Energy demand and carbon intensity of electricity (Figure 2.14)¶

Notebook sr15_2.4.1_final_energy¶

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).

sr15_2.4.1_final_energy

IPCC SR15 scenario assessment¶

Final energy demand and carbon intensity analysis¶

This notebook contains the scripts to generate Figure 2.14 showing final energy demand development and the carbon intensity across scenarios for 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

from utils import boxplot_by_cat

pyam - INFO: Running in a notebook, setting `pyam` logging level to `logging.INFO` and adding stderr handler

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')

pyam.utils - INFO: Reading `../data/iamc15_scenario_data_world_r2.0.xlsx`

In [3]:

sr1p5.load_meta('sr15_metadata_indicators.xlsx')

pyam.core - INFO: Importing metadata for 416 scenarios (for total of 416)

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')
all_cats = specs.pop('all_cats')
subcats = specs.pop('subcats')
all_subcats = specs.pop('all_subcats')
marker= specs.pop('marker')

Downselect scenario ensemble to categories of interest for this assessment¶

In [5]:

years = [2020, 2030, 2050, 2070, 2100]

In [6]:

df = sr1p5.filter(category=cats, year=years)

Set specifications for filter and plotting and initialize a data list¶

In [7]:

save_name = 'output/fig2.14{}.{}'

In [8]:

categories = cats.copy()
categories.remove('Above 2C')
column = 'category'

In [9]:

filter_args = dict(df=sr1p5, category=categories, marker=None, join_meta=True)

In [10]:

def plotting_args(name, filetype='png', hlines=[]):
    return {'categories': categories, 'column': 'category', 'years': years, 'add_marker': marker,
            'hlines': hlines, 'save': save_name.format(name, filetype)}

In [11]:

data = []

Development of final energy demand by category¶

In [12]:

fe = df.filter(variable='Final Energy').timeseries()
fe.index = fe.index.droplevel([2, 3, 4])

In [13]:

name = 'final_energy'
_fe = pyam.filter_by_meta(df.filter(variable='Final Energy').timeseries(), **filter_args)
boxplot_by_cat(_fe, **plotting_args('a_{}'.format(name)),
               ylabel='Final Energy (EJ)')

In [14]:

data.append(('Final energy', _fe))

In [15]:

fe_ele = df.filter(variable='Final Energy|Electricity').timeseries()
fe_ele.index = fe_ele.index.droplevel([2, 3, 4])

In [16]:

fe_ele_share = pyam.filter_by_meta(fe_ele / fe * 100, **filter_args)

In [17]:

name = 'ele_share'
boxplot_by_cat(fe_ele_share, **plotting_args('c_{}'.format(name)), legend=False,
               ylabel='Electricity share in Final Energy (%)')

In [18]:

data.append(('Electricity share', fe_ele_share))

Plot the development of the carbon intensity of electricity vs. the residual energy demand¶

In [19]:

co2 = df.filter(variable='Emissions|CO2').timeseries()
co2.index = co2.index.droplevel([2, 3, 4])

In [20]:

co2_ele = df.filter(variable='Emissions|CO2|Energy|Supply|Electricity').timeseries()
co2_ele.index = co2_ele.index.droplevel([2, 3, 4])

In [21]:

carbon_intensity_ele = pyam.filter_by_meta(co2_ele / fe_ele, **filter_args)

In [22]:

name = 'carbon_intensity_electricity'
boxplot_by_cat(carbon_intensity_ele, **plotting_args('b_{}'.format(name), hlines=[0]), legend=False,
               ylabel='Carbon intensity of electricity (gCO2/MJ)')

In [23]:

data.append(('Carbon intensity of electricity', carbon_intensity_ele))

In [24]:

carbon_intensity_residual = pyam.filter_by_meta((co2 - co2_ele) / (fe - fe_ele), **filter_args)

In [25]:

name = 'carbon_intensity_residual'
boxplot_by_cat(carbon_intensity_residual, **plotting_args('d_{}'.format(name), hlines=[0]), legend=False,
               ylabel='Carbon intensity of residual fuel mix (gCO2/MJ)')

In [26]:

data.append(('Carbon intensity of residual', carbon_intensity_residual))

Export timeseries data to `xlsx`¶

In [27]:

writer = pd.ExcelWriter('output/fig2.14_data_table.xlsx')
for (name, _df) in data:
    pyam.utils.write_sheet(writer, name, _df, index=True)
writer.save()

In [ ]: