Green supply chain management for construction waste: case study for turkey
Bibliographic record
Abstract
As the construction industry is growing rapidly, managing a project becomes more vital. The three major parameters to be optimised for a project are content, time and cost to reach a high level of quality. These parameters are also essential for a construction project to satisfy customers on time. Today, it is also critical to protect the environment either at a manufacturing or at a construction site. Environmental problems and the growth of construction industry cause a new topic to manage construction waste with the help of green supply chain management (GSCM). GSCM reduces energy usage and waste, so it prevents any problem that will occur in human health and environment. To decrease waste with the help of GSCM in construction site, waste management regulations must be set to force the producers and consumers for its application. The European Union Council published a waste management directive in 2008 that gives some goal numbers to manage construction waste to minimise the environmental effect. The goal is to reach a reduction of 70% of construction and demolition waste (CDW) that will be reused, recycled or recovered in 2020. The aim of this study is to explore the cost-benefit and socialbenefit reflections of GSCM practices in Turkey under the influence of recent government mandated regulations with an emphasis on green supply chain and reverse logistics in CDW compared to EU 2008 directive. As Turkey is a candidate EU member, this study is analysing how close it is to the directives mentioned above. A GSCM flow chart is established to understand the CDW management system clearly in Turkey. Based on the literature review and case study examples from Turkey a model is built and propositions regarding GSCM and reverse logistics are formulated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".