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Record W3124382548 · doi:10.15376/biores.16.1.862-881

Projected cross-laminated timber demand and lumber supply analysis

2020· article· en· W3124382548 on OpenAlexaboutno aff
Kristin Brandt, Greg Latta, Dane Camenzind, Dan Dolan, Don Bender, Alex Wilson, Michael P. Wolcott

Bibliographic record

VenueBioResources · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCross laminated timberSupply and demandAgricultural economicsProduction (economics)Market researchMarket penetrationPopulationEngineeringCivil engineeringBusinessEconomics

Abstract

fetched live from OpenAlex

The use of cross-laminated lumber (CLT) for building construction has gained interest in the United States (US) and Canada. Although anecdotal market size claims exist, few quantitative studies have estimated the potential market size or discussed the impact of CLT on lumber supply. This paper presents a method to quantify CLT markets and lumber supplies based on data for the Northwest US. The western US was chosen for its early adoption of CLT combined with a long history of commercial timber construction. Structural designs of archetype buildings were combined with projected multifamily residential and commercial building construction to estimate the demand for CLT. These figures were reduced to account for assumptions that address market penetration and population density. In the case study for the Northwest, the total potential market is less than the existing CLT production in western North America. Thus, the demand region was expanded to include the US and Canada west of the Rocky Mountains, resulting in an estimated demand of 800,000 m3/yr by 2030. A regional lumber supply study suggests that the lumber supply will support the existing CLT industry, which utilizes approximately 2% of the selected lumber classifications, with an unknown impact on lumber cost and production.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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