AIDRES: A Database for the Decarbonisation of the Heavy Industry in Europe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it