Global Scenarios of Resource and Emission Savings from Material Efficiency in Residential Buildings and Cars
Bibliographic record
Abstract
This dataset contains the full results of a scenario analysis for the impact of material efficiency on material use, energy consumption, and GHG emissions for passenger vehicles and residential buildings with global scope. The results were generated with v2.4 of the ODYM-RECC model (open dynamic material systems model for the resource efficiency and climate change mitigation project), which is a modular depiction of major end-use sectors and the material cycles for the climate-relevant bulk materials (https://github.com/YaleCIE/RECC-ODYM). Its system definition comprises the use phase of materials (in products) and the material cycle stages mining, primary production, manufacturing, waste management and scrap recovery, and remelting/recycling as well as an energy supply scenario. ODYM-RECC generates a set of what-if scenarios for the climate-relevant end-use sectors and bulk material cycles against different socioeconomic, technology deployment, and climate policy backgrounds. It does so by applying a mass-balanced framework for the material cycles. It allows us to study the impacts of a broad spectrum of sustainable development strategies on the material cycles and identify trade-offs and constraints. It does not assess the likelihood of realisation of any of the scenarios studied but checks if mass balance constraints (e.g. by long product lifetimes or limited scrap supply) render some scenarios unfeasible from a material cycle point of view.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".