographyWaste managementAsphaltArchaeologyEngineeringOceanographyComputer science

Abstract

fetched live from OpenAlex

The development of Alberta's oil sands will result in significant greenhouse gas (GHG) emissions. This paper summarizes the implications of this development for Canada's emissions profile and reviews briefly the rationale for biotic carbon sequestration as a means of offsetting GHG emissions. The paper then turns to eight important issues for sinks-based offsets. These issues are: (1) the legal foundation for biotic carbon sequestration; (2) the risk of project failure and leakage; (3) monitoring and verification; (4) market intermediaries; (5) environmental risks; (6) land-use conflicts; (7) the alignment of regulatory requirements, policies and incentives; and (8) collateral benefits and strategic objectives. While some of these issues were identified in the federal and Alberta climate change plans released in 2002, these plans fall far short of establishing a comprehensive legal and policy framework for sinks-based offsets. The paper concludes by arguing that this framework should include carbon rights legislation, a regulatory and certification regime, and non-market mechanisms to increase biotic carbon sequestration. The promotion of sinks-based offsets should also occur as part of an integrated approach to resource and environmental management.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.335
Teacher spread0.313 · 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 teacher head, 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

Citations1
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueOpen MINDSame topicGlobal Energy and Sustainability ResearchFrench-language works237,207