Analysing the source of stressors (flow matrix)¶
To calculate the source (in terms of regions and sectors) of a certain stressor or impact driven by consumption, one needs to diagonalize this stressor/impact. This section shows how to do this based on the small test mrio included in pymrio. The same procedure can be use for any other MRIO, but keep in mind that diagonalizing a stressor dramatically increases the memory need for the calculations.
Basic example¶
First we load the test mrio:
In [1]:
import pymrio
io = pymrio.load_test()
The test mrio includes several extensions:
In [2]:
list(io.get_extensions())
Out[2]:
['factor_inputs', 'emissions']
For the example here, we use ‘emissions’ - ‘emission_type1’:
In [3]:
io.emissions.F
Out[3]:
region | reg1 | reg2 | ... | reg5 | reg6 | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | food | mining | ... | transport | other | food | mining | manufactoring | electricity | construction | trade | transport | other | |
stressor | compartment | |||||||||||||||||||||
emission_type1 | air | 1848064.80 | 986448.090 | 23613787.00 | 28139100.00 | 2584141.80 | 4132656.3 | 21766987.0 | 7842090.6 | 1697937.30 | 347378.150 | ... | 42299319 | 10773826.0 | 15777996.0 | 6420955.5 | 113172450.0 | 56022534.0 | 4861838.5 | 18195621 | 47046542.0 | 21632868 |
emission_type2 | water | 139250.47 | 22343.295 | 763569.18 | 273981.55 | 317396.51 | 1254477.8 | 1012999.1 | 2449178.0 | 204835.44 | 29463.944 | ... | 4199841 | 7191006.3 | 4826108.1 | 1865625.1 | 12700193.0 | 753213.7 | 2699288.3 | 13892313 | 8765784.3 | 16782553 |
2 rows × 48 columns
In [4]:
et1_diag = io.emissions.diag_stressor(('emission_type1', 'air'), name = 'emtype1_diag')
The parameter name is optional, if not given the name is set to the stressor name + ‘_diag’
The new emission matrix now looks like this:
In [5]:
et1_diag.F.head(15)
Out[5]:
region | reg1 | reg2 | ... | reg5 | reg6 | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | food | mining | ... | transport | other | food | mining | manufactoring | electricity | construction | trade | transport | other | |
region | sector | |||||||||||||||||||||
reg1 | food | 1848064.8 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
mining | 0.0 | 986448.09 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
manufactoring | 0.0 | 0.00 | 23613787.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
electricity | 0.0 | 0.00 | 0.0 | 28139100.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
construction | 0.0 | 0.00 | 0.0 | 0.0 | 2584141.8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
trade | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 4132656.3 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
transport | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 21766987.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
other | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 7842090.6 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
reg2 | food | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 1697937.3 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
mining | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 347378.15 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
manufactoring | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
electricity | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
construction | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
trade | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | |
transport | 0.0 | 0.00 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
15 rows × 48 columns
And can be connected back to the system with:
In [6]:
io.et1_diag = et1_diag
Finally we can calulate the all stressor accounts with:
In [7]:
io.calc_all()
Out[7]:
<pymrio.core.mriosystem.IOSystem at 0x7f251808f518>
This results in a square footprint matrix. In this matrix, every column respresents the amount of stressor occuring in each region - sector driven by the consumption stated in the column header. Conversly, each row states where the stressor impacts occuring in the row are distributed due (from where they are driven).
