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: East Suffolk 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 East Suffolk 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 East Suffolk 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: East Suffolk
## 
##     0     1 
## 32716   137
## Number of selected LSOAs - merged data:
## [1] 137
##    
##     East Suffolk
##   1          137
## Test linkage of Census 2021 data - suspect some LSOAs have changed
##    c2021_nhhs     c2021_pc_elec_ch_2021    IMDScore       pc_fuelPoor   
##  Min.   : 460.0   Min.   : 0.7849       Min.   : 2.253   Min.   : 4.00  
##  1st Qu.: 638.5   1st Qu.: 3.9639       1st Qu.: 9.457   1st Qu.:10.00  
##  Median : 730.0   Median : 6.4953       Median :17.763   Median :14.00  
##  Mean   : 758.3   Mean   : 8.0614       Mean   :20.325   Mean   :14.06  
##  3rd Qu.: 864.5   3rd Qu.:10.7089       3rd Qu.:24.771   3rd Qu.:19.00  
##  Max.   :1192.0   Max.   :36.2275       Max.   :81.147   Max.   :29.00  
##  NA's   :2        NA's   :2
## # 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 East Suffolk as a case study.

  • Number of households (Census 2021): 102,372
  • Number of electricity meters (2018): 109,668
  • Number of gas meters (2018): 89,407

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, (East Suffolk) = 60664

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, (East Suffolk) = 109668
##    case_study mean_MSOA_totInc
## 1:          0         43913.02
## 2:          1         41073.72

## 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: East Suffolk

region

nLSOAs

mean_KgCo2ePerCap

sd_KgCo2ePerCap

East

137

7,905.8

2,558.7

## 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:         Waveney 014A Southwold and Reydon       1312        1448       746
## 2: Suffolk Coastal 004A            Aldeburgh       1282        1503       784
## 3: Suffolk Coastal 004B            Aldeburgh       1120        1252       654
## 4: Suffolk Coastal 008D           Woodbridge       1119        1310       764
## 5: Suffolk Coastal 013G      Felixstowe East       1107        1127       519
## 6: Suffolk Coastal 003C           Saxmundham       1031        1181       828
##                LSOA11NM                WD18NM nGasMeters nElecMeters epc_total
## 1: Suffolk Coastal 004A             Aldeburgh       1282        1503       784
## 2:         Waveney 014A  Southwold and Reydon       1312        1448       746
## 3: Suffolk Coastal 008D            Woodbridge       1119        1310       764
## 4: Suffolk Coastal 001B Wenhaston & Westleton         60        1289       661
## 5: Suffolk Coastal 001C Wenhaston & Westleton        236        1265       668
## 6: Suffolk Coastal 004B             Aldeburgh       1120        1252       654
Table 5.2: LSOAs with highest number of gas meters (after cleaning)

LSOA11NM

WD18NM

nGasMeters

nElecMeters

epc_total

Waveney 014A

Southwold and Reydon

1,312

1,448

746

Suffolk Coastal 004A

Aldeburgh

1,282

1,503

784

Suffolk Coastal 004B

Aldeburgh

1,120

1,252

654

Suffolk Coastal 008D

Woodbridge

1,119

1,310

764

Suffolk Coastal 013G

Felixstowe East

1,107

1,127

519

Suffolk Coastal 003C

Saxmundham

1,031

1,181

828

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 137
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 17057.90 6599.02 7399.33 12405.27 14732.31 20774.79 46429.45 ▇▅▂▁▁
CREDS_gas_kgco2e2018_pdw 0 1 2129.79 664.63 31.87 1904.20 2221.50 2470.97 3829.69 ▁▁▇▆▁
CREDS_elec_kgco2e2018_pdw 0 1 1077.97 238.08 782.99 933.83 1006.15 1120.88 1906.27 ▇▆▁▁▁
CREDS_measuredHomeEnergy_kgco2e2018_pdw 0 1 3207.75 560.95 1803.57 2894.56 3177.01 3447.01 4983.88 ▂▇▇▃▁
CREDS_otherEnergy_kgco2e2011_pdw 0 1 272.38 451.14 13.26 53.23 93.08 224.41 2219.17 ▇▁▁▁▁
CREDS_allHomeEnergy_kgco2e2018_pdw 0 1 3480.14 515.62 2353.37 3118.09 3403.30 3796.10 5298.43 ▂▇▅▂▁
CREDS_car_kgco2e2018_pdw 0 1 2423.50 746.10 787.94 1910.45 2348.53 2913.69 4269.19 ▂▇▇▅▂
CREDS_van_kgco2e2018_pdw 0 1 309.99 210.04 61.50 163.82 247.92 367.10 1114.83 ▇▃▁▁▁
CREDS_personalTransport_kgco2e2018_pdw 0 1 2733.49 846.58 1048.95 2108.10 2629.22 3240.95 5142.54 ▃▇▆▃▁

