Checking a database¶
It has happened ocassionally that the reported data is not internally consistent. Here we show how to make the most of pyam’s tools to check your database so you can double check the data in the IAM database.
In [1]:
import time
from pprint import pprint
import pyam
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
We start with the tutorial data, it contains only a fraction of the AR5 data so is not internally consistent and is hence the perfect dataset to start with.
In [2]:
df = pyam.IamDataFrame(data='tutorial_AR5_data.csv', encoding='utf-8')
INFO:root:Reading `tutorial_AR5_data.csv`
In [3]:
df.head()
Out[3]:
| model | scenario | region | variable | unit | year | value | |
|---|---|---|---|---|---|---|---|
| 0 | AIM-Enduse 12.1 | EMF27-450-Conv | ASIA | Emissions|CO2 | Mt CO2/yr | 2005 | 10540.74 |
| 3 | AIM-Enduse 12.1 | EMF27-450-Conv | LAM | Emissions|CO2 | Mt CO2/yr | 2005 | 3285.00 |
| 6 | AIM-Enduse 12.1 | EMF27-450-Conv | MAF | Emissions|CO2 | Mt CO2/yr | 2005 | 4302.21 |
| 9 | AIM-Enduse 12.1 | EMF27-450-Conv | OECD90 | Emissions|CO2 | Mt CO2/yr | 2005 | 12085.85 |
| 12 | AIM-Enduse 12.1 | EMF27-450-Conv | REF | Emissions|CO2 | Mt CO2/yr | 2005 | 3306.95 |
Summary¶
With the pyam.IamDataFrame.check_internal_consistency method, we can
check the internal consistency of a database. If this method returns
None, the database is internally consistent (i.e. the total
variables are the sum of the sectoral breakdowns and the regional
breakdown
In the rest of this tutorial, we give you a chance to better understand this method. We go through what it is actually doing and show you the kind of output you can expect.
Checking variables are the sum of their components¶
We are going to use the check_aggregate method of IamDataFrame
to check that the components of a variable sum to its total. This method
takes np.is_close arguments as keyword arguments, we show our
recommended settings here.
In [4]:
np_isclose_args = {
"equal_nan": True,
"rtol": 1e-03,
"atol": 1e-05,
}
Using check_aggregate on the IamDataFrame allows us to quickly
check if a single variable is equal to the sum of its sectoral
components (e.g. is Emissions|CO2 equal to
Emissions|CO2|Transport plus Emissions|CO2|Solvents plus
Emissions|CO2|Energy etc.). A returned DataFrame will show us
where the aggregate is not equal to the sum of components.
In [5]:
df.check_aggregate(
"Emissions|CO2",
**np_isclose_args
)
INFO:root:Emissions|CO2 - 1368 of 1522 data points are not aggregates of components
Out[5]:
| 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| variable | model | scenario | region | |||||||||||
| Emissions|CO2 | AIM-Enduse 12.1 | EMF27-450-Conv | ASIA | 10540.74 | 13160.18 | 11899.38 | 9545.81 | 7355.07 | 6119.50 | NaN | NaN | NaN | NaN | NaN |
| LAM | 3285.00 | 3294.54 | 3367.62 | 2856.65 | 2207.36 | 1537.72 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.54 | 4238.91 | 3956.19 | 3490.81 | 2082.24 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.33 | 11646.37 | 8272.30 | 4457.91 | 1625.18 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.42 | 3325.20 | 2991.24 | 1889.38 | 960.75 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38321.78 | 35588.66 | 28531.68 | 20287.46 | 13367.27 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-450-NoCCS | ASIA | 10540.74 | 13160.11 | 11893.80 | 9478.33 | 7367.07 | 5513.79 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3362.61 | 2837.11 | 1889.89 | 899.63 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4239.03 | 3619.25 | 2787.47 | 1671.29 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 11659.29 | 8708.81 | 5488.86 | 3355.22 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3322.95 | 3076.67 | 1977.78 | 1181.73 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 35588.85 | 28629.65 | 20458.10 | 13660.19 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-550-LimBio | ASIA | 10540.74 | 13160.11 | 14124.17 | 14218.08 | 13187.66 | 10019.56 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3445.63 | 3496.62 | 2986.08 | 1790.49 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4368.48 | 4519.64 | 4294.83 | 2733.76 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 12607.17 | 11752.01 | 9749.33 | 6501.31 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3826.80 | 3615.47 | 3258.31 | 3076.27 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 39531.61 | 38815.54 | 34676.38 | 25295.31 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-Base-FullTech | ASIA | 10540.74 | 13160.11 | 14149.89 | 16559.14 | 19658.68 | 23071.34 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3449.84 | 3660.68 | 3850.44 | 3866.20 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4371.98 | 4751.63 | 5389.48 | 6082.37 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 12642.70 | 13332.29 | 13742.93 | 14150.35 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3838.82 | 4220.97 | 4866.31 | 5615.39 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 