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: Southampton 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 Southampton 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 Southampton 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: Southampton
## 
##     0     1 
## 32705   148
## Number of selected LSOAs - merged data:
## [1] 148
##    
##     Southampton
##   1         148
## Test linkage of Census 2021 data - suspect some LSOAs have changed
##    c2021_nhhs     c2021_pc_elec_ch_2021    IMDScore       pc_fuelPoor   
##  Min.   : 418.0   Min.   : 1.202        Min.   : 5.749   Min.   : 4.00  
##  1st Qu.: 596.5   1st Qu.: 8.547        1st Qu.:16.532   1st Qu.: 8.00  
##  Median : 663.0   Median :15.887        Median :25.086   Median :10.00  
##  Mean   : 677.3   Mean   :18.919        Mean   :27.262   Mean   :10.91  
##  3rd Qu.: 735.2   3rd Qu.:26.803        3rd Qu.:36.081   3rd Qu.:12.00  
##  Max.   :1087.0   Max.   :84.966        Max.   :67.169   Max.   :25.00  
##  NA's   :4        NA's   :4
## # 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 Southampton as a case study.

  • Number of households (Census 2021): 97,527
  • Number of electricity meters (2018): 108,605
  • Number of gas meters (2018): 80,385

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, (Southampton) = 69044

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, (Southampton) = 108609
##    case_study mean_MSOA_totInc
## 1:          0         43912.44
## 2:          1         41414.19

## 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: Southampton

region

nLSOAs

mean_KgCo2ePerCap

sd_KgCo2ePerCap

South East

148

7,334.3

2,838.4

## 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: Southampton 005F Swaythling        881         976       719
## 2: Southampton 020D Freemantle        869        1139       881
## 3: Southampton 026A    Sholing        846         908       425
## 4: Southampton 021A Freemantle        750         914       555
## 5: Southampton 030C    Sholing        724         769       393
## 6: Southampton 019D  Millbrook        718         742       356
##            LSOA11NM     WD18NM nGasMeters nElecMeters epc_total
## 1: Southampton 031D   Woolston        560        1392      1140
## 2: Southampton 029C    Bargate        414        1379      1100
## 3: Southampton 023D    Bargate        342        1341      1080
## 4: Southampton 029G    Bargate        407        1258      1130
## 5: Southampton 020D Freemantle        869        1139       881
## 6: Southampton 029A    Bargate        586        1105       794
Table 5.2: LSOAs with highest number of gas meters (after cleaning)

LSOA11NM

WD18NM

nGasMeters

nElecMeters

epc_total

Southampton 005F

Swaythling

881

976

719

Southampton 020D

Freemantle

869

1,139

881

Southampton 026A

Sholing

846

908

425

Southampton 021A

Freemantle

750

914

555

Southampton 030C

Sholing

724

769

393

Southampton 019D

Millbrook

718

742

356

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 148
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 17126.66 7014.73 5845.64 11905.74 15464.94 21050.67 44958.42 ▆▇▃▁▁
CREDS_gas_kgco2e2018_pdw 0 1 1758.06 626.82 12.82 1358.01 1727.03 2166.35 3659.41 ▁▅▇▃▁
CREDS_elec_kgco2e2018_pdw 0 1 1004.41 109.16 655.50 938.64 988.98 1044.12 1459.95 ▁▆▇▂▁
CREDS_measuredHomeEnergy_kgco2e2018_pdw 0 1 2762.47 641.02 1123.58 2322.68 2744.44 3169.45 4876.37 ▁▆▇▂▁
CREDS_otherEnergy_kgco2e2011_pdw 0 1 117.63 166.94 0.00 40.87 64.42 112.06 1151.73 ▇▁▁▁▁
CREDS_allHomeEnergy_kgco2e2018_pdw 0 1 2880.09 584.12 1506.15 2476.45 2813.93 3235.29 5031.52 ▂▇▅▂▁
CREDS_car_kgco2e2018_pdw 0 1 1848.64 566.47 613.65 1413.59 1851.17 2258.37 3546.98 ▃▆▇▃▁
CREDS_van_kgco2e2018_pdw 0 1 266.07 335.98 22.61 130.24 194.83 265.58 2801.60 ▇▁▁▁▁
CREDS_personalTransport_kgco2e2018_pdw 0 1 2114.71 684.48 760.81 1607.59 2131.50 2558.57 4223.23 ▅▇▇▂▁

