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Record W1911934446 · doi:10.1139/cjfr-2014-0097

A financial analysis of establishing poplar plantations for carbon offsets using Alberta and British Columbia’s afforestation protocols

2014· article· en· W1911934446 on OpenAlexafffundvenueabout
Jay A. Anderson, Amanda Long, Martin K. Luckert

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of Alberta
FundersUniversity of British ColumbiaGenome British ColumbiaUniversity of AlbertaGenome Canada
KeywordsAfforestationCarbon offsetCarbon sequestrationGreenhouse gasTonneCarbon creditAgroforestryForestryTree plantingEnvironmental scienceEnvironmental protectionAgricultural economicsBusinessGeographyEconomicsCarbon dioxideEcology

Abstract

fetched live from OpenAlex

Both Alberta and British Columbia allow the use of carbon offsets for meeting government greenhouse gas emission targets, but the provinces have different offset protocols. In British Columbia, afforested lands may be harvested yet still receive carbon offsets, whereas in Alberta, according to a yet-to-be-approved draft protocol, offsets could be contingent upon afforested lands being set aside as conservation easements. Our work considers the regulatory differences between the provincial carbon protocols as they impact the financial viability of afforestation projects in Alberta and British Columbia. Our results suggest that carbon prices would have to rise to approximately $150 per tonne of carbon dioxide equivalent (tCO 2 e) before conservation afforestation projects using balsam poplar would be financially viable in Alberta. However, afforesting and harvesting short-rotation hybrid poplar in British Columbia yields financially viable results under current carbon prices if stumpage prices for standing timber were to rise above $50·m –3 . There may be other incentives such as the benefits from public relations associated with planting trees that may lead to the implementation of some afforestation carbon offset projects. However, it appears that financial considerations present a significant barrier, making it unlikely that afforestation of private land will play a significant role in generating carbon offsets for Alberta or British Columbia.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.321
Teacher spread0.283 · 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 designObservational
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

Citations12
Published2014
Admission routes4
Has abstractyes

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