pathways is a Python package that characterizes the
environmental impacts of products, sectors or transition scenarios
over time using Life Cycle Assessment (LCA).
Compared to traditional scenario results from energy models,
pathways provides a more detailed and transparent view of the
environmental impacts of a scenario by resolving supply chains
between producers and consumers (as an LCA does). Hence, direct
and indirect emissions are accounted for, and double-counting
issues are partially resolved.
pathways is initially designed to work with data packages produced
by premise, but can be used with any IAM scenarios and LCA databases.
pathways reads a scenario and a corresponding set of scenario-based LCA matrices
and calculates the environmental impacts of the scenario (or a subset of it) over time.
pathways requires Python 3.10 or 3.11. It also requires the packages listed in the requirements.txt file.
pathways is in an early development stage, and
can be installed from the Github repo with pip:
pip install git+https://github.com/polca/pathways.git
or alternatively, you can clone the repository and install it from the source:
git clone https://github.com/polca/pathways.git
cd pathways
pip install -r requirements.txt
pathways is also available via anaconda:
conda install -c conda-forge -c romainsacchi pathways
.. note:: If you use an ARM architecture, you may want to also install
the scikit-umfpack package from the conda-forge channel for faster calculation.
However, you need to make sure that numpy<=1.24.4 is installed, as bw2io is not compatible
with the latest version of numpy:
conda install -c conda-forge scikit-umfpack
pathways is a Python package, and can be used in Python scripts
or in a Python interpreter.
See the example notebook.
To use the Pathways class, you need to provide it with a datapackage that contains your scenario data, mapping information, and LCA matrices. The datapackage should be a zip file that contains the following files:
datapackage.json: a JSON file that describes the contents of the datapackage- a
mappingfolder containing amapping.yamlfile that describes the mapping between the IAM scenario and the LCA databases - a
inventoriesfolder containing the LCA matrices as CSV files - a
scenario_datafolder containing the IAM scenario data as CSV file
from pathways import Pathways
datapackage_path = "path/to/your/datapackage.zip"
p = Pathways(
datapackage=datapackage_path,
debug=True # optional, if you want to see the logs
)
# Define your parameters (leave any as None to use all available values)
methods = ["IPCC 2021", "ReCiPe 2016"]
models = ["ModelA", "ModelB"]
scenarios = ["Baseline", "Intervention"]
regions = ["Region1", "Region2"]
years = [2020, 2025]
variables = ["Electricity", "Transport"]
# Run the calculation
p.calculate(
methods=methods,
models=models,
scenarios=scenarios,
regions=regions,
years=years,
variables=variables,
use_distributions=0 # optional, if > 0: number of iterations for Monte Carlo analysis
)The list of available LCIA methods can be obtained like so:
print(p.lcia_methods)The argument datapackage is the path to the datapackage.zip file
that describes the scenario and the LCA databases -- see dev/sample.
The argument methods is a list of methods to be used for the LCA
calculations. The argument years is a list of years for which the
LCA calculations are performed. The argument regions is a list of
regions for which the LCA calculations are performed. The argument
scenarios is a list of scenarios for which the LCA calculations are
performed.
If not specified, all the methods, years, regions and scenarios defined in the datapackage.json file are used, which can be very time-consuming.
Once calculated, the results of the LCA calculations are stored in the .lcia_results
attribute of the Pathways object as an xarray.DataArray.
You can display the LCA results with an optional cutoff parameter to filter insignificant data:
results = p.display_results(cutoff=0.001)
print(results)It can be further formatted to a pandas' DataFrame or
exported to a CSV/Excel file using the built-in methods of xarray.
df = results.to_dataframe()
df.to_csv("results.csv")Or the result can be exported as a Parquet file for further use in pandas or dask:
p.export_results(filename="results.gzip")Results can be visualized using your favorite plotting library.
Finally, when running a Monte Carlo analysis (i.e., when use_distributions is greater than 0),
parameters of the Monte Carlo analysis (coordinates of uncertain exchanges, values for each iteration, etc.) are
stored in Excel files. It is possible to run Global Sensitivity Analysis (GSA) on the results of the
Monte Carlo analysis, like so:
from pathways import run_gsa
run_gsa(method="delta")The method argument can only be "delta" for now. It will run a Delta Moment-Independent Measure (DMIM) sensitivity analysis on the results of the Monte Carlo analysis, to rank the influence of each uncertain exchange on the results' distribution.
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Report bugs by filing issues on GitHub.
If you are reporting a bug, please include:
- Your operating system name and version.
- Any details about your local setup that might be helpful in troubleshooting.
- Detailed steps to reproduce the bug.
- For visual bugs, a screenshot or animated GIF of the bug in action.
Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.
Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.
pathways could always use more documentation, whether as part of
the official pathways docs, in docstrings, or even on the web in
blog posts, articles, and such.
The best way to send feedback is to file an issue on the GitHub repository.
pathways is licensed under the terms of the BSD 3-Clause License.