Simulating a local emissions levy to fund local energy efficiency retrofit

Case study: Portsmouth

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: Portsmouth 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 Portsmouth 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 Portsmouth 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.

• CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
• 2018 residential gas & electricity use data
• Indices of Deprivation 2019
• Urban/Rural classification at LSOA level
• various LSOA/Ward boundary files and look-up tables from geoportal

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

## Filter: Portsmouth

## 
##     0     1 
## 32728   125

## Number of selected LSOAs - merged data:

## [1] 125

##    
##     Portsmouth
##   1        125

## Test linkage of Census 2021 data - suspect some LSOAs have changed

##    c2021_nhhs     c2021_pc_elec_ch_2021    IMDScore       pc_fuelPoor   
##  Min.   : 488.0   Min.   : 1.786        Min.   : 4.174   Min.   : 2.00  
##  1st Qu.: 618.0   1st Qu.: 4.348        1st Qu.:17.856   1st Qu.: 8.00  
##  Median : 667.0   Median : 6.775        Median :23.088   Median :11.00  
##  Mean   : 689.2   Mean   :10.728        Mean   :26.822   Mean   :10.64  
##  3rd Qu.: 755.0   3rd Qu.:14.391        3rd Qu.:32.665   3rd Qu.:13.00  
##  Max.   :1136.0   Max.   :60.132        Max.   :72.083   Max.   :23.00

## # 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 Portsmouth as a case study.

• Number of households (Census 2021): 86,152
• Number of electricity meters (2018): 90,855
• Number of gas meters (2018): 78,765

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, (Portsmouth) = 57314

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, (Portsmouth) = 90855

##    case_study mean_MSOA_totInc
## 1:          0          43912.2
## 2:          1          41016.8

## 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: Portsmouth

region	nLSOAs	mean_KgCo2ePerCap	sd_KgCo2ePerCap
South East	125	7,343.0	2,729.2

## Do we have any missing energy data?

Table 5.1: caption

LAD11NM	LSOA11CD	LSOA01NM	nElecMeters	nGasMeters	CREDS_total_kgco2e	CREDS_elec_kgco2e2018	CREDS_gas_kgco2e2018	CREDS_otherEnergy_kgco2e2011	CREDS_allHomeEnergy_kgco2e2011

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

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

First check the n electricity meters logic…

##           LSOA11NM                  WD18NM nGasMeters nElecMeters epc_total
## 1: Portsmouth 017D                  Milton        937        1153       895
## 2: Portsmouth 024B                 St Jude        936        1255       973
## 3: Portsmouth 014F                 Baffins        931         942       536
## 4: Portsmouth 023B Eastney and Craneswater        909         953       571
## 5: Portsmouth 022C                 St Jude        853        1000       870
## 6: Portsmouth 002A                  Cosham        850         914       635

##           LSOA11NM                  WD18NM nGasMeters nElecMeters epc_total
## 1: Portsmouth 024B                 St Jude        936        1255       973
## 2: Portsmouth 024A Eastney and Craneswater        702        1176       963
## 3: Portsmouth 017D                  Milton        937        1153       895
## 4: Portsmouth 024E               St Thomas        306        1012       784
## 5: Portsmouth 022C                 St Jude        853        1000       870
## 6: Portsmouth 004C              Paulsgrove        463         999       575

Table 5.2: LSOAs with highest number of gas meters (after cleaning)

LSOA11NM	WD18NM	nGasMeters	nElecMeters	epc_total
Portsmouth 017D	Milton	937	1,153	895
Portsmouth 024B	St Jude	936	1,255	973
Portsmouth 014F	Baffins	931	942	536
Portsmouth 023B	Eastney and Craneswater	909	953	571
Portsmouth 022C	St Jude	853	1,000	870
Portsmouth 002A	Cosham	850	914	635