39612.60 | 43835.49 | 49027.80 | 54552.86 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-G8-EERE | ASIA | 10540.74 | 13152.56 | 13415.94 | 10147.89 | 7637.61 | 4435.80 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.52 | 3106.39 | 2825.27 | 1784.31 | 899.06 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.02 | 4091.19 | 3977.50 | 3659.80 | 3336.85 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12750.81 | 10276.06 | 8833.95 | 5845.24 | 3473.56 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3596.74 | 3453.29 | 3468.73 | 3376.25 | 3058.68 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38304.41 | 35425.96 | 30395.43 | 23536.71 | 16487.83 | NaN | NaN | NaN | NaN | NaN | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | |
| REMIND 1.5 | EMF27-450-NoCCS | OECD90 | 15111.39 | 15254.16 | 8082.87 | 2864.75 | 369.53 | 328.11 | 299.06 | 266.24 | 255.25 | 245.07 | 226.35 | |
| World | 33837.41 | 38224.94 | 25524.60 | 7358.64 | 1691.05 | 1663.77 | 1616.52 | 1555.47 | 1553.00 | 1665.20 | 1883.11 | |||
| EMF27-550-LimBio | ASIA | 10193.98 | 13239.55 | 14218.37 | 11920.79 | 8135.32 | 5963.84 | 4486.53 | 3100.11 | 2246.06 | 1843.16 | 1570.02 | ||
| LAM | 2926.60 | 3478.79 | 4413.41 | 1831.96 | 1357.42 | 934.84 | 712.03 | 523.57 | 418.39 | 359.64 | 107.38 | |||
| MAF | 4035.32 | 4381.03 | 4504.49 | 3368.89 | 3582.70 | 3883.52 | 3663.91 | 3349.07 | 3064.56 | 2919.43 | 3153.50 | |||
| OECD90 | 15111.39 | 15241.56 | 13016.52 | 10555.13 | 7238.06 | 4454.98 | 2745.80 | 1531.01 | 766.12 | 275.60 | -82.62 | |||
| World | 33837.41 | 37970.11 | 37657.41 | 28699.50 | 20936.83 | 15389.20 | 11536.73 | 8368.71 | 6360.33 | 5299.36 | 4644.20 | |||
| EMF27-Base-FullTech | ASIA | 10193.98 | 13478.78 | 20256.01 | 24006.74 | NaN | NaN | NaN | NaN | 34529.79 | 28622.83 | 23400.42 | ||
| LAM | 2926.60 | 3508.40 | 5067.35 | 5464.43 | 4402.98 | 5424.51 | 5869.57 | 5988.95 | 6096.94 | 5152.73 | 4074.45 | |||
| MAF | 4035.32 | 4381.09 | 5364.84 | 5862.75 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 15111.39 | 15234.63 | NaN | NaN | 16610.24 | 16943.56 | 16515.90 | 15922.43 | 14587.22 | 11864.62 | 9683.61 | |||
| World | 33837.41 | 38293.08 | 48134.42 | 53343.82 | 59836.10 | 70077.89 | 77941.21 | 82914.15 | 84109.23 | 75995.09 | 68004.38 | |||
| WITCH_EMF27 | EMF27-450-Conv | ASIA | 9895.45 | 13210.18 | 13914.12 | 12004.49 | 10538.51 | 8767.49 | 7410.94 | 6299.16 | 3794.59 | 2865.46 | 2437.77 | |
| LAM | 4660.57 | 4644.17 | 1851.46 | 1537.68 | 1421.58 | 658.62 | -161.02 | -1398.20 | -1659.55 | -1631.41 | -1586.79 | |||
| MAF | 2508.31 | 2673.95 | 2224.44 | 1932.65 | 1907.50 | 1703.00 | 1588.40 | 1493.36 | 1413.90 | 1303.02 | 1118.21 | |||
| OECD90 | 12644.40 | 12597.55 | 9780.38 | NaN | 4755.20 | 3257.79 | 2240.72 | 1399.83 | 795.67 | 359.17 | 224.28 | |||
| REF | 3870.58 | 4035.17 | 2381.75 | 2055.81 | 1733.11 | 1347.51 | 1064.51 | 815.36 | 611.13 | 444.37 | 383.41 | |||
| World | 33579.32 | 37161.03 | 30152.15 | 24091.32 | 20355.91 | 15734.40 | 12143.56 | 8609.50 | 4955.74 | 3340.62 | 2576.88 | |||
| EMF27-550-LimBio | ASIA | 9895.98 | 13341.76 | 17280.06 | 18745.41 | 16414.52 | 12419.72 | 10012.01 | 9373.38 | 8937.92 | 9270.47 | 9214.63 | ||
| LAM | 4660.84 | 4612.38 | 2729.37 | 2515.92 | 2286.12 | 1309.50 | 1048.52 | 677.15 | 470.62 | -66.27 | -210.35 | |||
| MAF | 2508.81 | 2621.97 | 2773.63 | 2885.03 | 2775.24 | 2552.54 | 2579.22 | 2758.68 | 2928.88 | 3067.60 | 3139.20 | |||
| OECD90 | 12645.78 | 12542.87 | 11852.60 | NaN | NaN | 5310.74 | 4461.18 | 4405.41 | 4236.36 | 4016.45 | 3860.45 | |||
| REF | 3871.04 | 4062.72 | 3764.23 | 3380.51 | 2617.25 | 1957.66 | 1686.23 | 1658.31 | 1628.48 | 1550.83 | 1510.95 | |||
| World | 33582.45 | 37181.70 | 38399.88 | 37802.01 | 32002.02 | 23550.17 | 19787.16 | 18872.93 | 18202.25 | 17839.07 | 17514.87 | |||
| EMF27-Base-FullTech | ASIA | 9893.46 | 13378.34 | 20016.55 | 26248.47 | 30889.38 | 34562.46 | 37566.05 | 40325.64 | 42647.52 | 44874.72 | 46657.52 | ||
| LAM | 4659.58 | 4623.98 | 4524.39 | 4644.99 | 4937.36 | 5250.67 | 5698.25 | 6117.40 | 6522.66 | 6945.51 | 7358.61 | |||
| MAF | 2506.45 | 2642.28 | 3291.19 | 4063.34 | 5028.41 | 6038.17 | 7017.40 | 8032.94 | 8851.50 | 9680.49 | 10373.40 | |||
| OECD90 | 12639.28 | 12598.84 | 13097.95 | 13835.62 | 14969.12 | 15784.59 | 16540.18 | 17249.21 | 17924.86 | 18566.23 | 19180.64 | |||
| REF | 3868.87 | 4077.28 | 4636.23 | 5039.14 | 5412.35 | 5886.80 | 6279.44 | 6439.80 | 6722.19 | 7040.23 | 7284.21 | |||
| World | 33567.64 | 37320.72 | 45566.30 | 53831.56 | 61236.62 | 67522.70 | 73101.32 | 78164.98 | 82668.74 | 87107.17 | 90854.38 |
140 rows × 11 columns
As we are missing most of the sectoral data in this subset of AR5, the total variables are mostly not equal to their components. The data table above shows us which model-scenario-region combinations this is the case for. As a user, we would then have to examine which sectors we have for each of these model-scenario-region combinations in order to determine what is missing.
