def check_ext_matrix(self, rel_tol=default_rel_tol):
is_correct = True
e1 = np.sum(self._ext, axis=1)
total_final_use = np.sum(self._sut.domestic_final_use, axis=1)
q2 = np.dot(mi.inverse(self.io_matrix_model_0()), total_final_use)
e2 = np.dot(self._ext, q2)
it = np.nditer(e1, flags=['f_index'])
while not it.finished and is_correct:
if not math.isclose(e1[it.index], e2[it.index], rel_tol=rel_tol):
is_correct = False
it.iternext()
return is_correct
def check_io_matrix(self, rel_tol=default_rel_tol):
is_correct = True
# in this case a scaling vector is calculated. If the net-supply matrix is correct
# it would contains 1s (or zero if product is absent)
total_final_use = np.sum(self._sut.domestic_final_use, axis=1)
s = np.dot(mi.inverse(self.io_matrix_model_0()), total_final_use)
it = np.nditer(s, flags=['f_index'])
while not it.finished and is_correct:
if not (math.isclose(s[it.index], 1, rel_tol=rel_tol)
or math.isclose(s[it.index], 0, rel_tol=rel_tol)):
is_correct = False
it.iternext()
return is_correct
def transformation_matrix(self):
supply = self._sut.supply
g = self._sut.total_industry_output
return np.dot(np.diag(g), mi.inverse(supply))
def ext_coefficients_matrix(self):
supply = self._sut.supply
return np.dot(self._ext, mi.inverse(supply))
def transformation_matrix(self):
supply = self._sut.supply
q = self._sut.total_product_domestic_use
return np.dot(mi.inverse(supply), np.diag(q))
def main():
"""This script will read supply-use tables for a series of countries and convert them
in four different input-output tables following transformation model A - D and model 0.
These four different input-output tables and model 0 are subsequently used to
calculate the total marginal output change and CO2 emissions for 1 million Euro
final demand of each product. Subsequently scatter plots are made to show
differences between the transformation models in terms of marginal changes in
total sector/product output and CO2 emissions."""
# 0. INITIALIZING
debug = False
show_graphics = False
save_graphics = True
resolution_graphics = Resolution.HIGH
show_zero_values = False
show_chart_titles = False
configuration = cf.Config()
if debug:
cntr_codes = ['NL']
cntr_names = ['Netherlands']
else:
cntr_codes = configuration.country_codes
cntr_names = configuration.country_names
if resolution_graphics == Resolution.LOW:
dpi_graphics = 50
elif resolution_graphics == Resolution.HIGH:
dpi_graphics = 200
else:
dpi_graphics = 50
yr = configuration.base_year
data_dir = configuration.data_dir
# create final demand matrix
fd_matrix = np.diag(np.ones(configuration.product_count))
for cntr_code in cntr_codes:
# 1. READING DATA
# 1.1. status message
cntr_name = cntr_names[cntr_codes.index(cntr_code)]
print('start analysis for country with country code: ' + cntr_name)
# 1.2. reading the sut file
fn = cntr_code + '_sut_' + str(yr) + '.xlsx'
full_fn = os.path.join(data_dir, fn)
reader = sr.SutReader(full_fn, cntr_code, yr)
st = reader.read_sut()
# 1.3. reading the environmental extensions file
env_reader = er.EnvReader(cntr_code, yr)
e_extensions = env_reader.get_extensions()
# 2. CREATE TRANSFORMATION MODELS
md_0 = m0.TransformationModel0(st, e_extensions)
md_a = ma.TransformationModelA(st, e_extensions)
md_b = mb.TransformationModelB(st, e_extensions)
md_c = mc.TransformationModelC(st, e_extensions)
md_d = md.TransformationModelD(st, e_extensions)
# 3. CREATE INPUT-OUTPUT COEFFICIENT MATRICES
model_0 = md_0.io_matrix_model_0()
model_a = md_a.io_coefficient_matrix()
model_b = md_b.io_coefficient_matrix()
model_c = md_c.io_coefficient_matrix()
model_d = md_d.io_coefficient_matrix()
# 4. CREATE EXTENSION COEFFICIENT MATRICES
model_0_ext = md_0.ext_transaction_matrix()
model_a_ext = md_a.ext_coefficients_matrix()
model_b_ext = md_b.ext_coefficients_matrix()
model_c_ext = md_c.ext_coefficients_matrix()
model_d_ext = md_d.ext_coefficients_matrix()