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 = -8.0118, df = 135, p-value = 4.704e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6714612 -0.4420446
## sample estimates:
##       cor 
## -0.567674
## Total emissions per dwelling (LSOA level) summary
##    LSOA11CD            WD18NM                         IMD_Decile_label All_Tco2e_per_dw
##  Length:137         Length:137         4                      :20      Min.   : 7.399  
##  Class :character   Class :character   5                      :19      1st Qu.:12.405  
##  Mode  :character   Mode  :character   9                      :16      Median :14.732  
##                                        10 (10% least deprived):15      Mean   :17.058  
##                                        6                      :14      3rd Qu.:20.775  
##                                        8                      :13      Max.   :46.429  
##                                        (Other)                :40
Table 5.4: Highest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01030205

Tower

10 (10% least deprived)

46.4

E01033444

Kesgrave East

10 (10% least deprived)

35.8

E01033442

Kesgrave East

9

33.5

E01030260

Lothingland

6

32.4

E01033447

Kesgrave East

10 (10% least deprived)

31.7

E01030180

Kesgrave West

10 (10% least deprived)

31.0

Table 5.4: Lowest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01030250

Harbour

1 (10% most deprived)

7.4

E01030247

Harbour

1 (10% most deprived)

8.7

E01030170

Felixstowe West

2

8.8

E01030279

St Margaret's

1 (10% most deprived)

9.0

E01030273

Pakefield

6

9.1

E01030249

Harbour

1 (10% most deprived)

9.1

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. 
##   31.87 1904.20 2221.50 2129.79 2470.97 3829.69
## `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 = -4.3719, df = 135, p-value = 2.44e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4908895 -0.1960308
## sample estimates:
##        cor 
## -0.3521679

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 = -2.7391, df = 135, p-value = 0.006994
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.38244882 -0.06420624
## sample estimates:
##        cor 
## -0.2294509

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 = -2.7391, df = 135, p-value = 0.006994
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.38244882 -0.06420624
## sample estimates:
##        cor 
## -0.2294509
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

Rural town and fringe

2,011.6

1,100.1

270.3

Rural town and fringe in a sparse setting

2,067.4

950.3

121.8

Rural village and dispersed

1,135.8

1,559.6

1,306.3

Rural village and dispersed in a sparse setting

2,876.1

1,120.9

227.1

Urban city and town

2,324.0

993.2

96.5

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 = 4.9338, df = 135, p-value = 2.334e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.2387915 0.5242063
## sample estimates:
##       cor 
## 0.3908537
## `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 = 8.4419, df = 135, p-value = 4.311e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4660234 0.6877157
## sample estimates:
##       cor 
## 0.5877971
## `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 = -10.017, df = 135, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7396864 -0.5449406
## sample estimates:
##        cor 
## -0.6529773
Table 5.6: Summary of per dwelling transport emissions for LSOAs by urban/rural code

RUC11

mean_car_kgco2e

mean_van_kgco2e

Rural town and fringe

2,362.7

401.3

Rural town and fringe in a sparse setting

1,990.4

281.1

Rural village and dispersed

3,345.9

575.2

Rural village and dispersed in a sparse setting

1,814.4

225.8

Urban city and town

2,296.6

247.0

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 = -0.51663, df = 135, p-value = 0.6063
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.2105675  0.1242202
## sample estimates:
##         cor 
## -0.04442077

