Exploring technologically, temporally and geographically-sensitive life cycle inventories for wind turbines: A parameterized model for Denmark

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A comprehensive parameterized model for wind turbine life cycle inventories.

An alternative to generic values for wind turbines carbon footprint.

Application of the model to 1,400 wind turbines in Denmark.

Systematic analysis of temporal, geographical and technological influence.


In life cycle assessments of wind turbines and, more generally, of Renewable Energy Systems (RES), environmental impacts are usually normalized by electricity production to express their performance per kilowatt-hour. For most RES, manufacture and installation dominate the impacts. Hence, results are sensitive to parameters governing both impacting phases and electricity production. Most available studies present the environmental performance of generic wind turbines with assumed fixed values for sensitive parameters (e.g. electricity production) that often vary between studies and fail to reflect specificities of wind farm projects. This study presents an approach to build a comprehensive parameterized model that generates unique wind turbine life cycle inventories conditioned by technologically, temporally and geographically-sensitive parameters. This approach allows for the characterization of the carbon footprint of five sets of turbines in Denmark, where wind power is highly developed. The analysis shows disparities even between turbines of similar power output, mostly explained by the service time, load factor and components weights but also by background processes (evolution of electricity mix and recycled steel content). Project-specific inventories with technologically, temporally and geographically-sensitive parameters are essential for supporting RES development projects. Such inventories are especially important to evaluate highly-renewable electricity mixes, such as that of Denmark.

Graphical abstract


Wind turbine

Parameterized model

Life Cycle Assessment

Spatio-temporal variability

Carbon footprint

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