Additional value of non-electricity generating services (co-benefits) provided by non-conventional dual use solar photovoltaic systems: A PRISMA review
Bibliographic record
Abstract
Solar photovoltaic (PV) systems play a pivotal role in the global transition to renewable energy, providing decentralized energy generation and socio-economic advantages. The land-use intensity of conventional PV , however, has led to growing interest in non-conventional systems such as floating PV (FPV), building-integrated PV (BIPV), agrivoltaics , PV carports, and grid-support applications. These alternatives offer additional non-electricity generation-related benefits, often overlooked by the prevailing evaluation metric in energy systems , the levelized cost of electricity (LCOE). While LCOE remains the dominant tool for assessing PV economics due to its simplicity, it fails to account for these non-electricity-related co-benefits, leading to an undervaluation of emerging PV technologies . To address this gap, a systematic review of 261 peer-reviewed articles was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Thirteen categories of additional benefits were identified, with greenhouse gas emissions reduction (42.5 %) and water loss reduction (38.7 %) being the most frequently cited. Most studies, however, lacked a quantitative framework for integrating these benefits into overall PV economic evaluations. As a result, evaluation methods were discussed for each of these benefit categories, creating the foundation for a comprehensive framework that would evaluate non-conventional PV benefits. Additionally, significant inconsistencies in reporting critical PV attributes were identified, with only 25 % of studies reporting efficiency and 53 % specifying power ratings, making evaluation of PV co-benefits challenging. To address these shortcomings, this study proposes a standardized PV Nameplate Data table for academic PV studies encompassing key parameters such as location, module technology, efficiency, lifetime, power rating, and energy generation. Furthermore, the review underscores the necessity of developing open-source tools to systematically evaluate PV benefits; thereby enabling robust systems comparison, establishing comprehensive evaluation methodologies, improving system design, and informing policymakers’ decisions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".