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	125
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	17285.30	6564.15	6065.37	12696.72	17132.42	20232.04	45546.56	▅▇▂▁▁
CREDS_gas_kgco2e2018_pdw	0	1	1953.64	500.56	353.62	1736.43	1997.90	2265.41	3315.13	▁▂▇▅▁
CREDS_elec_kgco2e2018_pdw	0	1	934.83	139.62	705.45	866.73	916.18	992.76	1816.82	▇▅▁▁▁
CREDS_measuredHomeEnergy_kgco2e2018_pdw	0	1	2888.48	536.17	1524.54	2620.31	2903.72	3185.37	4563.92	▂▅▇▂▁
CREDS_otherEnergy_kgco2e2011_pdw	0	1	65.05	85.49	5.63	28.07	46.16	72.48	696.52	▇▁▁▁▁
CREDS_allHomeEnergy_kgco2e2018_pdw	0	1	2953.53	497.23	1574.89	2686.30	2958.28	3223.52	4593.58	▁▅▇▂▁
CREDS_car_kgco2e2018_pdw	0	1	1710.34	550.06	457.84	1376.08	1751.80	2027.31	3521.34	▂▆▇▂▁
CREDS_van_kgco2e2018_pdw	0	1	540.81	2878.29	43.32	144.88	203.43	295.79	32186.23	▇▁▁▁▁
CREDS_personalTransport_kgco2e2018_pdw	0	1	2251.15	2883.36	528.81	1602.54	1967.47	2312.95	33215.59	▇▁▁▁▁

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'

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 = -8.2065, df = 123, p-value = 2.586e-13
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6975637 -0.4681088
## sample estimates:
##        cor 
## -0.5948201

## Total emissions per dwelling (LSOA level) summary

##    LSOA11CD            WD18NM                       IMD_Decile_label All_Tco2e_per_dw
##  Length:125         Length:125         4                    :29      Min.   : 6.065  
##  Class :character   Class :character   5                    :21      1st Qu.:12.697  
##  Mode  :character   Mode  :character   1 (10% most deprived):15      Median :17.132  
##                                        2                    :15      Mean   :17.285  
##                                        3                    :14      3rd Qu.:20.232  
##                                        6                    :12      Max.   :45.547  
##                                        (Other)              :19

Table 5.4: Highest emitting LSOAs (per dwelling)

LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01017053	Cosham	2	45.5
E01017062	Drayton and Farlington	10 (10% least deprived)	36.2
E01017056	Cosham	8	31.3
E01017057	Cosham	8	30.2
E01017086	Hilsea	7	30.1
E01017072	Eastney and Craneswater	6	28.8

Table 5.4: Lowest emitting LSOAs (per dwelling)

LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01033381	Charles Dickens	4	6.1
E01017133	St Thomas	3	6.4
E01017032	Charles Dickens	1 (10% most deprived)	7.1
E01017077	Fratton	1 (10% most deprived)	7.8
E01017034	Charles Dickens	4	7.8
E01017035	Charles Dickens	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. 
##   353.6  1736.4  1997.9  1953.6  2265.4  3315.1

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

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 = -7.2718, df = 123, p-value = 3.613e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.6603430 -0.4124235
## sample estimates:
##        cor 
## -0.5483192

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'

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

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -4.6008, df = 123, p-value = 1.031e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5235574 -0.2225472
## sample estimates:
##        cor 
## -0.3831797

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'

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

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDS_elec_kgco2e2018_pdw
## t = -4.6008, df = 123, p-value = 1.031e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5235574 -0.2225472
## sample estimates:
##        cor 
## -0.3831797

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	1,953.6	934.8	65.1

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 = 12.762, df = 123, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6676956 0.8215174
## sample estimates:
##       cor 
## 0.7548009

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

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

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

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDS_total_kgco2e_pdw and selectedLsoasDT$CREDS_allHomeEnergy_kgco2e2018_pdw
## t = 12.719, df = 123, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6662992 0.8206979
## sample estimates:
##       cor 
## 0.7537165

## `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'

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 = -10.658, df = 123, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.7742914 -0.5889535
## sample estimates:
##        cor 
## -0.6928982

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	1,710.3	540.8

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'

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 = 0.91534, df = 123, p-value = 0.3618
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09472168  0.25418994
## sample estimates:
##        cor 
## 0.08225404

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’

• 2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

• September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

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
125	370.8	42.4	20.8

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

WD20NM	nLSOAs	All emissions	Gas	Electricity
Drayton and Farlington	8	33.8	3.6	1.5
Cosham	9	32.6	3.1	1.4
Copnor	9	30.8	3.1	1.3
Eastney and Craneswater	8	29.1	3.2	1.5
Baffins	9	28.9	3.2	1.5
Milton	9	28.3	3.0	1.4
Central Southsea	9	28.3	3.4	1.4
Hilsea	9	27.0	3.1	1.4
Fratton	9	24.9	3.1	1.4
St Thomas	9	23.2	2.7	1.7
Paulsgrove	9	23.1	2.6	1.7
Nelson	10	22.1	3.1	1.5
St Jude	7	21.8	2.9	1.5
Charles Dickens	11	16.9	2.3	1.7

