Back in 2014, the UK’s Department for Energy and Climate Change was under pressure to show that green subsidies were not driving up household energy bills.
DECC produced handy infographic offering a transparent breakdown of average energy costs per household.
It’s pretty obvious that we all have to pay the wholesale costs of fuel. We also pay to maintain the pipes and wires that deliver gas an electricity to our doors. We pay (a low rate of) VAT and we cover the margin and supply costs of energy companies.
But there were a few things missing… whether we like it or not we are all paying for the energy inefficiency of housing: the costs of air pollution from all those boilers straining to keep houses warm, the NHS costs of ill-health from damp and cold homes, the cost of climate impacts and, as I discovered, the tax we pay to subsidise fossil fuels.
So I redrew that infographic with all those invisible costs.
Since I originally did this, the department of Energy and Climate Change has been renamed Business, Energy and Industrial Strategy. The government dropped the bit about pesky climate change. But in 2019 we have Greta Thunberg, we have climate strikes, we have Extinction Rebellion and we have talk of a Green New Deal.
We also have even less time to act.
So, maybe in 2019 there is room to change the story about how we really pay for energy inefficiency.
At about the same time that DECC published their infographic, the UK government was also publishing data on the health costs of poor quality housing, air pollution and climate impacts. The ODI and Oil Change International also conducted independent analysis of fossil fuel subsidies.
Where there was a range, I took a conservative estimate and where I had to make an approximation, I took the lowest possible numbers.
Health costs of poor quality housing
£ 857 million
Source: The cost of poor housing which gives annual savings to NHS: if excess cold in housing, dampness, carbon monoxide and excess heat in housing were fixed.
Government spending on fossil fuel subsidies (excluding spending on carbon capture and storage)
£ 7,089 million
Source: Empty promises G20 subsidies to oil, gas and coal production via tax relief (mainly decommisiong oil and gas), domestic public finance, overseas public finance and via shares in multilateral development banks
Health costs of air pollution, right down to the proportion of this pollution coming from domestic boilers (straining to heat that poor quality housing)
£ 1,500 million
Source: Air Pollution: Action in a Changing Climate costs of only PM2.5 air pollution in 2008 (best data I could find in 2014) estimated by government at £18 billion (in a range of £9-20billion) and according to the National Atmospheric Emissions Directory about 10% of PM2.5 pollution comes from ‘residential stationary combustion’ aka boilers.
Costs to the UK economy of climate change-related damage inside and outside the UK
£ 402 million
Source: Foresight Future Flooding very rough conservative estimate based on 10% of current average annual damage (1.4 bn) and management costs of flooding (0.8 bn) and International Dimensions of Climate Change with my very rough conservative estimate based on 10% of current overseas humanitarian aid costs ( due to climate change related conflict or disaster 435m + 600m) and aid spending on health (683m) and water and sanitation (106m). This is highly conservative as it does not cover costs to UK business with overseas assets, disruption of critical infrastructure.
The code is here and on github - please feel free to use it and improve it. I’ve left it a bit raw and clunky because it makes the laying out easier to tweak.
The data are crying out for an update with the latest and most reliable sources.
