Global Guidance On LCIA Indicators: Impacts Of Particulate Matter And Of Land Use
Bibliographic record
Abstract
Improving life cycle impact assessment models is crucial. The flagship project of the UNEP-SETAC Life Cycle Initiative provides global guidance and consensus on environmental LCIA indicators for climate change, particulate matter impacts, land use impact on biodiversity, water scarcity and water use impacts on human health. We present the recommendations and latest developments in two impact categories, particulate matter and land use. We present a framework for calculating characterization factors for indoor and outdoor emissions of primary PM2.5 and secondary PM2.5 precursors, enabling to account for the exposure to fine particulate matter (PM2.5) pollution in LCA, a major contributor to human disease burden. The model first provides default aggregated exposure factors for archetypal indoor and outdoor, urban and rural sources. It then customizes these archetypes for 3646 real-world urban areas in 16 sub-continental regions. Population intake fractions (iF) are highest in Southeast Asia, with 95% of the iF ranging from 4.3 to 160 ppm across 3646 cities (population-weighted mean of 39 ppm) and from 0.2 to 6.3 ppm (mean 2 ppm) across the 16 sub-continental rural regions. Intake fractions in residential and occupational indoor source environments range from 470 ppm to 62000 ppm, as function of air exchange rate and occupancy. Indoor exposure typically contributes 80–90% to overall exposure from outdoor sources. These intake fractions are then combined with average and marginal non-linear dose-response slope and severity factors to yield characterization factors expressed in DALY/kg precursor emitted. For land use, the selected model and indicator builds on species richness, incorporates the local effect of different land uses on biodiversity, links land use to species loss, includes the relative scarcity of affected ecosystems, and includes the threat level of species. Global average characterization factors (CFs) are interim recommended to quantify potential species loss (PSL) from land use and land use change, suitable for hotspot analysis in LCA. These CFs are not valid for comparative assertions. Developments are required before upgrading this interim recommendation to a full recommendation of CFs, including the refinement of land use classes, the inclusion of additional taxa, and the test of CFs in sufficient case studies.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".