Regioinvent: a regionalized version of ecoinvent integrating detailed trade data
Bibliographic record
Abstract
Abstract Life cycle inventory databases currently offer limited country-level coverage and often rely on outdated supply chain representations in their market processes. In this research, we combine ecoinvent with the BACI database—which provides detailed international trade statistics—to regionalize ecoinvent and model average supply chains based on up-to-date data. To achieve this, we automatically duplicate regional processes and adapt them to specific national contexts by modifying three key inputs in the ecoinvent database: electricity, heat, and municipal solid waste treatment. We also construct national consumption markets based on international trade and domestic production data to capture country-specific supply chain characteristics. These markets are fully relinked throughout the database, and elementary flows are spatialized. The resulting regioinvent adaptation of ecoinvent comprises 4,031 products regionalized across 225 countries, yielding 669,571 newly created processes. We assess the effect of regionalization by comparing each regionalized process with its corresponding original ecoinvent process. On average, regionalization introduces differences of 10.8% for climate change, 13.9% for human health, and 11.6% for ecosystem quality. Spatialization of elementary flows is found to exert a stronger influence on regionalized impact categories than inventory regionalization alone. Future developments of regioinvent will focus on addressing its current limitations, particularly the estimation of domestic production data and the contextualization of transportation within national consumption markets. Despite these limitations, we recommend that LCA practitioners use regioinvent, as it is expected to provide more accurate results than the traditional ecoinvent database.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.017 |
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".