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

137

439.2

56.2

29.2

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Kesgrave

8

33.9

3.5

1.4

Beccles & Worlingham

9

27.7

3.9

1.6

Oulton Broad

9

25.6

3.6

1.6

Orwell & Villages

7

24.5

2.3

1.4

Kirkley & Pakefield

10

23.2

3.7

1.6

Eastern Felixstowe

7

23.1

3.8

1.5

Western Felixstowe

8

22.1

3.0

1.4

Harbour & Normanston

10

20.1

3.6

1.7

Aldeburgh & Leiston

6

18.9

3.3

1.5

Bungay & Wainford

5

18.4

1.3

1.4

Halesworth & Blything

5

16.6

1.6

1.6

Carlton Colville

4

16.3

2.0

0.9

Martlesham & Purdis Farm

4

16.2

2.0

0.8

Gunton & St Margarets

6

15.1

2.4

1.1

Woodbridge

4

15.0

2.5

1.1

Carlton & Whitton

6

14.6

2.3

1.0

Carlford & Fynn Valley

3

13.7

0.9

1.0

Rushmere St Andrew

3

13.3

1.5

0.5

Framlingham

3

10.8

1.1

0.9

Lothingland

3

10.0

0.7

0.5

Southwold

3

9.4

1.7

0.6

Wickham Market

2

8.9

1.2

0.6

Wrentham, Wangford & Westleton

2

8.8

0.5

0.9

Kessingland

3

8.4

1.3

0.7

Rendlesham & Orford

2

7.7

0.4

0.7

Melton

2

7.2

0.9

0.5

Saxmundham

2

6.4

1.1

0.5

Kelsale & Yoxford

1

3.4

0.2

0.3

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Kesgrave

8

33.9

3.5

1.4

Beccles & Worlingham

9

27.7

3.9

1.6

Oulton Broad

9

25.6

3.6

1.6

Orwell & Villages

7

24.5

2.3

1.4

Kirkley & Pakefield

10

23.2

3.7

1.6

Eastern Felixstowe

7

23.1

3.8

1.5

Western Felixstowe

8

22.1

3.0

1.4

Harbour & Normanston

10

20.1

3.6

1.7

Aldeburgh & Leiston

6

18.9

3.3

1.5

Bungay & Wainford

5

18.4

1.3

1.4

Halesworth & Blything

5

16.6

1.6

1.6

Carlton Colville

4

16.3

2.0

0.9

Martlesham & Purdis Farm

4

16.2

2.0

0.8

Gunton & St Margarets

6

15.1

2.4

1.1

Woodbridge

4

15.0

2.5

1.1

Carlton & Whitton

6

14.6

2.3

1.0

Carlford & Fynn Valley

3

13.7

0.9

1.0

Rushmere St Andrew

3

13.3

1.5

0.5

Framlingham

3

10.8

1.1

0.9

Lothingland

3

10.0

0.7

0.5

Southwold

3

9.4

1.7

0.6

Wickham Market

2

8.9

1.2

0.6

Wrentham, Wangford & Westleton

2

8.8

0.5

0.9

Kessingland

3

8.4

1.3

0.7

Rendlesham & Orford

2

7.7

0.4

0.7

Melton

2

7.2

0.9

0.5

Saxmundham

2

6.4

1.1

0.5

Kelsale & Yoxford

1

3.4

0.2

0.3

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

4,179.2

521.8

264.1

785.9

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. 
##    1813    3039    3609    4179    5090   11375
Table 6.3: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01030205

Suffolk Coastal 011D

Tower

46.4

11,375.2

54,700

E01033444

Suffolk Coastal 010H

Kesgrave East

35.8

8,772.6

52,700

E01033442

Suffolk Coastal 010G

Kesgrave East

33.5

8,203.2

52,700

E01030260

Waveney 001E

Lothingland

32.4

7,926.8

42,500

E01033447

Suffolk Coastal 010I

Kesgrave East

31.7

7,778.3

52,700

E01030180

Suffolk Coastal 009B

Kesgrave West

31.0

7,603.1

46,600

Table 6.3: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01030250

Waveney 004D

Harbour

7.4

1,812.8

33,400

E01030247

Waveney 007A

Harbour

8.7

2,127.7

31,800

E01030170

Suffolk Coastal 014E

Felixstowe West

8.8

2,161.1

34,400

E01030279

Waveney 002D

St Margaret's

9.0

2,213.6

34,000

E01030273

Waveney 012F

Pakefield

9.1

2,224.3

37,800

E01030249

Waveney 004C

Harbour

9.1

2,235.3

33,400

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. 
##   7.809 466.529 544.268 521.798 605.387 938.274
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

