1 About

This report is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/

The model is similar in some respects to Evans et al (2023) Getting to net zero: Islington’s social housing stock (https://journal-buildingscities.org/articles/10.5334/bc.349) which estimated an overall cost of upgrading Islington’s social housing stock of 4500 buildings containing some 33,300 dwellings to be £1,600m.

1.1 Published versions

A paper based on this model as applied to the City of Southampton has been published in Buildings & Cities:

  • Anderson, B. (2023). A residential emissions-based carbon levy: city and neighbourhood consequences. Buildings and Cities, 4(1), pp. 1–20. DOI: https://doi.org/10.5334/bc.279

1.2 Citing this report

If you wish to re-use material from this report please cite it as:

Ben Anderson (2023) Simulating a local emissions levy to fund local energy efficiency retrofit: Islington as a case study. University of Southampton, United Kingdom

License: CC-BY

Share, adapt, give attribution.

2 Highlights

This report estimates the value of an emissions levy for Islington using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.

It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.

Key results:

Section 10 gives the overall summary of the annual £ emissions levy value and estimated retrofit costs

  • Figure 5.2 maps the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e - which areas emit the most?
  • Table 6.1 shows total levy generated under Scenario 1
  • Table 6.8 shows total levy generated under Scenario 2
  • Figure 6.11 compares the scenarios in terms of % of levy generated by areas in each IMD decile while Figure 6.12 compares the levy generated under each scenario at LSOA level. In both cases, Scenario 2 should be lower in more deprived areas and higher in less deprived areas. Figure 6.11 shows whether or not this is the case
  • Table 7.1 shows total retrofit costs and Figure 7.1 shows the LSOA level retrofit costs per dwelling by IMD decile for comparison with Figure 5.1
  • Figure 9.1 shows the years to pay back under Scenario 1 for an all emissions levy while Figure 9.7 does the same for Scenario 2
  • Figure ?? shows what would happen after year 1 if the levy were shared equally across LSOAs (all emissions, Scenario 1) and Figure ?? shows the same for Scenario 2.
  • Figure 9.2 maps the years to pay under Scenario 1
  • Figure 9.12 shows payback years under each Scenario assuming a constant all emissions levy

3 Introduction

This report estimates a model of an emissions levy for Islington using LSOA level data on emissions derived from the CREDS place-based emissions calculator.

The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.

The report then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

Finally the report compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the report also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.

4 Data

The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.

All analysis is at LSOA level. Cautions on inference from area level data apply.

## Filter: Islington
## 
##     0     1 
## 32730   123
## Number of selected LSOAs - merged data:
## [1] 123
##    
##     Islington
##   1       123
## Test linkage of Census 2021 data - suspect some LSOAs have changed
##    c2021_nhhs     c2021_pc_elec_ch_2021    IMDScore      pc_fuelPoor   
##  Min.   : 421.0   Min.   : 2.577        Min.   :10.73   Min.   : 6.00  
##  1st Qu.: 664.5   1st Qu.: 6.734        1st Qu.:20.68   1st Qu.:13.00  
##  Median : 755.0   Median : 9.482        Median :27.78   Median :15.00  
##  Mean   : 770.6   Mean   :12.091        Mean   :27.71   Mean   :14.78  
##  3rd Qu.: 873.0   3rd Qu.:13.934        3rd Qu.:33.42   3rd Qu.:17.00  
##  Max.   :1137.0   Max.   :46.163        Max.   :50.09   Max.   :21.00  
##  NA's   :3        NA's   :3
## # NAs are LSOAs that didn't match - only matters to analysis using Census 2021 data on electricity central heating

4.1 Case study context

This report takes Islington as a case study.

  • Number of households (Census 2021): 92,467
  • Number of electricity meters (2018): 105,006
  • Number of gas meters (2018): 85,924

Figure 4.1 shows the distribution of deprivation across the case study area using the IMD 2019 data.

If there are gaps in these maps then there may be LSOA level data linking errors, possibly due to name or boundary changes. We love name and boundary changes. If so the model results may be partial. #YMMV

Figure 4.1: IMD (LSOAs)

Figure 4.2 shows the distribution of fuel poverty across the case study area using the BEIS/DESNZ 2019 data.

Figure 4.2: Fuel poverty (LSOAs)

Observed EPCs - need to check bias with respect to e.g. Census data. EPCs more likely to be from new houses, rentals, re-sales (required) etc.

## Saving 7 x 5 in image
## Number of EPC coded dwellings in case study area, (Islington) = 69321

Imputed EPCs (see logic in Section 7.1) - this corrects for numbers but not the bias in the EPC sample

## Number of imputed EPC coded dwellings in case study area, (Islington) = 105015
##    case_study mean_MSOA_totInc
## 1:          0         43865.80
## 2:          1         53316.26

## Saving 7 x 5 in image

## Saving 7 x 5 in image

5 LSOA level emissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation.”

Source: https://www.carbon.place/

Notes:

