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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".