Sectoral indicators (Supplementary Material, Table 4.SM.1)¶
Notebook sr15_4.SM.1_supplementary_sectoral_indicators¶
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¶
Supplementary indicators of the pace of transformation
in 1.5°C pathways and selected sectoral studies¶
This notebook computes additional indicators of sectoral transformation for Table 4.SM.1 in the Special Report.
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 yaml
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')
Assign years of interest and downselect to scenarios of interest for this assessment¶
In [5]:
table_years = [2020, 2030, 2050]
compare_year = 2010
years = [compare_year] + table_years
In [6]:
cats.remove('Above 2C')
In [7]:
df = sr1p5.filter(category=cats + ['no-climate-assessment'], year=years)
Initialize a pyam.Statistics instance¶¶
In [8]:
filters = [
(('IAM pathways', 'no & lo os 1.5'), {'category': cats_15_no_lo}),
(('IAM pathways', 'lo os 1.5'), {'category': ['1.5C low overshoot']}),
(('IAM pathways', 'hi os 1.5'), {'category': ['1.5C high overshoot']}),
(('IAM pathways', 'S1'), {'marker': ['S1']}),
(('IAM pathways', 'S2'), {'marker': ['S2']}),
(('IAM pathways', 'S5'), {'marker': ['S5']}),
(('IAM pathways', 'LED'), {'marker': ['LED']}),
(('sectoral studies', 'Löffler et al. (2017)'), {'model': ['GENeSYS-MOD 1.0']}),
(('sectoral studies', 'IEA ETP (2017)'), {'model': ['IEA Energy Technology Perspective Model 2017']}),
(('sectoral studies', 'IEA WEM (2017)'), {'model': ['IEA World Energy Model 2017']})
]
stats = pyam.Statistics(df=df, filters=filters, rows=True)
Share of renewables in primary energy and electricity generation¶
In [9]:
def add_stats_share(var_list, header, total, total_name, df=df, years=table_years):
_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 {}'.format(header),
subheader='in {} (%)'.format(total_name), row=y)
In [10]:
pe = df.filter(variable='Primary Energy').timeseries()
pe.index = pe.index.droplevel([2, 3, 4])
In [11]:
pe_re_vars = [
'Primary Energy|Biomass',
'Primary Energy|Non-Biomass Renewables'
]
add_stats_share(pe_re_vars, 'renewables', pe, 'primary energy')
In [12]:
ele = df.filter(variable='Secondary Energy|Electricity').timeseries()
ele.index = ele.index.droplevel([2, 3, 4])
In [13]:
ele_re_vars = [
'Secondary Energy|Electricity|Biomass',
'Secondary Energy|Electricity|Non-Biomass Renewables'
]
add_stats_share(ele_re_vars, 'renewables', ele, 'electricity generation')
Share of fossil fuels in electricity generation¶
In [14]:
ele_foss_vars = [
'Secondary Energy|Electricity|Coal',
'Secondary Energy|Electricity|Gas',
'Secondary Energy|Electricity|Oil',
]
add_stats_share(ele_foss_vars, 'fossil fuels', ele, 'electricity generation')
Energy demand in buildings (relative to 2010)¶
In [15]:
bld = df.filter(variable='Final Energy|Residential and Commercial', year=years).timeseries()
In [16]:
for y in table_years:
stats.add(-(bld[y] / bld[compare_year] - 1) * 100,
header='Change in energy demand in buildings',
subheader='relative to {} (%)'.format(compare_year), row=y)
Direct emissions reductions from buildings (relative to 2010)¶
In [17]:
co2_bld = df.filter(variable='Emissions|CO2|Energy|Demand|Residential and Commercial', year=years).timeseries()
In [18]:
for y in table_years:
stats.add(-(co2_bld[y] / co2_bld[compare_year] - 1) * 100,
header='Direct emissions reductions from buildings',
subheader='relative to {} (%)'.format(compare_year), row=y)
Share of low-carbon fuels and electricity in the transport sector¶
For the scenario presented by "Löffler et al. (2017)", the share of low-carbon fuels was read directly from one of the figures in the manuscript.
In [19]:
trp = df.filter(variable='Final Energy|Transportation').timeseries()
trp.index = trp.index.droplevel([2, 3, 4])
In [20]:
trp_low_vars = [
'Final Energy|Transportation|Electricity',
'Final Energy|Transportation|Hydrogen',
'Final Energy|Transportation|Liquids|Biomass'
]
add_stats_share(trp_low_vars, 'low-carbon fuels', trp, 'transport')
In [21]:
trp_ele_vars = ['Final Energy|Transportation|Electricity']
add_stats_share(trp_ele_vars, 'electricity', trp, 'transport')
In [22]:
trp_bio_vars = ['Final Energy|Transportation|Liquids|Biomass']
add_stats_share(trp_bio_vars, 'biofuels', trp, 'transport')
Industrial emissions (relative to 2010)¶
In [23]:
co2_ind = df.filter(variable='Emissions|CO2|Energy|Demand|Industry', year=years).timeseries()
In [24]:
for y in table_years:
stats.add(-(co2_ind[y] / co2_ind[compare_year] - 1) * 100,
header='Industrial emissions reductions',
subheader='relative to {} (%)'.format(compare_year), row=y)
Display summary statistics table and export as xlsx¶
In [25]:
summary = stats.summarize(center='median', interquartile=True, custom_format='{:.2f}')
summary = (
summary
.swaplevel(0, 2, axis=0).swaplevel(1, 2, axis=0)
.sort_index(axis=0, level=0, sort_remaining=False)
)
summary
Out[25]:
In [26]:
summary.to_excel('output/table_4.SM.1_suppl_sectoral_indicators.xlsx')