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

WD20NM	nLSOAs	All emissions	Gas	Electricity
Drayton and Farlington	8	33.8	3.6	1.5
Cosham	9	32.6	3.1	1.4
Copnor	9	30.8	3.1	1.3
Eastney and Craneswater	8	29.1	3.2	1.5
Baffins	9	28.9	3.2	1.5
Milton	9	28.3	3.0	1.4
Central Southsea	9	28.3	3.4	1.4
Hilsea	9	27.0	3.1	1.4
Fratton	9	24.9	3.1	1.4
St Thomas	9	23.2	2.7	1.7
Paulsgrove	9	23.1	2.6	1.7
Nelson	10	22.1	3.1	1.5
St Jude	7	21.8	2.9	1.5
Charles Dickens	11	16.9	2.3	1.7

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
4,234.9	478.6	229.0	707.7

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'

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

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

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. 
##    1486    3111    4197    4235    4957   11159

Table 6.3: Highest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy	msoa_tot_annual_income_2018
E01017053	Portsmouth 002C	Cosham	45.5	11,158.9	36,800
E01017062	Portsmouth 026B	Drayton and Farlington	36.2	8,873.8	52,700
E01017056	Portsmouth 006B	Cosham	31.3	7,671.0	49,400
E01017057	Portsmouth 006C	Cosham	30.2	7,400.3	49,400
E01017086	Portsmouth 007A	Hilsea	30.1	7,364.8	40,900
E01017072	Portsmouth 025B	Eastney and Craneswater	28.8	7,068.1	46,600

Table 6.3: Lowest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy	msoa_tot_annual_income_2018
E01033381	Portsmouth 016G	Charles Dickens	6.1	1,486.0	24,900
E01017133	Portsmouth 018F	St Thomas	6.4	1,560.1	27,200
E01017032	Portsmouth 016B	Charles Dickens	7.1	1,745.9	24,900
E01017077	Portsmouth 015B	Fratton	7.8	1,907.7	37,800
E01017034	Portsmouth 016D	Charles Dickens	7.8	1,919.5	24,900
E01017035	Portsmouth 018A	Charles Dickens	8.0	1,949.6	27,200

Figure ?? repeats the analysis but just for gas.

Anything unusual?

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

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

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

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   86.64  425.43  489.48  478.64  555.02  812.21

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
E01017061	Portsmouth 003B	Drayton and Farlington	3.3	812.2	52,700
E01017072	Portsmouth 025B	Eastney and Craneswater	3.3	804.5	46,600
E01017063	Portsmouth 003C	Drayton and Farlington	3.0	738.8	52,700
E01017069	Portsmouth 023C	Eastney and Craneswater	2.9	719.0	49,300
E01017062	Portsmouth 026B	Drayton and Farlington	2.8	677.9	52,700
E01032604	Portsmouth 026D	Drayton and Farlington	2.7	655.5	52,700

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
E01017032	Portsmouth 016B	Charles Dickens	0.9	231.6	24,900
E01017035	Portsmouth 018A	Charles Dickens	0.9	226.7	27,200
E01017038	Portsmouth 013C	Charles Dickens	0.8	197.7	25,500
E01017114	Portsmouth 004C	Paulsgrove	0.7	176.2	44,500
E01033380	Portsmouth 024E	St Thomas	0.5	129.6	46,500
E01033381	Portsmouth 016G	Charles Dickens	0.4	86.6	24,900

Figure ?? repeats the analysis for electricity.