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 = -9.9011, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7213850 -0.5262861
## sample estimates:
##        cor 
## -0.6338111
## Total emissions per dwelling (LSOA level) summary
##    LSOA11CD            WD18NM                       IMD_Decile_label All_Tco2e_per_dw
##  Length:148         Length:148         4                    :26      Min.   : 5.846  
##  Class :character   Class :character   2                    :24      1st Qu.:11.906  
##  Mode  :character   Mode  :character   3                    :24      Median :15.465  
##                                        1 (10% most deprived):19      Mean   :17.127  
##                                        5                    :15      3rd Qu.:21.051  
##                                        6                    :14      Max.   :44.958  
##                                        (Other)              :26
Table 5.4: Highest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01017249

Shirley

8

45.0

E01017148

Bassett

8

43.5

E01017197

Freemantle

7

41.4

E01017224

Peartree

6

31.2

E01017180

Coxford

8

30.7

E01017214

Millbrook

6

30.2

Table 5.4: Lowest emitting LSOAs (per dwelling)

LSOA11CD

WD18NM

IMD_Decile_label

All_Tco2e_per_dw

E01017140

Bargate

3

5.8

E01017139

Bargate

2

7.0

E01017182

Coxford

2

7.3

E01032738

Bevois

3

7.9

E01017241

Redbridge

1 (10% most deprived)

7.9

E01017245

Redbridge

1 (10% most deprived)

8.0

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. 
##   12.82 1358.01 1727.03 1758.06 2166.35 3659.41
## `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 = -7.7513, df = 146, p-value = 1.421e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6450947 -0.4147371
## sample estimates:
##      cor 
## -0.53995

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 = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32744768 -0.01443342
## sample estimates:
##        cor 
## -0.1753689

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 = -2.1523, df = 146, p-value = 0.03301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.32744768 -0.01443342
## sample estimates:
##        cor 
## -0.1753689
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 city and town

1,758.1

1,004.4

117.6

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 = 17.213, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7571077 0.8655189
## sample estimates:
##       cor 
## 0.8184714
## `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 = 16.017, df = 146, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.7311467 0.8501570
## sample estimates:
##       cor 
## 0.7983163
## `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 = -5.833, df = 146, p-value = 3.37e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5570107 -0.2940157
## sample estimates:
##        cor 
## -0.4347367
Table 5.6: Summary of per dwelling transport emissions for LSOAs by urban/rural code

RUC11

mean_car_kgco2e

mean_van_kgco2e

Urban city and town

1,848.6

266.1

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 = 0.59071, df = 146, p-value = 0.5556
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1134079  0.2085304
## sample estimates:
##        cor 
## 0.04882944