E01030206

Suffolk Coastal 011E

Tower

3.8

938.3

54,700

E01030166

Suffolk Coastal 013F

Felixstowe East

3.7

897.3

45,400

E01030242

Waveney 001C

Gunton and Corton

3.2

784.8

42,500

E01030192

Suffolk Coastal 010E

Martlesham

3.2

775.2

52,700

E01030224

Waveney 010B

Beccles North

3.1

763.0

36,200

E01030204

Suffolk Coastal 011C

Tower

3.1

757.2

54,700

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

E01030198

Suffolk Coastal 010F

Kirton

0.6

139.4

52,700

E01030176

Suffolk Coastal 002E

Hacheston

0.4

104.2

44,700

E01030200

Suffolk Coastal 006B

Grundisburgh

0.3

74.1

49,800

E01030219

Suffolk Coastal 001B

Wenhaston & Westleton

0.1

23.7

46,300

E01030285

Waveney 013D

The Saints

0.1

14.5

40,500

E01030286

Waveney 011B

Wainford

0.0

7.8

44,200

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. 
##   191.8   228.8   246.5   264.1   274.6   467.0
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

E01030176

Suffolk Coastal 002E

Hacheston

1.9

467.0

44,700

E01033445

Suffolk Coastal 007G

Rendlesham

1.9

465.9

44,000

E01030200

Suffolk Coastal 006B

Grundisburgh

1.9

458.4

49,800

E01030286

Waveney 011B

Wainford

1.8

444.5

44,200

E01030285

Waveney 013D

The Saints

1.8

430.4

40,500

E01033449

Suffolk Coastal 007J

Rendlesham

1.7

420.0

44,000

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

E01030274

Waveney 008B

Pakefield

0.9

211.0

34,500

E01030278

Waveney 003D

St Margaret's

0.8

204.7

36,600

E01030161

Suffolk Coastal 014B

Felixstowe North

0.8

204.0

34,400

E01030247

Waveney 007A

Harbour

0.8

198.5

31,800

E01030276

Waveney 007E

Pakefield

0.8

193.9

31,800

E01030282

Waveney 014A

Southwold and Reydon

0.8

191.8

40,700

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. 
##   441.9   709.2   778.4   785.9   844.5  1221.1

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% 
##  7.399329 12.405269 14.732308 20.774785 46.429448
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name …[]
Number of rows 137
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 17.06 6.60 7.40 12.41 14.73 20.77 46.43 ▇▅▂▁▁
beis_GBPtotal_sc2_perdw 0 1 3140.15 2240.58 902.72 1513.44 2081.29 4301.16 13716.42 ▇▂▁▁▁
beis_GBPtotal_sc2 0 1 2318474.38 1315281.24 632570.00 1225584.90 1856693.69 3170908.14 6707328.25 ▇▃▂▂▁

Gas cuts:

## Cuts for gas per dw
##         0%        25%        50%        75%       100% 
## 0.03187364 1.90420144 2.22150376 2.47096774 3.82969035
## [1] 35.24069
## Cuts for elec per dw
##        0%       10%       20%       30%       40%       50%       60%       70%       80% 
## 0.7829903 0.8876588 0.9249715 0.9454646 0.9845754 1.0061538 1.0359388 1.0783696 1.1518579 
##       90%      100% 
## 1.4512718 1.9062691
## [1] 19.55299
Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01030205

