Creating just agrivoltaic transitions for large-scale solar: a comparative multi criteria analysis
Bibliographic record
Abstract
Agrivoltaics enables synergies between solar photovoltaics and agriculture, offering a dual solution to preserve agricultural activities while producing renewable electricity. Agrivoltaic systems promote agricultural, economic, social, and environmental outcomes, advancing Sustainable Development Goals SDG 2 (Zero Hunger); SDG 7 (Affordable and Clean Energy); SDG 12 (Responsible Consumption and Production); and (SDG 13 Climate Action). While the climate, technological, and agricultural productivity benefits of agrivoltaics are well understood, questions remain concerning its socio-technical opportunities and challenges as a catalyst for just transitions in both mature and emerging agrivoltaics jurisdictions. This study presents the first multi-criteria analysis (MCA)-based just transition assessment of agrivoltaics, providing a novel quantitative and socio-legal framework to evaluate its contribution to equitable energy transitions. Assessing the multifaceted contribution of agrivoltaics to climate, food, and energy security requires quantifying and evaluating benefits and risks to activate just transition-focused policy and legal reform. In turn this can enable the acceleration of socio-technical innovations to achieve landscape-level just agrivoltaics. The MCA framework is applied to three mature European agrivoltaic jurisdictions − Germany, Italy, and France − to guide emerging agrivoltaic practices in Alberta, Canada, and New South Wales, Australia. Applying quantitative and socio-legal functional comparative mixed methods MCA approach, the study provides a replicable framework to inform policy and regulatory design, highlighting opportunities to align agrivoltaic deployment with broader just transition objectives. The study findings offer actionable socio-legal insights for scaling agrivoltaics while embedding just transition principles, with broader implications for energy, climate, and agricultural policy.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".