1 About

Paper prepared for the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

2 Citation

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at International Conference on Evolving Cities, Southampton, United Kingdom

3 Introduction

Background blurb about emissions, retofit, carbon tax/levy etc

4 Emissions Levy Case Study - Southampton

In the reminder of this paper we develop a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. We apply 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. We then sum these values to given an overall levy revenue estimate for the area in the case study.

We 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.

We then compare the distributions of the two to understand whether sufficient revenue would be generated within each LSOA or in the case styudy area to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so we also analyse the extent to which redistribution of revenue from high emissions areas (households) would be required. It should be noted that this is area level analysis using mean emissions per household. It will not capture within-LSOA hetergeneity in emissions and so will almost certaonly underestimate the range of the household level emissions levy value.

4.1 Data and boundary files

We will use a number of datasets to analyse the patterns of emissions. Some of these are in the repo as they are public access, others are not (or too large).

This analysis is at LSOA level.

4.1.1 Useful LSOA labels and codes

Load lSOA look-up table

## Loading LSOA look-up table with useful labels

4.1.2 Boundaries

LSOA - this is all going to be LSOA analysis

## Loading Solent LSOA boundaries from file
## Rows of data: 1136
## Selecting Southampton
## Rows of data: 148

Check with a map…

## Boundary data co-ord system: 27700

Figure 4.1: LSOA check map (shows LSOA, MSOA and ward names when clicked

4.1.3 IMD 2019

Labeled as 2019 but actually 2018 data. Source: https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019

## Overall IMD decile counts
## [1] 32844
## 
##   1 (10% most deprived)                       2                       3 
##                    3284                    3284                    3285 
##                       4                       5                       6 
##                    3284                    3285                    3284 
##                       7                       8                       9 
##                    3284                    3285                    3284 
## 10 (10% least deprived) 
##                    3285
## # Southampton IMD decile counts
## [1] 148
## 
##   1 (10% most deprived)                       2                       3 
##                      19                      24                      24 
##                       4                       5                       6 
##                      26                      15                      14 
##                       7                       8                       9 
##                       7                      14                       4 
## 10 (10% least deprived) 
##                       1
## 
##   1 (10% most deprived)                       2                       3 
##             0.128378378             0.162162162             0.162162162 
##                       4                       5                       6 
##             0.175675676             0.101351351             0.094594595 
##                       7                       8                       9 
##             0.047297297             0.094594595             0.027027027 
## 10 (10% least deprived) 
##             0.006756757
## 
## 50% least deprived  50% most deprived 
##                 40                108
## 
## 50% least deprived  50% most deprived 
##          0.2702703          0.7297297

These are LSOA level deprivation indices. Decile is the English & Welsh decile:

  • 1 = 10% most deprived LSOAs in England & Wales;
  • 10 = 10% least deprived LSOA in England & Wales.

Figure 4.2: LSOA IMD map (shows LSOA, MSOA, ward names and IMD decile when clicked

4.1.4 Fuel poverty

2019 estimates - do we actually use this data?

Source: https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2021

4.2 CREDS place-based emmissions 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
## [1] 32844
Table 4.1: Data summary
Name credsLsoaDT
Number of rows 148
Number of columns 29
Key LSOA11CD
_______________________
Column type frequency:
character 7
factor 1
numeric 21
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
LAD11NM 0 1 11 11 0 1 0
WD18NM 0 1 6 13 0 16 0
LSOA11CD 0 1 9 9 0 148 0
LSOA11NM 0 1 16 16 0 148 0
WD20CD 0 1 9 9 0 16 0
RUC11 0 1 19 19 0 1 0
oacSuperGroupName 0 1 15 35 0 7 0

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
IMD_Decile_label 0 1 FALSE 10 4: 26, 2: 24, 3: 24, 1 (: 19