  • Emissions are presented as per capita…
  • Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc
Table 5.1: Summary of CREDS per capita data for LSOAs: Islington

region

nLSOAs

mean_KgCo2ePerCap

sd_KgCo2ePerCap

London

123

8,091.2

2,447.2

## Do we have any missing energy data?
Table 5.1: caption

LAD11NM

LSOA11CD

LSOA01NM

nElecMeters

nGasMeters

CREDS_total_kgco2e

CREDS_elec_kgco2e2018

CREDS_gas_kgco2e2018

CREDS_otherEnergy_kgco2e2011

CREDS_allHomeEnergy_kgco2e2011

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##          LSOA11NM      WD18NM nGasMeters nElecMeters epc_total
## 1: Islington 014A   Canonbury       1056        1093       660
## 2: Islington 016C   Canonbury        982        1019       514
## 3: Islington 004E    Junction        981        1072       620
## 4: Islington 015D    Holloway        961        1189       766
## 5: Islington 022D Clerkenwell        951        1225       868
## 6: Islington 016F   St Mary's        936        1076       674
##          LSOA11NM        WD18NM nGasMeters nElecMeters epc_total
## 1: Islington 020E    St Peter's        833        1795      1340
## 2: Islington 023D       Bunhill        688        1412       991
## 3: Islington 007B Finsbury Park        755        1381      1090
## 4: Islington 011I Highbury West        186        1363      1150
## 5: Islington 023C       Bunhill        619        1278      1040
## 6: Islington 022D   Clerkenwell        951        1225       868
Table 5.2: LSOAs with highest number of gas meters (after cleaning)

LSOA11NM

WD18NM

nGasMeters

nElecMeters

epc_total

Islington 014A

Canonbury

1,056

1,093

660

Islington 016C

Canonbury

982

1,019

514

Islington 004E

Junction

981

1,072

620

Islington 015D

Holloway

961

1,189

766

Islington 022D

Clerkenwell

951

1,225

868

Islington 016F

St Mary's

936

1,076

674

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7’. Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

5.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values
Table 5.3: Data summary
Name …[]
Number of rows 123
Number of columns 9
Key NULL
_______________________
Column type frequency:
numeric 9
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDS_total_kgco2e_pdw 0 1 18515.44 5616.67 8317.10 14150.29 17380.22 22835.63 34270.51 ▅▇▅▃▁
CREDS_gas_kgco2e2018_pdw 0 1 1824.31 576.19 153.08 1505.76 1888.75 2179.42 3458.33 ▁▃▇▆▁
CREDS_elec_kgco2e2018_pdw 0 1 824.56 144.11 557.35 722.92 800.00 895.29 1347.42 ▅▇▅▁▁
CREDS_measuredHomeEnergy_kgco2e2018_pdw 0 1 2648.87 604.01 774.71 2309.13 2630.27 3005.33 4615.72 ▁▃▇▃▁
CREDS_otherEnergy_kgco2e2011_pdw 0 1 215.54 249.80 30.42 82.54 121.17 214.25 1351.39 ▇▁▁▁▁
CREDS_allHomeEnergy_kgco2e2018_pdw 0 1 2864.41 482.95 1482.49 2576.97 2837.17 3115.08 4679.59 ▁▇▇▂▁
CREDS_car_kgco2e2018_pdw 0 1 615.72 203.94 209.70 467.48 626.20 731.49 1284.19 ▅▆▇▂▁
CREDS_van_kgco2e2018_pdw 0 1 70.08 65.61 6.41 40.00 54.92 83.78 656.91 ▇▁▁▁▁
CREDS_personalTransport_kgco2e2018_pdw 0 1 685.80 208.58 235.29 556.56 696.12 804.27 1320.62 ▃▆▇▂▁

Examine patterns of per dwelling emissions for sense.

5.1.1 All emissions

Figure 5.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level all consumption emissions per dwelling against IMD score

Figure 5.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score 

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_total_kgco2e_pdw
## t = -8.1442, df = 121, p-value = 3.936e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6984925 -0.4672230
## sample estimates:
##       cor 
## -0.595039
## Total emissions per dwelling (LSOA level) summary
##    LSOA11CD            WD18NM          IMD_Decile_label All_Tco2e_per_dw
##  Length:123         Length:123         3      :36       Min.   : 8.317  
##  Class :character   Class :character   2      :26       1st Qu.:14.150  
##  Mode  :character   Mode  :character   4      :22       Median :17.380  
##                                        5      :13       Mean   :18.515  
##                                        6      :12       3rd Qu.:22.836  
##                                        7      : 7       Max.   :34.271  
##                                        (Other): 7
Table 5.4: Highest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01033486

Holloway

2

34.3

E01002700

Barnsbury

7

32.3

E01002747

Highbury West

7

31.9

E01002745

Highbury West

6

30.6

E01002756

Hillrise

3

29.7

E01002742

Highbury East

6

29.0

Table 5.4: Lowest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01002803

St Peter's

4

8.3

E01002713

Caledonian

1 (10% most deprived)

8.4

E01002703

Bunhill

3

10.4

E01002704

Bunhill

4

10.4

E01002765

Holloway

3

10.9

E01002702

Bunhill

3

11.3

We should not be surprised that emissions are negatively correlated with deprivation. If you are, you should try reading an excellent paper by Milena Buchs and Sylke Schnepf, both formerly of the University of Southampton:

“whilst all types of emissions rise with income, low income, workless and elderly households are more likely to have high emissions from home energy than from other domains, suggesting that they may be less affected by carbon taxes on transport or total emissions. This demonstrates that fairness implications related to mitigation policies need to be examined for separate emission domains.”

