Citizen beliefs concerning wood as a construction material under extreme weather events
Bibliographic record
Abstract
Climate change places great pressure on the construction sector to decrease its greenhouse gas emissions and to create solutions that perform well in changing weather conditions. Our study explores citizen perceptions on wood usage as a building material under expected mitigation and adaptation measures aimed at a changing climate and extreme weather events. The data are founded on an internet-based survey material collected from a consumer panel from Finland and Sweden during May–June 2021, with a total of 2015 responses. By employing exploratory factor analysis, we identified similar belief structures for the two countries, consisting of both positive and negative views on wood construction. In linear regression models for predicting these beliefs, the perceived seriousness of climate change was found to increase positive views on wood construction but was insignificant for negative views. Both in Finland and Sweden, higher familiarity with wooden multistory construction was found to connect with more positive views on the potential of wood in building, e.g., due to carbon storage and material properties. Our findings underline the potential of wood material use as one avenue of climate change adaptation in the built environment. Future research should study how citizens’ concerns for extreme weather events affect their future material preferences in their everyday living environments, also beyond the Nordic region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".