Scenario-level LCA of energy systems and transition pathways
Project description
pathways
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.
Installation
pathways
is in an early development stage, and
can be installed from the Github repo with pip
:
pip install pathways
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
Usage
pathways
is a Python package, and can be used in Python scripts
or in a Python interpreter.
See the example notebook.
Python
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
mapping
folder containing amapping.yaml
file that describes the mapping between the IAM scenario and the LCA databases - a
inventories
folder containing the LCA matrices as CSV files - a
scenario_data
folder 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,
multiprocessing=True, # optional, use multiprocessing to speed up the calculation
use_distributions=0 # optional, 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 export to a CSV/Excel file using the built-in
methods of xarray
.
df = results.to_dataframe()
df.to_csv("results.csv")
Results can be visualized using your favorite plotting library.
Contributing
Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.
You can contribute in many ways:
Types of Contributions
Report Bugs
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.
Fix Bugs
Look through the GitHub issues for bugs. Anything tagged with "bug" and "help wanted" is open to whoever wants to implement it.
Implement Features
Look through the GitHub issues for features. Anything tagged with "enhancement" and "help wanted" is open to whoever wants to implement it.
Write Documentation
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.
Submit Feedback
The best way to send feedback is to file an issue on the GitHub repository.
Credits
Contributors
Financial Support
License
pathways
is licensed under the terms of the BSD 3-Clause License.
Project details
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