With that in mind, read on.

Figure 5.2: Annual mean T CO2e per dwelling (LSOAs)

5.1.2 Home energy use

Figure 5.3 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   153.1  1505.8  1888.7  1824.3  2179.4  3458.3
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level gas per dwelling emissions against IMD score

Figure 5.3: Scatter of LSOA level gas per dwelling emissions against IMD score 

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_gas_kgco2e2018_pdw
## t = -4.2361, df = 121, p-value = 4.462e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5043200 -0.1947253
## sample estimates:
##       cor 
## -0.359371

Figure 5.4 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

Figure 5.4: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits? 

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -4.2256, df = 121, p-value = 4.646e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5036600 -0.1938739
## sample estimates:
##        cor 
## -0.3586003

Figure 5.5 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

Figure 5.5: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits? 

## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -4.2256, df = 121, p-value = 4.646e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5036600 -0.1938739
## sample estimates:
##        cor 
## -0.3586003
Table 5.5: Summary of per dwelling energy emissions for LSOAs by urban/rural code

RUC11

mean_gas_kgco2e

mean_elec_kgco2e

mean_other_energy_kgco2e

Urban major conurbation

1,824.3

824.6

215.5

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDS_total_kgco2e_pdw and selectedLsoasDT$CREDS_measuredHomeEnergy_kgco2e2018_pdw
## t = 9.8914, df = 121, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.5576153 0.7561678
## sample estimates:
##       cor 
## 0.6686432
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDS_total_kgco2e_pdw and selectedLsoasDT$CREDS_allHomeEnergy_kgco2e2018_pdw
## t = 12.038, df = 121, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6455501 0.8094633
## sample estimates:
##       cor 
## 0.7382176
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

How does the correlation look now?

Figure 5.6: Annual mean T CO2e due to energy use per dwelling (LSOAs)

5.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 5.7 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level car use per dwelling emissions against IMD score

Figure 5.7: Scatter of LSOA level car use per dwelling emissions against IMD score 

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_car_kgco2e2018_pdw
## t = -3.7731, df = 121, p-value = 0.000251
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4742478 -0.1564034
## sample estimates:
##       cor 
## -0.324454
Table 5.6: Summary of per dwelling transport emissions for LSOAs by urban/rural code

RUC11

mean_car_kgco2e

mean_van_kgco2e

Urban major conurbation

615.7

70.1

Figure 5.8 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Scatter of LSOA level van use per dwelling emissions against IMD score

Figure 5.8: Scatter of LSOA level van use per dwelling emissions against IMD score 

## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_van_kgco2e2018_pdw
## t = 0.4862, df = 121, p-value = 0.6277
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1339245  0.2194754
## sample estimates:
##        cor 
## 0.04415677

Figure 5.9: Mean annual T CO2e per dwelling due to transport (LSOAs)

6 Estimating the annual emissions levy

Case studies:

  • Annual carbon tax
  • Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

EU carbon ‘price’

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

  • based on a Marginal Abatement Cost (MAC)
  • 2021:
    • Low: £122/T
    • Central: £245/T <- use the central value for now
    • High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

6.1 Scenario 1: Central carbon cost

Table 6.1 below shows the overall £ GBP emissions levy total for the case study area in £M under Scenario 1.

Table 6.1: Total £m GBP levy by source (Scenario 1)

nLSOAs

All emissions

Gas

Electricity

123

457.6

45.8

21.4

Table 6.1: Total £m GBP levy by source, ordered by highest emitting ward (Scenario 1)

WD20NM

nLSOAs

All emissions

Gas

Electricity

Highbury West

10

41.7

3.0

1.9

Highbury East

7

32.4

3.3

1.2

Holloway

9

32.2

2.9

1.3

Bunhill

8

29.5

2.2

1.9

St Peter's

7

29.2

2.9

1.5

Mildmay

8

28.3

3.1

1.1

Tollington

8

27.8

3.1

1.2

Hillrise

8

27.3

2.6

1.1

St Mary's

7

27.3

3.0

1.4

Barnsbury

7

27.2

2.7

1.3

Canonbury

7

27.1

3.1

1.1

Finsbury Park

8

26.4

3.0

1.5

St George's

7

26.3

3.0

1.2

Caledonian

8

26.0

2.5

1.4

Clerkenwell

7

25.5

2.5

1.2

Junction

7

23.5

2.8

1.1

Table 6.1: Total £m GBP levy by source, ordered by highest emitting ward (Scenario 1)

WD20NM

nLSOAs

All emissions

Gas

Electricity

Highbury West

10

41.7

3.0

1.9

Highbury East

7

32.4

3.3

1.2

Holloway

9

32.2

2.9

1.3

Bunhill

8

29.5

2.2

1.9

St Peter's

7

29.2

2.9

1.5

Mildmay

8

28.3

3.1

1.1

Tollington

8

27.8

3.1

1.2

Hillrise

8

27.3

2.6

1.1

St Mary's

7

27.3

3.0

1.4

Barnsbury

7

27.2

2.7

1.3

Canonbury

7

27.1

3.1

1.1

Finsbury Park

8

26.4

3.0

1.5

St George's

7

26.3

3.0

1.2

Caledonian

8

26.0

2.5

1.4

Clerkenwell

7

25.5

2.5

1.2

Junction

7

23.5

2.8

1.1

Emissions level due to total emissions, gas & electricity use by LSOA (Scenario 1)

Figure 6.1: Emissions level due to total emissions, gas & electricity use by LSOA (Scenario 1)

The table below shows the mean per dwelling value rounded to the nearest £10.

