Taxonomy of uncertainty in environmental life cycle assessment of infrastructure projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Environmental life cycle assessment (LCA) is increasingly being used to evaluate infrastructure products and to inform their funding, design and construction. As such, recognition of study limitations and consideration of uncertainty are needed; however, most infrastructure LCAs still report deterministic values. Compared to other LCA subfields, infrastructure LCA has developed relatively recently and lags in adopting uncertainty analysis. This paper presents four broad categories of infrastructure LCA uncertainty. These contain 11 drivers focusing on differences between infrastructure and manufactured products. Identified categories and drivers are: application of ISO 14040/14044 standards (functional unit, reference flow, boundaries of analysis); spatiotemporal realities underlying physical construction (geography, local context, manufacturing time); nature of the construction industry (repetition of production, scale, and division of responsibilities); and characteristics of infrastructure projects (agglomeration of other products, and recurring embodied energy). Infrastructure products are typically large, one-off projects with no two being exactly alike in terms of form, function, temporal or spatial context. As a result, strong variability between products is the norm and much of the uncertainty is irreducible. Given the inability to make significant changes to an infrastructure project ex-post and the unique nature of infrastructure, ex-ante analysis is of particular importance. This paper articulates the key drivers of infrastructure specific LCA uncertainty laying the foundation for future refinement of uncertainty consideration for infrastructure. As LCA becomes an increasingly influential tool in decision making for infrastructure, uncertainty analysis must be standard practice, or we risk undermining the fundamental goal of reduced real-world negative environmental impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it