Suffolk Coastal 011D

Tower

46.4

13,716.4

E01033444

Suffolk Coastal 010H

Kesgrave East

35.8

9,817.8

E01033442

Suffolk Coastal 010G

Kesgrave East

33.5

8,964.9

E01030260

Waveney 001E

Lothingland

32.4

8,550.9

E01033447

Suffolk Coastal 010I

Kesgrave East

31.7

8,328.5

E01030180

Suffolk Coastal 009B

Kesgrave West

31.0

8,065.9

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01030250

Waveney 004D

Harbour

7.4

902.7

E01030247

Waveney 007A

Harbour

8.7

1,059.5

E01030170

Suffolk Coastal 014E

Felixstowe West

8.8

1,076.1

E01030279

Waveney 002D

St Margaret's

9.0

1,102.3

E01030273

Waveney 012F

Pakefield

9.1

1,107.6

E01030249

Waveney 004C

Harbour

9.1

1,113.1

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01030206

Suffolk Coastal 011E

Tower

3.8

900.3

E01030166

Suffolk Coastal 013F

Felixstowe East

3.7

838.9

E01030242

Waveney 001C

Gunton and Corton

3.2

670.3

E01030192

Suffolk Coastal 010E

Martlesham

3.2

655.9

E01030224

Waveney 010B

Beccles North

3.1

637.8

E01030204

Suffolk Coastal 011C

Tower

3.1

629.0

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01030286

Waveney 011B

Wainford

0.0

3.9

E01030285

Waveney 013D

The Saints

0.1

7.2

E01030219

Suffolk Coastal 001B

Wenhaston & Westleton

0.1

11.8

E01030200

Suffolk Coastal 006B

Grundisburgh

0.3

36.9

E01030176

Suffolk Coastal 002E

Hacheston

0.4

51.9

E01030198

Suffolk Coastal 010F

Kirton

0.6

69.4

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01030176

Suffolk Coastal 002E

Hacheston

1.9

477.6

E01033445

Suffolk Coastal 007G

Rendlesham

1.9

475.9

E01030200

Suffolk Coastal 006B

Grundisburgh

1.9

464.7

E01030286

Waveney 011B

Wainford

1.8

443.8

E01030285

Waveney 013D

The Saints

1.8

422.7

E01033449

Suffolk Coastal 007J

Rendlesham

1.7

407.2

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01030282

Waveney 014A

Southwold and Reydon

0.8

95.5

E01030276

Waveney 007E

Pakefield

0.8

96.5

E01030247

Waveney 007A

Harbour

0.8

98.8

E01030161

Suffolk Coastal 014B

Felixstowe North

0.8

101.6

E01030278

Waveney 003D

St Margaret's

0.8

101.9

E01030274

Waveney 008B

Pakefield

0.9

105.1

## `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

137

317.6

35.2

19.6

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Kesgrave

8

32.2

2.3

0.9

Orwell & Villages

7

21.7

1.5

1.0

Beccles & Worlingham

9

19.5

2.4

0.9

Oulton Broad

9

17.3

2.2

1.0

Eastern Felixstowe

7

16.0

2.7

0.9

Western Felixstowe

8

15.1

1.8

0.8

Kirkley & Pakefield

10

14.9

2.1

0.9

Rushmere St Andrew

3

13.9

1.3

0.3

Bungay & Wainford

5

13.6

0.8

1.2

Martlesham & Purdis Farm

4

13.4

1.5

0.5

Carlton Colville

4

13.4

1.2

0.5

Carlford & Fynn Valley

3

11.4

0.6

0.9

Aldeburgh & Leiston

6

10.2

2.0

0.9

Harbour & Normanston

10

10.1

1.9

0.9

Gunton & St Margarets

6

9.9

1.6

0.7

Carlton & Whitton

6

9.4

1.5

0.6

Lothingland

3

9.3

0.4

0.4

Halesworth & Blything

5

9.2

0.9

1.2

Woodbridge

4

9.2

1.7

0.7

Framlingham

3

7.3

0.6

0.8

Wickham Market

2

7.1

0.8

0.5

Rendlesham & Orford

2

6.6

0.2

0.7

Melton

2

5.8

0.6

0.4

Southwold

3

5.5

1.1

0.4

Wrentham, Wangford & Westleton

2

5.3

0.2

0.8

Kessingland

3

4.4

0.7

0.4

Saxmundham

2

3.2

0.6

0.3

Kelsale & Yoxford

1

2.8

0.1

0.2

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

334.5

329.2

175.3

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

137

439.2

317.6

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.   :13.96  
##  1st Qu.:40.60  
##  Median :46.91  
##  Mean   :45.09  
##  3rd Qu.:50.65  
##  Max.   :60.27