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

148

435.3

44.8

26.7

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Shirley

9

33.6

3.4

1.5

Freemantle

9

32.7

3.1

1.9

Bassett

9

31.9

3.4

1.5

Bitterne Park

9

31.2

3.2

1.7

Millbrook

10

30.3

3.0

1.6

Peartree

9

28.5

3.1

1.6

Bargate

11

28.2

2.1

2.8

Sholing

9

27.7

3.0

1.5

Portswood

9

27.4

2.9

1.6

Woolston

9

27.2

2.5

1.6

Coxford

9

25.4

2.3

1.6

Harefield

9

24.3

2.9

1.6

Bevois

10

24.2

3.0

1.7

Redbridge

10

23.1

2.5

1.7

Bitterne

9

22.2

2.4

1.6

Swaythling

8

17.7

2.1

1.2

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Shirley

9

33.6

3.4

1.5

Freemantle

9

32.7

3.1

1.9

Bassett

9

31.9

3.4

1.5

Bitterne Park

9

31.2

3.2

1.7

Millbrook

10

30.3

3.0

1.6

Peartree

9

28.5

3.1

1.6

Bargate

11

28.2

2.1

2.8

Sholing

9

27.7

3.0

1.5

Portswood

9

27.4

2.9

1.6

Woolston

9

27.2

2.5

1.6

Coxford

9

25.4

2.3

1.6

Harefield

9

24.3

2.9

1.6

Bevois

10

24.2

3.0

1.7

Redbridge

10

23.1

2.5

1.7

Bitterne

9

22.2

2.4

1.6

Swaythling

8

17.7

2.1

1.2

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

4,196.0

430.7

246.1

676.8

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. 
##    1432    2917    3789    4196    5157   11015
Table 6.3: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01017249

Southampton 011C

Shirley

45.0

11,014.8

58,400

E01017148

Southampton 001D

Bassett

43.5

10,668.3

46,900

E01017197

Southampton 020E

Freemantle

41.4

10,150.1

51,500

E01017224

Southampton 024C

Peartree

31.2

7,650.4

49,000

E01017180

Southampton 002A

Coxford

30.7

7,522.4

47,300

E01017214

Southampton 019C

Millbrook

30.2

7,390.1

41,700

Table 6.3: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

msoa_tot_annual_income_2018

E01017140

Southampton 023D

Bargate

5.8

1,432.2

34,100

E01017139

Southampton 029A

Bargate

7.0

1,718.8

40,800

E01017182

Southampton 004A

Coxford

7.3

1,799.4

33,900

E01032738

Southampton 022F

Bevois

7.9

1,928.3

31,200

E01017241

Southampton 007B

Redbridge

7.9

1,928.6

38,600

E01017245

Southampton 012E

Redbridge

8.0

1,952.1

33,800

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. 
##    3.14  332.71  423.12  430.72  530.76  896.55
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

E01017249

Southampton 011C

Shirley

3.7

896.6

58,400

E01017148

Southampton 001D

Bassett

3.6

890.2

46,900

E01017197

Southampton 020E

Freemantle

3.0

734.5

51,500

E01032753

Southampton 009F

Portswood

2.9

721.7

51,300

E01017252

Southampton 011D

Shirley

2.9

716.4

58,400

E01017145

Southampton 001B

Bassett

2.9

711.4

46,900

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

E01017142

Southampton 029C

Bargate

0.7

171.4

40,800

E01032748

Southampton 029G

Bargate

0.7

160.0

40,800

E01017140

Southampton 023D

Bargate

0.6

143.9

34,100

E01017281

Southampton 032D

Woolston

0.3

81.6

31,400

E01032755

Southampton 029I

Bargate

0.2

59.0

40,800

E01032746

Southampton 029F

Bargate

0.0

3.1

40,800

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. 
##   160.6   230.0   242.3   246.1   255.8   357.7
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

E01032746

Southampton 029F

Bargate

1.5

357.7

40,800

E01017202

Southampton 016C

Harefield

1.3

318.4

38,700

E01017270

Southampton 003C

Swaythling

1.3

314.8

29,600

E01017170

Southampton 027D

Bitterne

1.3

310.1

34,800

E01032748

Southampton 029G

Bargate

1.3

310.0

40,800

E01017142

Southampton 029C

Bargate

1.3

309.2

40,800

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

E01017138

Southampton 023C

Bargate

0.9

212.1

34,100

E01017160

Southampton 017D

Bevois

0.8

198.3

41,900

E01017281

Southampton 032D

Woolston

0.8

193.7

31,400

E01017250

Southampton 010B

Shirley

0.8

193.3

40,200

E01017196

Southampton 020D

Freemantle

0.8

191.4

51,500

E01017278

Southampton 031D

Woolston

0.7

160.6

49,400

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. 
##   275.3   569.1   672.4   676.8   776.5  1194.7

