Carbon footprint and economic viability analysis of water pumping systems in a semi-arid region, Morocco
Bibliographic record
Abstract
Today, the world faces a significant challenge: climate change, mainly driven by greenhouse gas (GHG) emissions. These changes are seen in global warming and seasonal shifts, worsened by population growth and shifting lifestyles. In this context, effective management of water resources is vital. Renewable energies, especially photovoltaics (PVs), are emerging as a sustainable solution, particularly suited to arid and semi-arid regions with ample sunlight. This technology lowers energy costs and GHG emissions, providing an option that is both cost-effective and environmentally friendly. This article presents a comparative cost analysis of two pumping systems using different energy sources: PVs and electricity. The study, based on a 7.1 kW pumping system, indicates that PV pumping is not only the most environmentally friendly option, emitting only 1.14 tonnes of carbon dioxide (tCO2)/year compared with 7.8 tCO2/year from the electricity pumping system, but also the most economically feasible, with a cost of 0.11 Moroccan dirham per cubic metre of water (MAD/m³), compared with 1.48 MAD/m³ for the grid-powered system life-cycle cost analysis and evaluation of carbon dioxide emissions over the system’s operational lifetime, provide a comprehensive comparison to show importance of economic and environmental impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".