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

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

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

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   172.8   212.3   224.5   229.0   243.2   445.1

Table 6.5: Highest emitting LSOAs by £ GBP electricity levy

LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw	msoa_tot_annual_income_2018
E01017114	Portsmouth 004C	Paulsgrove	1.8	445.1	44,500
E01033380	Portsmouth 024E	St Thomas	1.4	339.2	46,500
E01017074	Portsmouth 023E	Eastney and Craneswater	1.3	323.1	49,300
E01017061	Portsmouth 003B	Drayton and Farlington	1.2	306.0	52,700
E01032604	Portsmouth 026D	Drayton and Farlington	1.2	294.0	52,700
E01033381	Portsmouth 016G	Charles Dickens	1.2	286.9	24,900

Table 6.5: Lowest emitting LSOAs by £ GBP electricity levy

LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw	msoa_tot_annual_income_2018
E01017040	Portsmouth 016F	Charles Dickens	0.7	181.1	24,900
E01017038	Portsmouth 013C	Charles Dickens	0.7	180.2	25,500
E01017035	Portsmouth 018A	Charles Dickens	0.7	179.1	27,200
E01017091	Portsmouth 007D	Hilsea	0.7	177.2	40,900
E01017132	Portsmouth 018E	St Thomas	0.7	174.0	27,200
E01017031	Portsmouth 016A	Charles Dickens	0.7	172.8	24,900

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

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

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'

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. 
##   373.5   642.0   711.4   707.7   780.4  1118.2

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% 
##  6.065372 12.696716 17.132420 20.232036 45.546559

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name	…[]
Number of rows	125
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	17.29	6.56	6.07	12.7	17.13	20.23	45.55	▅▇▂▁▁
beis_GBPtotal_sc2_perdw	0	1	3067.13	2007.88	739.98	1549.0	2626.18	3773.31	13063.74	▇▃▁▁▁
beis_GBPtotal_sc2	0	1	2094440.42	1144173.96	587844.80	1257978.6	1944245.61	2520568.28	6453485.42	▇▇▂▁▁

Gas cuts:

## Cuts for gas per dw

##        0%       25%       50%       75%      100% 
## 0.3536182 1.7364286 1.9978962 2.2654064 3.3151335

## [1] 25.93655

## Cuts for elec per dw

##        0%       10%       20%       30%       40%       50%       60%       70%       80% 
## 0.7054486 0.7894485 0.8410243 0.8737713 0.8893182 0.9161812 0.9362445 0.9795884 1.0168096 
##       90%      100% 
## 1.0500216 1.8168168

## [1] 13.15284

Table 6.7: Highest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01017053	Portsmouth 002C	Cosham	45.5	13,063.7
E01017062	Portsmouth 026B	Drayton and Farlington	36.2	9,640.7
E01017056	Portsmouth 006B	Cosham	31.3	7,839.0
E01017057	Portsmouth 006C	Cosham	30.2	7,433.6
E01017086	Portsmouth 007A	Hilsea	30.1	7,380.4
E01017072	Portsmouth 025B	Eastney and Craneswater	28.8	6,935.9

Table 6.7: Lowest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01033381	Portsmouth 016G	Charles Dickens	6.1	740.0
E01017133	Portsmouth 018F	St Thomas	6.4	776.9
E01017032	Portsmouth 016B	Charles Dickens	7.1	869.4
E01017077	Portsmouth 015B	Fratton	7.8	949.9
E01017034	Portsmouth 016D	Charles Dickens	7.8	955.8
E01017035	Portsmouth 018A	Charles Dickens	8.0	970.8

Table 6.7: Highest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_gasEmissions_levy
E01017061	Portsmouth 003B	Drayton and Farlington	3.3	759.3
E01017072	Portsmouth 025B	Eastney and Craneswater	3.3	747.8
E01017063	Portsmouth 003C	Drayton and Farlington	3.0	649.4
E01017069	Portsmouth 023C	Eastney and Craneswater	2.9	619.6
E01017062	Portsmouth 026B	Drayton and Farlington	2.8	558.1
E01032604	Portsmouth 026D	Drayton and Farlington	2.7	524.6

Table 6.7: Lowest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_gasEmissions_levy
E01033381	Portsmouth 016G	Charles Dickens	0.4	43.1
E01033380	Portsmouth 024E	St Thomas	0.5	64.5
E01017114	Portsmouth 004C	Paulsgrove	0.7	87.7
E01017038	Portsmouth 013C	Charles Dickens	0.8	98.4
E01017035	Portsmouth 018A	Charles Dickens	0.9	112.9
E01017032	Portsmouth 016B	Charles Dickens	0.9	115.3

