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Record W4285530709 · doi:10.1079/9781789248234.0083

Economic analysis of a softwood lumber quota regime and a policy to subsidize biomass generation of electricity.

2020· book-chapter· en· W4285530709 on OpenAlexaboutno aff
Fatemeh Mokhtarzadeh, G. Cornelis van Kooten

Bibliographic record

VenueCABI eBooks · 2020
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSoftwoodSubsidyEconomicsElectricityAgricultural economicsWelfareApplied general equilibriumInternational economicsRest (music)International tradePulp and paper industryMarket economyEngineering

Abstract

fetched live from OpenAlex

<title>Abstract</title> The REPA spatial price equilibrium model developed in Chapter 4 is used to investigate the regional welfare impacts of a quota on exports of Canadian softwood lumber to the U.S. In the model, Canada is divided into seven regions and the U.S. into five regions, with the rest of the world constituting a 13th region; the model is calibrated to the bilateral trade flows that existed in 2016 when there was free trade in lumber. Various quota levels are examined in terms of their impact on producers and consumers in both countries. Canadian producers are found to be better off with a hard quota compared with free trade, although the quota leads to a reduction in market share while driving a wedge between Canadian and U.S. prices, both of which are aggravated with harder quotas. Overall, the loss of export sales to the U.S. is not recouped with sales to the rest of the world. The REPA model is also used to examine the impact of EU demand for wood pellets to generate electricity. Results indicate that pellet prices will approximately double.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.847
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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