Package Overview

Beta status: pyaesa is currently in beta testing. Please report bugs, unexpected behavior, documentation gaps, and installation or workflow issues on the GitHub Issues page.

Package Scope

pyaesa supports AESA workflows from data preparation to deterministic and uncertainty results. The package follows three calculation phases:

  1. Phase A computes life-cycle assessment results, including IO-LCA from processed MRIO data.

  2. Phase B computes allocated carrying capacities, with aCC = aSoCC * CC.

  3. Phase C computes absolute sustainability ratios, with ASR = LCA / aCC.

Workflow Map

The figure below summarizes the main package data sources, public workflow functions, study objectives, and output families.

High level pyaesa package map

Example Output Figures

The example below illustrates some of the 83 figure families available with pyaesa. For steady-state carrying capacities, it shows allocated carrying capacity (aCC) trajectories and a single-year ASR polar figure. For dynamic climate change carrying capacities based on AR6 pathways, it shows global carrying capacity (CC) and allocated carrying capacity (aCC) trajectories and cumulative budgets.

Example outputs generated by the package

License

pyaesa source code is distributed under the GPL 3.0 license. Downloaded datasets remain governed by their original providers’ terms and conditions documented in the Workflow Reference.

Community

Contributing to pyaesa

pyaesa uses the GitHub Discussions Ideas category to collect feature ideas and development priorities from users.

Use Ideas to propose new features, request additional allocation methods, upvote existing proposals, and comment with your use case, data source, expected workflow, or implementation constraints.

To propose direct code modifications, for example implementing new allocation methods or other features, see CONTRIBUTING.md.

Sharing Reproducible Case Studies

Users are encouraged to share AESA case studies developed with pyaesa in the AESA_case_studies repository. This supports the AESA community by making case studies easier to understand, inspect, reproduce, compare, and extend.

Installation

pyaesa requires Python 3.11 to 3.14 and at least 4 GB of available RAM.

python -m pip install pyaesa

The Workflow Reference provides the main data sources, workflow functions, and study objectives supported by pyaesa.