Risk assessment of the procurement and permitting (pre-construction) process for green retrofitting in high-rise buildings in Jakarta: A risk model-based approach
Bibliographic record
Abstract
The importance of green building concepts is emphasized in the current era due to the drastic decline in global climate conditions. However, their development is hindered as they are primarily applied to new buildings, while almost two-thirds of the world's buildings are already constructed. This study aims to improve the efficiency of Green Retrofitting, accelerating the growth of green buildings in Indonesia. It identifies the procurement and permitting processes for Green Retrofitting in high-rise office buildings in Jakarta, along with high-risk activities from these processes. Additionally, it develops a model of the relationship between these high-risk activities and the implementation efficiency of green retrofitting, using a Monte Carlo approach based on the Regulation of the Minister of Public Works and Housing No. 21 of 2021 and the Green Building Council Indonesia. The analysis uses data from 26 expert respondents on green retrofitting procurement and permitting, finding 83 activities with 214 risk indicators influencing green retrofitting efficiency, including 57 high risks. Identifying the most risky activities, the study develops a relationship model and conducts simulation and optimization to improve project time efficiency, ultimately accelerating the growth of green buildings in Indonesia.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".