Copyright (c) 2022 IIASA; this repository is released under the MIT License.
In the NAVIGATE project (Work Package 2), we compared IAMs based on observed characteristics of model results that are closely linked to the specification of technologies in the models, even if details of the implementation in the models differ.
In particular, we looked at the growth rate of various energy sector technologies and the implied learning rates, where investment costs for technologies become cheaper as cumulative installed capacity increases. The aim was to identify the ranges of these parameters across models, distinguishing between granular and “lumpy” technologies (i.e., technologies with few but large units, e.g., nuclear power plants).
This repository contains a notebook to easily create plots to compare indicators across models, regions or technologies.
The notebook uses the Python package pyam, an open-source community toolbox for analysis & visualization of scenario data. The package was developed to facilitate working with timeseries scenario data conforming to the format developed by the Integrated Assessment Modeling Consortium (IAMC). The package is used in ongoing assessments by the IPCC and in many model comparison projects at the global and national level, including several Horizon 2020 projects.
Read the docs for more information!
To run the notebooks on your machine, please install Python version 3.7 or higher. To install the required packages and dependencies, download or git-clone this repository and run the following command in the root folder:
pip install -r requirements.txt
Then, you can start a Jupyter notebook using
jupyter notebook