# 5. CHECK TRANSFORMATION MODELS
if not md_a.check_io_transaction_matrix():
print('Model A transaction matrix not correct')
if not md_a.check_io_coefficients_matrix():
print('Model A coefficients matrix not correct')
if not md_a.check_ext_transaction_matrix():
print('Model A extension matrix not correct')
if not md_a.check_ext_coefficient_matrix():
print('Model A extension coefficients matrix not correct')
if not md_b.check_io_transaction_matrix():
print('Model B transaction matrix not correct')
if not md_b.check_io_coefficients_matrix():
print('Model B coefficients matrix not correct')
if not md_b.check_ext_transaction_matrix():
print('Model B extension matrix not correct')
if not md_b.check_ext_coefficient_matrix():
print('Model B extension coefficients matrix not correct')
if not md_c.check_io_transaction_matrix():
print('Model C transaction matrix not correct')
if not md_c.check_io_coefficients_matrix():
print('Model C coefficients matrix not correct')
if not md_c.check_ext_transaction_matrix():
print('Model C extension matrix not correct')
if not md_c.check_ext_coefficient_matrix():
print('Model C extension coefficients matrix not correct')
if not md_d.check_io_transaction_matrix():
print('Model D transaction matrix not correct')
if not md_d.check_io_coefficients_matrix():
print('Model D coefficients matrix not correct')
if not md_d.check_ext_transaction_matrix():
print('Model D extension matrix not correct')
if not md_d.check_ext_coefficient_matrix():
print('Model D extension coefficients matrix not correct')
# model 0 does not have coefficient matrices
if not md_0.check_io_matrix():
print('Model 0 technology matrix not correct')
if not md_0.check_ext_matrix():
print('Model 0 extensions matrix not correct')
# 6. CREATE FINAL DEMAND 'SHOCK' VECTORS
# 6.1. take out non-existing products
fd = np.sum(st.domestic_final_use, axis=1)
fd_mask = fd != 0
fd_mask = fd_mask * 1
fd_mask = np.diag(fd_mask)
fd_matrix = np.dot(fd_matrix, fd_mask)
# 6.2. transform final demand
delta_fd_model_0 = md_0.final_demand(fd_matrix)
delta_fd_model_a = md_a.final_demand(fd_matrix)
delta_fd_model_b = md_b.final_demand(fd_matrix)
delta_fd_model_c = md_c.final_demand(fd_matrix)
delta_fd_model_d = md_d.final_demand(fd_matrix)
# 7. CALCULATE CHANGES IN INDUSTRY/PRODUCT OUTPUT
# 7.1. Industry output and product output
eye = np.diag(np.ones(configuration.product_count))
delta_s_model_0 = mi.inverse(model_0) @ delta_fd_model_0
delta_q_model_a = mi.inverse(eye - model_a) @ delta_fd_model_a
delta_q_model_b = mi.inverse(eye - model_b) @ delta_fd_model_b
delta_g_model_c = mi.inverse(eye - model_c) @ delta_fd_model_c
delta_g_model_d = mi.inverse(eye - model_d) @ delta_fd_model_d
# 7.2. Make scatter plots of total output
delta_q_mod_a = np.sum(delta_q_model_a, axis=0, keepdims=True)
delta_q_mod_b = np.sum(delta_q_model_b, axis=0, keepdims=True)
delta_g_mod_c = np.sum(delta_g_model_c, axis=0, keepdims=True)
delta_g_mod_d = np.sum(delta_g_model_d, axis=0, keepdims=True)
if not show_zero_values:
delta_q_mod_a = _remove_zero_value(delta_q_mod_a)
delta_q_mod_b = _remove_zero_value(delta_q_mod_b)
delta_g_mod_c = _remove_zero_value(delta_g_mod_c)
delta_g_mod_d = _remove_zero_value(delta_g_mod_d)
# 7.2.1. model a vs model b, total product supply per product
plt.rcParams['font.size'] = 12
plt.scatter(delta_q_mod_a, delta_q_mod_b)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$ output')
plt.xlabel('$\Delta q^{(A)}$ (million Euro)')
plt.ylabel('$\Delta q^{(B)}$ (million Euro)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_output_model_a_b.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 7.7.2. model c vs model d, industry output per industry
plt.rcParams['font.size'] = 12
plt.scatter(delta_g_mod_c, delta_g_mod_d)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$ output')
plt.xlabel('$\Delta g^{(C)}$ (million Euro)')
