Model to assess the circularity of PV panels
Bibliographic record
Abstract
The transition from a linear to a circular economy (CE) is a key concern for industry, research institutions and stakeholders, representing a sustainable solution to the growth in waste generated by industrial processes. The effective management of waste produced by photovoltaic (PV) modules throughout their operational lifespan is vital to facilitate the conversion of photovoltaic energy in a more sustainable manner. The photovoltaic (PV) industry, critical to the transition to renewable energy, faces significant challenges related to end-of-life management of solar panels. This thesis investigates the application of the principles of the circular economy to the photovoltaic sector, with the aim of optimizing the use of resources and minimizing the environmental impact. By developing and evaluating a mathematical method to assess the circularity of photovoltaic panels throughout their entire life cycle, this research addresses key aspects such as recycling, reuse, energy use and CO₂ emissions. The proposed framework provides a comprehensive tool for stakeholders and PV panels suppliers to measure and improve the circularity of PV modules, promoting sustainable practices within the solar energy industry. This work contributes to the broader goal of achieving sustainable resource management by providing a solid foundation for the implementation of circular economy strategies in the renewable energy sector. The methodology employed in this thesis begins with an identification of the necessity for precise frameworks to measure circular economy aspects in PV module suppliers. A flexible framework was developed, with a particular focus on energy, recycling, reuse, and CO₂ emissions. The methodology comprises the creation of indicators for each aspect, derived from both scientific and industrial sources, and their subsequent application to a case study of a PV power plant in Spain. The indicators are constituted of sub-factors, and the scores are averaged in order to assess the circularity of each aspect. The findings of the case study demonstrated the efficacy of the assessment method. A comparative analysis of Canadian solar energy utilization, employing two distinct scenarios, the first scenario resulted in a score of 33.74% while the second scenario revealed a notable enhancement in the circularity performance of the latter. This is exemplified by the incorporation of a second-hand market strategy enhancing the circular performance of the PV panels in a 15,63%. For future work, enhancing the software tool to include pre-determined pathways could be highly beneficial. If the tool were to provide tailored guidelines for further improvement based on the company’s score, it could offer actionable recommendations for advancing circularity. These pathways would enable companies to receive specific, step-by-step guidance on how to enhance their practices, ultimately fostering more effective and strategic improvements in their circularity efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".