In [8]:
io.et1_diag.D_cba.head(20)
Out[8]:
region | reg1 | reg2 | ... | reg5 | reg6 | |||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | food | mining | ... | transport | other | food | mining | manufactoring | electricity | construction | trade | transport | other | |
region | sector | |||||||||||||||||||||
reg1 | food | 609347.998747 | 34.963223 | 1.987631e+05 | 7.678755e+02 | 2.873371e+03 | 5.603158e+04 | 1.448778e+03 | 5.225312e+04 | 4.826852e+04 | 0.453097 | ... | 74.449012 | 222.953289 | 25525.516392 | 6.557600 | 1.382548e+05 | 38.345126 | 408.937891 | 4.425472e+04 | 63.940215 | 4.876200e+02 |
mining | 2527.449441 | 61271.249639 | 1.232716e+05 | 5.406781e+04 | 1.632967e+05 | 1.459774e+04 | 4.916876e+03 | 4.975184e+04 | 4.311608e+02 | 288.903690 | ... | 261.326848 | 829.871069 | 268.406793 | 1990.840054 | 7.400739e+04 | 6327.871035 | 299.916699 | 1.732632e+04 | 242.874444 | 4.038056e+03 | |
manufactoring | 1199.041530 | 38.236679 | 4.686837e+06 | 8.108636e+02 | 1.816229e+04 | 7.713610e+03 | 2.825229e+03 | 1.634290e+04 | 3.205155e+02 | 2.886944 | ... | 424.008556 | 517.344586 | 145.900439 | 72.195039 | 3.424703e+06 | 94.046696 | 129.127059 | 7.826193e+03 | 241.185186 | 9.015651e+02 | |
electricity | 148505.091902 | 12764.784297 | 1.519466e+06 | 1.167804e+07 | 5.265922e+05 | 1.307806e+06 | 4.851957e+05 | 4.308489e+06 | 1.908031e+04 | 240.730963 | ... | 9294.381551 | 28937.569016 | 7947.002552 | 476.106897 | 1.072302e+06 | 38647.049939 | 495.159380 | 9.712017e+05 | 3850.133826 | 9.804940e+03 | |
construction | 49.018479 | 6.053459 | 4.019302e+02 | 3.081457e+02 | 2.568908e+06 | 7.291250e+02 | 5.853107e+02 | 9.718253e+03 | 4.408230e+00 | 0.037972 | ... | 1.679791 | 4.461035 | 2.211530 | 0.213951 | 2.898921e+02 | 1.682977 | 12.667370 | 5.303592e+02 | 0.988979 | 3.859558e+00 | |
trade | 138.041355 | 3.139596 | 1.880042e+03 | 8.852940e+01 | 1.420349e+03 | 2.390871e+06 | 4.371531e+02 | 2.383210e+03 | 2.637857e+01 | 0.095853 | ... | 36.433641 | 50.392051 | 42.098945 | 1.141682 | 1.240785e+03 | 4.989788 | 14.192623 | 1.726377e+06 | 45.633464 | 4.276151e+01 | |
transport | 521.216924 | 122.504968 | 1.585636e+04 | 1.428149e+03 | 6.467383e+03 | 2.741408e+04 | 1.018383e+07 | 4.388313e+04 | 9.410163e+01 | 2.099087 | ... | 2854.767892 | 222.900909 | 568.393996 | 55.916912 | 1.130829e+04 | 285.596244 | 127.388007 | 2.198931e+04 | 8734.726973 | 1.309008e+03 | |
other | 537.062679 | 24.688020 | 7.752597e+03 | 1.170368e+03 | 8.587431e+03 | 1.322798e+04 | 4.448616e+03 | 7.516913e+06 | 6.837072e+01 | 0.767113 | ... | 56.726997 | 435.703359 | 36.640953 | 3.856648 | 5.346664e+03 | 20.750945 | 15.152425 | 1.029493e+04 | 58.278076 | 1.356728e+03 | |
reg2 | food | 234.870108 | 0.041282 | 1.213308e+03 | 3.241119e+00 | 9.719899e+00 | 1.004924e+01 | 4.822982e-01 | 1.281313e+01 | 1.694248e+06 | 0.041642 | ... | 3.026472 | 0.475024 | 80.139206 | 0.025141 | 2.183449e+02 | 0.472522 | 7.011110 | 1.605455e+02 | 0.455175 | 3.092008e+00 |