Table 6.2: Mean £ GBP levy per dwelling by source (Scenario 1)

All_emissions

Gas

Electricity

Gas + Electricity

4,536.3

447.0

202.0

649.0

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 6.2: £k per LSOA revenue using BEIS central carbon price 

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 6.3: £k per LSOA revenue using BEIS central carbon price 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2038    3467    4258    4536    5595    8396
Table 6.3: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01033486

Islington 011E

Holloway

34.3

8,396.3

58,700

E01002700

Islington 017C

Barnsbury

32.3

7,908.1

67,200

E01002747

Islington 006B

Highbury West

31.9

7,819.7

65,100

E01002745

Islington 013C

Highbury West

30.6

7,505.6

65,600

E01002756

Islington 001B

Hillrise

29.7

7,287.7

46,300

E01002742

Islington 006A

Highbury East

29.0

7,109.7

65,100

Table 6.3: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01002803

Islington 020E

St Peter's

8.3

2,037.7

63,500

E01002713

Islington 015C

Caledonian

8.4

2,048.3

42,600

E01002703

Islington 023C

Bunhill

10.4

2,553.4

48,900

E01002704

Islington 023D

Bunhill

10.4

2,554.3

48,900

E01002765

Islington 015D

Holloway

10.9

2,662.9

42,600

E01002702

Islington 023B

Bunhill

11.3

2,774.0

48,900

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 6.4: £k per LSOA incurred via gas using BEIS central carbon price 

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 6.5: £k per LSOA incurred via gas using BEIS central carbon price 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    37.5   368.9   462.7   447.0   534.0   847.3
Table 6.4: Highest emitting LSOAs by £ GBP gas levy

LSOA11CD

LSOA01NM

WD18NM

gas_T_CO2e_pdw

GBP_gas_levy_perdw

msoa_tot_annual_income_2018

E01002700

Islington 017C

Barnsbury

3.5

847.3

67,200

E01002789

Islington 008D

St George's

2.9

721.8

60,300

E01002785

Islington 008B

St George's

2.8

677.2

60,300

E01002759

Islington 002D

Hillrise

2.7

664.7

51,600

E01002769

Islington 008A

Junction

2.7

655.9

60,300

E01002742

Islington 006A

Highbury East

2.7

650.8

65,100

Table 6.4: Lowest emitting LSOAs by £ GBP gas levy

LSOA11CD

LSOA01NM

WD18NM

gas_T_CO2e_pdw

GBP_gas_levy_perdw

msoa_tot_annual_income_2018

E01002709

Islington 019C

Caledonian

0.8

192.5

44,400

E01002703

Islington 023C

Bunhill

0.8

188.6

48,900

E01002701

Islington 023A

Bunhill

0.7

160.8

48,900

E01002800

Islington 018E

St Peter's

0.6

146.3

53,600

E01033492

Islington 011I

Highbury West

0.4

87.9

58,700

E01033493

Islington 006F

Highbury West

0.2

37.5

65,100

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 6.6: £k per LSOA incurred via electricity using BEIS central carbon price 

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via electricity using BEIS central carbon price

Figure 6.7: £k per LSOA incurred via electricity using BEIS central carbon price 

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   136.6   177.1   196.0   202.0   219.3   330.1
Table 6.5: Highest emitting LSOAs by £ GBP electricity levy

LSOA11CD

LSOA01NM

WD18NM

elec_T_CO2e_pdw

GBP_elec_levy_perdw

msoa_tot_annual_income_2018

E01002751

Islington 005E

Highbury West

1.3

330.1

41,600

E01002731

Islington 007B

Finsbury Park

1.3

319.1

52,700

E01033489

Islington 022G

Bunhill

1.2

304.7

53,800

E01002787

Islington 010E

St George's

1.2

294.4

44,500

E01002700

Islington 017C

Barnsbury

1.2

283.6

67,200

E01002711

Islington 021B

Caledonian

1.2

281.9

50,900

Table 6.5: Lowest emitting LSOAs by £ GBP electricity levy

LSOA11CD

LSOA01NM

WD18NM

elec_T_CO2e_pdw

GBP_elec_levy_perdw

msoa_tot_annual_income_2018

E01002773

Islington 004D

Junction

0.6

156.2

47,100

E01033493

Islington 006F

Highbury West

0.6

152.3

65,100

E01002723

Islington 022C

Clerkenwell

0.6

148.7

53,800

E01002713

Islington 015C

Caledonian

0.6

148.0

42,600

E01002697

Islington 019B

Barnsbury

0.6

147.5

44,400

E01033487

Islington 011F

Holloway

0.6

136.6

58,700

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 6.8: £k per LSOA incurred via electricity and gas using BEIS central carbon price 

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 6.9: £k per LSOA incurred via electricity and gas using BEIS central carbon price 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   189.8   565.7   644.4   649.0   736.3  1130.9

