Impacts of agrisolar co-location on the food–energy–water nexus and economic security
Bibliographic record
Abstract
Understanding how solar PV installations affect the landscape and its critical resources is crucial to achieve sustainable net-zero energy production. To enhance this understanding, we investigate the consequences of converting agricultural fields to solar photovoltaic installations, which we refer to as ‘agrisolar’ co-location. We present a food, energy, water and economic impact analysis of agricultural output offset by agrisolar co-location for 925 arrays (2.53 GWp covering 3,930 ha) spanning the California Central Valley. We find that agrisolar co-location displaces food production but increases economic security and water sustainability for farmers. Given the unprecedented pace of solar PV expansion globally, these results highlight the need for a deeper understanding of the multifaceted outcomes of agricultural and solar PV co-location decisions. This analysis looks at the impacts and outcomes from installing solar arrays on agricultural land, finding that these ‘agrisolar’ projects can displace food production but simultaneously provide economic security and offset water use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".