Checking multiple variables¶
We can then wrap this altogether to check all or a subset of the
variables in an IamDataFrame.
In [6]:
for variable in df.filter(level=1).variables():
diff = df.check_aggregate(
variable,
**np_isclose_args
)
# you could then make whatever summary you wanted
# with diff
INFO:root:Emissions|CO2 - 1368 of 1522 data points are not aggregates of components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Price|Carbon - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check aggregate because it has no components
The output tells us where there are issues as well as where it is not possible to actually check sums because no components have been reported.
Checking that regions sum to aggregate regions¶
Similarly to checking that the sum of a variable’s components give the declared total, we can check that summing regions gives the intended total.
To do this, we use the check_aggregate_regions method of
IamDataFrame. By default, this method checks that all the regions in
the dataframe sum to World.
Using check_aggregate_regions on the IamDataFrame allows us to
quickly check if a regional total for a single variable is equal to the
sum of its regional contributors. A returned DataFrame will show us
where the aggregate is not equal to the sum of components.
In [7]:
df.check_aggregate_regions(
"Emissions|CO2",
**np_isclose_args
)
INFO:root:Emissions|CO2 - 503 of 503 data points are not aggregates of regional components
Out[7]:
| 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region | model | scenario | variable | |||||||||||
| World | AIM-Enduse 12.1 | EMF27-450-Conv | Emissions|CO2 | 34492.05 | 38321.78 | 35588.66 | 28531.68 | 20287.46 | 13367.27 | NaN | NaN | NaN | NaN | NaN |
| EMF27-450-NoCCS | Emissions|CO2 | 34492.05 | 38313.59 | 35588.85 | 28629.65 | 20458.10 | 13660.19 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-550-LimBio | Emissions|CO2 | 34492.05 | 38313.59 | 39531.61 | 38815.54 | 34676.38 | 25295.31 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-Base-FullTech | Emissions|CO2 | 34492.05 | 38313.59 | 39612.60 | 43835.49 | 49027.80 | 54552.86 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-G8-EERE | Emissions|CO2 | 34492.05 | 38304.41 | 35425.96 | 30395.43 | 23536.71 | 16487.83 | NaN | NaN | NaN | NaN | NaN | ||
| GCAM 3.0 | AMPERE3-450 | Emissions|CO2 | 31473.40 | 31678.13 | 38660.77 | 45110.97 | 44768.14 | 34990.09 | 19397.62 | 1208.73 | -17387.30 | -37099.22 | -57844.17 | |
| AMPERE3-450P-CE | Emissions|CO2 | 31473.40 | 31678.14 | 38603.90 | 46071.54 | 43844.74 | 34636.41 | 19108.64 | 1129.57 | -17399.41 | -37076.52 | -57817.45 | ||
| AMPERE3-450P-EU | Emissions|CO2 | 31473.40 | 31678.14 | 39487.87 | 47419.61 | 45118.79 | 35081.54 | 19182.91 | 1166.07 | -17384.21 | -37079.51 | -57832.34 | ||
| AMPERE3-550 | Emissions|CO2 | 31473.40 | 31678.13 | 39660.52 | 47541.01 | 50744.18 | 46992.91 | 34172.78 | 17064.62 | -2639.86 | -21628.98 | -42437.11 | ||
| AMPERE3-Base-EUback | Emissions|CO2 | 31473.40 | 31678.13 | 41826.41 | 52214.95 | 63459.29 | 75453.50 | 81730.83 | 86384.17 | 89308.29 | 92285.81 | 96090.28 | ||
| AMPERE3-CF450P-EU | Emissions|CO2 | 31473.40 | 31678.13 | 41826.41 | 52214.95 | 49660.44 | 36881.63 | 19617.99 | 995.01 | -17475.49 | -37146.77 | -57853.52 | ||
| AMPERE3-RefPol | Emissions|CO2 | 31473.40 | 31678.14 | 39787.42 | 48131.28 | 52770.92 | 54537.50 | 54976.51 | 54792.56 | 53594.22 | 51287.98 | 49551.47 | ||
| EMF27-450-Conv | Emissions|CO2 | 31473.62 | 34700.97 | 12161.32 | 11121.98 | 6976.38 | 5488.54 | 3289.90 | 1023.41 | -1751.60 | -3619.36 | -6129.59 | ||
| EMF27-450-NoCCS | Emissions|CO2 | 31473.62 | 34685.29 | 11238.06 | 13109.25 | 12112.07 | 10342.93 | 6317.67 | 2467.32 | -1664.16 | -4832.87 | -8236.19 | ||
| EMF27-550-LimBio | Emissions|CO2 | 31473.62 | 34685.29 | 19169.88 | 23277.01 | 21186.04 | 15170.25 | 10413.05 | 8098.03 | 6605.72 | 6446.21 | 5829.69 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 31473.62 | 34685.29 | 43318.48 | 51233.36 | 57308.36 | 63987.64 | 68381.59 | 72124.98 | 75247.84 | 77206.61 | 77589.17 | ||
| IMAGE 2.4 | AMPERE3-450 | Emissions|CO2 | 34111.29 | 35344.27 | 36059.37 | 32234.66 | 19608.88 | 15150.28 | 6668.57 | -677.70 | -3156.89 | -6273.64 | -7112.29 | |
| AMPERE3-450P-CE | Emissions|CO2 | 34111.14 | 35343.93 | 38844.85 | 40453.35 | 31255.38 | 21628.42 | 13883.06 | 5169.80 | -2754.38 | -6409.44 | -8174.45 | ||