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. 
##   216.0   338.0   417.0   442.8   519.0   947.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   474.0   666.0   748.0   800.5   903.0  1503.0
## [1] 100
## [1] 100

variable

nDwellings

pc

i.nDwellings

i.pc

nDwellings_A

158.74

0.15

12,737.80

0.17

nDwellings_B

10,321.23

9.61

778,932.47

10.59

nDwellings_C

30,189.62

28.11

2,208,676.15

30.04

nDwellings_D

44,967.00

41.87

2,939,176.13

39.98

nDwellings_E

17,660.97

16.44

881,518.85

11.99

nDwellings_F

2,716.00

2.53

502,229.00

6.83

nDwellings_G

1,389.63

1.29

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] 832.952
## Number of dwellings: 62628
## To retrofit F-G (£m)
## [1] 170.7278
## Number of dwellings: 6370
## To retrofit D-G (£m)
## [1] 1003.68
## To retrofit D-G (mean per dwelling)
## [1] 14371.81
Table 7.1: Retrofit cost totals (£m GBP):

Mean total £m per LSOA

Total £m

7.3

1,003.7

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

Ward

Mean total £m per LSOA

Total £m

Harbour

8.6

43.0

Kirkley

8.0

40.0

Woodbridge

9.6

38.5

Felixstowe East

8.8

35.3

Pakefield

6.8

34.0

Wenhaston & Westleton

16.8

33.6

Aldeburgh

10.8

32.3

Whitton

6.3

31.4

Felixstowe South

7.4

29.6

Normanston

5.8

29.2

Beccles North

9.2

27.5

Southwold and Reydon

9.1

27.2

Kesgrave West

6.5

25.9

Grundisburgh

12.5

25.0

Kessingland

8.2

24.5

Carlton Colville

6.1

24.4

Felixstowe West

6.0

24.1

Oulton Broad

7.8

23.5

St Margaret's

4.7

23.5

Gunton and Corton

7.6

22.9

Halesworth

7.6

22.8

Bungay

7.6

22.8

The Trimleys

5.7

22.7

Saxmundham

7.1

21.3

Leiston

7.1

21.2

Melton

6.7

20.1

Beccles South

6.3

18.8

Carlton

6.2

18.7

Felixstowe North

6.1

18.4

Martlesham

6.1

18.3

Worlingham

5.8

17.4

Tower

5.5

16.4

Oulton

5.2

15.7

The Saints

14.6

14.6

Hacheston

14.2

14.2

Kirton

6.7

13.4

Wainford

13.1

13.1

Wrentham

12.9

12.9

Blything

12.7

12.7

Lothingland

6.2

12.4

Framlingham

6.0

12.0

Nacton & Purdis Farm

5.8

11.6

Rendlesham

5.3

10.7

Kesgrave East

2.4

9.5

Wickham Market

9.3

9.3

Fynn Valley

7.4

7.4

## `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. 
##   1.169   2.852   3.896   3.888   4.796   8.019
## `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: 3.9

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. 
##   11.52   16.13   18.33   18.99   20.03   40.10
## `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: 18.33

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

E01030219

Suffolk Coastal 001B

Wenhaston & Westleton

17.2

5.4

40.1

24.8

25.9

17.1

7.6

E01030222

Suffolk Coastal 001C

Wenhaston & Westleton

16.4

4.8

32.4

29.5

25.1

16.2

5.1

E01030282

Waveney 014A

Southwold and Reydon

15.5

6.1

19.2

30.7

28.2

8.3

2.5

E01030152

Suffolk Coastal 004A

Aldeburgh

14.9

4.9

15.1

42.0

17.6

5.6

1.9

E01030285

Waveney 013D

The Saints

14.6

3.1

38.2

35.8

27.5

18.3

5.6

E01030176

Suffolk Coastal 002E

Hacheston

14.2

3.6

29.4

27.6

36.2

16.1

6.1

E01030200

Suffolk Coastal 006B

Grundisburgh

13.7

2.9

31.6

32.8

30.4

19.0

3.5

E01030286

Waveney 011B

Wainford

13.1

3.2

36.4

36.2

30.0

15.2

5.1

E01030295

Waveney 014D

Wrentham

12.9

4.0

26.4

34.1

27.8

9.4

2.7

E01030211

Suffolk Coastal 008D

Woodbridge

12.9

4.9

17.7

35.6

22.9

6.0

1.7

9.2 Scenario 2

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.9696  3.2980  6.9502  6.6596  9.4885 16.1039
## `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: 6.95

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   12.76   25.09   31.49   30.63   36.25   52.58
## `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

