Calculate CO2 emissions for personal mobility.
Calculate CO2 (equivalent) emissions for a given transport trip and provide a simple interpretation of the result.
Install from PyPI via
pip install transport-co2
You can list the currently supported modes like so:
from transport_co2 import Mode list(Mode)
Mode allows you to estimate the CO2 emission per passenger for a journey given its distance (in km):
Mode.SMALL_CAR.estimate_co2(distance_in_km=100) # 11200.0
This estimate is based on an average occupancy (in this example
1.5). You can also specify the occupancy:
Mode.SMALL_CAR.estimate_co2(distance_in_km=100, occupancy=3) # 5600.0
If you don't want to work with the
Mode enum, you can instead work with the
from transport_co2 import estimate_co2 estimate_co2(mode="light_rail", distance_in_km=100) # 1400.0
You can also specify the occupancy like above:
estimate_co2(mode="light_rail", distance_in_km=100, occupancy=250) # 873.6
Running unit tests
In order to run the unit tests for this project:
- activate the virtual environment with
This project is intended to be used to help raise awareness about the cost of transportation choices, so people can make informed decisions.
We are initially focused on ground (road and rail) transport, as it is the most significant source on transport greenhouse gas emissions.
This library intends to provide:
- a statistical estimate of greenhouse gase emissions given information about a trip (origin/destination, mode, and/or distance)
- a simple interpretation of the statistical emissions estimate, in terms such as "high" or "low"
We try to provide a "best effort" estimate of CO2 emissions for transportation modes defined by the OpenTripPlanner project.
Our baseline estimates come from an infographic published by the European Environment Agency: CO2 emissions from passenger transport.
For modes not covered in the EEA infographic, we have used data from other sources (described below). In some cases, we have simply averaged values for several modes to provide estimates for more ambiguous modes, like CAR and TRANSIT.
Some mode energy estimates are based on megajoules for the petroleum consumed. For those modes, we used a petroleum average of 12g CO2 eq. per megajoule (The International Council on Clean Transportation, 2010).
For FERRY mode calculation we use the average of 6.8 MJ per passenger kilometer with an average occupancy of 91 (Schiller & Kenworthy, 2017, p. 143-144). We then estimate that each ferry passenger kilometer approximates to 81.6 g CO2, based on the above petroleum megajoule to grams CO2 conversion factor (6.8 * 12).
For SUBWAY (metro) mode calculation we use the average of 0.52 MJ per passenger kilometer with an average occupancy of 31 (Schiller & Kenworthy, 2017, pp. 143-144). We then estimate that each metro passenger kilometer approximates to 6.24 grams of CO2, based on the above petroleum megajoule to grams CO2 conversion factor (0.52 * 12).
- The International Council on Clean Transportation. (2010). Carbon Intensity of Crude Oil in Europe. Retrieved from https://theicct.org/sites/default/files/ICCT_crudeoil_Eur_Dec2010_sum.pdf
- Schiller, P. L., & Kenworthy, J. R. (2017). An Introduction to Sustainable Transportation. Abingdon, United Kingdom: Routledge.
Further improvements to the model may come from other sources, such as the following.
Initial package structure forked from navdeep-G/setup.py.
Friendly nod to jamiebull1/transport-carbon.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for transport_co2-0.3.0-py2.py3-none-any.whl