mining | 215.562762 | 1690.186670 | 5.892279e+04 | 1.862733e+03 | 7.746514e+02 | 7.570607e+02 | 1.582065e+02 | 2.302825e+03 | 1.787512e+03 | 10287.905967 | ... | 200.678830 | 58.083367 | 66.185618 | 1351.809333 | 1.430140e+04 | 2650.008184 | 42.230072 | 8.909866e+03 | 79.981946 | 3.551366e+03 | |
manufactoring | 75.621346 | 4.229463 | 7.185296e+06 | 6.763336e+01 | 7.171199e+02 | 4.429766e+02 | 1.914508e+02 | 1.076869e+03 | 9.941057e+02 | 5.361056 | ... | 352.701574 | 125.165588 | 44.659838 | 19.157594 | 1.140699e+06 | 19.220461 | 50.982784 | 3.009360e+03 | 74.837190 | 2.460570e+02 | |
electricity | 4.912183 | 4.392270 | 6.448813e+03 | 1.947867e+02 | 1.552407e+01 | 2.864877e+01 | 1.010699e+01 | 9.682693e+01 | 1.143809e+03 | 23.763711 | ... | 225.020323 | 2.614112 | 0.740525 | 3.162466 | 1.079151e+03 | 8.601151 | 0.254639 | 2.214440e+03 | 1.186302 | 1.102287e+01 | |
construction | 0.179274 | 0.201490 | 4.711030e+02 | 8.221412e-01 | 1.045043e+02 | 1.884894e+00 | 1.624084e+00 | 1.924495e+01 | 1.001309e+02 | 1.098256 | ... | 130.390027 | 4.804088 | 0.230208 | 0.152699 | 7.708346e+01 | 0.545433 | 47.750232 | 3.449526e+02 | 1.023520 | 5.208169e+00 | |
trade | 9.487802 | 0.442851 | 2.547850e+03 | 5.931388e+00 | 3.093165e+01 | 2.414790e+02 | 8.294245e+00 | 5.515915e+01 | 3.073687e+02 | 1.313536 | ... | 159.340296 | 69.421120 | 25.278297 | 0.783272 | 5.438996e+02 | 2.795486 | 8.852273 | 1.174931e+06 | 20.099409 | 2.543311e+01 | |
transport | 30.167917 | 11.691703 | 1.095783e+04 | 7.704484e+01 | 2.369344e+02 | 1.171461e+03 | 4.962901e+03 | 1.299130e+03 | 9.083313e+02 | 15.109529 | ... | 789093.398671 | 117.822465 | 101.020895 | 7.816896 | 2.491430e+03 | 51.079756 | 28.609582 | 8.615321e+03 | 2234.585845 | 4.125342e+02 | |
other | 4.710152 | 0.999656 | 3.487999e+03 | 1.504331e+01 | 6.607788e+01 | 8.958452e+01 | 3.266330e+01 | 1.104173e+03 | 2.621005e+02 | 3.670327 | ... | 376.984093 | 213.700696 | 7.178914 | 1.523151 | 6.818453e+02 | 7.576198 | 7.380601 | 3.072026e+03 | 37.695142 | 7.442119e+02 | |
reg3 | food | 79.487995 | 0.012420 | 2.707179e+03 | 3.212251e-01 | 1.118965e+00 | 8.545640e+00 | 3.848642e-01 | 2.342262e+01 | 6.321926e+01 | 0.001120 | ... | 0.660378 | 1.095579 | 96.427089 | 0.060430 | 1.350698e+03 | 0.136524 | 0.253732 | 5.444486e+02 | 0.330214 | 1.007920e+02 |
mining | 1.660826 | 9.283144 | 2.805174e+03 | 4.683637e+00 | 3.721404e+00 | 1.903501e+01 | 1.984584e+00 | 1.130052e+02 | 1.043411e+00 | 1.482323 | ... | 1.579297 | 4.760043 | 1.028577 | 9.018513 | 1.370585e+03 | 19.208836 | 0.609243 | 7.625285e+02 | 1.234952 | 6.075141e+02 | |
manufactoring | 256.950787 | 12.496966 | 1.951101e+07 | 1.576456e+02 | 1.369020e+03 | 9.774252e+02 | 4.651207e+02 | 9.975075e+03 | 2.274549e+02 | 2.093210 | ... | 856.521423 | 1696.838781 | 304.748914 | 109.539716 | 9.493385e+06 | 71.802017 | 260.584083 | 6.010179e+04 | 587.491671 | 3.817698e+04 | |
electricity | 346.618944 | 75.166170 | 3.907669e+06 | 8.377082e+03 | 1.955704e+03 | 3.618932e+03 | 1.930657e+03 | 5.954611e+05 | 9.697584e+02 | 33.309324 | ... | 1833.141385 | 6869.669139 | 1750.805701 | 1639.927136 | 1.908654e+06 | 44575.821242 | 628.061059 | 6.364065e+06 | 4888.472424 | 3.661473e+06 |