import pandas as pd
import matplotlib.pyplot as plt
import squarify
# pip install squarify (algorithm for treemap)
# find location by running squarify in a notebook
# modify the following 4 lines of squarify code to rotate the treemap by 90degs
#x = [rect["y"] for rect in rects]
#y = [-rect["x"] for rect in rects]
#dx = [rect["dy"] for rect in rects]
#dy = [-rect["dx"] for rect in rects]
import seaborn as sns
import numpy as np
import matplotlib.gridspec as gridspec
# Activate Seaborn
sns.set()
# data
mutual_costs = {
"Fossil fuel subsidy via tax": 314,
"Air pollution from housing":66,#cost of air pollution from housing
"Poor housing":38,#cost of poor housing on health
"C.I.":18}#climate impacts
UK_data_2014 = {
"Wholesale energy costs":637,
"Other supply costs and margins":291,
"Network costs":286,
"Support costs":89,
"VAT @5%":66
}
Support_Breakdown = {
"Large-scale renewables, such as wind (RO)": 36,
"Carbon taxes":23,
"Small-scale renewables such as solar (FITs)": 9,
"Price reduction from low carbon policies":-5,
"ECO":36,
"Warm Home Discount Support":13,
"Smart Meters":3,
"Government Electricity Rebate":-12,
"Warm Home Discount rebate (average)":-13}
mutual = sum(mutual_costs.values())
household = sum(UK_data_2014.values())
subs = sum(Support_Breakdown.values())
# setting up scaling dimensions
total_costs = mutual+household
total_area = 100
b_house = 10
b_ext = 6
h_ext = (total_area * mutual/total_costs) / b_ext
b_subs = 4
h_subs = 3
# creating dataframes to play with
df1 = pd.DataFrame()
df1['Description'] = mutual_costs.keys()
df1['Costs'] = mutual_costs.values()
df2 = pd.DataFrame()
df2['Description'] = UK_data_2014.keys()
df2['Costs'] = UK_data_2014.values()
df3 = pd.DataFrame()
df3['Description'] = Support_Breakdown.keys()
df3['Costs'] = Support_Breakdown.values()
df3['AbsCosts'] = abs(df3.Costs)
# creating text labels for infographic
label_text1 = []
for row, col in df1.iterrows():
#text = str(df1.loc[row,'Description'])+" £"+str(df1.loc[row,'Costs'])
text = str(df1.loc[row,'Description'])
label_text1.append(text)
label_text1
df1['Label'] = label_text1
df1
label_text2 = []
for row, col in df2.iterrows():
#text = str(df2.loc[row,'Description'])+" £"+str(df2.loc[row,'Costs'])
text = str(df2.loc[row,'Description'])
label_text2.append(text)
label_text2
df2['Label'] = label_text2
df2
label_text3 = []
for row, col in df3.iterrows():
text = str(df3.loc[row,'Description'])+" £"+str(df3.loc[row,'Costs'])
label_text3.append(text)
label_text3
df3['Label'] = label_text3
df3
from textwrap import fill
labels1 = [fill(l, 15) for l in df1.loc[:,'Label']]
labels2 = [fill(l, 20) for l in df2.loc[:,'Label']]
labels3 = [fill(l, 20) for l in df3.loc[:,'Label']]
# generating squarify coordinates for house data
rect = squarify.squarify(df2['Costs'],0,0,np.sqrt(household),np.sqrt(household))
coords = pd.concat([
pd.DataFrame.from_dict(rect[0],orient='index').T,
pd.DataFrame.from_dict(rect[1],orient='index').T,
pd.DataFrame.from_dict(rect[2],orient='index').T,
pd.DataFrame.from_dict(rect[3],orient='index').T,
pd.DataFrame.from_dict(rect[4],orient='index').T
],ignore_index=True)
coords
all_data = pd.concat([df2,coords],axis=1,sort=False)
# generating squarify coordinates for externalities data
rect_m = squarify.squarify(df1['Costs'],0,0,np.sqrt(mutual),np.sqrt(mutual))
coords_m = pd.concat([
pd.DataFrame.from_dict(rect_m[0],orient='index').T,
pd.DataFrame.from_dict(rect_m[1],orient='index').T,
pd.DataFrame.from_dict(rect_m[2],orient='index').T,
pd.DataFrame.from_dict(rect_m[3],orient='index').T,
],ignore_index=True)
coords_m
all_data_m = pd.concat([df1,coords_m],axis=1,sort=False)
font_size = []
for row, col in all_data.iterrows():
if col[1] > 500:
font_size.append(30)
elif col[1] < 100:
font_size.append(20)
else:
font_size.append(25)
font_size
all_data['font_size'] = font_size
font_size_m = []
for row, col in all_data_m.iterrows():
if col[1] > 500:
font_size.append(25)
elif col[1] < 100:
font_size_m.append(14)
else:
font_size_m.append(25)
font_size_m
all_data_m['font_size'] = font_size_m
wrapping = [25,20,25,12,5]
wrapping_m = [50,25,10,12]
all_data['wrapping'] = wrapping
all_data_m['wrapping'] = wrapping_m
#Create plot
plt.style.use('fivethirtyeight')
plt.figure(figsize= [b_ext,h_ext])
plt.rc('font', weight='bold')# controls default text sizes
plt.rc('text', color='w')
fig = plt.figure(figsize= [25,10])
fig.patch.set_facecolor('w')
fig.patch.set_alpha(0)
#split the plot into 3 axes on a grid
gs1 = gridspec.GridSpec(1, 3, figure=fig,wspace=0)
gs1.tight_layout(fig, rect=[0, 0, 1, 1],pad=0,w_pad=0,h_pad=0)
ax1 = fig.add_subplot(gs1[0])
ax2 = fig.add_subplot(gs1[1])
ax3 = fig.add_subplot(gs1[2])
# generate tree map
squarify.plot(sizes=df1['Costs'],ax=ax1,norm_x=np.sqrt(mutual),norm_y=np.sqrt(mutual),color='r',linewidth='10',edgecolor=(1,1,1,0),alpha=1) #color = colors,
squarify.plot(sizes=df2['Costs'],ax=ax2,norm_x=37,norm_y=37, color='#9d9a01',linewidth='10',edgecolor=(1,1,1,0),alpha=1) #color = colors,
# lay out of axis to plot areas so that they are equivalent
ax1.set_xlim(-(41-np.sqrt(mutual)),np.sqrt(mutual))
ax1.set_ylim(-np.sqrt(mutual),37+2)
ax2.set_xlim(-2,37+2)
ax2.set_ylim(-37,np.sqrt(mutual))
ax3.set_xlim(-.15,.85)
ax3.set_ylim(0,1)
# transparent background
ax1.patch.set_facecolor('w')
ax1.patch.set_alpha(0)
ax2.patch.set_facecolor('w')
ax2.patch.set_alpha(0)
ax3.patch.set_facecolor('w')
ax3.patch.set_alpha(0)
# data labels for household energy
for row, col in all_data.iterrows():
x = col[4]
y = (-col[3])
ax2.text(x+1,y-5,fill(col[0],col[8]),ha='left',va='baseline',fontsize=16)
ax2.text(x+1,(y-7.5),'£ ',ha='left',va='baseline',fontsize=16)
ax2.text(x+2,(y-7.5),col[1],ha='left',va='baseline',fontsize=col[7])
# data labels for externalities
for row, col in all_data_m.iterrows():
x = col[4]
y = (-col[3])
ax1.text(x+0.8,y-1,fill(col[0],col[8]),ha='left',va='top',fontsize=12)
ax1.text(x+0.8,(y-5),'£ ',ha='left',va='baseline',fontsize=12)
ax1.text(x+1.4,(y-5),col[1],ha='left',va='baseline',fontsize=col[7])
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
# disaggregated support costs with labels
ax3.text(0.01,.99,fill("Support for cleaner energy and keeping the lights on",35),ha='left',va='top',color='w',fontsize=18)
