NRC light-duty vehicle life cycle assessment model
Bibliographic record
Abstract
This document provides an overview of the National Research Council’s (NRC’s) light-duty vehicle life cycle assessment (LCA) model that was developed as part of the Public Services and Procurement Canada’s (PSPC’s) Low-Carbon Procurement Project (LCPP). It includes a brief description of the methodology, a detailed overview of the development of the inventory of modelled processes, and instructions on setting-up and using the model to evaluate the life cycle greenhouse gas (GHG) emissions of light-duty vehicles. The NRC’s light-duty vehicle LCA model can be used to evaluate the life cycle greenhouse gas (GHG) emissions of 15 types of vehicles that vary by powertrain type and vehicle class: • five types of powertrains, including internal combustion engine (ICEV), hybrid (HEV), plug-in hybrid (PHEV), all-electric (BEV) and fuel cell electric (FCEV), and • three classes of vehicle, including a passenger car, sport utility vehicle (SUV) and pickup truck (PUT). This model builds on NRC’s previous LCA work on light-duty vehicles It uses openLCA – an open-source LCA software platform, includes detailed information about material and energy inputs of vehicle component production and assembly, incorporates upstream energy use information from Environment and Climate Change Canada’s Fuel LCA model, uses Ecoinvent v3 as the life cycle inventory background database, and allows the users to define vehicle-specific parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".