Climate change impacts and annual electricity production of all wind turbines installed in Europe until 2020

This repository provides datasets and analysis scripts supporting the study:

Huber et al. (2026). "Integrating geographic data into greenhouse gas emission footprinting: a spatial analysis of European wind turbines". International Journal of Life Cycle Assessment.

The data were generated using the ReWind model, a spatially resolved life cycle assessment (LCA) framework for onshore and offshore wind energy systems across Europe. The model combines turbine-specific parameters with geographically explicit data (e.g. wind resources, bathymetry, and grid distances) to calculate greenhouse gas emission footprints (GHGFs).

The ReWind model is publicly available on GitHub:

https://github.com/EVERGi/ReWind

 

The repository contains the following files:

1.  cc_impacts_eu_wind_fleet_corrected.csv
   
   This dataset provides the calculated greenhouse gas emission footprints (GHGFs) for the European wind fleet at turbine level. 
   Each record represents one turbine and includes the following variables:
   • ISO_code: Country code 
   • Latitude, Longitude: Geographic coordinates 
   • sea_depth_m: Water depth (for offshore turbines) 
   • Total: GHG emission footprint (kg CO₂eq per kWh)
   • Capacity_factors: Capacity factor (fraction) 
   • Lifetime_production_kWh: Total electricity production over the assumed lifetime 
2.  Statistical_summary.csv
   This file summarizes statistical relationships between key variables across turbine groups. 
   Columns include:
   • Foundation_type: Offshore foundation type (e.g. monopile, semi-submersible) 
   • Target / variable: Variables included in the correlation analysis 
   • Pearson: Pearson correlation coefficient 
   • Spearman: Spearman correlation coefficient 
   • Mutual_info: Mutual information metric 
   • n: Number of observations 
3. problematic_offshore_turbines.csv
   This dataset lists identified offshore turbines with potentially implausible or inconsistent input parameters (e.g. anomalous sea depth values), used for data cleaning and filtering during the analysis.
4. Fleet_basic_charts_validation.ipynb
   Jupyter Notebook used for validation and exploratory analysis of the European wind fleet dataset. Includes basic descriptive statistics and visualizations.
5. Offshore_analysis.ipynb
   Jupyter Notebook containing detailed analysis of offshore turbine characteristics, including the evaluation of foundation types, turbine size, and their influence on GHG emission footprints.
6. High_impact_turbines.ipynb
   Jupyter Notebook focusing on turbines with high GHG emission footprints. The notebook explores the drivers behind extreme values, including turbine size, capacity factor, and spatial clustering.

The datasets provided here represent processed outputs of the ReWind model. While they enable validation and reproduction of the analytical results presented in the associated publication, full reproduction of the model calculations requires:

• Access to licensed life cycle inventory data (e.g. ecoinvent)
• Large external geospatial datasets (e.g. GEBCO bathymetry)
• Substantial computational resources for full fleet evaluation

To ensure transparency, the GitHub repository provides the model implementation and a reduced example workflow, enabling users to reproduce the methodology and verify calculations on smaller datasets.

 

If you use this dataset, please cite both the publication and this dataset:

Huber et al. (2026). Integrating geographic data into greenhouse gas emission footprinting: a spatial analysis of European wind turbines. International Journal of Life Cycle Assessment.

 

• Coordinates are provided in decimal degrees (WGS84)
• GHG emission footprints are expressed in kg CO₂eq per kWh
• Capacity factors are expressed as fractions (not percentages)