Defining and Achieving Next-Generation Green Electronics: A Perspective on Best Practices Through the Lens of Hybrid Printed Electronics
Bibliographic record
Abstract
Interest in green electronics has grown significantly as global electronic production, electronic waste (e-waste) generation, and resource depletion rise in parallel with international efforts to reduce carbon emissions. The persistent challenges of successful e-waste recycling and the environmental impact of resource consumption highlight the urgent need for transformative solutions in the electronics sector. Without major breakthroughs, conventional electronics as currently manufactured and consumed fall short of contributing to climate and other sustainability targets. Therefore, to support the transition to greener electronics, this work reviews existing research and legislation relevant to the field and considers the perspectives and ongoing efforts of the EU Green Electronics Working Group (comprised of 12 green electronics-focused Horizon Europe projects) to identify and define the most important aspects of green electronics. Given the absence of a widely accepted definition of what makes electronics truly “green,” the most critical aspects are clarified to support the development of a common, unified definition:Electronics that, when measured against their alternatives over their whole lifecycle and value chain, have a reduced environmental impact in terms of greenhouse gas emissions, toxicity, and resource depletion and avoid burden shifting from one impact to another or along the value chain, while fulfilling a given function. A set of recommendations and best practices, informed by the latest advancements and ongoing research developments in green electronics, is then provided to address the entire lifecycle of electronic devices. These strategies offer a framework to guide the development and adoption of greener electronics.
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 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 | 0.016 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".