This repo contains a dump of the data that is used in the Green Cooling Initiative's Country Data map.

The data was accessed with the following command:

curl 'https://www.green-cooling-initiative.org/typo3conf/ext/worldmaptool/Resources/Public/Javascript/countries.json' --output raw.json

The output is stored as raw.json, which contains the following fields:

• data: 73944 rows of data. Each row here is a country-year-sector-scenario combination.
• mapping: column names for data: ['scenario', 'country', 'sector', 'year', 'indirect', 'direct', 'stock', 'sales', 'value', 'consumption'].
• identifiers: the list ['scenario', 'country', 'sector', 'year'], confirming our finding about the uniqueness of rows in data
• entities: the list ['country', 'scenario', 'sector', 'subsector', 'region', 'regionSelect']. I don't know what this means.
• oldEntities: auxiliary data that maps to the identifiers used in data. For example, the country column in data uses a country id, and this contains the full country dataset.

A few notes about this dataset:

• There is no distinction made between past data and projections. The 2 scenarios have almost identical data up to and including 2016, so I believe everything 2018 and later is a projection.
• The year field in the raw data is "years since 2000". In the output files we change this to be the full year.
• This does not include detailed documentation on the meaning of the columns. The best I see is in oldEntities['labels'] which has:

{
    "indirect": "Indirect emissions",
    "direct": "Direct emissions",
    "total": "Total emissions",
    "erp": "Emission reduction potential",
    "stock": "Appliances in use",
    "sales": "Unit sales",
    "value": "Value",
    "allSectors": "cooling sector"
}

This repo contains the following files:

• raw.json: the raw data that is sent to your browser to construct the map (the result of the curl command above)
• data.csv: the full dataset in csv form
• cleaned.csv: like data.csv but aggregated on the country-year level, only for past data. Uses country names instead of ids.
• main.ipynb: a jupyter notebook that takes raw.json and outputs the remaining files, plus shows how to load some of the other useful data (countries and sectors).

Update June 2024

Green Cooling Initiative has released updated data, dumped here as updated.json. If you're interested in cleaned/csv versions of that data please let me know.