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Record W4406628723 · doi:10.5539/jsd.v18n1p94

A Systematic Review of Floating Photovoltaic Plant Environmental Impacts

2025· review· en· W4406628723 on OpenAlexvenueno aff
Larissa Faria, Michael Männich, Marcelo Coelho, Jucimara Andreza Rigotti, Tobias Bleninger, Jean Ricardo Simões Vitule

Bibliographic record

VenueJournal of Sustainable Development · 2025
Typereview
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Solar photovoltaic installations are growing fast worldwide as a renewable alternative for power generation, although, there are some disadvantages of the conventional land-based solar power plants, such as the need of a large land area. Thus, floating photovoltaic power plants (FPV) have emerged as a new solution in solar energy; however, the environmental impacts of such installations are still being investigated. Here, we performed a systematic review to compile reported environmental impacts of FPV installations on water bodies. Twenty-nine papers were retrieved and had data related to environmental impacts of FPV extracted. We looked for any physical, chemical, and biotic parameters that were quantified to evaluate FPV effects (FPV versus a control without FPV). Alterations in parameters were classified as positive or negative impacts according to each study. We found very few studies with primary quantitative data, which does not allow us to draw a general pattern of impacts. The most reported alterations were decreased temperature and evaporation from the water body after FPV installation (~35% of the studies). We found 17 alterations classified as a negative impact, and only six as positive. As FPVs are an emergent energy alternative more investments are needed in studies directly focused on assessing its environmental impacts, particularly making an explicit comparison of FPV installation and a control to disentangle their effects on water quality, phytoplankton and the biota.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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