Table 6.7: Highest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_elecEmissions_levy
E01017114	Portsmouth 004C	Paulsgrove	1.8	467.1
E01033380	Portsmouth 024E	St Thomas	1.4	308.3
E01017074	Portsmouth 023E	Eastney and Craneswater	1.3	284.3
E01017061	Portsmouth 003B	Drayton and Farlington	1.2	258.6
E01032604	Portsmouth 026D	Drayton and Farlington	1.2	240.7
E01033381	Portsmouth 016G	Charles Dickens	1.2	230.0

Table 6.7: Lowest levy LSOAs

LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_elecEmissions_levy
E01017031	Portsmouth 016A	Charles Dickens	0.7	86.1
E01017132	Portsmouth 018E	St Thomas	0.7	86.6
E01017091	Portsmouth 007D	Hilsea	0.7	88.3
E01017035	Portsmouth 018A	Charles Dickens	0.7	89.2
E01017038	Portsmouth 013C	Charles Dickens	0.7	89.7
E01017040	Portsmouth 016F	Charles Dickens	0.7	90.2

## `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
125	261.8	25.9	13.2

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

WD20NM	nLSOAs	All emissions	Gas	Electricity
Drayton and Farlington	8	29.8	2.8	1.1
Cosham	9	26.9	1.9	0.9
Copnor	9	26.3	2.2	0.9
Eastney and Craneswater	8	22.2	2.3	0.9
Baffins	9	20.8	1.9	0.9
Milton	9	20.1	1.7	0.8
Hilsea	9	19.8	2.0	0.9
Central Southsea	9	19.0	2.2	0.8
Paulsgrove	9	15.5	1.4	1.3
Fratton	9	15.2	1.7	0.8
St Thomas	9	13.5	1.5	1.2
Nelson	10	12.4	1.7	0.9
St Jude	7	11.9	1.6	0.9
Charles Dickens	11	8.4	1.1	0.9

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
687.4	297.2	145.2

6.3 Compare scenarios

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

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

nLSOAs	sum_total_sc1	sum_total_sc2
125	370.8	261.8

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.

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.   :10.55  
##  1st Qu.:31.41  
##  Median :37.84  
##  Mean   :38.01  
##  3rd Qu.:45.28  
##  Max.   :60.34

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. 
##   222.0   346.0   447.0   458.5   536.0   973.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   494.0   644.0   695.0   726.8   800.0  1255.0

## [1] 100

## [1] 100

variable	nDwellings	pc	i.nDwellings	i.pc
nDwellings_A	34.67	0.04	12,737.80	0.17
nDwellings_B	6,895.02	7.66	778,932.47	10.59
nDwellings_C	26,719.81	29.68	2,208,676.15	30.04
nDwellings_D	41,284.86	45.86	2,939,176.13	39.98
nDwellings_E	12,870.05	14.30	881,518.85	11.99
nDwellings_F	1,448.00	1.61	502,229.00	6.83
nDwellings_G	770.16	0.86	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] 720.2602

## Number of dwellings: 54155

## To retrofit F-G (£m)

## [1] 81.756

## Number of dwellings: 3051

## To retrofit D-G (£m)

## [1] 802.0162

## To retrofit D-G (mean per dwelling)

## [1] 13983

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

Mean total £m per LSOA	Total £m
6.4	802.0

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

Ward	Mean total £m per LSOA	Total £m
Central Southsea	7.8	70.2
Fratton	7.5	67.6
St Jude	9.3	65.3
Nelson	6.5	65.1
Eastney and Craneswater	7.6	61.0
Copnor	6.7	60.7
Baffins	6.5	58.9
Milton	6.5	58.4
Drayton and Farlington	7.0	56.1
Paulsgrove	5.9	53.0
Hilsea	5.7	51.1
St Thomas	5.7	50.9
Cosham	5.5	49.6
Charles Dickens	3.1	34.2

## `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'

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. 
##   1.245   2.824   3.326   3.828   4.512   9.306

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

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: 3.33

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. 
##   13.05   17.81   19.56   20.53   21.58   37.03

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

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

## Median years: 19.56

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

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.

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).

