How to run the script:

(0) Download all required databases in database_requirements.txt and store them in the folder "Databases".
Global Energy Monitor:            https://globalenergymonitor.org
Wang (2023) material demand data: https://zenodo.org/records/7023703 (only files ending with "_mats.csv" to "/Databases/Wang_2023_data"
Global Steel Production Costs database: https://www.transitionzero.org/insights/global-steel-production-costs
Add data in "additional_oil_and_gas_production_data.xlsx" to the "Production & reserves" sheet in the "Global Oil and Gas Extraction Tracker" Database file.

(1) Setup the conda environment by creating it from the «conda_requirements.txt» via:  “conda create --name env_name --file conda_requirements.txt -c conda-forge” in your console. Then, activate your newly created environment via “conda activate env_name” and install the missing requirements via “python -m pip install -r pip_requirements.txt”

2) Run "Setup.ipnyb" for loading life cycle inventories and life cycle impact assessment methods. If you have premise databases stored locally, the fastest way is to import them as bw2package files (option 1). The second fastest way is to import them via a plugin in the activity-browser (option 2). Alternatively, import via premise is also possible (option 3).

3) Run "Stocks_calc.ipnyb" for calculating material stocks in fossil infrastructure.

4) Run "Representive_distances_calc.ipnyb" for calculating representative
   steel scrap transport distances and to write them to life cycle inventories.

5) Run "LCIA_calc.ipnyb" for calculating environmental impacts and externality costs of
   steel production and renewable energy systems.

6) Run "Plot.ipnyb" for reproducing figures.

-> If there is any help required, please contact the corresponding authors of the study.