Planning the swedish economy¶

Let's begin importing the data in a dictionary for the periods we want to plan. The data has been saved from the excel files using the save_data.py script.

In [1]:

from pathlib import Path
from pickle import load

import numpy as np

from cybersyn import Economy, TargetEconomy


with Path('data', 'economy.pkl').open('rb') as f:
    economy_dict = load(f)
    economy = Economy(**economy_dict)

with Path('data', 'target_economy.pkl').open('rb') as f:
    target_economy_dict = load(f)
    target_economy = TargetEconomy(**target_economy_dict)

from pathlib import Path from pickle import load import numpy as np from cybersyn import Economy, TargetEconomy with Path('data', 'economy.pkl').open('rb') as f: economy_dict = load(f) economy = Economy(**economy_dict) with Path('data', 'target_economy.pkl').open('rb') as f: target_economy_dict = load(f) target_economy = TargetEconomy(**target_economy_dict)

The products and production units are the same since the data is aggregated (except there is a CO2 product but not a CO2 sector):

In [2]:

economy.product_names[:5]

economy.product_names[:5]

Out[2]:

['Products of agriculture, hunting and related services',
 'Products of forestry, logging and related services',
 'Fish and other fishing products; aquaculture products; support services to fishing',
 'Mining and quarrying',
 'Food products, beverages and tobacco products']

The plan¶

We will plan the economy for 15 periods with a horizon of 5 periods and revising the plan after 3 periods (apply these 3 periods and discard the other 2).

In [3]:

len(economy.supply)

len(economy.supply)

Out[3]:

17

In [4]:

from cybersyn import OptimizePlan

periods = 15
horizon_periods = 5
revise_periods = 3

plan = OptimizePlan(
    periods = periods,
    horizon_periods = horizon_periods, 
    revise_periods = revise_periods,
    economy = economy,
    ecology = None
)

planned = plan(
    target_economy,
    target_ecology=None,
    init_surplus=target_economy.domestic[0] / 12,  # np.zeros(economy.products)
    init_export_deficit=0  
)

from cybersyn import OptimizePlan periods = 15 horizon_periods = 5 revise_periods = 3 plan = OptimizePlan( periods = periods, horizon_periods = horizon_periods, revise_periods = revise_periods, economy = economy, ecology = None ) planned = plan( target_economy, target_ecology=None, init_surplus=target_economy.domestic[0] / 12, # np.zeros(economy.products) init_export_deficit=0 )

In [5]:

activity = np.array(planned.activity).T
production = np.array(planned.production).T
surplus = np.array(planned.surplus).T
total_import = np.array(planned.total_import).T
export_deficit = np.array(planned.export_deficit)
worked_hours = np.array(planned.worked_hours)

activity = np.array(planned.activity).T production = np.array(planned.production).T surplus = np.array(planned.surplus).T total_import = np.array(planned.total_import).T export_deficit = np.array(planned.export_deficit) worked_hours = np.array(planned.worked_hours)

Visualizing the results¶

In [6]:

import matplotlib.pyplot as plt
from numpy import linspace

plt.style.use("seaborn-v0_8-whitegrid")

colors = plt.cm.nipy_spectral(linspace(0, 1, economy.products))

import matplotlib.pyplot as plt from numpy import linspace plt.style.use("seaborn-v0_8-whitegrid") colors = plt.cm.nipy_spectral(linspace(0, 1, economy.products))

We planned for 9 years and the interpolation between them (mYEAR).

In [7]:

periods = [
    "2008",
    "m2008",
    "2009",
    "m2009",
    "2010",
    "m2010",
    "2011",
    "m2011",
    "2012",
    "m2012",
    "2013",
    "m2013",
    "2014",
    "m2014",
    "2015",
    "m2015",
    "2016",
    "m2016",
]

periods = [ "2008", "m2008", "2009", "m2009", "2010", "m2010", "2011", "m2011", "2012", "m2012", "2013", "m2013", "2014", "m2014", "2015", "m2015", "2016", "m2016", ]

Production unit activity¶

In [8]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Evolution of the activity in each period.", size=16)

for i in range(economy.sectors):
    ax.plot(periods[: plan.periods], activity[i, :], c=colors[i, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Activity of the unit", size=14)

plt.savefig(Path("figures", "unit_activity.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Evolution of the activity in each period.", size=16) for i in range(economy.sectors): ax.plot(periods[: plan.periods], activity[i, :], c=colors[i, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Activity of the unit", size=14) plt.savefig(Path("figures", "unit_activity.png"))

Production¶

Note that a period with negative production means that more product was used than supplied.

