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Record W2144807203 · doi:10.1139/cjfr-2013-0337

Elasticity of demand for Canadian logs and lumber in China and Japan

2013· article· en· W2144807203 on OpenAlexaffvenueabout
Kurt Niquidet, Jingwen Tang

Bibliographic record

VenueCanadian Journal of Forest Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of VictoriaCanadian Forest Service
Fundersnot available
KeywordsAlmost ideal demand systemChinaPrice elasticity of demandEconomicsBoomAgricultural economicsElasticity (physics)Derived demandEconomyDemand curveProduction (economics)MicroeconomicsGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Over the past decade, Chinese demand for forest products has increased substantially. While domestic production has risen, this increased demand has been met mostly by imports. The boom has been a welcome benefit to large forest products exporting countries such as Canada, especially in light of the weakened demand that occurred in the United States (US) following the financial crisis. While estimates of demand parameters for the US are vast, little is known about Canada’s other two leading markets: China and Japan. Furthermore, many existing trade models assume that logs and lumber are homogenous, perfectly substitutable regardless of their origin. In this note, we apply a source-differentiated Almost Ideal Demand System (AIDS) model to Chinese and Japanese log and lumber imports, with an emphasis on imports originating from Canada. Results indicate that the price elasticity of demand for Canadian lumber and logs in China is elastic. In contrast, in Japan, the price elasticity of demand for Canadian lumber is inelastic, but like China, demand for Canadian log imports is elastic. Expenditure elasticities suggest that Canadian wood products are expected to be favoured relative to other countries as demand in China grows; this finding may be attributed to marketing programs.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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.

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

Citations22
Published2013
Admission routes3
Has abstractyes

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