Environmental Benefits of Reusable Modular Mass Timber Construction for Residential use in Japan: an LCA Approach
Bibliographic record
Abstract
The demand for wooden buildings in Japan consistently reaches figures over 50 million square meters per year. However, the building industry in Japan is based on the constant renewal of the building stock, leading to a short average lifespan, and an unsustainable and wasteful system both from the environmental and economic point of view. As an alternative to this situation, this study assesses the environmental benefits of a modular mass timber system using CLT, designed for consecutive reuse. First, the study presents an award-winning proposal for a modular, reusable mass timber system for residential construction in Japan. After, the study calculates the Global Warming Potential of construction (GWP) of the reusable system in comparison to a conventional mass timber system (benchmark), using the Lifecycle Assessment (LCA) method. The study evaluates the proposed system in two different locations, within 60 years. Three different scenarios for forest resources are considered during the above time-frame, namely stable forest (standard), growing forest (optimistic) and decreasing forest resources (pessimist) to understand how changes in the carbon flow of forests could impact the environmental output of the construction. The results show a modular construction system can be used to provide high-quality dwellings in Japan leading to a significant potential for mitigating the impacts of construction on the environment. More specifically, a growing forest scenario provides the smallest GWP, more than 100% smaller than the benchmark.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".