6.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw
##        0%       25%       50%       75%      100% 
##  8.317103 14.150294 17.380224 22.835626 34.270510
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name …[]
Number of rows 123
Number of columns 3
Key NULL
_______________________
Column type frequency:
numeric 3
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
V1 0 1 18.52 5.62 8.32 14.15 17.38 22.84 34.27 ▅▇▅▃▁
beis_GBPtotal_sc2_perdw 0 1 3196.75 1787.16 1014.69 1726.24 2474.44 4519.80 8716.40 ▇▃▂▂▁
beis_GBPtotal_sc2 0 1 2554341.93 1209394.26 940400.40 1603522.32 2067237.34 3276354.82 5749251.01 ▇▃▃▁▂

Gas cuts:

## Cuts for gas per dw
##        0%       25%       50%       75%      100% 
## 0.1530753 1.5057601 1.8887469 2.1794192 3.4583333
## [1] 30.21635
## Cuts for elec per dw
##        0%       10%       20%       30%       40%       50%       60%       70%       80% 
## 0.5573507 0.6799301 0.7075384 0.7318494 0.7629907 0.8000000 0.8341897 0.8776577 0.9104766 
##       90%      100% 
## 0.9955876 1.3474211
## [1] 14.40809
Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01033486

Islington 011E

Holloway

34.3

8,716.4

E01002700

Islington 017C

Barnsbury

32.3

7,985.1

E01002747

Islington 006B

Highbury West

31.9

7,852.7

E01002745

Islington 013C

Highbury West

30.6

7,382.2

E01002756

Islington 001B

Hillrise

29.7

7,055.9

E01002742

Islington 006A

Highbury East

29.0

6,789.2

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01002803

Islington 020E

St Peter's

8.3

1,014.7

E01002713

Islington 015C

Caledonian

8.4

1,020.0

E01002703

Islington 023C

Bunhill

10.4

1,271.5

E01002704

Islington 023D

Bunhill

10.4

1,271.9

E01002765

Islington 015D

Holloway

10.9

1,326.0

E01002702

Islington 023B

Bunhill

11.3

1,381.3

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01002700

Islington 017C

Barnsbury

3.5

853.6

E01002789

Islington 008D

St George's

2.9

665.6

E01002785

Islington 008B

St George's

2.8

598.8

E01002759

Islington 002D

Hillrise

2.7

580.0

E01002769

Islington 008A

Junction

2.7

566.9

E01002742

Islington 006A

Highbury East

2.7

559.3

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01033493

Islington 006F

Highbury West

0.2

18.7

E01033492

Islington 011I

Highbury West

0.4

43.8

E01002800

Islington 018E

St Peter's

0.6

72.8

E01002701

Islington 023A

Bunhill

0.7

80.1

E01002703

Islington 023C

Bunhill

0.8

93.9

E01002709

Islington 019C

Caledonian

0.8

95.8

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01002751

Islington 005E

Highbury West

1.3

324.6

E01002731

Islington 007B

Finsbury Park

1.3

308.1

E01033489

Islington 022G

Bunhill

1.2

286.5

E01002787

Islington 010E

St George's

1.2

271.0

E01002700

Islington 017C

Barnsbury

1.2

254.8

E01002711

Islington 021B

Caledonian

1.2

252.3

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01033487

Islington 011F

Holloway

0.6

68.0

E01002697

Islington 019B

Barnsbury

0.6

73.4

E01002713

Islington 015C

Caledonian

0.6

73.7

E01002723

Islington 022C

Clerkenwell

0.6

74.0

E01033493

Islington 006F

Highbury West

0.6

75.8

E01002773

Islington 004D

Junction

0.6

77.8

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Table 6.8: Total £m GBP levy by source (Scenario 2)

nLSOAs

All emissions

Gas

Electricity

123

314.2

30.2

14.4

Table 6.8: Total £m GBP levy by source, ordered by highest emitting ward (Scenario 2)

WD20NM

nLSOAs

All emissions

Gas

Electricity

Highbury West

10

32.2

2.0

1.4

Highbury East

7

26.1

2.5

0.8

Holloway

9

22.7

1.7

0.8

Hillrise

8

21.2

2.0

0.7

St Peter's

7

20.9

1.9

1.1

Mildmay

8

19.8

2.1

0.6

Canonbury

7

19.3

2.2

0.7

St Mary's

7

18.0

2.0

1.0

Barnsbury

7

18.0

1.9

0.9

St George's

7

18.0

2.1

0.8

Bunhill

8

17.4

1.1

1.4

Tollington

8

17.4

2.0

0.7

Caledonian

8

17.0

1.5

1.0

Clerkenwell

7

16.5

1.6

0.8

Finsbury Park

8

15.4

1.8

1.1

Junction

7

14.2

1.9

0.7

Total levy due to all emissions, gas & electricity use by LSOA (Scenario 2)

Figure 6.10: Total levy due to all emissions, gas & electricity use by LSOA (Scenario 2)

The table below shows the mean per dwelling value rounded to the nearest £10.

Table 6.9: Mean £ GBP levy per dwelling by source (Scenario 2)

All_emissions

Gas

Electricity

484.4

298.6

134.2

6.3 Compare scenarios

Figure 6.11 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.

Table 6.10: Compare totals for Scenario 1 & Scenario 2 (£m)

nLSOAs

sum_total_sc1

sum_total_sc2

123

457.6

314.2

Comparing £ levy under each scenario by IMD decile - all consumption emissions

Figure 6.11: Comparing £ levy under each scenario by IMD decile - all consumption emissions 

## Saving 7 x 5 in image

Figure 6.12 compares the £ levy under each scenario for all consumption.

## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
Comparing £ levy under each scenario - all consumption emissions

Figure 6.12: Comparing £ levy under each scenario - all consumption emissions

7 Estimate retofit costs

  • from A-E <- £13,300
  • from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

7.1 Impute EPC counts

There are likely to be a range of biases in the EPC sample such as:

  • required for new builds
  • required for rental
  • required for sale
  • required after some retrofit

Check distributions of EPCs against known building stock:

  • Census dwelling type vs EPC dwelling type
  • Census dwelling age (?) vs EPC building age
  • Census main heating type ? vs EPC “mainfuel_mainsgas” “mainfuel_electric” “mainfuel_oil” “mainfuel_coal” “mainfuel_lpg” “mainfuel_biomass”

##  pc_missing_epcs 
##  Min.   :-18.75  
##  1st Qu.: 29.73  
##  Median : 37.07  
##  Mean   : 34.78  
##  3rd Qu.: 42.83  
##  Max.   : 56.40

These plots suggest:

  • Lots of detached houses missing - especially in lower deprivation areas
  • Lots of semis missing
  • Terrace houses better represented
  • Flats fairly well represented
  • Overall compared to the number of electricity meters, we have about 40% of the EPCs we should have

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. We simply upweight the EPC counts proportionally so that the total matches the number of electricity meters.

Note that this assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption given what we have seen above but there is currently no way to re-weight them (e.g. to fit the dwelling type counts) with the data we have - we do not know the of EPCs of each Band that come from different dwelling types.

Note to self: this could be done by re-weighting the case level EPC data using Census outcomes at LSOA level - e.g. via IPF

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   255.0   438.0   526.0   563.5   643.5  1340.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   451.0   723.0   813.0   853.7   953.0  1795.0
## [1] 100
## [1] 100

variable

nDwellings

pc

i.nDwellings

i.pc

nDwellings_A

116.78

0.11

12,737.80

0.17

nDwellings_B

15,060.15

14.41

778,932.47

10.59

nDwellings_C

39,644.39

37.95

2,208,676.15

30.04

nDwellings_D

37,433.06

35.83

2,939,176.13

39.98

nDwellings_E

10,827.07

10.36

881,518.85

11.99

nDwellings_F

1,006.00

0.96

502,229.00

6.83

nDwellings_G

390.93

0.37

28,665.51

0.39

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

7.2 Estimate costs - sheep dip D to G

Upgrade all D-G:

  • upgrading A-E to ‘acceptable standard’: £13,300
  • upgrading F-G to ‘acceptable standard’: £26,800

Source: estimates of the cost of retrofitting dwellings in different EPC bands ‘to an appropriate standard’ published in a recent English Housing Survey technical report (MHCLG 2018) (see https://journal-buildingscities.org/articles/10.5334/bc.279#T2)

The model does not upgrade A-C as it was assumed that dwellings in the least energy-efficient EPC bands (D–G) would be prioritised and that EPC bands A–C would not be retrofitted since they would already conform to the current UK policy objective of:

“as many homes as possible to achieve EPC band C by 2035 where cost-effective, practical and affordable, and to ensure as many fuel poor homes as reasonably practicable achieve a band C rating by the end of 2030.” https://www.gov.uk/government/news/plan-to-drive-down-the-cost-of-clean-heat

Table 7.1 reports total retofit costs.

## To retrofit D-E (£m)
## [1] 641.8596
## Number of dwellings: 48260
## To retrofit F-G (£m)
## [1] 51.81009
## Number of dwellings: 1933
## To retrofit D-G (£m)
## [1] 693.6697
## To retrofit D-G (mean per dwelling)
## [1] 13800.12
Table 7.1: Retrofit cost totals (£m GBP):

Mean total £m per LSOA

Total £m

5.6

693.7

Table 7.1: Retrofit cost totals by ward (£m GBP):

Ward

Mean total £m per LSOA

Total £m

Highbury West

5.1

50.7

Tollington

6.2

49.8

St George's

6.8

47.4

Highbury East

6.6

46.1

Mildmay

5.7

45.4

St Mary's

6.3

44.4

Finsbury Park

5.5

44.1

Barnsbury

6.2

43.7

Hillrise

5.4

42.9

Canonbury

6.1

42.5

Holloway

4.6

41.1

Junction

5.8

40.3

St Peter's

5.7

40.1

Clerkenwell

5.6

39.5

Bunhill

4.8

38.1

Caledonian

4.7

37.6

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Figure 7.1 shows the LSOA level retofit costs per dwelling by IMD decile.

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
LSOA level retofit costs per dwelling by IMD score

Figure 7.1: LSOA level retofit costs per dwelling by IMD score 

## Saving 7 x 5 in image

Figure 7.2: Total estimated retorfit costs per LSOA

7.2.1 Estimate costs - sheep dip D to G but upgrades in 20% poorest areas funded by HM Gov (somehow)

Set aside areas in IMD decile 1 & 2? Assume costs met by HM Treasury or elsewhere e.g. energy levy etc

to do

8 Compare levy with costs

8.1 Scenario 1

Totals

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

8.2 Scenario 2

Totals

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Repeat per dwelling

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

9 Years to pay…

9.1 Scenario 1

Figure 9.1 shows years to pay under Scenario 1 (all emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.692   2.512   3.210   3.323   3.996   6.768
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Years to pay under Scenario 1 (all em issions)

Figure 9.1: Years to pay under Scenario 1 (all em issions) 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Median years: 3.21

Figure 9.2 maps the distribution of years to pay for all LSOAs in the case study.

