Linking service provision to material cycles: A new framework for studying the resource efficiency–climate change (RECC) nexus
Bibliographic record
Abstract
Abstract Material production accounts for 23% of all greenhouse gas emissions. More efficient use of materials—through decoupling of services that support human well‐being from material use—is imperative as other emission mitigation options are expensive. An interdisciplinary scientific assessment of material efficiency and its links to service provision, material cycle management, and climate policy is needed to identify effective strategies and help design the policy framework required for their implementation. We present the resource efficiency–climate change (RECC) mitigation framework, a first step toward such a comprehensive assessment. RECC is based on dynamic material flow analysis and links the services provided (individual motorized transport and shelter) to the operation of in‐use stocks of products (passenger vehicles and residential buildings), to their expansion and maintenance, and to their material cycles to model mitigation strategies and analyze trade‐offs for environmental impacts along the products’ life cycle. A key innovation of RECC is the upscaling of product archetypes with different degrees of material and energy efficiency, which are simulated with engineering tools. We utilize RECC with augmented storylines of the shared socioeconomic pathways to describe future service demand and associated material requirements. Ten material efficiency strategies at different stages of the material cycle can be assessed by ramping up their implementation rates to the identified technical potentials. RECC provides scenario results for the life cycle impacts of ambitious service–material decoupling concurrent with energy system decarbonization, giving detailed insights on the RECC mitigation nexus to policy‐makers worldwide.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".