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% 
##  5.845638 11.905745 15.464936 21.050667 44.958416
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name …[]
Number of rows 148
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 17.13 7.01 5.85 11.91 15.46 21.05 44.96 ▆▇▃▁▁
beis_GBPtotal_sc2_perdw 0 1 3221.47 2302.13 713.17 1452.80 2329.22 4374.47 13148.61 ▇▃▂▁▁
beis_GBPtotal_sc2 0 1 2184016.07 1266099.70 650296.60 1113364.33 1881431.98 2831522.94 6640047.94 ▇▅▂▁▁

Gas cuts:

## Cuts for gas per dw
##        0%       25%       50%       75%      100% 
## 0.0128181 1.3580128 1.7270291 2.1663532 3.6594059
## [1] 31.10013
## Cuts for elec per dw
##        0%       10%       20%       30%       40%       50%       60%       70%       80% 
## 0.6554957 0.8912341 0.9254283 0.9517786 0.9721885 0.9889768 1.0163918 1.0329692 1.0666977 
##       90%      100% 
## 1.1367568 1.4599524
## [1] 16.02575
Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01017249

Southampton 011C

Shirley

45.0

13,148.6

E01017148

Southampton 001D

Bassett

43.5

12,629.6

E01017197

Southampton 020E

Freemantle

41.4

11,853.3

E01017224

Southampton 024C

Peartree

31.2

8,108.8

E01017180

Southampton 002A

Coxford

30.7

7,917.2

E01017214

Southampton 019C

Millbrook

30.2

7,718.9

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_allEmissions_levy

E01017140

Southampton 023D

Bargate

5.8

713.2

E01017139

Southampton 029A

Bargate

7.0

855.9

E01017182

Southampton 004A

Coxford

7.3

896.0

E01032738

Southampton 022F

Bevois

7.9

960.2

E01017241

Southampton 007B

Redbridge

7.9

960.4

E01017245

Southampton 012E

Redbridge

8.0

972.0

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01017249

Southampton 011C

Shirley

3.7

965.3

E01017148

Southampton 001D

Bassett

3.6

955.8

E01017197

Southampton 020E

Freemantle

3.0

722.6

E01032753

Southampton 009F

Portswood

2.9

703.4

E01017252

Southampton 011D

Shirley

2.9

695.5

E01017145

Southampton 001B

Bassett

2.9

687.9

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_gasEmissions_levy

E01032746

Southampton 029F

Bargate

0.0

1.6

E01032755

Southampton 029I

Bargate

0.2

29.4

E01017281

Southampton 032D

Woolston

0.3

40.6

E01017140

Southampton 023D

Bargate

0.6

71.7

E01032748

Southampton 029G

Bargate

0.7

79.7

E01017142

Southampton 029C

Bargate

0.7

85.3

Table 6.7: Highest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01032746

Southampton 029F

Bargate

1.5

313.3

E01017202

Southampton 016C

Harefield

1.3

254.4

E01017270

Southampton 003C

Swaythling

1.3

249.0

E01017170

Southampton 027D

Bitterne

1.3

242.0

E01032748

Southampton 029G

Bargate

1.3

241.8

E01017142

Southampton 029C

Bargate

1.3

240.7

Table 6.7: Lowest levy LSOAs

LSOA11CD

LSOA01NM

WD18NM

T_CO2e_pdw

GBP_elecEmissions_levy

E01017278

Southampton 031D

Woolston

0.7

80.0

E01017196

Southampton 020D

Freemantle

0.8

95.3

E01017250

Southampton 010B

Shirley

0.8

96.3

E01017281

Southampton 032D

Woolston

0.8

96.4

E01017160

Southampton 017D

Bevois

0.8

98.7

E01017138

Southampton 023C

Bargate

0.9

105.6

## `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

148

323.2

31.1

16.0

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

WD20NM

nLSOAs

All emissions

Gas

Electricity

Shirley

9

29.9

2.8

0.9

Bassett

9

29.0

3.0

0.9

Bitterne Park

9

25.3

2.3

1.0

Millbrook

