Improved sustainability assessment of the G20’s supply chains of materials, fuels, and food
Bibliographic record
Abstract
Abstract Transparency in global value chains of materials, fuels, and food is critical for the implementation of sustainability policies. Such policies should be led by the G20, who represent more than 80% of global material, fuel, and food consumption. Multi-regional input–output analysis plays an important role for consumption-based assessment, including supply chains and their environmental impacts. However, previous accounting schemes were unable to fully assess the impacts of materials, fuels, and food. To close this gap, we provide an improved method to map key aspects of sustainability along value chains of materials, fuels, and food. The results show that the rise in global coal-related greenhouse gas (GHG) emissions between 1995 and 2015 was driven by the G20’s metals and construction materials industry. In 2015, the G20 accounted for 96% of global coal-related GHG emissions, of which almost half was from the extraction and processing of metals and construction materials in China and India. Major drivers include China’s rising infrastructure and exports of metals embodied in machinery, transport, and electronics consumed by other G20 members. In 2015, the vast majority (70%–95%) of the GHG emissions of metals consumed by the EU, USA, Canada, Australia, and other G20 members were emitted abroad, mostly in China. In contrast, hotspots in the impact displacement of water stress, land-use related biodiversity loss, and low-paid workforce involve the G20’s food imports from non-G20 members. Particularly high-income members have contributed to the G20’s rising environmental footprints by their increasing demand for materials, food, and fuels extracted and processed in lower-income regions with less strict environmental policies, higher water stress, and more biodiversity loss. Our results underline the G20’s importance of switching to renewable energy, substituting high-impact materials, improving supply chains, and using site-specific competitive advantages to reduce impacts on water and ecosystems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".