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Record W2231620869

Property Rights and the Legal Framework for Carbon Sequestration on Agricultural Land

2006· article· en· W2231620869 on OpenAlexaffabout
Steven A. Kennett, Arlene Kwasniak, Alastair R. Lucas

Bibliographic record

VenueSSRN Electronic Journal · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCarbon sequestrationProperty rightsGreenhouse gasBusinessNatural resource economicsKyoto ProtocolEasementProperty lawClimate change mitigationLand lawStatutory lawAgricultural landAgricultureLaw and economicsEnvironmental resource managementLand tenurePolitical scienceEconomicsLawGeography
DOInot available

Abstract

fetched live from OpenAlex

Kyoto Protocol Annex 1 Parties can elect to include carbon sequestered on agricultural land in calculating their net greenhouse gas emissions. Canada has proposed in its Climate Change Plan to include such carbon sinks as a source of greenhouse gas offsets. This raises questions about the design of a Canadian legal and institutional framework necessary to facilitate investment in sequestration projects on agricultural land. Focus of the article is on (1) definition of underlying legal rights to sequestration potential and sequestered carbon and (2) establishment of a property rights regime for sequestered carbon. A major conclusion is that common law property rights and the statutory regime for conservation easements do not provide an adequate legal basis for sequestration transactions, with the consequence that specific property rights legislation is required. Clarity is necessary on initial ownership of sequestration potential and sequestered carbon, and a property rights regime is needed to facilitate transfer of interests in carbon assets. Criteria are identified to guide the design of such a property rights regime.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.312
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0090.032
Scholarly communication0.0120.008
Open science0.0030.004
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations6
Published2006
Admission routes2
Has abstractyes

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