10

24.8

2.1

0.9

Freemantle

9

23.6

1.9

1.1

Peartree

9

23.0

2.3

1.0

Sholing

9

20.9

2.1

0.9

Portswood

9

19.8

2.1

0.9

Woolston

9

18.8

1.5

0.9

Coxford

9

18.6

1.4

1.0

Harefield

9

17.3

2.0

0.9

Bargate

11

15.7

1.2

1.8

Bevois

10

15.6

2.1

1.0

Redbridge

10

15.0

1.5

0.9

Bitterne

9

14.2

1.4

1.0

Swaythling

8

11.8

1.5

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

551.6

307.2

147.9

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

148

435.3

323.2

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.   :-7.926  
##  1st Qu.:29.659  
##  Median :40.557  
##  Mean   :37.655  
##  3rd Qu.:46.984  
##  Max.   :58.592

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. 
##   211.0   341.8   409.0   466.5   548.2  1140.0
## N elec meters
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   430.0   631.5   695.5   733.8   800.8  1392.0
## [1] 100
## [1] 100

variable

nDwellings

pc

i.nDwellings

i.pc

nDwellings_A

123.76

0.12

12,737.80

0.17

nDwellings_B

12,081.33

11.29

778,932.47

10.59

nDwellings_C

33,672.06

31.47

2,208,676.15

30.04

nDwellings_D

41,797.24

39.06

2,939,176.13

39.98

nDwellings_E

15,469.05

14.46

881,518.85

11.99

nDwellings_F

2,763.00

2.58

502,229.00

6.83

nDwellings_G

1,105.28

1.03

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] 761.6416
## Number of dwellings: 57266
## To retrofit F-G (£m)
## [1] 146.4769
## Number of dwellings: 5466
## To retrofit D-G (£m)
## [1] 908.1185
## To retrofit D-G (mean per dwelling)
## [1] 14417.74
Table 7.1: Retrofit cost totals (£m GBP):

Mean total £m per LSOA

Total £m

6.1

908.1

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

Ward

Mean total £m per LSOA

Total £m

Freemantle

7.1

63.9

Sholing

6.9

62.0

Shirley

6.9

61.9

Millbrook

6.1

61.0

Bitterne

6.7

60.6

Peartree

6.7

60.2

Bevois

5.9

58.9

Bitterne Park

6.5

58.7

Harefield

6.5

58.2

Coxford

6.3

57.0

Portswood

6.0

53.8

Woolston

5.9

53.0

Bassett

5.8

52.6

Redbridge

5.1

51.4

Bargate

4.7

51.3

Swaythling

5.5

43.6

## `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. 
##   1.301   2.707   3.745   4.032   4.977  10.307
## `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: 3.74

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. 
##   11.69   18.00   21.43   22.74   25.60   57.64
## `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: 21.43

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

E01017154

Southampton 022B

Bevois

14.2

6.6

37.9

15.1

28.1

19.1

12.2

E01017202

Southampton 016C

Harefield

11.1

7.4

31.7

27.8

24.1

21.2

6.3

E01017192

Southampton 021A

Freemantle

10.2

3.8

22.1

47.7

18.2

7.2

1.6

E01017185

Southampton 002D

Coxford

9.7

4.7

22.9

41.9

21.6

13.7

1.8

E01017260

Southampton 026D

Sholing

9.7

5.5

24.7

40.7

22.9

17.8

4.0

E01032753

Southampton 009F

Portswood

9.4

2.8

14.5

45.1

22.4

6.0

0.6

E01017219

Southampton 028B

Peartree

8.7

7.0

27.0

33.6

12.9

9.7

1.4

E01017256

Southampton 026A

Sholing

8.6

3.3

16.8

51.8

17.2

0.7

0.5

E01017151

Southampton 006C

Bassett

8.5

4.2

21.8

49.0

24.1

5.6

0.0

E01017257

Southampton 026B

Sholing

8.4

5.6

27.1

40.1

14.1

10.3

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. 
##   1.090   3.216   6.080   6.824   9.741  20.699
## `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: 6.08

