EIOA Class: Integrated Group Assignment(IGA)
Each group is expected to perform a research question about two real-world issues: BREXIT and national stimulus plans.
in a post COVID-19 world.
Tasks
- Load from pickle
- Calculate for a specific country territorial CO2 emissions
- Calculate for a specific country consumption-based CO2 emissions
- Identify top-5 sectors that contribute to territorial emissions of a specific country
- Identify top-5 final demand expenditure on products that contribute to consumption-based emissions of a specific country
- Identify top-5 countries where the specific country displaced its emissions to
June 11 at 17:00 (deadline):Code and report(.pdf) in Brightspace/Assignments.
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import numpy as np
import time
import pickle as pkl
import os
from pathlib import Path
np.set_printoptions(precision=2)
tstart = time.time()
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TASK 1: Load MRIO from Pickle
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Folder settings: Change the current working directory to reflect the location in your computer
(run os.getcwd() to find out what that is)
Folder to read MRIO from
os.getcwd()
mrio_dir = Path("/…/EIOA/IGA/MRIO") # Fill " " with your working directory path
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Load MRIO
tstart = time.time()
mrio_name = ‘mrio.pkl’
mrio_str = mrio_dir.joinpath(mrio_name)
pkl_in = open(mrio_str,“rb”) # ‘open’ the mrio.pkl file
mrio = pkl.load(pkl_in) # load the mrio.pkl file to mrio
pkl_in.close()
tend = time.time()
print(‘Done reading in %5.2f s\n’% (tend - tstart))
category counts
nr = mrio[’label’][‘region’].count()[0]
ns = mrio[’label’][‘product’].count()[0]
ny = mrio[’label’][‘final’].count()[0]
MRIO matrix/vector variables
L = mrio[‘L’]
Y = mrio[‘Y’]
V = mrio[‘V’]
F = mrio[‘F’]
F_co2 = mrio[‘F_co2’]
H = mrio[‘H’]
H_co2 = mrio[‘H_co2’]
calculate total output
x = L @ Y.sum(1)
calculate direct emissions intensity
f_co2 = F_co2/x # resulting in na or inf when x = 0
f_co2 = np.nan_to_num(f_co2, copy=False, nan=0.0, posinf=0.0, neginf=0.0) # replace na and inf with 0
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Configuration for all following tasks
change to choose different region: c in 0 to 48
position 20 is Netherlands
explore mrio[’label’][‘region’] to find others
c = 20
To locate country columns or rows in A and L: ‘c*ns : (c+1)*ns’
To locate country columns in Y: ‘c*ny : (c+1)*ny’
convert units from kg to Mt (*1e-9)
conv = 1e-9
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TASK 2. Calculate territorial emissions for a specific country
We want to report three cols: production-related, households and total, plus a global contribution percentage
pba = np.zeros((1,4))
pba[:,0] = F_co2[c*ns : (c+1)*ns].sum() conv # direct emissions by producers
pba[:,1] = H_co2[cny : (c+1)*ny].sum() *conv # direct emissions by households
pba[:,2] = pba[:,0:2].sum()
pba[:,3] = pba[:,2]/((F_co2.sum()+H_co2.sum())*conv)*100
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TASK 3. Calculate for a specific country consumption-based CO2 emissions
We want to report three cols: production-generated, households and total, plus a global contribution percentage
cba = np.zeros((1,4))
y = Y[:, c*ny : (c+1)*ny].sum(1) # aggregate final demand to one column
cba[:,0] = f_co2 @ L @ y conv # footprint traced to production emissions
cba[:,1] = H_co2[cny : (c+1)*ny].sum()*conv # footprint traced to household direct emissions
cba[:,2] = cba[:,:2].sum()