| AMPERE3-450P-EU | Emissions|CO2 | 34111.14 | 35343.93 | 40612.22 | 46400.38 | 37347.97 | 25392.08 | 17060.16 | 7214.90 | -2789.63 | -6821.32 | -7928.68 | ||
| AMPERE3-550 | Emissions|CO2 | 34111.29 | 35318.50 | 37365.86 | 37226.82 | 32352.89 | 30493.37 | 22154.07 | 12673.92 | 7180.83 | 909.23 | -2504.16 | ||
| AMPERE3-RefPol | Emissions|CO2 | 34124.32 | 35746.98 | 40855.54 | 46771.34 | 48448.44 | 51487.30 | 48906.51 | 43724.96 | 40676.94 | 36602.42 | 32884.37 | ||
| EMF27-550-LimBio | Emissions|CO2 | 34259.27 | 35594.34 | 37292.93 | 36467.66 | 28910.72 | 24195.19 | 19530.54 | 13307.58 | 10956.33 | 8196.68 | 5984.36 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 34391.39 | 35246.31 | 40384.52 | 44757.28 | 48671.22 | 55920.66 | 61887.17 | 65748.83 | 72569.20 | 78338.48 | 82774.78 | ||
| MERGE_EMF27 | EMF27-450-Conv | Emissions|CO2 | 28501.22 | 33303.16 | 30506.60 | 22718.89 | 13174.43 | 4174.12 | -628.46 | -2784.94 | -4736.63 | -5526.81 | -6008.83 | |
| EMF27-550-LimBio | Emissions|CO2 | 28501.22 | 33303.16 | 36019.89 | 34031.54 | 26705.70 | 17872.93 | 10598.82 | 7675.84 | 4542.86 | 6515.47 | 5797.86 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 28501.22 | 33303.16 | 43021.90 | 54681.24 | 64214.82 | 73116.83 | 81405.99 | 90072.94 | 98476.95 | 108882.84 | 120493.31 | ||
| EMF27-G8-EERE | Emissions|CO2 | 28501.22 | 33303.16 | 31636.36 | 27381.84 | 19962.34 | 14278.68 | 10533.13 | 9599.27 | 9211.55 | 8061.24 | 8393.79 | ||
| MESSAGE V.4 | AMPERE3-450 | Emissions|CO2 | 34474.59 | 36035.69 | 36941.46 | 35238.71 | 26747.96 | 15173.32 | 4329.48 | -1304.69 | -5447.10 | -8728.73 | -11209.52 | |
| AMPERE3-450P-EU | Emissions|CO2 | 34474.49 | 36036.02 | 40821.65 | 46438.23 | 38929.54 | 27622.31 | 12469.82 | 2786.95 | -2585.87 | -6268.49 | -8535.53 | ||
| AMPERE3-550 | Emissions|CO2 | 34474.51 | 36035.96 | 39222.41 | 42988.07 | 40487.80 | 34363.56 | 20847.26 | 10424.52 | 2479.89 | -2730.61 | -6474.82 | ||
| AMPERE3-RefPol | Emissions|CO2 | 34474.46 | 36035.98 | 40886.23 | 47008.04 | 51379.09 | 53497.39 | 50990.57 | 46103.64 | 41339.83 | 34733.89 | 27562.64 | ||
| EMF27-550-LimBio | Emissions|CO2 | 34491.02 | 36087.09 | 34675.76 | 30326.54 | 22000.71 | 11312.70 | 9846.46 | 8570.05 | 7230.63 | 5834.56 | 4500.82 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 34491.02 | 36087.09 | 42809.72 | 48375.99 | 55957.77 | 64431.68 | 71728.32 | 75668.28 | 77297.82 | 77283.35 | 75904.56 | ||
| REMIND 1.5 | AMPERE3-450 | Emissions|CO2 | 33841.49 | 37365.91 | 35255.18 | 31679.69 | 25439.84 | 16908.36 | 6524.72 | -910.93 | -5015.05 | -8196.08 | -10192.88 | |
| AMPERE3-450P-CE | Emissions|CO2 | 33841.49 | 37357.58 | 39260.04 | 43283.25 | 34864.17 | 20741.40 | 7005.88 | -622.25 | -4786.58 | -8275.44 | -10291.58 | ||
| AMPERE3-450P-EU | Emissions|CO2 | 33841.49 | 37356.37 | 41565.70 | 47902.12 | 37928.16 | 21615.59 | 6958.30 | -563.45 | -4783.46 | -8277.63 | -10290.40 | ||
| AMPERE3-550 | Emissions|CO2 | 33841.49 | 37366.12 | 38324.51 | 39015.27 | 36963.66 | 31733.51 | 22831.66 | 13927.89 | 5749.59 | -327.77 | -3755.24 | ||
| AMPERE3-550P-EU | Emissions|CO2 | 33841.49 | 37360.58 | 41568.63 | 47908.67 | 46792.96 | 37445.25 | 24413.62 | 14572.43 | 6804.20 | 86.14 | -3693.90 | ||
| AMPERE3-Base-EUback | Emissions|CO2 | 33841.49 | 37371.87 | 44020.67 | 50296.87 | 58575.08 | 71744.59 | 82786.79 | 87993.16 | 85663.03 | 75402.53 | 66716.49 | ||
| AMPERE3-CF450P-EU | Emissions|CO2 | 33841.49 | 37365.98 | 44028.80 | 50295.90 | 39726.70 | 22501.37 | 6855.42 | -602.89 | -4769.69 | -8268.81 | -10300.93 | ||
| AMPERE3-RefPol | Emissions|CO2 | 33841.49 | 37372.53 | 41615.44 | 48455.22 | 55203.05 | 61590.57 | 64595.04 | 64737.59 | 62246.20 | 56447.99 | 51261.41 | ||
| EMF27-450-Conv | Emissions|CO2 | 33837.41 | 37977.31 | 29314.49 | 13503.92 | 6281.74 | 3040.79 | 787.74 | -526.27 | -1744.61 | -1641.29 | -1413.97 | ||
| EMF27-450-NoCCS | Emissions|CO2 | 33837.41 | 38224.94 | 25524.60 | 7358.64 | 1691.05 | 1663.77 | 1616.52 | 1555.47 | 1553.00 | 1665.20 | 1883.11 | ||
| EMF27-550-LimBio | Emissions|CO2 | 33837.41 | 37970.11 | 37657.41 | 28699.50 | 20936.83 | 15389.20 | 11536.73 | 8368.71 | 6360.33 | 5299.36 | 4644.20 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 33837.41 | 38293.08 | 48134.42 | 53343.82 | 59836.10 | 70077.89 | 77941.21 | 82914.15 | 84109.23 | 75995.09 | 68004.38 | ||
