Working with the EXIOBASE EE MRIO database

Getting EXIOBASE

EXIOBASE 1 (developed in the fp6 project EXIOPOL), EXIOBASE 2 (outcome of the fp7 project CREEA) and EXIOBASE 3 (outcome of the fp7 project DESIRE) are available on the EXIOBASE webpage.

You need to register before you can download the full dataset.

Further information on the different EXIOBASE versions can be found in corresponding method papers.

EXIOBASE 1: Tukker et al. 2013. Exiopol – Development and Illustrative Analyses of a Detailed Global MR EE SUT/IOT. Economic Systems Research 25(1), 50-70
EXIOBASE 2: Wood et al. 2015. Global Sustainability Accounting—Developing EXIOBASE for Multi-Regional Footprint Analysis. Sustainability 7(1), 138-163
EXIOBASE 3: Stadler et al. 2018. EXIOBASE 3: Developing a Time Series of Detailed Environmentally Extended Multi‐Regional Input‐Output Tables. Journal of Industrial Ecology 22(3), 502-515

EXIOBASE 1

To download EXIOBASE 1 for the use with pymrio, navigate to the EXIOBASE webpage - section(tab) “Data Download” - “EXIOBASE 1 - full dataset” and download either

pxp_ita_44_regions_coeff_txt for the product by product (pxp) MRIO system or
ixi_fpa_44_regions_coeff_txt for the industry by industry (ixi) MRIO system or
pxp_ita_44_regions_coeff_src_txt for the product by product (pxp) MRIO system with emission data per source or
ixi_fpa_44_regions_coeff_src_txt for the industry by industry (ixi) wMRIO system with emission data per source.

The links above directly lead to the required file(s), but remember that you need to be logged in to access them.

The Pymrio parser works with the compressed (zip) files as well as the unpacked files. If you want to unpack the files, make sure that you store them in different folders since they unpack in the current directory.

EXIOBASE 2

EXIOBASE 3 is available at the EXIOBASE webpage at the section (tab) tab “Data Download” - “EXIOBASE 2 - full dataset”.

You can download either

MrIOT PxP ita coefficient version2 2 2 for the product by product (pxp) MRIO system or
MrIOT IxI fpa coefficient version2 2 2 for the industry by industry (ixi) MRIO system.

The links above directly lead to the required file(s), but remember that you need to be logged in to access them.

The pymrio parser works with the compressed (zip) files as well as the unpacked files. You can unpack the files together in one directory (unpacking creates a separate folder for each EXIOBASE 2 version). The unpacking of the PxP version also creates a folder “__MACOSX” - you can delete this folder.

EXIOBASE 3

EXIOBASE 3 is available at the EXIOBASE webpage at the section (tab) tab “Data Download” - “EXIOBASE 3 - monetary”. The EXIOBASE 3 parser works with both, the compressed zip archives and the extracted database.

Parsing

[1]:

import pymrio

For each publically available version of EXIOBASE pymrio provides a specific parser. All exiobase parser work with the zip archive (as downloaded from the exiobase webpage) or the extracted data.

To parse EXIOBASE 1 use:

[2]:

exio1 = pymrio.parse_exiobase1(
    path="/tmp/mrios/exio1/zip/121016_EXIOBASE_pxp_ita_44_regions_coeff_txt.zip"
)

/home/konstans/proj/pymrio/pymrio/tools/ioparser.py:373: FutureWarning: The default value of regex will change from True to False in a future version.
  _new_unit = _unit.unit.str.replace("/" + mon_unit, "")

The parameter ‘path’ needs to point to either the folder with the extracted EXIOBASE1 files for the downloaded zip archive.

Similarly, EXIOBASE 2 can be parsed by:

[3]:

exio2 = pymrio.parse_exiobase2(
    path="/tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip",
    charact=True,
    popvector="exio2",
)

The additional parameter ‘charact’ specifies if the characterization matrix provided with EXIOBASE 2 should be used. This can be specified with True or False; in addition, a custom one can be provided. In the latter case, pass the full path to the custom characterisatio file to ‘charact’.

The parameter ‘popvector’ allows to pass information about the population per EXIOBASE2 country. This can either be a custom vector of, if ‘exio2’ is passed, the one provided with pymrio.

EXIOBASE 3 can be parsed by:

[4]:

exio3 = pymrio.parse_exiobase3(path="/tmp/mrios/exio3/zip/exiobase3.4_iot_2009_pxp.zip")

Currently, no characterization or population vectors are provided for EXIOBASE 3.

For the rest of the tutorial, we use exio2; deleting exio1 and exio3 to free some memory:

[5]:

del exio1
del exio3

Exploring EXIOBASE

After parsing a EXIOBASE version, the handling of the database is the same as for any IO. Here we use the parsed EXIOBASE2 to explore some characteristics of the EXIBOASE system.

