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Record W3033017992 · doi:10.1021/acs.estlett.0c00349

Economic Potential for Rainfed Agrivoltaics in Groundwater-Stressed Regions

2020· article· en· W3033017992 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Science & Technology Letters · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsUniversity of Victoria
FundersH2020 Societal ChallengesEuropean Commission
KeywordsGroundwaterEnvironmental scienceCost of electricity by sourceWater resource managementSustainabilityAgricultureElectricity generationNatural resource economicsHydrology (agriculture)EconomicsGeographyEcologyEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Agrivoltaics co-locate crops with solar photovoltaics (PV) to provide sustainability benefits across land, energy, and water systems. Policies supporting a switch from irrigated farming to rainfed, grid-connected agrivoltaics in regions experiencing groundwater stress can mitigate both groundwater depletion and CO2 from electricity generation. Here, hydrology, crop, PV, and financial models are integrated to assess the economic potential for rainfed agrivoltaics in groundwater-stressed regions. The analysis reveals 11.2–37.6 PWh/yr of power generation potential, equivalent to 40%–135% of the global electricity supply in 2018. Almost 90% of groundwater depletion in 2010 (∼150 km3) occurred where the levelized cost for grid-connected rainfed agrivoltaic generation is 50–100 USD/MWh. Potential revenue losses following the switch from irrigated to rainfed crops represent 0%–34% of the levelized generation cost. Future cost–benefit analysis must value the avoided groundwater stress from the perspective of long-term freshwater availability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.198
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it