| WITCH_EMF27 | EMF27-450-Conv | Emissions|CO2 | 33579.32 | 37161.03 | 30152.15 | 24091.32 | 20355.91 | 15734.40 | 12143.56 | 8609.50 | 4955.74 | 3340.62 | 2576.88 | |
| EMF27-550-LimBio | Emissions|CO2 | 33582.45 | 37181.70 | 38399.88 | 37802.01 | 32002.02 | 23550.17 | 19787.16 | 18872.93 | 18202.25 | 17839.07 | 17514.87 | ||
| EMF27-Base-FullTech | Emissions|CO2 | 33567.64 | 37320.72 | 45566.30 | 53831.56 | 61236.62 | 67522.70 | 73101.32 | 78164.98 | 82668.74 | 87107.17 | 90854.38 |
Again, as the AR5 snapshot is incomplete, all World sums are not equal to the regions provided.
Once again, we can repeat this analysis over all the variables of
interest in an IamDataFrame.
In [8]:
for variable in df.variables():
diff = df.check_aggregate_regions(
variable,
**np_isclose_args
)
# you could then make whatever summary you wanted
# with diff
if diff is not None:
eg = diff
eg.head(20)
INFO:root:Emissions|CO2 - 503 of 503 data points are not aggregates of regional components
INFO:root:Emissions|CO2|Fossil Fuels and Industry - 239 of 239 data points are not aggregates of regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Price|Carbon - cannot check regional aggregate because it has no regional components
INFO:root:Primary Energy - 502 of 503 data points are not aggregates of regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Fossil|w/ CCS - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Temperature|Global Mean|MAGICC6|MED - cannot check regional aggregate because it has no regional components
Out[8]:
| 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| region | model | scenario | variable | |||||||||||
| World | AIM-Enduse 12.1 | EMF27-450-Conv | Primary Energy | 458.20 | 518.89 | 500.15 | 521.23 | 569.53 | 581.44 | NaN | NaN | NaN | NaN | NaN |
| EMF27-450-NoCCS | Primary Energy | 458.20 | 518.81 | 500.24 | 493.64 | 583.82 | 614.23 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-550-LimBio | Primary Energy | 458.20 | 518.81 | 544.28 | 592.53 | 639.70 | 679.98 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-Base-FullTech | Primary Energy | 458.20 | 518.81 | 545.24 | 619.43 | 715.12 | 816.88 | NaN | NaN | NaN | NaN | NaN | ||
| EMF27-G8-EERE | Primary Energy | 458.20 | 518.64 | 487.22 | 463.48 | 499.48 | 555.22 | NaN | NaN | NaN | NaN | NaN | ||
| GCAM 3.0 | AMPERE3-450 | Primary Energy | 460.41 | 504.35 | 618.51 | 743.09 | 848.71 | 935.92 | 1001.75 | 1091.21 | 1177.40 | 1281.61 | 1418.91 | |
| AMPERE3-450P-CE | Primary Energy | 460.41 | 504.35 | 618.74 | 753.34 | 849.79 | 942.16 | 1005.56 | 1092.56 | 1177.72 | 1281.71 | 1418.59 | ||
| AMPERE3-450P-EU | Primary Energy | 460.41 | 504.35 | 624.50 | 769.43 | 857.29 | 943.23 | 1002.71 | 1089.48 | 1177.26 | 1282.19 | 1420.23 | ||
| AMPERE3-550 | Primary Energy | 458.03 | 501.89 | 622.66 | 751.83 | 863.50 | 956.22 | 1007.66 | 1064.33 | 1146.19 | 1221.76 | 1331.16 | ||
| AMPERE3-Base-EUback | Primary Energy | 457.76 | 501.61 | 632.27 | 788.66 | 934.72 | 1073.33 | 1174.84 | 1261.38 | 1328.94 | 1395.49 | 1470.71 | ||
| AMPERE3-CF450P-EU | Primary Energy | 460.41 | 504.35 | 635.96 | 793.04 | 870.95 | 942.83 | 1001.18 | 1087.41 | 1176.79 | 1282.69 | 1420.85 | ||
| AMPERE3-RefPol | Primary Energy | 457.76 | 501.61 | 622.68 | 764.56 | 884.66 | 995.11 | 1080.88 | 1155.01 | 1214.59 | 1275.17 | 1325.32 | ||
| EMF27-450-Conv | Primary Energy | 458.82 | 504.59 | 556.01 | 630.79 | 694.18 | 749.23 | 791.67 | 824.53 | 834.82 | 822.03 | 794.94 | ||
| EMF27-450-NoCCS | Primary Energy | 458.82 | 504.45 | 546.22 | 599.28 | 628.39 | 654.74 | 684.87 | 733.00 | 787.27 | 834.91 | NaN | ||
| EMF27-550-LimBio | Primary Energy | 458.82 | 504.45 | 576.89 | 663.16 | 729.41 | 777.90 | 817.73 | 865.14 | 911.34 | 939.80 | 964.83 | ||
| EMF27-Base-FullTech | Primary Energy | 458.82 | 504.45 | 613.10 | 730.03 | 841.52 | 952.45 | 1049.69 | 1139.91 | 1218.93 | 1276.80 | 1319.59 | ||
| IMAGE 2.4 | AMPERE3-450 | Primary Energy | 441.25 | 473.91 | 544.13 | 577.42 | 638.17 | 685.99 | 751.05 | 763.70 | 778.29 | 821.22 | 863.04 | |
| AMPERE3-450P-CE | Primary Energy | 441.25 | 473.91 | 580.13 | 654.46 | 676.13 | 695.56 | 750.32 | 766.26 | 792.33 | 837.14 | 879.08 | ||
| AMPERE3-450P-EU | Primary Energy | 441.25 | 473.91 | 598.26 | 696.60 | 692.53 | 699.38 | 747.23 | 763.69 | 791.18 | 836.82 | 879.48 | ||