plt.ylabel('$\Delta g^{(D)}$ (million Euro)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_output_model_c_d.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 7.7.3. model a vs model c, product supply per product and
# industry output per industry
plt.rcParams['font.size'] = 12
plt.scatter(delta_q_mod_a, delta_g_mod_c)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$ output')
plt.xlabel('$\Delta q^{(A)}$ (million Euro)')
plt.ylabel('$\Delta g^{(C)}$ (million Euro)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_output_model_a_c.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 7.7.4. model b vs model d, product supply per product and
# industry output per industry
plt.rcParams['font.size'] = 12
plt.scatter(delta_q_mod_b, delta_g_mod_d)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$ output')
plt.xlabel('$\Delta q^{(B)}$ (million Euro)')
plt.ylabel('$\Delta g^{(D)}$ (million Euro)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_output_model_b_d.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8. CALCULATE CHANGES IN CO2 EMISSIONS
# 8.1 calculate change in emissions for every 1 million Euro product
delta_ext_model_0 = model_0_ext @ delta_s_model_0
delta_ext_model_a = model_a_ext @ delta_q_model_a
delta_ext_model_b = model_b_ext @ delta_q_model_b
delta_ext_model_c = model_c_ext @ delta_g_model_c
delta_ext_model_d = model_d_ext @ delta_g_model_d
# 8.2 take out CO2 emissions and convert from kg to metric tonne
delta_carbon_model_0 = delta_ext_model_0[0, :] / 1000
delta_carbon_model_a = delta_ext_model_a[0, :] / 1000
delta_carbon_model_b = delta_ext_model_b[0, :] / 1000
delta_carbon_model_c = delta_ext_model_c[0, :] / 1000
delta_carbon_model_d = delta_ext_model_d[0, :] / 1000
# 8.3 make scatter plots
# 8.3.1. model a vs model b, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_a, delta_carbon_model_b)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(A)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(B)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_a_b.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8.3.2 model c vs model d, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_c, delta_carbon_model_d)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(C)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(D)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_c_d.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8.3.3 model a vs model c, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_a, delta_carbon_model_c)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(A)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(C)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_a_c.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8.3.4 model b vs model d, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_b, delta_carbon_model_d)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(B)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(D)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_b_d.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8.3.5 model 0 vs model a, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_0, delta_carbon_model_a)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(0)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(A)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_0_a.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()
# 8.3.6 model 0 vs model c, carbon emissions
plt.rcParams['font.size'] = 12
plt.scatter(delta_carbon_model_0, delta_carbon_model_c)
if show_chart_titles:
plt.title(cntr_name + ' - $\Delta$CO$_{2}$ emissions')
plt.xlabel('$\Delta$ CO$_{2}$$^{(0)}$ (thousand Tonne)')
plt.ylabel('$\Delta$ CO$_{2}$$^{(C)}$ (thousand Tonne)')
plt.axis('scaled')
plt.xlim(_get_range(plt))
plt.ylim(_get_range(plt))
if save_graphics:
plot_fn = cntr_code + '_carbon_model_0_c.png'
full_plot_fn = os.path.join(configuration.results_dir, plot_fn)
plt.savefig(full_plot_fn, dpi=dpi_graphics)
if show_graphics:
plt.show()
plt.close()