Assessment for statements in the SPM¶
Notebook spm_sr15_statements¶
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¶
Statements of the Summary for Policymakers
derived from the scenario assessment¶
This notebook computes the summary statistics highlighted in the Summary for Policymakers of 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)
cats = specs.pop('cats')
cats_15 = specs.pop('cats_15')
cats_15_no_lo = specs.pop('cats_15_no_lo')
cats_2 = specs.pop('cats_2')
Downselect scenario ensemble to categories of interest for this assessment¶
In [5]:
cats.remove('Above 2C')
In [6]:
compare_year = 2010
In [7]:
sr1p5.meta.rename(columns={'Kyoto-GHG|2010 (SAR)': 'kyoto_ghg_2010'}, inplace=True)
In [8]:
df = sr1p5.filter(category=cats)
Define filters for pyam.Statistics instance¶
In [9]:
filters_15_no_lo = [('no & lo os 1.5', {'category': cats_15_no_lo})]
filters_compare = filters_15_no_lo + [('2.0 lo', {'category': 'Lower 2C'})]
Statement C1: Net CO2 emissions by warming categories and year of crossing the net-zero threshold¶
To reduce potential bias by many scenarios from the same modelling framework, 13 scenarios submitted by the 'AIM' model are excluded from the assessment underpinning this statement (cf. Table 2.4).
In [10]:
filter_args_aim = dict(model='AIM*',
scenario=['SFCM*_1p5Degree', 'EMF33_Med2C_nofuel', 'EMF33_Med2C_none'],
keep=False)
In [11]:
stats_c1 = pyam.Statistics(df=df, filters=filters_compare, rows=True)
In [12]:
co2 = (
df.filter(kyoto_ghg_2010='in range', variable='Emissions|CO2')
.filter(**filter_args_aim)
.convert_unit('Mt CO2/yr', 'Gt CO2/yr')
.timeseries()
)
In [13]:
for y in [2030]:
stats_c1.add((1 - co2[y] / co2[compare_year]) * 100,
header='Reduction in emissions by {}'.format(y),
subheader='relative to {} (%)'.format(compare_year),
row='Net CO2 emissions')
Only include scenarios in this statistic that reach net-zero CO2 emissions before the end of the century
In [14]:
net_zero = (
df.filter(kyoto_ghg_2010='in range')
.filter(**filter_args_aim)
).meta['year of netzero CO2 emissions']
netzero = net_zero[net_zero < 2100]
In [15]:
stats_c1.add(net_zero,
header='Year of net-zero'.format(y),
subheader=''.format(compare_year),
row='Net CO2 emissions')
In [16]:
stats_c1.summarize(center='median', interquartile=True, custom_format='{:.0f}')
Out[16]:
Statement C1.2: Reductions of methane and black carbon¶
In [17]:
stats_c12 = pyam.Statistics(df=df, filters=filters_15_no_lo, rows=True)
In [18]:
ch4 = df.filter(kyoto_ghg_2010='in range', variable='Emissions|CH4').timeseries()
In [19]:
for y in [2050]:
stats_c12.add((1 - ch4[y] / ch4[compare_year]) * 100,
header='Reduction in emissions by {}'.format(y),
subheader='relative to {} (%)'.format(compare_year),
row='Methane (CH4)')
In [20]:
bc = df.filter(variable='Emissions|BC').timeseries()
In [21]:
for y in [2050]:
stats_c12.add((1 - bc[y] / bc[compare_year]) * 100,
header='Reduction in emissions by {}'.format(y),
subheader='relative to {} (%)'.format(compare_year),
row='Black carbon')
In [22]:
stats_c12.summarize(center='mean', fullrange=True, custom_format='{:.0f}')
Out[22]:
Statement C2.2: Energy system transformation¶
In [23]:
def add_stats_share(stats, var_list, name, total, total_name, years, df=df):
_df = df.filter(variable=var_list)
for v in var_list:
_df.require_variable(v, exclude_on_fail=True)
_df.filter(exclude=False, inplace=True)
component = (
_df.timeseries()
.groupby(['model', 'scenario']).sum()
)
share = component / total * 100
for y in years:
stats.add(share[y], header='Share of {} in {}'.format(total_name, y),
subheader='(%)', row=name)
In [24]:
stats_c22 = pyam.Statistics(df=df, filters=filters_15_no_lo, rows=True)
In [25]:
ele = df.filter(variable='Secondary Energy|Electricity').timeseries()
ele.index = ele.index.droplevel([2, 3, 4])
In [26]:
ele_re_vars = [
'Secondary Energy|Electricity|Biomass',
'Secondary Energy|Electricity|Non-Biomass Renewables'
]
add_stats_share(stats_c22, ele_re_vars, 'renewables', ele, 'electricity generation', [2050])
In [27]:
ele_gas = ['Secondary Energy|Electricity|Gas']
add_stats_share(stats_c22, ele_gas, 'natural gas', ele, 'electricity generation', [2050])
In [28]:
ele_coal = ['Secondary Energy|Electricity|Coal']
add_stats_share(stats_c22, ele_coal, 'coal', ele, 'electricity generation', [2050])
In [29]:
stats_c22.summarize(center='mean', interquartile=True, custom_format='{:.0f}')
Out[29]:
Statement C2.3: CO2 emissions from industry¶
In [30]:
stats_c23 = pyam.Statistics(df=df, filters=filters_compare)
In [31]:
co2_ind = df.filter(variable='Emissions|CO2|Energy|Demand|Industry').timeseries()
In [32]:
for y in [2050]:
stats_c23.add((1 - co2_ind[y] / co2_ind[compare_year]) * 100,
header='Industrial emissions reductions relative to {} (%)'.format(compare_year),
subheader=y)
In [33]:
stats_c23.summarize(center='median', interquartile=True, custom_format='{:.0f}')
Out[33]:
Statement C2.4: Urban infrastructure and transport¶
In [34]:
stats_c24 = pyam.Statistics(df=df, filters=filters_compare, rows=True)
In [35]:
bld = df.filter(variable='Final Energy|Residential and Commercial').timeseries()
bld.index = bld.index.droplevel([2, 3, 4])
In [36]:
bld_ele_vars = ['Final Energy|Residential and Commercial|Electricity']
add_stats_share(stats_c24, bld_ele_vars, 'electricity', bld, 'energy demand in buildings', [2050])
In [37]:
trp = df.filter(variable='Final Energy|Transportation').timeseries()
trp.index = trp.index.droplevel([2, 3, 4])
In [38]:
var_trp_low = [
'Final Energy|Transportation|Electricity',
'Final Energy|Transportation|Hydrogen',
'Final Energy|Transportation|Liquids|Biomass'
]
add_stats_share(stats_c24, var_trp_low, 'low-emission energy', trp, 'energy demand in transport', [2050])
In [39]:
stats_c24.summarize(center='median', interquartile=True, custom_format='{:.0f}')
Out[39]:
Statement C2.5: Transitions of land use¶
Please refer to Figure 2.24 in Chapter 2 of the SR15 for details.
Statement C2.6: Energy-related investment costs¶
Please refer to Figure 2.27 in Chapter 2 of the SR15 for details.
Statement C2.7: Marginal abatement costs¶
Please refer to the notebook sr15_2.5_carbon_price_analysis in this folder for the detailed assessment.
Statement C3: Cumulative carbon dioxide removal¶
Please refer to the notebook sr15_2.3.4_carbon_dioxide_removal in this folder for the detailed assessment.
Statement C3.2: Carbon capture and sequestration¶
Please refer to the notebook sr15_2.3.4_carbon_dioxide_removal in this folder for the detailed assessment.
Statement D1.1: CO2 emissions reductions¶
In [40]:
stats_d11 = pyam.Statistics(df=df, filters=filters_15_no_lo)
In [41]:
ghg_ar4_sar = (
df.filter(kyoto_ghg_2010='in range', variable='Emissions|Kyoto Gases (AR4-GWP100)')
.rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})
.convert_unit('Mt CO2e/yr','Gt CO2e/yr')
.timeseries()
)
In [42]:
stats_d11.add(ghg_ar4_sar[[compare_year, 2030]], header='Kyoto GHG emissions (Gt CO2-eq/yr)')
In [43]:
stats_d11.add((ghg_ar4_sar[2030] / ghg_ar4_sar[compare_year] - 1) * 100,
header='Reduction of Kyoto GHG emissions',
subheader='2030 relative to {} (%)'.format(compare_year))
In [44]:
stats_d11.summarize(interquartile=True, custom_format='{:.1f}')
Out[44]:
Checking 1.5°C pathways with limited overshoot that do not reduce CO2 emissions to below 35Gt in 2030¶
In [45]:
ghg_ar4 = (
df.filter(variable='Emissions|Kyoto Gases (AR4-GWP100)')
.rename(unit={'Mt CO2-equiv/yr': 'Mt CO2e/yr'})
.convert_unit('Mt CO2e/yr','Gt CO2e/yr')
.timeseries()
)
In [46]:
ghg_ar4_15 = pyam.filter_by_meta(ghg_ar4, df, category=cats_15_no_lo)
ghg_ar4_15[ghg_ar4_15[2030] > 35].dropna(axis=1)
Out[46]: