Introducing an Integrated Municipal Solid Waste Management System: Assessment in Jordan
Bibliographic record
Abstract
Municipal solid waste management (MSWM) is considered one of the challenging environmental problems in the Middle East and North Africa (MENA) region. Municipal solid waste increased significantly due to rapid population growth and fast urbanization, change in lifestyles and consumption patterns. Major problems associated with MSWM are poor collection rates, open dumping, and improper recycling that pose environmental damages. An environmental impact analysis of Jordan’s MSWM was required to look into opportunities for bringing in an integrated solid waste management (ISWM). In this paper, we analyzed the country’s MSWM as a case study in the MENA region. Our goal was to identify the most environmentally-friendly and economically-viable alternative to the current situation. Based on the Life Cycle Assessment (LCA), we evaluated the potential environmental and economic impacts of 10 MSWM scenarios adopting different waste treatment technologies. Indicators of the environmental performance used were four impact categories of EDIP 2003 assessment method: Climate Change (GWP 100a), Acidification Potential, Eutrophication Potential and Human Toxicity. The results showed that improving the current MSWM with 72% of sanitary landfills with energy recovery and 28% of dry recyclable materials was the best scenario in terms of environmental impacts and economic cost. The cost recovery of this scenario was 155% compared to an average of 55.5% of the current cost recovery. The study also revealed that the materials recycled could be increased by 33.5% if the waste separation was applied at the source of generation.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".