DLR Institute of Networked Energy Systems
Published February 18, 2026 | Version 1.0.1
Software Open

Code and data for publication: Future Environmental Impacts of Global Iron and Steel Production

Authors/Creators

  • 1. ROR icon Leiden University
  • 2. ROR icon Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
  • 3. ROR icon Paul Scherrer Institute
  • 4. ROR icon Lund University
  • 5. ROR icon Netherlands Environmental Assessment Agency
  • 6. Utrecht University
  • 7. ROR icon Netherlands Organisation for Applied Scientific Research
  • 8. Leiden University (CML)

Description

This repository contains the python code and data for the publication:
 
Harpprecht, C., Sacchi, R., Naegler, T., van Sluisveld, M., Daioglou, V.,  Tukker, A., Steubing, B. (2025). Future Environmental Impacts of Global Iron and Steel Production. Energy & Environmental Science. https://doi.org/10.1039/D5EE01356A 
 
This repository is structured into 2 sub-projects:
  1. Scenario data processing and analysis, LCA data analysis and plotting (folder:  1_data_processing)
  2. Creation of LCI databases using premise (folder:  2_premise_database_creation)

Folder 1_data_processing: Scenario data processing and analysis, LCA data analysis and plotting

This folder provides the Python code and data for:
 
  1. processing and converting scenario data on future steel production from the Integrated Assessment Model (IAM) IMAGE into the data format required by the prospective LCA software tool premise (specifically, premise community scenarios);
  2. processing and analysing prospective LCA (pLCA) results of the scenarios for steel production;
  3. generating the plots for the publication about the IMAGE steel scenario data and the pLCA results of future steel production.
 
The code represents a typical pipeline for data processing and allows to reproduce the aforementioned results and plots.
 
Data inputs:
  • scenario data for steel production from the IAM IMAGE (included)
  • pLCA results of future steel production calculated using the database produced with the code in folder 2_premise_database_creation (not included, as it contains proprietary data from ecoinvent)
 
Outputs:
  • steel scenario data for premise (input for Folder 2: 2_premise_database_creation)
  • plots
  • underlying data of analyses

Folder 2_premise_database_creation: Creation of LCI databases using premise

This folder provides the Python code and data for creating pLCA databases using premise and its function for external scenarios. The Python code is in the Jupyter Notebook.
 
Data inputs:
  • steel scenario data for premise (from Folder 1: 1_data_processing)
  • inputs required for external scenarios for premise:
    1. config.yaml (included)
    2. lcis_steel_ei3.9.1.xlsx (included only partially as "lcis_steel_ei3.9.1_no-ecoinvent-data.xlsx", as proprietary data points from ecoinvent are excluded )
    3. steel_scenario_data.csv (included)
    4. datapackage.json (included)
  • ecoinvent 3.9.1. (excluded)
 
Outputs:
  • pLCA database(s) (e.g. as superstructure database)
  • scenario-difference-file.xlsx (excluded)

Metadata

The LCIs are documented in the excel file lcis_steel_ei3.9.1.xlsx / "lcis_steel_ei3.9.1_no-ecoinvent-data.xlsx". 
 
How to get proprietary data
Interested parties with a valid ecoinvent license are asked to contact the corresponding author or Romain Sacchi (romain.sacchi@psi.ch) directly to receive the input files that contain ecoinvent data points. With these files, the entire workflow can be executed.
Alternatively, with a valid ecoinvent license, premise  can also be used directly to incorporate the steel LCIs into ecoinvent, as the inventories are by now also available in premise (as desribed in the documentation). During extraction, premise fills in the data gaps by extracting them from ecoinvent automatically. Please note, that the steel scenarios depend on the IMAGE version. Thus, they might differ for scenarios from another IMAGE version than used in the original publication. 
 
Additional information
More information is found in the READMEs in each folder.
 
Authors and acknowledgment
The Code was written by Carina Harpprecht with input from Romain Sacchi, Tobias Naegler, Amelie Müller, and Benjamin Fuchs. The Python code was developed at DLR in parallel with a project of Amelie Müller who conducted here Master thesis at DLR supervised by Carina Harpprecht about future environmental impacts of cement production (Müller et al., 2024). Analogies in the code are marked with a tag: # ref-to-cement. We would like to thank Amelie for her work. Benjamin Fuchs served as technical advisor.
 
Licenses
  • for Python Code: BSD 3-Clause License
  • for Data: specified in the respective folder

Project status

This project has been finalized and will not be further developed.

Contact
Carina Harpprecht: carina.harpprecht@dlr.de
 
References
Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., & Steubing, B. (2024). Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Journal of Cleaner Production, 450, 141884. https://doi.org/10.1016/j.jclepro.2024.141884
Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., & Steubing, B. (2023). Code and data for publication: Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Zenodo. https://doi.org/10.5281/zenodo.10255594

Files

Code_and_data_Harpprecht et al. 2025_Future Environmental Impacts of Global Iron and Steel Production.zip

Files (7.1 MB)

Additional details

Related works

Is supplement to
Journal article: 10.1039/D5EE01356A (DOI)

Software

Programming language
Python
Development Status
Unsupported

References

  • Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., & Steubing, B. (2024). Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Journal of Cleaner Production, 450, 141884. https://doi.org/10.1016/j.jclepro.2024.141884
  • Müller, A., Harpprecht, C., Sacchi, R., Maes, B., van Sluisveld, M., Daioglou, V., Šavija, B., & Steubing, B. (2023). Code and data for publication: Decarbonizing the cement industry: Findings from coupling prospective life cycle assessment of clinker with integrated assessment model scenarios. Zenodo. https://doi.org/10.5281/zenodo.10255594