Figure 9.2: Years to pay per LSOA (Scenbario 1, all emissions levy, no redistribution)

Figure 9.3 shows years to pay under Scenario 1 (energy emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.52   18.77   21.07   22.68   24.32   70.07
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Years to pay under Scenario 1 (energy emissions)

Figure 9.3: Years to pay under Scenario 1 (energy emissions) 

## Median years: 21.07

Figure 9.4 shows those LSOAs which would be in surplus (above the y = 0 line) after Year 1.

Surplus £ after year 1 for each LSOA (all emissions levy)

Figure 9.4: Surplus £ after year 1 for each LSOA (all emissions levy)

Figure 9.5 shows the same distribution but for the energy emissions levy. Clearly there is a lower level of surplus due to the reduced levy value at LSOA level.

Surplus £ after year 1 for each LSOA (energy levy)

Figure 9.5: Surplus £ after year 1 for each LSOA (energy levy)

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

Figure 9.6 shows the year 1 outcome at ward level if levy is shared equally across all LSOAs (all emissions levy - orange dots).

Year 1 outcome if levy is shared equally (all emissions levy)

Figure 9.6: Year 1 outcome if levy is shared equally (all emissions levy) 

## Saving 7 x 5 in image
Table 9.1: Longest years to pay under Scenario 1 (sorted by total retrofit cost)

LSOA

LSOA name

Ward

Retrofit cost £m

Years to pay (all emissions levy)

Years to pay (energy emissions levy)

% EPC D

% EPC E

% EPC F

% EPC G

E01002731

Islington 007B

Finsbury Park

9.9

5.2

23.2

33.2

12.9

3.0

0.9

E01002762

Islington 011C

Holloway

9.4

2.5

19.3

52.3

14.2

1.9

1.0

E01002789

Islington 008D

St George's

9.0

2.2

14.9

51.5

18.4

3.6

0.2

E01002774

Islington 004E

Junction

8.9

4.2

20.8

44.8

13.1

1.6

0.5

E01002696

Islington 020A

Barnsbury

8.8

3.0

18.7

47.3

13.4

2.1

0.3

E01002792

Islington 017E

St Mary's

8.7

3.0

17.9

44.8

14.2

2.3

0.4

E01002798

Islington 018C

St Peter's

8.6

2.5

17.4

38.4

19.9

2.5

0.7

E01002718

Islington 014B

Canonbury

8.6

2.1

16.3

52.8

16.8

2.3

0.9

E01002698

Islington 017A

Barnsbury

8.3

3.7

21.7

35.8

13.4

1.2

0.6

E01002727

Islington 021C

Clerkenwell

8.1

3.1

24.8

38.6

16.3

3.8

0.9

9.2 Scenario 2

Figure 9.7 shows years to pay under Scenario 2 (all emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.630   3.110   5.580   5.670   8.009  13.592
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Years to pay under Scenario 2 (all emissions)

Figure 9.7: Years to pay under Scenario 2 (all emissions) 

## Median years: 5.58

Figure 9.8 shows years to pay under Scenario 2 (energy emissions)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.77   26.66   33.60   37.06   43.13  140.72
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Years to pay under Scenario 2 (energy emissions)

Figure 9.8: Years to pay under Scenario 2 (energy emissions)

Figure 9.9 shows those LSOAs which would be in surplus (above the y = 0 line) after Year 1.

Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)

Figure 9.9: Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)

Figure 9.10 shows the same distribution for the Scenario 2 energy levy.

Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)

Figure 9.10: Surplus £ after year 1 for each LSOA (Scenario 2, all emissions levy)

What happens in Year 2 totally depends on the rate of upgrades…

Figure 9.11 shows the year 1 outcome at ward level if levy is shared equally across all LSOAs (all emissions levy - orange dots).

Year 1 outcome if levy is shared equally (Scenario 2, all emissions levy)

Figure 9.11: Year 1 outcome if levy is shared equally (Scenario 2, all emissions levy) 

## Saving 7 x 5 in image
Table 9.2: Longest years to pay under Scenario 2 (sorted by total retrofit cost)

LSOA

LSOA name

Ward

Retrofit cost £m

Years to pay (all emissions levy)

Years to pay (energy emissions levy)

% EPC D

% EPC E

% EPC F

% EPC G

E01002731

Islington 007B

Finsbury Park

9.9

10.4

31.4

33.2

12.9

3.0

0.9

E01002762

Islington 011C

Holloway

9.4

3.0

28.2

52.3

14.2

1.9

1.0

E01002789

Islington 008D

St George's

9.0

2.4

16.9

51.5

18.4

3.6

0.2

E01002774

Islington 004E

Junction

8.9

8.5

34.1

44.8

13.1

1.6

0.5

E01002696

Islington 020A

Barnsbury

8.8

4.5

26.5

47.3

13.4

2.1

0.3

E01002792

Islington 017E

St Mary's

8.7

4.6

24.1

44.8

14.2

2.3

0.4

E01002798

Islington 018C

St Peter's

8.6

3.0

22.6

38.4

19.9

2.5

0.7

E01002718

Islington 014B

Canonbury

8.6

2.3

20.1

52.8

16.8

2.3

0.9

E01002698

Islington 017A

Barnsbury

8.3

6.9

34.1

35.8

13.4

1.2

0.6

E01002727

Islington 021C

Clerkenwell

8.1

4.7

40.4

38.6

16.3

3.8

0.9

9.3 Compare scenarios

Figure 9.12 compares pay-back times for the two scenarios - who does the rising block tariff help?

