The Awareness Level of Green Procurement at the District Assemblies in Western Region in Ghana
Bibliographic record
Abstract
The present study seeks to examine the awareness level of green procurement at the district assemblies in Ghana and also help create the consciousness of implementing and enforcing green procurement in the public sector effectively. The research was carried out in Eighteen (18) District Assemblies in the Western Region of Ghana using non-standard interview as the main data collection technique. The findings of the research revealed that, there are key legislations, standards and regulations regulating the assemblies’ activities on the environment and the society which affect green procurement, though there is no specific law/policy titled green procurement. These key legislations, standards and regulations have contributed to increased awareness of green procurement at the District Assemblies in Western Region tied with the recent introduction of sustainable procurement which has been included in the Public Procurement Amendment Act 2016 (Act 914). However, the study identified that the significant impact of public procurement as a policy instrument on environment appeared not to be clear to the assemblies. It is recommended that the procurement policy and the Act 663/914 should be reformed to reflect green procurement to help the districts in their quest in creating sustainable environment. The district assemblies and EPA should organise joint seminars and workshops to sensitize and create awareness among the assembly’s staff on the benefits of green procurement and the need to adopt it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".