Driving factors for responsible sourcing in Europe: Motivations of renewable energy technology manufacturers
Bibliographic record
Abstract
• The study focuses on mineral sourcing by solar PV and wind turbine manufacturers. • Expert interviews explore drivers of responsible sourcing policies in Europe. • Altruism and compliance motivate manufacturers to implement responsible sourcing. • Complexity impedes responsible sourcing implementation in upstream supply chains. • Justice in supply chains needs more comprehensive approaches. The paper highlights the urgent demand for sustainable energy transitions within planetary boundaries while addressing social injustices. This transformation significantly relies on increasing the proportion of renewable energy sources, which requires extensive mining and utilisation of energy transition metals like copper, cobalt, and lithium. Particular concerns arise when Indigenous lands are involved in mining operations, raising issues of human rights and environmental integrity. The European Union and the United States of America have responded to these concerns with legislative measures to enhance supply chain transparency and prevent conflicts stemming from unethical practices. The study aims to explore responsible sourcing efforts among renewable energy technology manufacturers operating in Europe in the context of these regulations and the obstacles they encounter. Through semi-structured interviews with sustainability and procurement managers, the research investigates internal and external drivers for responsible sourcing, identifying altruistic values and regulatory compliance as critical factors. Despite acknowledging the importance of responsible sourcing, supply chain complexity and resource limitations persist. Ultimately, the study suggests that while responsible sourcing initiatives have the potential to promote justice within supply chains, there is a pressing need for holistic approaches to overcome existing barriers and effectively implement sustainable practices across the renewable energy sector.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".