A methodology for assessing environmental impact of building integrated PV in low carbon footprint electricity generation context
Bibliographic record
Abstract
With the growing interest in renewable energy to mitigate climate change, photovoltaics are increasingly relevant due to their limited carbon emissions in the use phase. This study focuses on integrating photovoltaic technologies in countries with low-carbon electricity mixes, specifically the cases of France and Norway. It presents a comprehensive methodology assessing the environmental impact of PV technologies and their application in a French and a Norwegian building. The research includes a case study of a single-family house, modeled in TRNSYS for dynamic thermal systems simulation, operating in both locations. The photovoltaic panels' life cycle assessment is conducted using the SimaPro software and a functional unit of 1 kWh from mono-crystalline panels with an expected service life of 25 years. Such analysis aims to evaluate the environmental impact through key performance indicators during the life span of the photovoltaic panel, from cradle to use with a focus on the raw material use, manufacturing processes, transportation, use phase replacements and electricity production. The indicators analysed are global warming potential, cumulative energy demand (non-renewable, fossil), energy payback time and energy return on energy invested. The study also explores the impact of different manufacturing, transportation and installation scenarios of the photovoltaic panels, including a 100% European low carbon footprint electricity mix. In summary, the findings demonstrate that in countries with low-carbon electricity production, the use of photovoltaic panels presents a favorable outcome in terms of global warming potential for the French case (25-38.6 g C O 2 e/kWh), regardless of their place of manufacturing. For the Norwegian scenarios (spanning 29.5-45.6 g C O 2 e/kWh), the life cycle benefit in terms of emission reductions is only evident if the panels are locally produced in Europe. This conclusion is based on electricity from the photovoltaic installation modeled to replace the Norwegian production mix of electricity. Thus, the geographical system boundaries in relation to the replaced electricity is an important parameter. Cumulative energy demand (non-renewable, fossil) was found to vary between 0.34 MJ/kWh and 0.44 MJ/kWh, the Norwegian scenarios consistently showing higher numbers than the French. Energy payback times of the mono crystalline photovoltaic panel ranged between 0.75 to 0.97 years depending on the solar potential of the installation place and the scenarios of manufacturing.
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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".