| AMPERE3-550 | Primary Energy | 441.25 | 473.79 | 559.87 | 603.66 | 689.49 | 758.18 | 815.96 | 844.50 | 862.31 | 905.22 | 942.64 |
An internally consistent database¶
If we have an internally consistent database, the returned DataFrame
will always be none.
Repeating the same analysis as above can then confirm that all is well with the database as well as give us some insight into which variables do not have regional or sectoral breakdowns reported.
In [9]:
consistent_df = pyam.IamDataFrame(data="tutorial_check_database.csv", encoding='utf-8')
INFO:root:Reading `tutorial_check_database.csv`
In [10]:
for variable in consistent_df.filter(level=1).variables():
diff = consistent_df.check_aggregate(
variable,
**np_isclose_args
)
assert diff is None
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Gas - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CF4 - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CH4 - cannot check aggregate because it has no components
In [11]:
for variable in consistent_df.filter(level=1).variables():
diff = consistent_df.check_aggregate_regions(
variable,
**np_isclose_args
)
assert diff is None
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6 - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CF4 - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
Putting it altogether¶
Finally, we provide the check_internal_consistency method which does
all the above for you and returns a dictionary with all of the
dataframes which document the errors.
Note: at the moment, this method’s regional checking is limited to checking that all the regions sum to the World region. We cannot make this more automatic unless we start to store how the regions relate, see this issue.
In [12]:
# if all is good, None is returned
print("Checking consistent data"); time.sleep(0.5)
assert consistent_df.check_internal_consistency() is None
# otherwise we get a dict back
print("Checking AR5 subset"); time.sleep(0.5)
errors = df.check_internal_consistency()
Checking consistent data
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Cars - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Power - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Gas - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6 - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6|Industry - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6|Industry - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6|Solvents - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|C2F6|Solvents - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CF4 - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CF4 - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CH4 - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Aggregate Agg - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Aggregate Agg - cannot check regional aggregate because it has no regional components
Checking AR5 subset
INFO:root:Emissions|CO2 - 1390 of 1522 data points are not aggregates of components
INFO:root:Emissions|CO2 - 503 of 503 data points are not aggregates of regional components
INFO:root:Emissions|CO2|Fossil Fuels and Industry - 1258 of 1258 data points are not aggregates of components
INFO:root:Emissions|CO2|Fossil Fuels and Industry - 239 of 239 data points are not aggregates of regional components
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply - 239 of 239 data points are not aggregates of components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Emissions|CO2|Fossil Fuels and Industry|Energy Supply|Electricity - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Price|Carbon - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Price|Carbon - cannot check regional aggregate because it has no regional components
INFO:root:Primary Energy - 1522 of 1522 data points are not aggregates of components
INFO:root:Primary Energy - 503 of 503 data points are not aggregates of regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Coal - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Fossil|w/ CCS - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Primary Energy|Fossil|w/ CCS - cannot check regional aggregate because it has no regional components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Temperature|Global Mean|MAGICC6|MED - cannot check aggregate because it has no components
WARNING:root:Filtered IamDataFrame is empty!
