End-of-Life management of wind turbines, PV modules and Lithium-Ion batteries: Current practices and closing the circular economy gap
Bibliographic record
Abstract
Renewable energy generation and increased electrification are pivotal to reducing greenhouse gas emissions and mitigating climate change. Consequently, global deployment of wind turbines, PV modules and electric vehicles has soared, and the trend is expected to continue. These technologies have only recently started reaching the end of their design lives, and rapid escalation of end-of-life (EoL) waste volumes are projected. This study responds to the imminent waste issue by researching current EoL management practices, initiatives and regulations of these three technologies in Canada and globally. Through extensive literature review and communications with select experts in the EoL field, it also seeks to identify factors that impede current EoL management efforts to close the circular economy gap and those that can support the overall sustainability of deploying these technologies. \n \nThe EoL management of these technologies is in the early stages and many innovative initiatives are being explored and developed. There are currently few proven business cases, and barriers to the EoL strategies’ profitability and effectiveness include insufficient waste feedstock, inadequate collection infrastructure and second-life markets, and uncertainty about the assets’ remaining useful life. Designing for circularity, collaboration between supply chain stakeholders, circular business models and technology-specific regulations that incorporate extended producer responsibility, second-life targets and circular solutions can help progress the technologies toward improved sustainability. \n \nThe research found that EoL management is a complex but necessary undertaking that needs to consider multiple, often conflicting factors. Additionally, the technologies and their EoL management practices are dynamic and fast-changing. Hence this study's findings are best viewed as compelling evidence of the increasing need for robust EoL management and a demonstration of potential solutions rather than absolute conclusions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".