E01030219

Suffolk Coastal 001B

Wenhaston & Westleton

17.2

10.2

42.6

24.8

25.9

17.1

7.6

E01030222

Suffolk Coastal 001C

Wenhaston & Westleton

16.4

8.5

41.3

29.5

25.1

16.2

5.1

E01030282

Waveney 014A

Southwold and Reydon

15.5

12.3

30.7

30.7

28.2

8.3

2.5

E01030152

Suffolk Coastal 004A

Aldeburgh

14.9

9.8

20.0

42.0

17.6

5.6

1.9

E01030285

Waveney 013D

The Saints

14.6

3.6

39.5

35.8

27.5

18.3

5.6

E01030176

Suffolk Coastal 002E

Hacheston

14.2

4.5

31.7

27.6

36.2

16.1

6.1

E01030200

Suffolk Coastal 006B

Grundisburgh

13.7

3.0

33.6

32.8

30.4

19.0

3.5

E01030286

Waveney 011B

Wainford

13.1

3.7

36.8

36.2

30.0

15.2

5.1

E01030295

Waveney 014D

Wrentham

12.9

6.3

36.5

34.1

27.8

9.4

2.7

E01030211

Suffolk Coastal 008D

Woodbridge

12.9

9.8

27.8

35.6

22.9

6.0

1.7

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: East Suffolk

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

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

Scenario

nLSOAs

All emissions levy

Gas emissions levy

Electricity emissions levy

Scenario 1

137

439.2

56.2

29.2

Scenario 2

137

317.6

35.2

19.6

Table 10.2 shows the summary overall retrofit cost results for East Suffolk.

Table 10.2: Retrofit cost totals (£m GBP): East Suffolk

Total retrofit cost £m

Mean total retrofit cost £m per LSOA

1,003.7

7.3

As a reminder:

  • Number of households (Census 2021): 102,372
  • Estimated number of domestic electricity meters (2018): 109,668
  • Estimated number of domestic gas meters (2018): 89,407

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)

Harbour

43.0

8.6

Kirkley

40.0

8.0

Woodbridge

4

15.0

2.5

1.1

9.2

1.7

0.7

38.5

9.6

Felixstowe East

35.3

8.8

Pakefield

34.0

6.8

Wenhaston & Westleton

33.6

16.8

Aldeburgh

32.3

10.8

Whitton

31.4

6.3

Felixstowe South

29.6

7.4

Normanston

29.2

5.8

Beccles North

27.5

9.2

Southwold and Reydon

27.2

9.1

Kesgrave West

25.9

6.5

Grundisburgh

25.0

12.5

Kessingland

3

8.4

1.3

0.7

4.4

0.7

0.4

24.5

8.2

Carlton Colville

4

16.3

2.0

0.9

13.4

1.2

0.5

24.4

6.1

Felixstowe West

24.1

6.0

Oulton Broad

9

25.6

3.6

1.6

17.3

2.2

1.0

23.5

7.8

St Margaret's

23.5

4.7

Gunton and Corton

22.9

7.6

Halesworth

22.8

7.6

Bungay

22.8

7.6

The Trimleys

22.7

5.7

Saxmundham

2

6.4

1.1

0.5

3.2

0.6

0.3

21.3

7.1

Leiston

21.2

7.1

Melton

2

7.2

0.9

0.5

5.8

0.6

0.4

20.1

6.7

Beccles South

18.8

6.3

Carlton

18.7

6.2

Felixstowe North

18.4

6.1

Martlesham

18.3

6.1

Worlingham

17.4

5.8

Tower

16.4

5.5

Oulton

15.7

5.2

The Saints

14.6

14.6

Hacheston

14.2

14.2

Kirton

13.4

6.7

Wainford

13.1

13.1

Wrentham

12.9

12.9

Blything

12.7

12.7

Lothingland

3

10.0

0.7

0.5

9.3

0.4

0.4

12.4

6.2

Framlingham

3

10.8

1.1

0.9

7.3

0.6

0.8

12.0

6.0

Nacton & Purdis Farm

11.6

5.8

Rendlesham

10.7

5.3

Kesgrave East

9.5

2.4

Wickham Market

2

8.9

1.2

0.6

7.1

0.8

0.5

9.3

9.3

Fynn Valley

7.4

7.4

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.