After reading the raw files, metadata about EXIOBASE can be accessed within the meta field:

[6]:

exio2.meta

[6]:

Description: Metadata for pymrio
MRIO Name: EXIOBASE
System: pxp
Version: 2.2.2
File: None
History:
20210224 11:48:56 - FILEIO -  EXIOBASE data F_Y_materials parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFDMaterials_version2.2.2.txt
20210224 11:48:56 - FILEIO -  EXIOBASE data F_Y_emissions parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFDEmissions_version2.2.2.txt
20210224 11:48:56 - FILEIO -  EXIOBASE data S_resources parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrResources_version2.2.2.txt
20210224 11:48:55 - FILEIO -  EXIOBASE data S_materials parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrMaterials_version2.2.2.txt
20210224 11:48:54 - FILEIO -  EXIOBASE data S_emissions parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrEmissions_version2.2.2.txt
20210224 11:48:52 - FILEIO -  EXIOBASE data S_factor_inputs parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFactorInputs_version2.2.2.txt
20210224 11:48:51 - FILEIO -  EXIOBASE data Y parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFinalDemand_version2.2.2.txt
20210224 11:48:51 - FILEIO -  EXIOBASE data A parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrIot_version2.2.2.txt

Custom points can be added to the history in the meta record. For example:

[7]:

exio2.meta.note("First test run of EXIOBASE 2")
exio2.meta

[7]:

Description: Metadata for pymrio
MRIO Name: EXIOBASE
System: pxp
Version: 2.2.2
File: None
History:
20210224 11:50:36 - NOTE -  First test run of EXIOBASE 2
20210224 11:48:56 - FILEIO -  EXIOBASE data F_Y_materials parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFDMaterials_version2.2.2.txt
20210224 11:48:56 - FILEIO -  EXIOBASE data F_Y_emissions parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFDEmissions_version2.2.2.txt
20210224 11:48:56 - FILEIO -  EXIOBASE data S_resources parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrResources_version2.2.2.txt
20210224 11:48:55 - FILEIO -  EXIOBASE data S_materials parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrMaterials_version2.2.2.txt
20210224 11:48:54 - FILEIO -  EXIOBASE data S_emissions parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrEmissions_version2.2.2.txt
20210224 11:48:52 - FILEIO -  EXIOBASE data S_factor_inputs parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFactorInputs_version2.2.2.txt
20210224 11:48:51 - FILEIO -  EXIOBASE data Y parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrFinalDemand_version2.2.2.txt
20210224 11:48:51 - FILEIO -  EXIOBASE data A parsed from /tmp/mrios/exio2/zip/mrIOT_PxP_ita_coefficient_version2.2.2.zip/mrIOT_PxP_ita_coefficient_version2.2.2/mrIot_version2.2.2.txt

To check for sectors, regions and extensions:

[8]:

exio2.get_sectors()

[8]:

Index(['Paddy rice', 'Wheat', 'Cereal grains nec', 'Vegetables, fruit, nuts',
       'Oil seeds', 'Sugar cane, sugar beet', 'Plant-based fibers',
       'Crops nec', 'Cattle', 'Pigs',
       ...
       'Paper for treatment: landfill',
       'Plastic waste for treatment: landfill',
       'Inert/metal/hazardous waste for treatment: landfill',
       'Textiles waste for treatment: landfill',
       'Wood waste for treatment: landfill',
       'Membership organisation services n.e.c.',
       'Recreational, cultural and sporting services', 'Other services',
       'Private households with employed persons',
       'Extra-territorial organizations and bodies'],
      dtype='object', name='sector', length=200)

[9]:

exio2.get_regions()

[9]:

Index(['AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GR',
       'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'MT', 'NL', 'PL', 'PT', 'RO', 'SE',
       'SI', 'SK', 'GB', 'US', 'JP', 'CN', 'CA', 'KR', 'BR', 'IN', 'MX', 'RU',
       'AU', 'CH', 'TR', 'TW', 'NO', 'ID', 'ZA', 'WA', 'WL', 'WE', 'WF', 'WM'],
      dtype='object', name='region')

[10]:

list(exio2.get_extensions())

[10]:

['factor_inputs', 'emissions', 'materials', 'resources', 'impact']

Calculating the system and extension results

The following command checks for missing parts in the system and calculates them. In case of the parsed EXIOBASE this includes A, L, multipliers M, footprint accounts, ..