Comparing pay-back times across scenarios

Figure 9.12: Comparing pay-back times across scenarios 

## Saving 7 x 5 in image

10 Summary: Islington

Table 10.1 shows the summary overall levy value results for Islington by Scenario.

Table 10.1: Levy total (Year 1 £m GBP): Islington

Scenario

nLSOAs

All emissions levy

Gas emissions levy

Electricity emissions levy

Scenario 1

123

457.6

45.8

21.4

Scenario 2

123

314.2

30.2

14.4

Table 10.2 shows the summary overall retrofit cost results for Islington.

Table 10.2: Retrofit cost totals (£m GBP): Islington

Total retrofit cost £m

Mean total retrofit cost £m per LSOA

693.7

5.6

As a reminder:

  • Number of households (Census 2021): 92,467
  • Estimated number of domestic electricity meters (2018): 105,006
  • Estimated number of domestic gas meters (2018): 85,924

The model is similar in some respects to Evans et al (2023) Getting to net zero: Islington’s social housing stock (https://journal-buildingscities.org/articles/10.5334/bc.349) which estimated an overall cost of upgrading Islington’s social housing stock (only) of 4500 buildings containing some 33,300 dwellings to be £1,600m, compared to this model’s estimate of £693.67m for all required dwellings.

The following table (Table 10.3) summarises all results by ward.

Table 10.3: All results by ward (£m GBP, sorted by total retrofit cost):

WD20NM

nLSOAs

All emissions levy (£m, Scenario 1)

Gas emissions levy (£m, Scenario 1)

Electricity emissions levy (£m, Scenario 1)

All emissions levy (£m, Scenario 2)

Gas emissions levy (£m, (Scenario 2)

Electricity emissions levy (£m, Scenario 2)

Total retrofit cost (£m)

Mean total retrofit cost per LSOA (£m)

Highbury West

10

41.7

3.0

1.9

32.2

2.0

1.4

50.7

5.1

Tollington

8

27.8

3.1

1.2

17.4

2.0

0.7

49.8

6.2

St George's

7

26.3

3.0

1.2

18.0

2.1

0.8

47.4

6.8

Highbury East

7

32.4

3.3

1.2

26.1

2.5

0.8

46.1

6.6

Mildmay

8

28.3

3.1

1.1

19.8

2.1

0.6

45.4

5.7

St Mary's

7

27.3

3.0

1.4

18.0

2.0

1.0

44.4

6.3

Finsbury Park

8

26.4

3.0

1.5

15.4

1.8

1.1

44.1

5.5

Barnsbury

7

27.2

2.7

1.3

18.0

1.9

0.9

43.7

6.2

Hillrise

8

27.3

2.6

1.1

21.2

2.0

0.7

42.9

5.4

Canonbury

7

27.1

3.1

1.1

19.3

2.2

0.7

42.5

6.1

Holloway

9

32.2

2.9

1.3

22.7

1.7

0.8

41.1

4.6

Junction

7

23.5

2.8

1.1

14.2

1.9

0.7

40.3

5.8

St Peter's

7

29.2

2.9

1.5

20.9

1.9

1.1

40.1

5.7

Clerkenwell

7

25.5

2.5

1.2

16.5

1.6

0.8

39.5

5.6

Bunhill

8

29.5

2.2

1.9

17.4

1.1

1.4

38.1

4.8

Caledonian

8

26.0

2.5

1.4

17.0

1.5

1.0

37.6

4.7

As ever, #YMMV.

11 R environment

11.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • flextable (Gohel 2021)
  • ggplot2 (Wickham 2009)
  • here (Müller 2017)
  • knitr (Xie 2016b)
  • leaflet (Cheng, Karambelkar, and Xie 2023)
  • lubridate (Grolemund and Wickham 2011)
  • rmarkdown (Allaire et al. 2018)
  • sf (Pebesma 2018)
  • skimr (Arino de la Rubia et al. 2017)
  • viridisLite (Garnier et al. 2023)

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.
Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.
Cheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2023. Leaflet: Create Interactive Web Maps with the JavaScript ’Leaflet’ Library. https://CRAN.R-project.org/package=leaflet.
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
Garnier, Simon, Ross, Noam, Rudis, Robert, Camargo, et al. 2023. viridis(Lite) - Colorblind-Friendly Color Maps for r. https://doi.org/10.5281/zenodo.4678327.
Gohel, David. 2021. Flextable: Functions for Tabular Reporting. https://CRAN.R-project.org/package=flextable.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.
Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.
Pebesma, Edzer. 2018. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal. https://journal.r-project.org/archive/2018/RJ-2018-009/index.html.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.