INFO:root:Temperature|Global Mean|MAGICC6|MED - cannot check regional aggregate because it has no regional components
In [13]:
pprint([k for k in errors.keys()])
['Emissions|CO2-aggregate',
'Emissions|CO2-regional',
'Emissions|CO2|Fossil Fuels and Industry-aggregate',
'Emissions|CO2|Fossil Fuels and Industry-regional',
'Emissions|CO2|Fossil Fuels and Industry|Energy Supply-aggregate',
'Primary Energy-aggregate',
'Primary Energy-regional']
In [14]:
errors["Emissions|CO2-aggregate"]
Out[14]:
| 2005 | 2010 | 2020 | 2030 | 2040 | 2050 | 2060 | 2070 | 2080 | 2090 | 2100 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| variable | model | scenario | region | |||||||||||
| Emissions|CO2 | AIM-Enduse 12.1 | EMF27-450-Conv | ASIA | 10540.74 | 13160.18 | 11899.38 | 9545.81 | 7355.07 | 6119.50 | NaN | NaN | NaN | NaN | NaN |
| LAM | 3285.00 | 3294.54 | 3367.62 | 2856.65 | 2207.36 | 1537.72 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.54 | 4238.91 | 3956.19 | 3490.81 | 2082.24 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.33 | 11646.37 | 8272.30 | 4457.91 | 1625.18 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.42 | 3325.20 | 2991.24 | 1889.38 | 960.75 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38321.78 | 35588.66 | 28531.68 | 20287.46 | 13367.27 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-450-NoCCS | ASIA | 10540.74 | 13160.11 | 11893.80 | 9478.33 | 7367.07 | 5513.79 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3362.61 | 2837.11 | 1889.89 | 899.63 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4239.03 | 3619.25 | 2787.47 | 1671.29 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 11659.29 | 8708.81 | 5488.86 | 3355.22 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3322.95 | 3076.67 | 1977.78 | 1181.73 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 35588.85 | 28629.65 | 20458.10 | 13660.19 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-550-LimBio | ASIA | 10540.74 | 13160.11 | 14124.17 | 14218.08 | 13187.66 | 10019.56 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3445.63 | 3496.62 | 2986.08 | 1790.49 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4368.48 | 4519.64 | 4294.83 | 2733.76 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 12607.17 | 11752.01 | 9749.33 | 6501.31 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3826.80 | 3615.47 | 3258.31 | 3076.27 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 39531.61 | 38815.54 | 34676.38 | 25295.31 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-Base-FullTech | ASIA | 10540.74 | 13160.11 | 14149.89 | 16559.14 | 19658.68 | 23071.34 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.68 | 3449.84 | 3660.68 | 3850.44 | 3866.20 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.49 | 4371.98 | 4751.63 | 5389.48 | 6082.37 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12744.16 | 12642.70 | 13332.29 | 13742.93 | 14150.35 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3604.39 | 3838.82 | 4220.97 | 4866.31 | 5615.39 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38313.59 | 39612.60 | 43835.49 | 49027.80 | 54552.86 | NaN | NaN | NaN | NaN | NaN | |||
| EMF27-G8-EERE | ASIA | 10540.74 | 13152.56 | 13415.94 | 10147.89 | 7637.61 | 4435.80 | NaN | NaN | NaN | NaN | NaN | ||
| LAM | 3285.00 | 3286.52 | 3106.39 | 2825.27 | 1784.31 | 899.06 | NaN | NaN | NaN | NaN | NaN | |||
| MAF | 4302.21 | 4487.02 | 4091.19 | 3977.50 | 3659.80 | 3336.85 | NaN | NaN | NaN | NaN | NaN | |||
| OECD90 | 12085.85 | 12750.81 | 10276.06 | 8833.95 | 5845.24 | 3473.56 | NaN | NaN | NaN | NaN | NaN | |||
| REF | 3306.95 | 3596.74 | 3453.29 | 3468.73 | 3376.25 | 3058.68 | NaN | NaN | NaN | NaN | NaN | |||
| World | 34492.05 | 38304.41 | 35425.96 | 30395.43 | 23536.71 | 16487.83 | NaN | NaN | NaN | NaN | NaN | |||
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | |