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.74   25.14   35.65   37.27   47.06  107.89
## `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

E01017154

Southampton 022B

Bevois

14.2

13.3

69.2

15.1

28.1

19.1

12.2

E01017202

Southampton 016C

Harefield

11.1

14.8

47.5

27.8

24.1

21.2

6.3

E01017192

Southampton 021A

Freemantle

10.2

6.1

36.6

47.7

18.2

7.2

1.6

E01017185

Southampton 002D

Coxford

9.7

8.1

35.9

41.9

21.6

13.7

1.8

E01017260

Southampton 026D

Sholing

9.7

10.6

40.4

40.7

22.9

17.8

4.0

E01032753

Southampton 009F

Portswood

9.4

3.3

16.2

45.1

22.4

6.0

0.6

E01017219

Southampton 028B

Peartree

8.7

14.0

47.0

33.6

12.9

9.7

1.4

E01017256

Southampton 026A

Sholing

8.6

4.8

22.2

51.8

17.2

0.7

0.5

E01017151

Southampton 006C

Bassett

8.5

7.3

37.7

49.0

24.1

5.6

0.0

E01017257

Southampton 026B

Sholing

8.4

11.2

50.6

40.1

14.1

10.3

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: Southampton

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

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

Scenario

nLSOAs

All emissions levy

Gas emissions levy

Electricity emissions levy

Scenario 1

148

435.3

44.8

26.7

Scenario 2

148

323.2

31.1

16.0

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

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

Total retrofit cost £m

Mean total retrofit cost £m per LSOA

908.1

6.1

As a reminder:

  • Number of households (Census 2021): 97,527
  • Estimated number of domestic electricity meters (2018): 108,605
  • Estimated number of domestic gas meters (2018): 80,385

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)

Freemantle

9

32.7

3.1

1.9

23.6

1.9

1.1

63.9

7.1

Sholing

9

27.7

3.0

1.5

20.9

2.1

0.9

62.0

6.9

Shirley

9

33.6

3.4

1.5

29.9

2.8

0.9

61.9

6.9

Millbrook

10

30.3

3.0

1.6

24.8

2.1

0.9

61.0

6.1

Bitterne

9

22.2

2.4

1.6

14.2

1.4

1.0

60.6

6.7

Peartree

9

28.5

3.1

1.6

23.0

2.3

1.0

60.2

6.7

Bevois

10

24.2

3.0

1.7

15.6

2.1

1.0

58.9

5.9

Bitterne Park

9

31.2

3.2

1.7

25.3

2.3

1.0

58.7

6.5

Harefield

9

24.3

2.9

1.6

17.3

2.0

0.9

58.2

6.5

Coxford

9

25.4

2.3

1.6

18.6

1.4

1.0

57.0

6.3

Portswood

9

27.4

2.9

1.6

19.8

2.1

0.9

53.8

6.0

Woolston

9

27.2

2.5

1.6

18.8

1.5

0.9

53.0

5.9

Bassett

9

31.9

3.4

1.5

29.0

3.0

0.9

52.6

5.8

Redbridge

10

23.1

2.5

1.7

15.0

1.5

0.9

51.4

5.1

Bargate

11

28.2

2.1

2.8

15.7

1.2

1.8

51.3

4.7

Swaythling

8

17.7

2.1

1.2

11.8

1.5

0.7

43.6

5.5

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.