Land Use in LCA: Including Regionally Altered Precipitation to Quantify Ecosystem Damage
Bibliographic record
Abstract
The incorporation of soil moisture regenerated by precipitation, or green water, into life cycle assessment has been of growing interest given the global importance of this resource for terrestrial ecosystems and food production. This paper proposes a new impact assessment model to relate land and water use in seasonally dry, semiarid, and arid regions where precipitation and evapotranspiration are closely coupled. We introduce the Precipitation Reduction Potential midpoint impact representing the change in downwind precipitation as a result of a land transformation and occupation activity. Then, our end-point impact model quantifies terrestrial ecosystem damage as a function of precipitation loss using a relationship between woody plant species richness, water and energy regimes. We then apply the midpoint and end-point models to the production of soybean in Southeastern Amazonia which has resulted from the expansion of cropland into tropical forest, with noted effects on local precipitation. Our proposed cause-effect chain represents a complementary approach to previous contributions which have focused on water consumption impacts and/or have represented evapotranspiration as a loss to the water cycle.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".