Decarbonization of the construction sector in Sweden : exploring barriers to and drivers for increased use of wood-based materials in the construction industry
Bibliographic record
Abstract
The European Union intends to reach climate neutrality by 2050, which will require substantial reductions in greenhouse gas emissions. Construction is an energy and carbon-intensive sector, which needs further decarbonization. The use of wood in buildings can be effective measures for decarbonization in the sector compared to conventional building materials, such as concrete and steel. Despite Sweden's long history of building with wood-based products and extensive forest cover, the share of multi-storey buildings manufactured with wood frames are low. Wood-based building materials offer a potential for decreasing carbon emissions, storing carbon in buildings for a long-time span and offering high cascading potential. As wood resources are limited, it is important to examine the possibilities of utilizing wood in long-lasting products and plan the use of them to contribute to decarbonization. Therefore, it is important to map and analyse the wood flow from forests to various wood industries and to the building sector. In this study, the wood material flow is mapped based on the mass conservation principle and the availability of wood resources for wood-based building material is investigated. The results show that gross and net felling is predicted to be relatively constant in the coming years, but that the view is that the market for high value wood products, such as wood-frames, textiles etc, will increase and thereby increase competition for the raw material. A significant role in achieving the decarbonization goals can be played by stakeholders in the building industry by choosing low-carbon technologies, including wood-based building materials. It is important to review the perspective of experts with experience of working with wood-based buildings or material from different disciplines within the construction process such as building material companies, architects, designers, and construction companies to identify barriers and drivers associated with the selection of wood as building material. Therefore, in addition to a wood-flow study semi-structured interviews were conducted with a group of these experts in the field to investigate their perspectives of using an increased amount of wood products in buildings and the potential for decarbonization. The main conclusion from these interviews is that the experts see an increase in semi-high-rise wood-framed buildings, and that local initiatives and clear sustainability goals are important drivers. The respondents also emphasize the learning process as well as a continuous need for good examples of successful projects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".