Methane Emissions from Artificial Waterbodies Dominate the Carbon Footprint of Irrigation: A Study of Transitions in the Food–Energy–Water–Climate Nexus (Spain, 1900–2014)
Bibliographic record
Abstract
Irrigation in the Mediterranean region has been used for millennia and has greatly expanded with industrialization. Irrigation is critical for climate change adaptation, but it is also an important source of greenhouse gas emissions. This study analyzes the carbon (C) footprint of irrigation in Spain, covering the complete historical process of mechanization. A 21-fold total, 6-fold area-based, and 4-fold product-based increase in the carbon footprint was observed during the 20th century, despite an increase in water use efficiency. CH 4 emissions from waterbodies, which had not previously been considered in the C footprint of irrigation systems, dominated the emission budget during most of the analyzed period. Technologies to save water and tap new water resources greatly increased energy and infrastructure demand, while improvements in power generation efficiency had a limited influence on irrigation emissions. Electricity production from irrigation dams may contribute to climate change mitigation, but the amount produced in relation to that consumed in irrigation has greatly declined. High uncertainty in CH 4 emission estimates from waterbodies stresses a need for more spatially resolved data and an improved empirical knowledge of the links between water quality, water level fluctuations, and emissions at the regional scale.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".