Primary energy supply in illustrative pathways and by category (Figure 2.15)¶
Notebook sr15_2.4.2.1_primary_energy_marker-scenarios¶
This notebook is based on the Release 1.1 of the IAMC 1.5C Scenario Explorer and Data and refers to the published version of the IPCC Special Report on Global Warming of 1.5C (SR15).
The notebook is run with pyam release 0.5.0.
The source code of this notebook is available on GitHub (release 2.0.2).
IPCC SR15 scenario assessment¶
Analysis of the primary energy development
in illustrative pathways¶
This notebook computes indicators and diagnostics of the primary-energy timeseries by fuel in Figure 2.15, focusing on the illustrative pathways and the IEA's 'Faster Transition Scenario' using the 'World Energy Model' in the IPCC's "Special Report on Global Warming of 1.5°C".
The scenario data used in this analysis can be accessed and downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.
Load pyam package and other dependencies¶
In [1]:
import pandas as pd
import numpy as np
import io
import itertools
import yaml
import math
import matplotlib.pyplot as plt
plt.style.use('style_sr15.mplstyle')
%matplotlib inline
import pyam
Import scenario data, categorization and specifications files¶
The metadata file with scenario categorisation and quantitative indicators can be downloaded at https://data.ene.iiasa.ac.at/iamc-1.5c-explorer.
Alternatively, it can be re-created using the notebook sr15_2.0_categories_indicators.
The last cell of this section loads and assigns a number of auxiliary lists as defined in the categorization notebook.
In [2]:
sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r2.0.xlsx')
In [3]:
sr1p5.load_meta('sr15_metadata_indicators.xlsx')
In [4]:
with open("sr15_specs.yaml", 'r') as stream:
specs = yaml.load(stream, Loader=yaml.FullLoader)
rc = pyam.run_control()
for item in specs.pop('run_control').items():
rc.update({item[0]: item[1]})
cats = specs.pop('cats')
cats_15_no_lo = specs.pop('cats_15_no_lo')
marker = specs.pop('marker')
Downselect scenario ensemble to categories of interest for this assessment¶
In [5]:
cats.remove('Above 2C')
In [6]:
years = [2030, 2050, 2100]
In [7]:
df = sr1p5.filter(category=cats, year=years)
Set specifications for filter and plotting¶
In [8]:
save_name = 'output/fig2.15{}.png'
In [9]:
filter_args = dict(df=sr1p5, category=cats, marker=None, join_meta=True)
Retrieve historical data from IEA Energy Statistics and rename variables¶
In [10]:
hist = sr1p5.filter(model='Reference', scenario='IEA Energy Statistics (r2017)')
In [11]:
fossil_vars = [
'Primary Energy|Coal',
'Primary Energy|Oil',
'Primary Energy|Gas'
]
ren_vars = [
'Primary Energy|Geothermal',
'Primary Energy|Hydro',
'Primary Energy|Ocean',
]
hist_mapping = {}
for f in fossil_vars:
hist_mapping.update({f: 'Fossil without CCS'})
for r in ren_vars:
hist_mapping.update({r: 'Other renewables'})
hist_mapping.update({'Primary Energy|Biomass': 'Biomass without CCS'})
hist.rename({'variable': hist_mapping}, inplace=True)
In [12]:
hist_args = dict(color='black', linestyle='dashed', linewidth=1)
Add IEA's 'Faster Transition Scenario' to the set of marker scenarios for comparison¶
In [13]:
m = 'IEA WEM'
col = 'marker'
df.set_meta(m, col,
df.filter(model='IEA World Energy Model 2017',
scenario='Faster Transition Scenario'))
rc.update({'marker': {col: {m: 'o'}},
'c': {col: {m: 'red'}},
'edgecolors': {col: {m: 'black'}}}
)
marker += [m]
Rename variables for plots¶
In [14]:
variable_mapping = [
('Fossil without CCS', 'Primary Energy|Fossil|w/o CCS', 'black'),
('Fossil with CCS', 'Primary Energy|Fossil|w/ CCS', 'grey'),
('Biomass without CCS',
['Primary Energy|Biomass|Modern|w/o CCS',
'Primary Energy|Biomass|Traditional'], 'forestgreen'),
('Biomass with CCS', 'Primary Energy|Biomass|Modern|w/ CCS', 'limegreen'),
('Nuclear', 'Primary Energy|Nuclear', 'firebrick'),
('Wind', 'Primary Energy|Wind', 'lightskyblue'),
('Solar', 'Primary Energy|Solar', 'gold'),
('Other renewables',
['Primary Energy|Ocean',
'Primary Energy|Geothermal',
'Primary Energy|Hydro'], 'darkorange'),
]
In [15]:
variables = []
mapping = {}
for (name, variable, color) in variable_mapping:
variables.append(name)
if isinstance(variable, list):
for v in variable:
mapping.update({v: name})
else:
mapping.update({variable: name})
rc.update({'color': {'marker': {name: color}}})
df.rename({'variable': mapping}, inplace=True)
hist.rename({'variable': mapping}, inplace=True)
The "LowEnergyDemand" scenario by design does not have CCS, and does not explicitly include these variables. Therefore, the variable mapping is implemented differently.
