Assessment of the ecological footprint associated with consumer goods and waste management activities of south mediterranean cities: Case of Algiers and Tipaza
Bibliographic record
Abstract
Despite the management strategies implemented, urban waste management in Algeria is a massive concern, especially entities in charge focus primarily on eliminating waste, neglecting material and energy use which might have a substantial environmental impact. This study uses combined urban metabolism analysis and EF assessment model to estimate the surface required to absorb greenhouse gas (GHG) generated, intending to demonstrate the reliability of Ecological Footprint (EF) as an environmental indicator in managing activities related to product manufacturing and waste management. Therefore, the research considers two neighboring coastal cities Algiers and Tipaza, contrasting demographically and economically. The results obtained from urban metabolism demonstrate that material produced from raw inputs is associated with higher GHG emissions. Similarly, emissions generated from waste management activities are dominated by transportation at more than 40% in both cities. Thus, proportional to the amount of GHGs emitted, EF in terms of energy is higher in the disposed material representing 90% consumer goods' EF compared to the Diverted material. These emissions require a total amount of 578 190.05 gha and 92 950.7gha in Algiers and Tipaza respectively. As for waste management, transportation requires the most significant EF values exceeding 5 thousand gha to sequester carbon in Algiers. Eventually, the investigations reveal disparities in data collection and structure in both cities and shortcomings in waste management. Thus, this empirical study highlights EF's reliability for understanding the tangible impact of economic growth on the environment that supports the development of cities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".