cba[:,3] = cba[:,2]/((F_co2.sum()+H_co2.sum())*conv)*100
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TASK 4. Identify top-5 sectors that contribute to production-based emissions
pba_s: we want to report in rows each sector plus household
label_s = list(mrio[’label’][‘product’][‘Name’]) + [‘Household direct’]
pba_s = F_co2[c*ns : (c+1)*ns] *conv
pba_s = np.append(pba_s, pba[0,1]) # append household direct emissions to the end
pba_s_sorted: we want to report in rows the ‘sorted’ (in descending order) sectoral plus household results;
3 cols: sectoral emissions, percent, and cumulative percent
pba_s_sorted = np.zeros((ns+1,3))
rank = np.argsort(pba_s) # returns indices that’d sort the array low to high
rank = np.flip(rank) # flip so it goes ‘high to low’
pba_s_sorted[:,0] = np.sort(pba_s) # sort the values from ’low to high’
pba_s_sorted[:,0] = np.flip(pba_s_sorted[:,0]) # flip so the values go from high to low
pba_s_sorted[:,1] = pba_s_sorted[:,0]/pba[0,2]*100 # percentage contribution
pba_s_sorted[:,2] = np.cumsum(pba_s_sorted[:,1]) # cumulative percentage contribution
sector names of the top 5 emitters
for i in range(0,5) :
print(“Top”, i+1, label_s[rank[i]],"(sector index =", rank[i], “);”)
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TASK 5. Identify top-5 final demand spending sectors that contribute to consumption-based emissions
TASK 6. Identify top-5 countries where the specific country displaced emissions to
label_s = list(mrio[’label’][‘product’][‘Name’]) + [‘Household direct’]
cba_s = f_co2 @ L @ np.diag(y)*conv
cba_rs = np.reshape(cba_s,(nr,ns)) # pay attention to get the new shape dimension right
cba_s = cba_rs.sum(0) # by final demand sector
cba_s = np.append(cba_s,cba[0,1]) # append household direct emissions to the end
cba_r = cba_rs.sum(1) # by emitting regions
cba_r[c] = cba_r[c] + cba[0,1] # add HH direct emissions
cba_s_sort: we want to report in rows each final demand sector(product) plus household
3 cols: sectoral emissions, percent, and cumulative percent
cba_s_sorted = np.zeros((ns+1,3))
rank = np.argsort(cba_s) # sort from ’low to high’ and returns indices
rank = np.flip(rank) # flip so it goes ‘high to low’
cba_s_sorted[:,0] = np.sort(cba_s) # sort the values from ’low to high’
cba_s_sorted[:,0] = np.flip(cba_s_sorted[:,0]) # flip so the values go from high to low
cba_s_sorted[:,1] = cba_s_sorted[:,0]/cba[0,2]*100 # percentage contribution
cba_s_sorted[:,2] = np.cumsum(cba_s_sorted[:,1]) # cumulative percentage contribution
sector names of the top 5 final demand product categories
for i in range(0,5) :
print(“Top”, i+1, label_s[rank[i]],"(sector index =", rank[i], “);”)
cba_r_sort: we want to report in rows each country of origin
3 cols: country emissions, percent, and cumulative percent
label_r = list(mrio[’label’][‘region’][‘CountryName’])
cba_r_sorted = np.zeros((nr,3))
rank = np.argsort(cba_r) # sort from ’low to high’ and returns indices
rank = np.flip(rank) # flip so it goes ‘high to low’
cba_r_sorted[:,0] = np.sort(cba_r) # sort the values from ’low to high’
cba_r_sorted[:,0] = np.flip(cba_r_sorted[:,0]) # flip so the values go from high to low
cba_r_sorted[:,1] = cba_r_sorted[:,0]/cba[0,2]*100 # percentage contribution
cba_r_sorted[:,2] = np.cumsum(cba_r_sorted[:,1]) # cumulative percentage contribution
country names of the top 5 displacement locations
if c in rank[0:5] :
for i in range(0,6) :
if rank[i] != c :
print(“Top”, i+1, label_r[rank[i]],"(country index =", rank[i], “);”)
else :
for i in range(0,5) :
print(“Top”, i+1, label_r[rank[i]],"(country index =", rank[i], “);”)