20 rows × 48 columns
The total footprints of a region - sector are given by summing the footprints along rows:
In [9]:
io.et1_diag.D_cba.sum(axis=0).reg1
Out[9]:
sector
food 2.056183e+06
mining 1.794235e+05
manufactoring 9.749300e+07
electricity 1.188759e+07
construction 3.342906e+06
trade 3.885884e+06
transport 1.075027e+07
other 1.582152e+07
dtype: float64
In [10]:
io.emissions.D_cba.reg1
Out[10]:
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | |
---|---|---|---|---|---|---|---|---|---|
stressor | compartment | ||||||||
emission_type1 | air | 2.056183e+06 | 179423.535893 | 9.749300e+07 | 1.188759e+07 | 3.342906e+06 | 3.885884e+06 | 1.075027e+07 | 1.582152e+07 |
emission_type2 | water | 2.423103e+05 | 25278.192086 | 1.671240e+07 | 1.371303e+05 | 3.468292e+05 | 7.766205e+05 | 4.999628e+05 | 8.480505e+06 |
The total stressor in a sector corresponds to the sum of the columns:
In [11]:
io.et1_diag.D_cba.sum(axis=1).reg1
Out[11]:
sector
food 1848064.80
mining 986448.09
manufactoring 23613787.00
electricity 28139100.00
construction 2584141.80
trade 4132656.30
transport 21766987.00
other 7842090.60
dtype: float64
In [12]:
io.emissions.F.reg1
Out[12]:
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | |
---|---|---|---|---|---|---|---|---|---|
stressor | compartment | ||||||||
emission_type1 | air | 1848064.80 | 986448.090 | 23613787.00 | 28139100.00 | 2584141.80 | 4132656.3 | 21766987.0 | 7842090.6 |
emission_type2 | water | 139250.47 | 22343.295 | 763569.18 | 273981.55 | 317396.51 | 1254477.8 | 1012999.1 | 2449178.0 |
Aggregation of source footprints¶
If only one specific aspect of the source is of interest for the analysis, the footprint matrix can easily be aggregated with the standard pandas groupby function.
For example, to aggregate to the source region of stressor, do:
In [13]:
io.et1_diag.D_cba.groupby(level='region', axis=0).sum()
Out[13]:
region | reg1 | reg2 | ... | reg5 | reg6 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | food | mining | ... | transport | other | food | mining | manufactoring | electricity | construction | trade | transport | other |
region | |||||||||||||||||||||
reg1 | 7.628249e+05 | 74265.619882 | 6.554229e+06 | 1.173668e+07 | 3.296308e+06 | 3.818391e+06 | 1.068369e+07 | 1.199973e+07 | 6.829377e+04 | 535.974719 | ... | 1.300377e+04 | 3.122120e+04 | 3.453617e+04 | 2.606829e+03 | 4.727453e+06 | 4.542033e+04 | 1.502541e+03 | 2.799800e+06 | 1.323776e+04 | 1.794454e+04 |
reg2 | 5.755115e+02 | 1712.185384 | 7.269345e+06 | 2.227236e+03 | 1.955463e+03 | 2.743145e+03 | 5.365729e+03 | 5.967041e+03 | 1.699751e+06 | 10338.264024 | ... | 7.905415e+05 | 5.920865e+02 | 3.254335e+02 | 1.384431e+03 | 1.160092e+06 | 2.740299e+03 | 1.930713e+02 | 1.201258e+06 | 2.449865e+03 | 4.998925e+03 |