| REMIND 1.5 | EMF27-450-NoCCS | OECD90 | 15111.39 | 15254.16 | 8082.87 | 2864.75 | 369.53 | 328.11 | 299.06 | 266.24 | 255.25 | 245.07 | 226.35 | |
| World | 33837.41 | 38224.94 | 25524.60 | 7358.64 | 1691.05 | 1663.77 | 1616.52 | 1555.47 | 1553.00 | 1665.20 | 1883.11 | |||
| EMF27-550-LimBio | ASIA | 10193.98 | 13239.55 | 14218.37 | 11920.79 | 8135.32 | 5963.84 | 4486.53 | 3100.11 | 2246.06 | 1843.16 | 1570.02 | ||
| LAM | 2926.60 | 3478.79 | 4413.41 | 1831.96 | 1357.42 | 934.84 | 712.03 | 523.57 | 418.39 | 359.64 | 107.38 | |||
| MAF | 4035.32 | 4381.03 | 4504.49 | 3368.89 | 3582.70 | 3883.52 | 3663.91 | 3349.07 | 3064.56 | 2919.43 | 3153.50 | |||
| OECD90 | 15111.39 | 15241.56 | 13016.52 | 10555.13 | 7238.06 | 4454.98 | 2745.80 | 1531.01 | 766.12 | 275.60 | -82.62 | |||
| World | 33837.41 | 37970.11 | 37657.41 | 28699.50 | 20936.83 | 15389.20 | 11536.73 | 8368.71 | 6360.33 | 5299.36 | 4644.20 | |||
| EMF27-Base-FullTech | ASIA | 10193.98 | 13478.78 | 20256.01 | 24006.74 | 28404.78 | 33016.66 | 35977.35 | 36397.92 | 34529.79 | 28622.83 | 23400.42 | ||
| LAM | 2926.60 | 3508.40 | 5067.35 | 5464.43 | 4402.98 | 5424.51 | 5869.57 | 5988.95 | 6096.94 | 5152.73 | 4074.45 | |||
| MAF | 4035.32 | 4381.09 | 5364.84 | 5862.75 | 8659.61 | 12865.82 | 17680.13 | 22674.41 | 27242.60 | 29150.29 | 29981.48 | |||
| OECD90 | 15111.39 | 15234.63 | 15486.57 | 16326.59 | 16610.24 | 16943.56 | 16515.90 | 15922.43 | 14587.22 | 11864.62 | 9683.61 | |||
| World | 33837.41 | 38293.08 | 48134.42 | 53343.82 | 59836.10 | 70077.89 | 77941.21 | 82914.15 | 84109.23 | 75995.09 | 68004.38 | |||
| WITCH_EMF27 | EMF27-450-Conv | ASIA | 9895.45 | 13210.18 | 13914.12 | 12004.49 | 10538.51 | 8767.49 | 7410.94 | 6299.16 | 3794.59 | 2865.46 | 2437.77 | |
| LAM | 4660.57 | 4644.17 | 1851.46 | 1537.68 | 1421.58 | 658.62 | -161.02 | -1398.20 | -1659.55 | -1631.41 | -1586.79 | |||
| MAF | 2508.31 | 2673.95 | 2224.44 | 1932.65 | 1907.50 | 1703.00 | 1588.40 | 1493.36 | 1413.90 | 1303.02 | 1118.21 | |||
| OECD90 | 12644.40 | 12597.55 | 9780.38 | 6560.69 | 4755.20 | 3257.79 | 2240.72 | 1399.83 | 795.67 | 359.17 | 224.28 | |||
| REF | 3870.58 | 4035.17 | 2381.75 | 2055.81 | 1733.11 | 1347.51 | 1064.51 | 815.36 | 611.13 | 444.37 | 383.41 | |||
| World | 33579.32 | 37161.03 | 30152.15 | 24091.32 | 20355.91 | 15734.40 | 12143.56 | 8609.50 | 4955.74 | 3340.62 | 2576.88 | |||
| EMF27-550-LimBio | ASIA | 9895.98 | 13341.76 | 17280.06 | 18745.41 | 16414.52 | 12419.72 | 10012.01 | 9373.38 | 8937.92 | 9270.47 | 9214.63 | ||
| LAM | 4660.84 | 4612.38 | 2729.37 | 2515.92 | 2286.12 | 1309.50 | 1048.52 | 677.15 | 470.62 | -66.27 | -210.35 | |||
| MAF | 2508.81 | 2621.97 | 2773.63 | 2885.03 | 2775.24 | 2552.54 | 2579.22 | 2758.68 | 2928.88 | 3067.60 | 3139.20 | |||
| OECD90 | 12645.78 | 12542.87 | 11852.60 | 10275.13 | 7908.88 | 5310.74 | 4461.18 | 4405.41 | 4236.36 | 4016.45 | 3860.45 | |||
| REF | 3871.04 | 4062.72 | 3764.23 | 3380.51 | 2617.25 | 1957.66 | 1686.23 | 1658.31 | 1628.48 | 1550.83 | 1510.95 | |||
| World | 33582.45 | 37181.70 | 38399.88 | 37802.01 | 32002.02 | 23550.17 | 19787.16 | 18872.93 | 18202.25 | 17839.07 | 17514.87 | |||
| EMF27-Base-FullTech | ASIA | 9893.46 | 13378.34 | 20016.55 | 26248.47 | 30889.38 | 34562.46 | 37566.05 | 40325.64 | 42647.52 | 44874.72 | 46657.52 | ||
| LAM | 4659.58 | 4623.98 | 4524.39 | 4644.99 | 4937.36 | 5250.67 | 5698.25 | 6117.40 | 6522.66 | 6945.51 | 7358.61 | |||
| MAF | 2506.45 | 2642.28 | 3291.19 | 4063.34 | 5028.41 | 6038.17 | 7017.40 | 8032.94 | 8851.50 | 9680.49 | 10373.40 | |||
| OECD90 | 12639.28 | 12598.84 | 13097.95 | 13835.62 | 14969.12 | 15784.59 | 16540.18 | 17249.21 | 17924.86 | 18566.23 | 19180.64 | |||
| REF | 3868.87 | 4077.28 | 4636.23 | 5039.14 | 5412.35 | 5886.80 | 6279.44 | 6439.80 | 6722.19 | 7040.23 | 7284.21 | |||
| World | 33567.64 | 37320.72 | 45566.30 | 53831.56 | 61236.62 | 67522.70 | 73101.32 | 78164.98 | 82668.74 | 87107.17 | 90854.38 |
140 rows × 11 columns