[11]:

exio2.calc_all()

[11]:

<pymrio.core.mriosystem.IOSystem at 0x7f337397a520>

Exploring the results

[12]:

import matplotlib.pyplot as plt

plt.figure(figsize=(15, 15))
plt.imshow(exio2.A, vmax=1e-3)
plt.xlabel("Countries - sectors")
plt.ylabel("Countries - sectors")
plt.show()

../_images/notebooks_working_with_exiobase_37_0.png

The available impact data can be checked with:

[13]:

list(exio2.impact.get_rows())

[13]:

['Value Added',
 'Employment',
 'Employment hour',
 'abiotic depletion (elements, ultimate ultimate reserves)',
 'abiotic depletion (fossil fuels)',
 'abiotic depletion (elements, reserve base)',
 'abiotic depletion (elements, economic reserve)',
 'Landuse increase of land competition',
 'global warming (GWP100)',
 'global warming net (GWP100 min)',
 'global warming net (GWP100 max)',
 'global warming (GWP20)',
 'global warming (GWP500)',
 'ozone layer depletion (ODP steady state)',
 'ozone layer depletion (ODP5)',
 'ozone layer depletion (ODP10)',
 'ozone layer depletion (ODP15)',
 'ozone layer depletion (ODP20)',
 'ozone layer depletion (ODP25)',
 'ozone layer depletion (ODP30)',
 'ozone layer depletion (ODP40)',
 'human toxicity (HTP inf)',
 'Freshwater aquatic ecotoxicity (FAETP inf)',
 'Marine aquatic ecotoxicity (MAETP inf)',
 'Freshwater sedimental ecotoxicity (FSETP inf)',
 'Marine sedimental ecotoxicity (MSETP inf)',
 'Terrestrial ecotoxicity (TETP inf)',
 'human toxicity (HTP20)',
 'Freshwater aquatic ecotoxicity (FAETP20)',
 'Marine aquatic ecotoxicity (MAETP20)',
 'Freshwater sedimental ecotoxicity (FSETP20)',
 'Marine sedimental ecotoxicity (MSETP20)',
 'Terrestrial ecotoxicity (TETP20)',
 'human toxicity (HTP100)',
 'Freshwater aquatic ecotoxicity (FAETP100)',
 'Marine aquatic ecotoxicity (MAETP100)',
 'Freshwater sedimental ecotoxicity (FSETP100)',
 'Marine sedimental ecotoxicity (MSETP100)',
 'Terrestrial ecotoxicity (TETP100)',
 'human toxicity (HTP500)',
 'Freshwater aquatic ecotoxicity (FAETP500)',
 'Marine aquatic ecotoxicity (MAETP500)',
 'Freshwater sedimental ecotoxicity (FSETP500)',
 'Marine sedimental ecotoxicity (MSETP500)',
 'Terrestrial ecotoxicity (TETP500)',
 'Human toxicity (USEtox) 2008',
 'Fresh water Ecotoxicity (USEtox) 2008',
 'Human toxicity (USEtox) 2010',
 'Fresh water Ecotoxicity (USEtox) 2010',
 'photochemical oxidation (high NOx)',
 'photochemical oxidation (low NOx)',
 'photochemical oxidation (MIR; very high NOx)',
 'photochemical oxidation (MOIR; high NOx)',
 'photochemical oxidation (EBIR; low NOx)',
 'acidification (incl. fate, average Europe total, A&B)',
 'acidification (fate not incl.)',
 'eutrophication (fate not incl.)',
 'eutrophication (incl. fate, average Europe total, A&B)',
 'radiation',
 'odour',
 'EPS',
 'Carcinogenic effects on humans (H.A)',
 'Respiratory effects on humans caused by organic substances (H.A)',
 'Respiratory effects on humans caused by inorganic substances (H.A)',
 'Damages to human health caused by climate change (H.A)',
 'Human health effects caused by ionising radiation (H.A)',
 'Human health effects caused by ozone layer depletion (H.A)',
 'Damage to Ecosystem Quality caused by ecotoxic emissions (H.A)',
 'Damage to Ecosystem Quality caused by the combined effect of acidification and eutrophication (H.A)',
 'Damage to Ecosystem Quality caused by land occupation (H.A)',
 'Damage to Ecosystem Quality caused by land conversion (H.A)',
 'Damage to Resources caused by extraction of minerals (H.A)',
 'Damage to Resources caused by extraction of fossil fuels (H.A)',
 'Carcinogenic effects on humans (E.E)',
 'Respiratory effects on humans caused by organic substances (E.E)',
 'Respiratory effects on humans caused by inorganic substances (E.E)',
 'Damages to human health caused by climate change (E.E)',
 'Human health effects caused by ionising radiation (E.E)',
 'Human health effects caused by ozone layer depletion (E.E)',
 'Damage to Ecosystem Quality caused by ecotoxic emissions (E.E))',
 'Damage to Ecosystem Quality caused by the combined effect of acidification and eutrophication (E.E)',
 'Damage to Ecosystem Quality caused by land occupation (E.E)',
 'Damage to Ecosystem Quality caused by land conversion (E.E)',
 'Damage to Resources caused by extraction of minerals (E.E)',
 'Damage to Resources caused by extraction of fossil fuels (E.E)',
 'Carcinogenic effects on humans (I.I)',
 'Respiratory effects on humans caused by organic substances (I.I)',
 'Respiratory effects on humans caused by inorganic substances (I.I)',
 'Damages to human health caused by climate change (I.I)',
 'Human health effects caused by ionising radiation (I.I)',
 'Human health effects caused by ozone layer depletion (I.I)',
 'Damage to Ecosystem Quality caused by ecotoxic emissions (I.I)',
 'Damage to Ecosystem Quality caused by the combined effect of acidification and eutrophication (I.I)',
 'Damage to Ecosystem Quality caused by land occupation (I.I)',
 'Damage to Ecosystem Quality caused by land conversion (I.I)',
 'Damage to Resources caused by extraction of minerals (I.I)',
 'Damage to Resources caused by extraction of fossil fuels (I.I)',
 'photochemical oxidation (high NOx)(incl. NOx average, NMVOC average)',
 'photochemical oxidation (high NOx)(incl. NMVOC average)',
 'human toxicity HTP inf. (incl. PAH average, Xylene average, NMVOC average)',
 'Freshwater aquatic ecotoxicity FAETP inf. (incl. PAH average, Xylene average, NMVOC average)',
 'Marine aquatic ecotoxicity MAETP inf. (incl. PAH average, Xylene average, NMVOC average)',
 'Terrestrial ecotoxicity TETP inf. (incl. PAH average, Xylene average, NMVOC average)',
 'global warming GWP100 (incl. NMVOC average)',
 'ozone layer depletion ODP steady state (incl. NMVOC average)',
 'Total Emission relevant energy use',
 'Total Energy inputs from nature',
 'Total Energy supply',
 'Total Energy Use',
 'Total Heat rejected to fresh water',
 'Domestic Extraction',
 'Unused Domestic Extraction',
 'Water Consumption Green - Agriculture',
 'Water Consumption Blue - Agriculture',
 'Water Consumption Blue - Livestock',
 'Water Consumption Blue - Manufacturing',
 'Water Consumption Blue - Electricity',
 'Water Consumption Blue - Domestic',
 'Water Consumption Blue - Total',
 'Water Withdrawal Blue - Manufacturing',
 'Water Withdrawal Blue - Electricity',
 'Water Withdrawal Blue - Domestic',
 'Water Withdrawal Blue - Total',
 'Land use']

