Parsing the Eora26 EE MRIO database¶

Getting Eora26¶

The Eora 26 database is available at http://www.worldmrio.com . You can download these files with the pymrio automatic downloader as described at Eora26 download.

In the most simple case, you can get the full database in basic prices with (you need to agree to license conditions before download):

In [1]:

import pymrio

In [2]:

eora_storage = '/tmp/mrios/eora26'

In [3]:

eora_meta = pymrio.download_eora26(storage_folder=eora_storage, prices=['bp'])

The Eora MRIO is free for academic (university or grant-funded) work at degree-granting institutions. All other uses require a data license before the results are shared.

 When using Eora, the Eora authors ask you cite these publications:

 Lenzen, M., Kanemoto, K., Moran, D., Geschke, A. Mapping the Structure of the World Economy (2012). Env. Sci. Tech. 46(15) pp 8374-8381. DOI:10.1021/es300171x

 Lenzen, M., Moran, D., Kanemoto, K., Geschke, A. (2013) Building Eora: A Global Multi-regional Input-Output Database at High Country and Sector Resolution, Economic Systems Research,  25:1, 20-49, DOI:10.1080/09535314.2013.769 938


Do you agree with these conditions [y/n]: y

Parse¶

To parse a single year do:

In [4]:

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

/home/konstans/bin/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py:2530: 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:

In [5]:

eora.get_regions()

Out[5]:

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.

In [6]:

import country_converter as coco

In [7]:

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

Out[7]:

<pymrio.core.mriosystem.IOSystem at 0x7f4acfe64278>

In [8]:

eora.get_regions()

Out[8]:

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

In [9]:

eora.calc_all()

Out[9]:

<pymrio.core.mriosystem.IOSystem at 0x7f4acfe64278>

In [10]:

import matplotlib.pyplot as plt
with plt.style.context('ggplot'):
    eora.Q.plot_account(('Total cropland area', 'Total'), figsize=(8,5))
    plt.show()

/home/konstans/proj/pymrio/pymrio/core/mriosystem.py:886: PerformanceWarning: indexing past lexsort depth may impact performance.
  _data = pd.DataFrame(getattr(self, accounts[key]).ix[row].T)

../_images/notebooks_working_with_eora26_18_1.png

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