AIDRES: A Database for the Decarbonisation of the Heavy Industry in Europe
Bibliographic record
Abstract
The AIDRES database aims to support the long-term objective of a fully integrated industrial strategy in the EU-27, providing a service to the European Commission and a catalogue for industries to understand the effectiveness, efficiency and cost of potential innovation pathways for achieving carbon neutral processes in the steel, chemical, cement, glass, fertilizers and refineries sectors by 2050.The approach considers the geographical distribution of the annual production of key products quantified at EU-NUTS3 regional level.Process integration techniques are used to generate and evaluate the reference and future optimal production routes, providing a quantitative, technical and multi-criteria estimate of energy demand in Europe's major industrial sectors.Decarbonisation of the production considers routes achieving (i) substitution of less energy intensive products, (ii) electrification of the production, (iii) use of oxy-combustion, (iv) carbon capture transport and storage, (v) use of alternative fuels and (vi) biomass.This results in a per-ton-of-product database containing energy demand, direct emissions at the plant, amount of captured CO 2 and the associated investment and operation costs.Scenarios 2018-2050 for the energy prices, indirect upstream emissions, CO 2 allowance and production shift are considered to foreseen the operation expenditure and total emissions.Finally, the per-ton database is scaled-up at the NUTS3 level by the regional production capacity.The application of the database is demonstrated at the EU level for the analysis of the present and future evolution of selected heavy industrial sectors, reaching a direct emission reduction between 90-95% compared with 2015-2019 average.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".