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
E01017123	Portsmouth 024B	St Jude	10.9	5.1	24.2	37.4	15.1	5.1	1.1
E01017127	Portsmouth 022C	St Jude	10.7	5.4	24.3	47.5	17.8	6.2	1.1
E01017122	Portsmouth 022A	St Jude	10.2	4.6	22.1	42.8	19.5	5.9	1.5
E01017068	Portsmouth 023B	Eastney and Craneswater	9.6	3.3	21.1	57.8	12.1	1.9	0.9
E01017114	Portsmouth 004C	Paulsgrove	9.5	2.8	26.3	29.6	15.7	12.3	0.7
E01017124	Portsmouth 020E	St Jude	9.1	3.5	20.0	48.3	19.4	6.5	1.7
E01017126	Portsmouth 025D	St Jude	9.1	4.1	21.2	45.8	20.1	5.2	1.1
E01017025	Portsmouth 019B	Central Southsea	9.0	3.6	20.0	63.3	13.8	1.8	1.2
E01017073	Portsmouth 024A	Eastney and Craneswater	8.6	6.6	31.0	28.8	16.4	4.4	0.5
E01017076	Portsmouth 015A	Fratton	8.5	4.1	21.1	62.0	16.2	1.6	1.2

9.2 Scenario 2

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.064   3.703   5.269   6.564   9.058  18.689

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

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

## Median years: 5.27

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

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.33   26.77   33.49   34.96   40.13   73.01

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

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.

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.

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).

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
E01017123	Portsmouth 024B	St Jude	10.9	10.3	47.5	37.4	15.1	5.1	1.1
E01017127	Portsmouth 022C	St Jude	10.7	10.8	48.9	47.5	17.8	6.2	1.1
E01017122	Portsmouth 022A	St Jude	10.2	9.1	39.6	42.8	19.5	5.9	1.5
E01017068	Portsmouth 023B	Eastney and Craneswater	9.6	5.3	39.7	57.8	12.1	1.9	0.9
E01017114	Portsmouth 004C	Paulsgrove	9.5	3.3	29.4	29.6	15.7	12.3	0.7
E01017124	Portsmouth 020E	St Jude	9.1	5.7	33.1	48.3	19.4	6.5	1.7
E01017126	Portsmouth 025D	St Jude	9.1	7.4	38.1	45.8	20.1	5.2	1.1
E01017025	Portsmouth 019B	Central Southsea	9.0	6.1	35.1	63.3	13.8	1.8	1.2
E01017073	Portsmouth 024A	Eastney and Craneswater	8.6	13.2	62.2	28.8	16.4	4.4	0.5
E01017076	Portsmouth 015A	Fratton	8.5	7.8	40.0	62.0	16.2	1.6	1.2

9.3 Compare scenarios

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

Figure 9.12: Comparing pay-back times across scenarios

## Saving 7 x 5 in image

10 Summary: Portsmouth

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

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

Scenario	nLSOAs	All emissions levy	Gas emissions levy	Electricity emissions levy
Scenario 1	125	370.8	42.4	20.8
Scenario 2	125	261.8	25.9	13.2

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

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

Total retrofit cost £m	Mean total retrofit cost £m per LSOA
802.0	6.4

As a reminder:

• Number of households (Census 2021): 86,152
• Estimated number of domestic electricity meters (2018): 90,855
• Estimated number of domestic gas meters (2018): 78,765

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)
Central Southsea	9	28.3	3.4	1.4	19.0	2.2	0.8	70.2	7.8
Fratton	9	24.9	3.1	1.4	15.2	1.7	0.8	67.6	7.5
St Jude	7	21.8	2.9	1.5	11.9	1.6	0.9	65.3	9.3
Nelson	10	22.1	3.1	1.5	12.4	1.7	0.9	65.1	6.5
Eastney and Craneswater	8	29.1	3.2	1.5	22.2	2.3	0.9	61.0	7.6
Copnor	9	30.8	3.1	1.3	26.3	2.2	0.9	60.7	6.7
Baffins	9	28.9	3.2	1.5	20.8	1.9	0.9	58.9	6.5
Milton	9	28.3	3.0	1.4	20.1	1.7	0.8	58.4	6.5
Drayton and Farlington	8	33.8	3.6	1.5	29.8	2.8	1.1	56.1	7.0
Paulsgrove	9	23.1	2.6	1.7	15.5	1.4	1.3	53.0	5.9
Hilsea	9	27.0	3.1	1.4	19.8	2.0	0.9	51.1	5.7
St Thomas	9	23.2	2.7	1.7	13.5	1.5	1.2	50.9	5.7
Cosham	9	32.6	3.1	1.4	26.9	1.9	0.9	49.6	5.5
Charles Dickens	11	16.9	2.3	1.7	8.4	1.1	0.9	34.2	3.1

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.