reg3 | 1.054578e+03 | 215.654591 | 2.382082e+07 | 9.500254e+03 | 7.044809e+03 | 1.285847e+04 | 2.265449e+04 | 2.844767e+06 | 1.740953e+03 | 54.246936 | ... | 2.026441e+04 | 3.336623e+04 | 5.910854e+03 | 2.129253e+03 | 1.160211e+07 | 4.640647e+04 | 2.506927e+03 | 2.295789e+07 | 4.824482e+04 | 1.756144e+07 |
reg4 | 1.147382e+03 | 2323.792084 | 1.219510e+07 | 3.931814e+03 | 6.717898e+03 | 2.272241e+03 | 2.088695e+03 | 1.054927e+04 | 8.986664e+02 | 5812.809417 | ... | 1.818417e+03 | 1.930517e+03 | 2.402317e+03 | 1.257375e+06 | 8.856003e+06 | 7.878447e+03 | 2.121123e+03 | 6.469320e+06 | 5.546359e+03 | 1.756339e+04 |
reg5 | 1.283812e+06 | 6596.713907 | 1.588478e+07 | 1.642030e+04 | 1.234768e+04 | 1.598377e+04 | 2.387931e+04 | 2.906402e+04 | 1.828518e+04 | 1121.255607 | ... | 4.126106e+07 | 1.129572e+07 | 4.599517e+04 | 7.759982e+03 | 1.519859e+07 | 1.879346e+04 | 8.076635e+03 | 7.701012e+06 | 4.094084e+04 | 2.125918e+04 |
reg6 | 6.769053e+03 | 94309.570045 | 3.176873e+07 | 1.188346e+05 | 1.853198e+04 | 3.363451e+04 | 1.259087e+04 | 9.314423e+05 | 4.368062e+03 | 1283.054207 | ... | 8.360619e+03 | 2.378125e+04 | 1.508319e+07 | 7.406276e+04 | 2.990651e+07 | 3.671043e+07 | 1.822296e+06 | 1.286404e+06 | 4.794367e+07 | 1.839977e+07 |
6 rows × 48 columns
In addition, the aggregation function of pymrio also work on the diagonalized footprints. Here as example together with the country converter coco:
In [14]:
import country_converter as coco
io.aggregate(region_agg = coco.agg_conc(original_countries=io.get_regions(),
aggregates={'reg1': 'World Region A',
'reg2': 'World Region A',
'reg3': 'World Region A',},
missing_countries='World Region B'))
Out[14]:
<pymrio.core.mriosystem.IOSystem at 0x7f251808f518>
In [15]:
io.et1_diag.D_cba
Out[15]:
region | World Region A | World Region B | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
sector | food | mining | manufactoring | electricity | construction | trade | transport | other | food | mining | manufactoring | electricity | construction | trade | transport | other | |
region | sector | ||||||||||||||||
World Region A | food | 6.413682e+06 | 5.952471e+01 | 6.070321e+05 | 9.326086e+02 | 3.306995e+03 | 5.987980e+04 | 3.514385e+03 | 5.762977e+04 | 3.437446e+04 | 7.789050e+00 | 3.648174e+05 | 6.373523e+01 | 1.339737e+03 | 4.505792e+04 | 1.557188e+02 | 1.163124e+03 |
mining | 6.832129e+03 | 2.421509e+06 | 4.487266e+05 | 1.936672e+05 | 1.984123e+05 | 2.126768e+04 | 1.676197e+04 | 7.595762e+04 | 4.373682e+02 | 3.805693e+03 | 2.419141e+05 | 1.079248e+04 | 1.073022e+04 | 2.708989e+04 | 8.090257e+02 | 1.908491e+04 | |
manufactoring | 1.575255e+04 | 4.337974e+03 | 5.857218e+07 | 5.217160e+03 | 1.108166e+05 | 1.787260e+04 | 4.264953e+04 | 8.572782e+04 | 1.196817e+03 | 2.389005e+02 | 5.612155e+07 | 1.442696e+03 | 6.427781e+03 | 7.159629e+04 | 3.128223e+03 | 9.631846e+04 | |
electricity | 1.148908e+06 | 8.329886e+05 | 1.095357e+07 | 5.960881e+07 | 1.529502e+06 | 1.771262e+06 | 3.062426e+06 | 9.812779e+06 | 1.717518e+04 | 2.384122e+03 | 1.167721e+07 | 3.179723e+05 | 1.151092e+04 | 7.346021e+06 | 2.360035e+04 | 8.544805e+06 | |