In [16]:
led_mapping = {
'Primary Energy|Biomass': 'Biomass without CCS',
'Primary Energy|Coal': 'Fossil without CCS',
'Primary Energy|Gas': 'Fossil without CCS',
'Primary Energy|Oil': 'Fossil without CCS',
}
df.data = df.data.append(
df.filter(scenario='LowEnergyDemand',
variable=led_mapping)
.rename({'variable': led_mapping})
.data
)
In [17]:
df.filter(variable=variables, inplace=True)
hist.filter(variable=variables + ['Primary Energy'],
inplace=True)
Plot energy system development by marker scenario¶
In [18]:
w, h = plt.figaspect(0.30)
fig = plt.figure(figsize=(w, h))
ymax = 1150
hist_yr = 2015
_years = len(years) - 1
label_list = []
w = 0.5 / len(years)
for i, m in enumerate(marker):
_df = df.filter(marker=m).timeseries()
meta = _df.iloc[0].name[0:2]
_label = '{}\n{}\n({})'.format(meta[0], meta[1], m)
_df.index = _df.index.droplevel([0, 1, 2, 4])
# use _df.columns because not all scenarios extend until 2100
pos = [0.5 / _years * (j - (len(_df.columns) - 1) / 2) + i
for j in range(len(_df.columns))]
b = [0] * len(_df.columns)
for v in variables:
if v in _df.index:
lst = _df.loc[v]
plt.bar(x=pos, height=lst, bottom=b, width=w,
color=rc['color']['marker'][v],
edgecolor='black', label=None)
b += _df.loc[v]
val = (
hist.filter(variable='Primary Energy')
.timeseries()[hist_yr]
)
plt.hlines(y=val, xmin=(-.4 + i),
xmax=(.4 + i), **hist_args,
label=None)
label_list.append(_label)
# add years at the top
for j, yr in enumerate(_df.columns):
plt.text(pos[j] - 0.1, ymax * 1.05, yr)
# add legend entries
plt.hlines(y=[], xmin=[], xmax=[], **hist_args,
label='{} Primary Energy (IEA Energy Statistics 2017)'.format(hist_yr))
for v in variables:
plt.scatter(x=[], y=[], color=rc['color']['marker'][v], label=v)
plt.legend()
plt.grid(False)
plt.xlim(-0.6, (i + 0.6))
plt.xticks(range(0, i + 1), label_list)
plt.vlines(x=[_i + 0.5 for _i in range(i)], ymin=0, ymax=ymax, colors='white')
plt.ylim(0, ymax)
plt.ylabel('Primary energy by illustrative pathway (EJ/y)')
fig.savefig(save_name.format('a_primary_energy_by_marker'))
Plot energy system development by fuel for all 1.5°C pathways with limited overshoot¶
In [19]:
w, h = plt.figaspect(0.3)
fig = plt.figure(figsize=(w, h))
ymax = 550
hist_yr = 2015
_years = len(years) - 1
label_list = []
def marker_args(m):
return dict(zorder=4,
edgecolors=rc['edgecolors']['marker'][m],
c=rc['c']['marker'][m],
marker=rc['marker']['marker'][m],
linewidths=1)
for i, v in enumerate(variables):
_df = df.filter(variable=v).timeseries()
_df = pyam.filter_by_meta(_df, df, category=None, marker=None, join_meta=True)
for j, y in enumerate(years):
_df_15 = _df[_df.category.isin(cats_15_no_lo)]
lst = _df_15[y][~np.isnan(_df[y])]
pos = 0.5 / _years * (j - _years / 2) + i
outliers = len(lst[lst > ymax])
if outliers > 0:
plt.text(pos - 0.01 * len(years), ymax * 1.01, outliers)
p = plt.boxplot(lst, positions=[pos],
whis='range',
patch_artist=True)
plt.setp(p['boxes'], color=rc['color']['marker'][v])
plt.setp(p['medians'], color='black')
for m in marker:
val = _df.loc[_df.marker == m, y]
if not val.empty:
plt.scatter(x=pos, y=val, **marker_args(m),
s=40, label=None)
if v in list(hist.variables()):
val = hist.filter(variable=v).timeseries()[hist_yr]
plt.hlines(y=val, xmin=(-.4 + i), xmax=(.4 + i), **hist_args,
label=None)
label_list.append(v)
# add legend entries
plt.hlines(y=[], xmin=[], xmax=[], **hist_args,
label='{} Primary Energy (IEA Energy Statistics 2017)'.format(hist_yr))
for m in marker:
meta = df.filter(marker=m).timeseries().iloc[0].name[0:2]
_label = '{}|{} ({})'.format(meta[0], meta[1], m)
plt.scatter(x=[], y=[], **marker_args(m), s=60, label=_label)
# add years at the top
for _i in range(0, i + 1):
for j, yr in enumerate(years):
plt.text(0.5 / _years * (j - _years / 2) + _i - 0.1,
ymax * 1.05, yr)
#plt.legend()
plt.grid(False)
plt.xlim(-0.6, (i + 0.6))
plt.xticks(range(0, i + 1), label_list)
plt.vlines(x=[_i + 0.5 for _i in range(i)], ymin=0, ymax=ymax, colors='white')
plt.ylim(0, ymax)
plt.ylabel('Primary energy by fuel type (EJ/y)')
fig.savefig(save_name.format('b_primary_energy_by_fuel'))
Export timeseries data to xlsx¶
In [20]:
writer = pd.ExcelWriter('output/fig2.15_data_table.xlsx')
pyam.utils.write_sheet(writer, name,
pyam.filter_by_meta(df.timeseries(), df, category=None, marker=None, join_meta=True),
index=True)
writer.save()