Assessing the Dual Impact of Construction Waste on Project Inefficiencies and Environmental Sustainability in Iraq
Bibliographic record
Abstract
The construction sector in Iraq has escalating problems due to excessive waste production, which not only intensifies project inefficiencies but also presents considerable environmental issues. This research examines the dual effects of construction and demolition (C&D) waste on project performance, particularly on time delays and cost overruns, as well as environmental sustainability. A mixed-methods approach was used to gather data via structured questionnaires and semi-structured interviews with 15 industry experts, including project managers, site engineers, and contractors. A thorough study was performed using the Relative Importance Index (RII) and component categorization to rank and categorize 40 essential waste-related aspects during various phases of construction. The findings indicate that the primary factors contributing to construction waste are frequent design changes, inadequate material storage and handling, and inefficient on-site waste management techniques. These inefficiencies result in substantial cost increases and timetable delays, while also compromising sustainability objectives. The results highlight the critical need for cohesive waste management strategies and legislative changes that advocate for circular economy concepts, optimize site-level practices, and strengthen stakeholder collaboration. This study offers practical insights and strategic suggestions to enhance waste governance in Iraq's construction industry and facilitate the shift towards more sustainable building practices.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".