And to get for example the footprint of a specific impact do:

[14]:

print(exio2.impact.unit.loc["global warming (GWP100)"])
exio2.impact.D_cba_reg.loc["global warming (GWP100)"]

unit    kg CO2 eq.
Name: global warming (GWP100), dtype: object

[14]:

region
AT    1.450787e+11
BE    1.991422e+11
BG    6.266676e+10
CY    1.556996e+10
CZ    1.471491e+11
DE    1.394892e+12
DK    1.079304e+11
EE    2.381673e+10
ES    6.079175e+11
FI    1.153875e+11
FR    8.019998e+11
GR    2.247927e+11
HU    9.096635e+10
IE    9.591233e+10
IT    8.419421e+11
LT    3.366823e+10
LU    1.467799e+10
LV    2.255212e+10
MT    5.014763e+09
NL    2.992112e+11
PL    4.136385e+11
PT    1.120749e+11
RO    1.543358e+11
SE    1.282029e+11
SI    3.239223e+10
SK    6.911104e+10
GB    1.073548e+12
US    7.591895e+12
JP    1.825128e+12
CN    6.986984e+12
CA    7.142173e+11
KR    7.566406e+11
BR    5.595118e+11
IN    1.658771e+12
MX    6.219372e+11
RU    1.635710e+12
AU    5.715893e+11
CH    1.201448e+11
TR    4.939783e+11
TW    2.924074e+11
NO    8.791708e+10
ID    4.552600e+11
ZA    3.547961e+11
WA    1.224565e+12
WL    7.970228e+11
WE    4.931660e+11
WF    6.100073e+11
WM    1.329488e+12
Name: global warming (GWP100), dtype: float64

Visualizing the data

[15]:

with plt.style.context("ggplot"):
    exio2.impact.plot_account(["global warming (GWP100)"], figsize=(15, 10))
    plt.show()

../_images/notebooks_working_with_exiobase_43_0.png

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