In [9]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Evolution of planned production in each period.", size=16)

for i in range(economy.products):
    ax.plot(periods[: plan.periods], production[i, :], c=colors[i, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Production", size=14)

plt.savefig(Path("figures", "planned_prod.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Evolution of planned production in each period.", size=16) for i in range(economy.products): ax.plot(periods[: plan.periods], production[i, :], c=colors[i, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Production", size=14) plt.savefig(Path("figures", "planned_prod.png"))

Excess production¶

In [10]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Evolution of excess production in each period.", size=16)

for i in range(economy.products):
    ax.plot(periods[: plan.periods], surplus[i, :], c=colors[i, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Excess production", size=14)

plt.savefig(Path("figures", "excess_prod.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Evolution of excess production in each period.", size=16) for i in range(economy.products): ax.plot(periods[: plan.periods], surplus[i, :], c=colors[i, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Excess production", size=14) plt.savefig(Path("figures", "excess_prod.png"))

Note how carbon dioxide keeps rising since it is not used.

In [11]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle(r"Excess $CO_2$ in each period.", size=16)

ax.plot(periods[: plan.periods], surplus[59, :], c=colors[59, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel(economy.product_names[59], size=14)

plt.savefig(Path("figures", "excess_co2.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle(r"Excess $CO_2$ in each period.", size=16) ax.plot(periods[: plan.periods], surplus[59, :], c=colors[59, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel(economy.product_names[59], size=14) plt.savefig(Path("figures", "excess_co2.png"))

In [12]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Excess food in each period.", size=16)

ax.plot(periods[: plan.periods], surplus[4, :], c=colors[4, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel(economy.product_names[4], size=14)

plt.savefig(Path("figures", "excess_food.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Excess food in each period.", size=16) ax.plot(periods[: plan.periods], surplus[4, :], c=colors[4, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel(economy.product_names[4], size=14) plt.savefig(Path("figures", "excess_food.png"))

Imported final goods¶

In [13]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Evolution of imported final goods in each period.", size=16)

for i in range(economy.products):
    ax.plot(periods[: plan.periods], total_import[i, :], c=colors[i, :])

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Imported final", size=14)

plt.savefig(Path("figures", "planned_prod.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Evolution of imported final goods in each period.", size=16) for i in range(economy.products): ax.plot(periods[: plan.periods], total_import[i, :], c=colors[i, :]) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Imported final", size=14) plt.savefig(Path("figures", "planned_prod.png"))

Export deficit¶

In [14]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Export deficit in each period.", size=16)

ax.plot(periods[: plan.periods], export_deficit)

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Deficit in $", size=14)

plt.savefig(Path("figures", "export_deficit.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Export deficit in each period.", size=16) ax.plot(periods[: plan.periods], export_deficit) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Deficit in $", size=14) plt.savefig(Path("figures", "export_deficit.png"))

Total worked hours¶

In [15]:

fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fig.suptitle("Total worked hours in each period.", size=16)

ax.plot(periods[: plan.periods], worked_hours)

plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1])
ax.set_xlabel("Period", size=14)
ax.set_ylabel("Time in hours", size=14)

plt.savefig(Path("figures", "worked_hours.png"))

fig, ax = plt.subplots(1, 1, figsize=(8, 6)) fig.suptitle("Total worked hours in each period.", size=16) ax.plot(periods[: plan.periods], worked_hours) plan_lims = ax.set_xlim(periods[0], periods[plan.periods - 1]) ax.set_xlabel("Period", size=14) ax.set_ylabel("Time in hours", size=14) plt.savefig(Path("figures", "worked_hours.png"))