Prospective life cycle assessment as a tool for environmentally responsible innovation
Bibliographic record
Abstract
Life cycle assessment (LCA), which provides a framework for assessing the potential environmental impact of technologies across their life cycle, has been identified as a potential tool for environmentally responsible innovation (RI). Traditional LCA approaches are insufficient for RI because they tend to be retrospective and underemphasize stakeholder engagement. Recent framing studies on LCA of emerging technology, which includes prospective LCA, suggest that uncertainty, data availability, methodological challenges, applicable evaluations techniques, and type of decisions supported are related to technology and market maturity. This study evaluates this framing based on two prospective LCAs of emerging technologies conducted by the authors. Uncertainty and methodological challenges were related to technology readiness. However, data availability was a challenge regardless of technology maturity, and was best addressed by engagement with technology developers and end-users. Furthermore, questions explored and evaluation techniques used were more diverse than those reported by the framing studies and were related to both analyst and stakeholder interests and technology and market maturity. While initial framing provides important guidance towards incorporating anticipation in prospective LCA, future interactions must not overlook the importance of engaging with stakeholders to guide model development and inform environmentally responsible development and innovation.
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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 | 0.056 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".