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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).

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