More rationality and inclusivity are imperative in reference transition scenarios based on IAMs and shared socioeconomic pathways - recommendations for prospective LCA
Bibliographic record
Abstract
Prospective life cycle assessment (pLCA) is a key tool for evaluating future environmental impacts and supporting environmental policies. Recent pLCA methods integrate technological projections from transition scenarios modeled with integrated assessment models (IAMs), leveraging the shared socioeconomic pathways (SSPs) and representative concentration pathways developed within the framework of the Intergovernmental Panel for Climate Change (IPCC). However, this computational framework is influenced by subjective modeling choices within IAMs and SSPs, which can affect the robustness and relevance of future technological scenarios thus pLCA results. This article starts by highlighting these subjective choices through the lens of Science and Technology Studies, to then provide recommendations to enhance pLCA practices within this computational framework, especially through the selection of more (a) rational and (b) inclusive technological scenarios. The first step toward better practices is recognizing the inherited choices and limitations of borrowed models. Our recommendations then address the selection of future technological scenarios for pLCA: these scenarios could (a.1) account for the whole variability of mainstream transition scenarios from the latest IPCC report and its effect on pLCA results, (a.2) include only screened IPCC mainstream IAM scenarios based on proposed reality check criteria, (b.1) integrate scenarios rooted in alternative economic schools of thought, such as post-Keynesian economics or ecological macroeconomics, explore scenarios based on alternative (b.2) indicators prioritizing strong sustainability, justice, and well-being, and (b.3) societal narratives such as economic downscaling avenues and degrowth. Finally, we emphasize the need to incorporate ethical considerations into modeling, offering recommendations to (b.4) prioritize more equitable scenarios.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".