Parsing the Eora26 EE MRIO database

Getting Eora26

You need to register at http://www.worldmrio.com to download Eora 26 database. You can download the files from http://www.worldmrio.com/simplified. Or you can download these files with the pymrio automatic downloader as described at Eora26 download.

Parse

To parse a single year do:

import pymrio

eora_storage = "/tmp/mrios/eora26"

eora_meta = pymrio.download_eora26(
    storage_folder=eora_storage,
    email="<Your Eora account email>",
    password="<Your Eora account password>",
)

eora = pymrio.parse_eora26(year=2005, path=eora_storage)

/home/konstans/bin/anaconda3/envs/pymrio_dev/lib/python3.7/site-packages/pandas/core/generic.py:3887: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.
  obj = obj._drop_axis(labels, axis, level=level, errors=errors)

Explore

Eora includes (almost) all countries:

eora.get_regions()

Index(['AFG', 'ALB', 'DZA', 'AND', 'AGO', 'ATG', 'ARG', 'ARM', 'ABW', 'AUS',
       ...
       'TZA', 'USA', 'URY', 'UZB', 'VUT', 'VEN', 'VNM', 'YEM', 'ZMB', 'ZWE'],
      dtype='object', name='region', length=189)

This can easily be aggregated to, for example, the OECD/NON_OECD countries with the help of the country converter coco.

import country_converter as coco

eora.aggregate(
    region_agg=coco.agg_conc(
        original_countries="Eora", aggregates=["OECD"], missing_countries="NON_OECD"
    )
)

<pymrio.core.mriosystem.IOSystem at 0x7fb7f2a14e90>

eora.get_regions()

Index(['NON_OECD', 'OECD', 'Unspecified region'], dtype='object', name='region')

eora.calc_all()

<pymrio.core.mriosystem.IOSystem at 0x7fb7f2a14e90>

import matplotlib.pyplot as plt

with plt.style.context("ggplot"):
    eora.Q.plot_account(("Total cropland area", "Total"), figsize=(8, 5))
    plt.show()

See the other notebooks for further information on aggregation and file io.