Welcome to Carculator documentation¶

carculator is a parameterized model that allows to generate and characterize life cycle inventories for different vehicle configurations, according to selected:

• powertrain technologies (9): petrol engine, diesel engine, electric motor, hybrid, plugin-hybrid, etc.,

• year of operation (2): 2000, 2010, 2020, 2040 (with the possibility to interpolate in between, and up to 2050)

• and sizes (9): Micro, Mini, Large, etc.

The methodology used to develop carculator is explained in an article by [Romain and others, n.d.].

The tool has a focus on passenger cars. It is initially based on the model developed in [Cox and others, 2018].

More specifically, carculator generates Brightway2 and SimaPro inventories, but also directly provides characterized results against several midpoint indicators from the impact assessment method ReCiPe, ILCD, as well as life cycle cost indicators.

carculator is a special in the way that it uses time- and energy-scenario-differentiated background inventories for the future, resulting from the coupling between the ecoinvent database and the scenario outputs of PIK’s integrated assessment model REMIND. This allows to perform prospective study and consider future expected changes in regard to the production of electricity, cement, steel, heat, etc.

Objective¶

The objective is to produce life cycle inventories for vehicles in a transparent, comprehensive and quick manner, to be further used in prospective LCA of transportation technologies.

Why?¶

Many life cycle assessment (LCA) models of passenger cars exist. Yet, because LCA of vehicles, particularly for electric battery vehicles, are sensitive to assumptions made in regards to electricity mix used for charging, lifetime of the battery, etc., it has led to mixed conclusions being published in the scientific literature. Because the underlying calculations are kept undocumented, it is not always possible to explain the disparity in the results given by these models, which can contribute to adding confusion among the public.

Because carculator is kept as open as possible, the methods and assumptions behind the generation of results are easily identifiable and adjustable. Also, there is an effort to keep the different modules (classes) separated, so that improving certain areas of the model is relatively easy and does not require changing extensive parts of the code. In that regard, contributions are welcome.

Finally, beside being more flexible and transparent, carculator provides interesting features, such as:

• a stochastic mode, that allows fast Monte Carlo analyses, to include uncertainty at the vehicle level

• possibility to override any or all of the 200+ default input car parameters (e.g., number of passengers, drag coefficient) but also calculated parameters (e.g., driving mass).

• hot pollutants emissions as a function of the driving cycle, using HBEFA 4.1 data, further divided between rural, suburban and urban areas

• noise emissions, based on CNOSSOS-EU models for noise emissions and [Stefano and others, 2019] for inventory modelling and mid- and endpoint characterization of noise emissions, function of driving cycle and further divided between rural, suburban and urban areas

• export of inventories as an Excel/CSV file, to be used with Brightway2 or Simapro, including uncertainty information. This requires the user to have ecoinvent installed on the LCA software the car inventories are exported to.

• export inventories directly into Brightway2, as a LCIImporter object to be registered. Additionally, when run in stochastic mode, it is possible to export arrays of pre-sampled values using the presamples library to be used together with the Monte Carlo function of Brightway2.

• development of an online graphical user interface: carculator online

Get started with Installation and continue with an overview about how to use the library.

User’s Guide¶

API Reference¶