Copyright 2021 Daniel Huppmann
This repository is licensed under the Apache License, Version 2.0 (the "License"); see the LICENSE for details.
This repository contains solutions for the advanced assignment of the Modelling Lab of the Climate Risks Academy 2021 organized by the European University Institute (EUI) Florence School of Banking and Finance in cooperation with Oliver Wyman. See here for more information.
The exercise uses the scenario ensemble compiled by a cooperation between several integrated-assessment modelling teams and the Network for Greening the Financial System (NGFS), in particular Phase 2 (June 2021) of the scenario data. See this presentation about the release of Phase 2.
The scenario data is available via the NGFS Scenario Explorer hosted by IIASA
A user should learn how to retrieve scenario data from an IIASA Scenario Explorer instance into a Python computing environment and use pyam to create plots for data analysis (including computing indicators derived from timeseries data).
A user should be familiar with the following items before starting this assignment:
- Basic Python syntax: variables, functions, lists, dictionaries, ...
- If you’ve never used Python before, Anaconda is an easy option to install the basics
- See this Getting started guide
- More resources to learn Python and its syntax
- Jupyer notebooks, see jupyter.org
- The first steps tutorial of the pyam package
- Retrieve the data from the NGFS Scenario Explorer hosted by IIASA,
- By downloading the Excel file from the Downloads section of the web user interface saving the file on your machine, and then importing it into a Jupyter notebook, or
- Retrieving the data directly from Python via the pyam.iiasa.Connection class.
(see this tutorial,
connect to the
ngfsdatabase)
- Implement a grouping (meta indicator) of Paris-compatible pathways (vs. exceeding 2°C)
- Select several scenarios and compare the energy mix as suitable plots
- Create several plots showing the development of different energy sources by region
- Compute relative indicators of the trajectories of energy sources between different scenarios
This repository shows one way to implement the tasks. Following good practice of scientific programming, the tasks are separated into disctint notebooks, available in the notebooks folder.
This exercise 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