The tools in this project were developed in the context of an UBA Global Stocktake project to assess and monitor collective progress toward the Paris Agreement Goals.
The tools enable the user to explore emissions, energy and socioeconomic data to assess collective progres in a number of ways. The two key tools (1) produce histograms of averages and trends in data, and (2) examine how many countries have peaked emissions (or other variables). Some notebooks also allow the user to assess the availability of data across sectors, gases, and years.
Author: Louise Jeffery
Contact: louise.jeffery@pik-potsdam.de / l.jeffery@newclimate.org
Project begin: August, 2018
Last Update: November, 2019
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The analysis can all be run using Jupyter notebooks that use the gst_tools code in this repository. Jupyter notebooks run in a browser and are easy and convenient ways to run, visualise, and document your code. Jupyter notebooks run on all platforms and can be installed following the instructions here.
Python package requirements for the performance distribution tools can be found in requirements.txt.
The country converter tool used to convert country names to ISO codes is:
https://pypi.org/project/country_converter/
conda install -c konstantinstadler country_converter
The tools all work on country-year data in a specified csv format. Data ready for plotting is stored in the proc-data folder in a specified format. For an example, please see the example in the proc-data/example-data folder.
Dta will commonely need to be pre-processed and formatted by the user before plotting. For the PRIMAP-hist dataset we also provide a tool for extracting the data of interest (gas, category) from the dataset version available online (see prepare-PRIMAP-hist-data-for-collective-progress-plots.ipynb).
Pre-processed data should be stored in the input-data folder. For processed data, we recommend the following naming convention for files: source-sector-entity.csv, where source is the name of the original source of the data, sector is the sector covered (e.g. economy-wide, energy, agriculture), and entity is the main variable (e.g. CO2, energy, population).
A second step of data processing is also possible with the calculate-indicators.ipnyb notebook. This notebook takes two pre-processed datasets and divides one by the other to generate variables such as "emissions per capita" or "GDP per capita".
The main tools for assessing collective progress are:
Please see the notebooks themselves for more detailed descriptions.
The tools currently provide two types of output. The first, is general statistics or overviews that are written to the screen in the notebooks. The second is plots generated by the scripts. These plots are automatically saved to the 'output/plots' folder.
For more information and description of the toolset, please see the forthcoming UBA report documenting the project.