Prospective life cycle assessment of two-wheelers made blazing fast.
A fully parameterized Python model developed by the Technology Assessment group of the Paul Scherrer Institut to perform life cycle assessments (LCA) of two-wheelers. Builds upon the initial LCA model developed by Cox et al. 2018.
See the documentation for more detail, validation, etc.
carculator_two_wheeler allows yout to:
- produce life cycle assessment (LCA) results that include conventional midpoint impact assessment indicators as well cost indicators
carculator_two_wheeleruses time- and energy scenario-differentiated background inventories for the future, based on outputs of Integrated Asessment Model REMIND.- calculate hot pollutant and noise emissions based on a specified driving cycle
- produce error propagation analyzes (i.e., Monte Carlo) while preserving relations between inputs and outputs
- control all the parameters sensitive to the foreground model (i.e., the vehicles) but also to the background model (i.e., supply of fuel, battery chemistry, etc.)
- and easily export the vehicle models as inventories to be further imported in the Brightway2 LCA framework or the SimaPro LCA software.
carculator_two_wheeler integrates well with the Brightway2 LCA framework.
carculator_two_wheeler is at an early stage of development and is subject to continuous change and improvement.
Three ways of installing carculator_two_wheeler are suggested.
We recommend the installation on Python 3.7 or above.
conda install -c romainsacchi carculator_two_wheelerpip install carculator_two_wheelerFor more examples, see examples.
carculator_two_wheeler has a graphical user interface for fast comparisons of vehicles.
Do not hesitate to contact the development team at [email protected].
See contributing.
BSD-3-Clause. Copyright 2020 Paul Scherrer Institut.