construction | 1.287094e+03 | 3.530581e+03 | 7.855368e+03 | 4.364648e+03 | 1.082799e+07 | 1.811093e+03 | 1.193404e+04 | 4.796826e+04 | 8.117404e+00 | 1.278994e+00 | 9.064738e+03 | 2.977577e+01 | 1.183350e+03 | 1.057865e+04 | 1.704971e+02 | 5.278625e+04 | |
trade | 1.302177e+04 | 3.358650e+03 | 1.320568e+05 | 5.581957e+03 | 7.080932e+04 | 4.827615e+06 | 3.359931e+04 | 6.189364e+04 | 1.772741e+03 | 3.095513e+01 | 1.747324e+05 | 5.736669e+02 | 2.618051e+03 | 1.843441e+07 | 4.414173e+03 | 9.440231e+04 | |
transport | 1.661673e+04 | 1.013917e+04 | 2.026243e+05 | 1.186026e+04 | 9.463786e+04 | 6.605838e+04 | 6.900978e+07 | 2.981319e+05 | 3.570924e+03 | 2.342901e+02 | 2.223755e+05 | 2.064082e+03 | 4.978304e+03 | 3.583977e+05 | 8.792139e+05 | 3.666801e+05 | |
other | 4.973365e+04 | 5.119490e+04 | 4.157135e+05 | 3.141636e+04 | 2.451423e+05 | 7.347058e+04 | 3.463245e+05 | 3.192544e+07 | 1.468825e+03 | 2.988605e+02 | 5.284564e+05 | 3.388346e+03 | 7.111777e+03 | 6.871237e+05 | 8.921976e+03 | 3.158392e+07 | |
World Region B | food | 1.331074e+06 | 5.840707e+01 | 1.158034e+06 | 7.157090e+02 | 2.277605e+03 | 2.977910e+04 | 1.009061e+03 | 6.741954e+03 | 2.366136e+07 | 1.599984e+02 | 7.393735e+05 | 3.069128e+03 | 2.404678e+04 | 1.717301e+05 | 3.012820e+04 | 7.478749e+04 |
mining | 1.120813e+04 | 1.223669e+05 | 6.244537e+06 | 3.478494e+05 | 3.383509e+04 | 1.050007e+05 | 3.107988e+04 | 1.118387e+05 | 3.852121e+04 | 1.612039e+06 | 6.627586e+06 | 1.172268e+06 | 5.475534e+05 | 1.556577e+05 | 8.352660e+04 | 2.849191e+05 | |
manufactoring | 1.165415e+04 | 1.112145e+03 | 1.403366e+08 | 4.253396e+03 | 1.644812e+04 | 4.518924e+04 | 1.350120e+04 | 3.504348e+04 | 7.406372e+04 | 3.204186e+03 | 1.254308e+08 | 3.173150e+04 | 2.217880e+05 | 8.865177e+04 | 1.225773e+05 | 1.739262e+05 | |
electricity | 6.624049e+03 | 2.433130e+04 | 5.205461e+06 | 1.790907e+05 | 1.709844e+04 | 2.343087e+06 | 1.540263e+04 | 8.184191e+05 | 7.886875e+05 | 2.316855e+04 | 6.039318e+06 | 1.225545e+08 | 4.700901e+05 | 2.298746e+05 | 1.006105e+06 | 2.680870e+06 | |
construction | 2.441345e+03 | 3.338858e+02 | 6.498032e+04 | 1.067936e+03 | 2.843476e+04 | 1.094280e+04 | 2.562950e+03 | 1.372537e+04 | 1.229142e+04 | 2.193007e+03 | 4.813644e+04 | 3.446390e+04 | 1.097151e+07 | 3.431794e+04 | 6.586141e+04 | 3.153222e+05 | |
trade | 7.186649e+02 | 1.172395e+02 | 8.340493e+04 | 3.915868e+02 | 7.298241e+02 | 1.845264e+07 | 1.527523e+03 | 1.037099e+04 | 1.802717e+04 | 4.401980e+02 | 9.207769e+04 | 4.792922e+03 | 3.300157e+04 | 1.960537e+07 | 4.424292e+04 | 4.933951e+04 | |
transport | 1.118710e+04 | 1.051599e+03 | 3.382896e+05 | 3.512873e+03 | 4.336422e+03 | 6.536434e+04 | 3.522339e+06 | 2.451637e+04 | 3.616496e+04 | 2.977747e+03 | 2.337329e+05 | 3.716876e+04 | 9.823204e+04 | 2.505787e+05 | 1.102146e+08 | 2.615115e+05 | |
other | 4.082899e+02 | 1.690002e+02 | 4.761138e+04 | 5.086643e+02 | 7.504197e+02 | 2.293492e+04 | 2.199852e+03 | 4.906621e+06 | 5.528324e+03 | 8.635489e+02 | 4.987069e+04 | 8.685131e+03 | 2.601768e+04 | 3.556939e+04 | 3.218539e+04 | 4.074021e+07 |