Integration of Sustainability Criteria and Life Cycle Sustainability Assessment Method into Construction Material Selection in Developing Countries: The Case of Vietnam
Bibliographic record
Abstract
A sustainable development concerning economic, environmental, and social aspects is a global need as well as challenge in general and especially regarding the selection of construction materials. However, it is assumed that the importance of sustainability criteria is different in developed and developing countries. This is relevant for the application of Life Cycle Sustainability Assessment, a method that integrates the established methods for economic, ecological, and social evaluation (Life Cycle Costing, Life Cycle Assessment, and Social Life Cycle Assessment) without explicitly including importance weightings. This paper aims to review the reality of sustainable development in construction material selection in Vietnam, a developing country. A list of 18 sustainability criteria was set up by reviewing previous studies and using a questionnaire. These criteria were ranked and used to calculate the importance of weightings based on the Analytic Hierarchy Process method and a Likert scale. The results showed that the “price of material” was ranked as the first among the sustainability criteria. It is also pointed out that 42.06, 29.96, and 27.98 are the weightings of Life Cycle Costing, Life Cycle Assessment, and Social Life Cycle Assessment results, respectively. Besides, 11 obstacles for integrating sustainability criteria into material selection were identified in the questionnaire, and 4 out of them were marked as showing “high” importance.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".