Hybridizing Life Cycle Assessment (LCA) with Local Stakeholder Inputs
Bibliographic record
Abstract
Life cycle assessment (LCA) is the centerpiece of ISO 14040/44 certification as well as the United Nations’ Environmental Program. However, LCA and similar sustainability tools have been criticized as leading to loose coupling, misalignment, and symbolic usage. LCA experts have partly addressed these problems by developing Social LCA or by incorporating stakeholder multi-criteria preferences into LCA decisions. But systematic methods for engaging with stakeholders, including their local logics, and for weighting their preferences are lacking. In this study, we draw on a combination of stakeholder and institutional theory to detail a co-design engagement method that also accounts for differences among local stakeholder logics in order to hybridize LCA as a sustainability tool. In Study 1, we construct a standard LCA of two novel Oil Sands wetland remediation products: chicken feathers and biochar. In Study 2, we run an experiment with local stakeholders to identify those with more balanced (versus polarized) environmental and economic logics and then incorporate their preferences in a modified LCA of the remediation materials. Our research contributes to sustainability tool development as well as to stakeholder and institutional theory.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".