ax3.text(0.01,.67,fill("Support for vulnerable households, energy efficiency, and Government Electricity Rebate",35),ha='left',va='top',color='w',fontsize=18)
for row, col in df3.iterrows():
if row in range(0,4):
#print(row)
x = col[0]
y = col[1]
#y = (-col[3])
if len(str(y)) == 1:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 2:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 3:
y = str('£ '+str(y))
ax3.text(0.01,0.91-((1+row)/20),fill(col[0],45),ha='left',va='baseline',fontsize=12)
ax3.text(.5,0.91-((1+row)/20),y,ha='left',va='baseline',fontsize=16)
if row in range(4,8):
#print(row)
x = col[0]
y = col[1]
if len(str(y)) == 1:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 2:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 3:
y = str('£ '+str(y))
ax3.text(0.01,.79-((1+row)/20),fill(col[0],45),ha='left',va='baseline',fontsize=12)
ax3.text(0.5,.79-((1+row)/20),y,ha='left',va='baseline',fontsize=16)
if row in range(8,9):
#print(row)
x = col[0]
y = col[1]
if len(str(y)) == 1:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 2:
y = str('£ '+str(y))
#print(str('£ '+str(y)))
elif len(str(y)) == 3:
y = str('£ '+str(y))
ax3.text(0.01,.32,fill(col[0],40),ha='left',va='baseline',fontsize=12)
ax3.text(0.5,.32,y,ha='left',va='baseline',fontsize=16)
#triangular roof
t2= plt.Polygon([[-2,-12.1], [(37)/2,8], [39,-12.1]], color='#9d9a01')
ax2.add_patch(t2)
#cleaner energy
t3= plt.Polygon([[0,0.7], [0,1], [.6,1],[.6,.7]], color='#9d9a01')
ax3.add_patch(t3)
#vulnerable groups
t4= plt.Polygon([[0,.38], [0,.68], [.6,.68],[.6,0.38]], color='#9d9a01')
ax3.add_patch(t4)
#rebate
t5= plt.Polygon([[0,0.3], [0,.36], [.6,.36],[.6,.3]], color='#616266')
ax3.add_patch(t5)
#arrows
a1= plt.Polygon([[-.15,.97], [-0.15,.99], [0,.99],[0,0.97]], color='#9d9a01')
a2= plt.Polygon([[-.15,.3], [-.15,.97], [-.13,.97],[-.13,0.3]], color='#9d9a01')
a3= plt.Polygon([[-.11,.64], [-.11,.66], [0,.66],[0,0.64]], color='#9d9a01')
a4= plt.Polygon([[-.11,.26], [-.11,.64], [-.09,.64],[-.09,0.26]], color='#9d9a01')
a5= plt.Polygon([[-.07,.32], [-.07,.34], [-0,.34],[0,0.32]], color='#9d9a01')
a6= plt.Polygon([[-.07,.22], [-.07,.32], [-.05,.32],[-.05,0.22]], color='#9d9a01')
a7= plt.Polygon([[-.15,.26], [-.15,.28], [-.11,.28],[-.11,0.26]], color='#9d9a01')
a8= plt.Polygon([[-.15,.22], [-.15,.24], [-.07,.24],[-.07,0.22]], color='#9d9a01')
ax3.add_patch(a1)
ax3.add_patch(a2)
ax3.add_patch(a3)
ax3.add_patch(a4)
ax3.add_patch(a5)
ax3.add_patch(a6)
ax3.add_patch(a7)
ax3.add_patch(a8)
yaxis_length = 37+np.sqrt(mutual)
x1 = 36
x2 = 39
y1 = -(0.32*yaxis_length)
y2 = y1 - (0.02*yaxis_length)
y21 = -(0.36*yaxis_length)
y22 = y21 - (0.02*yaxis_length)
y31 = -(0.40*yaxis_length)
y32 = y31 - (0.02*yaxis_length)
b1 = plt.Polygon([[x1,y2], [x1,y1], [x2,y1],[x2,y2]], color='#9d9a01')
b2 = plt.Polygon([[x1,y22], [x1,y21], [x2,y21],[x2,y22]], color='#9d9a01')
b3 = plt.Polygon([[x1,y32], [x1,y31], [x2,y31],[x2,y32]], color='#9d9a01')
ax2.add_patch(b1)
ax2.add_patch(b2)
ax2.add_patch(b3)
plt.show()
Thanks to Kevin Wubert for the blog on how to embed python code in Squarespace.