Substitution impacts of wood use at the market level: a systematic review
Bibliographic record
Abstract
Abstract There is strong evidence that wood-based products are typically associated with lower fossil-based emissions over their lifecycle than functionally equivalent products made from other materials. However, the potential impact of large-scale material substitution at the market level remains challenging to quantify and is subject to assumptions and system boundary considerations. This paper presents a systematic review covering 44 peer-reviewed studies that quantify the substitution impacts of wood use at the level of a region or sector, to assess the commonalities and differences in scopes, system boundaries and key assumptions. We estimated the average and range of market-level substitution impacts and identify the caveats and knowledge gaps for such assessments. The results indicate an average substitution factor of 0.55 tonnes of fossil C avoided per tonne of C contained in wood harvested, with a range of 0.27–1.16 tC/tC for baseline scenarios covering all wood flows. This value depicts the average efficiency of avoided fossil emissions per unit of wood used for a certain wood use structure based on published studies but is of limited practical use as it is strictly context specific. A direct comparison between studies is complicated because a notable proportion of the studies provided insufficient information to estimate substitution factors or were not transparent in their assumptions, such as specifying which wood product is assumed to substitute for which non-wood product. A growing number of studies focus on policy-relevant analyses of the climate change mitigation potential associated with marginal changes in wood use, but market dynamics are generally considered to a limited extent. To further support decision-making, future studies could focus on changes in those end uses where increased substitution impacts could realistically be expected, while considering the various market dynamics and uncertainties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".