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
CREDStotal_kgco2e 0 1 12005970.95 3950315.01 5330300.00 8440200.00 12166000.00 14846325.00 22704000.00 ▇▆▇▃▁
CREDSgas_kgco2e2018 0 1 1234937.68 382064.37 10767.20 1017372.50 1233750.00 1459772.50 2586400.00 ▁▃▇▂▁
CREDSelec_kgco2e2018 0 1 735265.41 195095.99 418140.00 614700.00 694160.00 825945.00 1740510.00 ▇▇▁▁▁
CREDSotherEnergy_kgco2e2011 0 1 90723.71 153254.16 0.00 27785.00 47460.50 80246.00 1279200.00 ▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2011 0 1 2151650.51 473661.33 1185534.00 1866010.00 2114738.00 2380087.50 4765800.00 ▅▇▂▁▁
CREDScar_kgco2e2018 0 1 1310046.55 355771.51 333540.00 1094005.00 1357330.00 1542900.00 2099440.00 ▁▅▇▇▂
CREDSvan_kgco2e2018 0 1 199990.55 291846.23 11328.00 94336.75 140101.00 197145.00 2807200.00 ▇▁▁▁▁
pop_2018 0 1 1707.84 412.64 1080.00 1460.00 1620.00 1762.50 3900.00 ▇▅▁▁▁
energy_pc 0 1 18.47 5.29 9.50 14.69 17.28 21.88 44.48 ▇▇▂▁▁
pc_Heating_Electric 0 1 18.63 13.32 2.49 8.75 15.44 24.71 85.27 ▇▅▁▁▁
epc_total 0 1 466.49 184.08 211.00 341.75 409.00 548.25 1140.00 ▇▆▂▁▁
epc_newbuild 0 1 82.12 101.68 11.00 33.00 52.00 83.25 798.00 ▇▁▁▁▁
epc_A 0 1 0.55 2.10 0.00 0.00 0.00 0.00 13.00 ▇▁▁▁▁
epc_B 0 1 57.51 85.92 0.00 9.75 27.50 69.25 606.00 ▇▁▁▁▁
epc_C 0 1 147.58 86.28 39.00 87.00 122.50 184.25 492.00 ▇▅▁▁▁
epc_D 0 1 172.40 41.86 37.00 144.25 168.50 197.00 322.00 ▁▅▇▂▁
epc_E 0 1 64.81 27.76 17.00 45.00 62.50 76.25 220.00 ▇▇▂▁▁
epc_F 0 1 18.67 20.27 1.00 7.00 12.50 22.00 150.00 ▇▁▁▁▁
epc_G 0 1 4.99 9.73 0.00 1.00 2.50 5.00 96.00 ▇▁▁▁▁
IMD_Decile 0 1 4.11 2.31 1.00 2.00 4.00 6.00 10.00 ▇▇▅▃▁
IMDScore 0 1 27.26 13.62 5.75 16.53 25.09 36.08 67.17 ▆▇▅▂▁ 
## 
## Southampton 
##         148

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…

## LSOAs (check):
## [1] 148

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.

That assumption seems sensible…

4.2.1 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 4.2: 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
CREDStotal_kgco2e_pdw 0 1 17126.66 7014.73 5845.64 11905.74 15464.94 21050.67 44958.42 ▆▇▃▁▁
CREDSgas_kgco2e2018_pdw 0 1 1758.06 626.82 12.82 1358.01 1727.03 2166.35 3659.41 ▁▅▇▃▁
CREDSelec_kgco2e2018_pdw 0 1 1004.41 109.16 655.50 938.64 988.98 1044.12 1459.95 ▁▆▇▂▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw 0 1 2762.47 641.02 1123.58 2322.68 2744.44 3169.45 4876.37 ▁▆▇▂▁
CREDSotherEnergy_kgco2e2011_pdw 0 1 117.63 166.94 0.00 40.87 64.42 112.06 1151.73 ▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw 0 1 2880.09 584.12 1506.15 2476.45 2813.93 3235.29 5031.52 ▂▇▅▂▁
CREDScar_kgco2e2018_pdw 0 1 1848.64 566.47 613.65 1413.59 1851.17 2258.37 3546.98 ▃▆▇▃▁
CREDSvan_kgco2e2018_pdw 0 1 266.07 335.98 22.61 130.24 194.83 265.58 2801.60 ▇▁▁▁▁
CREDSpersonalTransport_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.

4.2.1.1 All emissions

Figure 4.3 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 per dwelling emissions against IMD score

Figure 4.3: Scatter of LSOA level all per dwelling emissions against IMD score 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDStotal_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
##    LSOA11CD            WD18NM          All_Tco2e_per_dw
##  Length:148         Length:148         Min.   : 5.846  
##  Class :character   Class :character   1st Qu.:11.906  
##  Mode  :character   Mode  :character   Median :15.465  
##                                        Mean   :17.127  
##                                        3rd Qu.:21.051  
##                                        Max.   :44.958
##     LSOA11CD     WD18NM All_Tco2e_per_dw
## 1: E01017249    Shirley         44.95842
## 2: E01017148    Bassett         43.54419
## 3: E01017197 Freemantle         41.42910
## 4: E01017224   Peartree         31.22609
## 5: E01017180    Coxford         30.70376
## 6: E01017214  Millbrook         30.16370
##     LSOA11CD    WD18NM All_Tco2e_per_dw
## 1: E01017245 Redbridge         7.967564
## 2: E01017241 Redbridge         7.871967
## 3: E01032738    Bevois         7.870684
## 4: E01017182   Coxford         7.344557
## 5: E01017139   Bargate         7.015385
## 6: E01017140   Bargate         5.845638

Figure 4.4: Annual mean T CO2e per dwelling for all SouthamptonLSOAs

4.2.1.2 Home energy use

Figure 4.5 uses the same plotting method to show emissions per dwelling due to gas use. This preserves the negative correlation shown in the previou splot for ‘all emissions’ but with some variation, notably for LSOAs which have a higher % ofelectric heating.

## 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 4.5: Scatter of LSOA level gas per dwelling emissions against IMD score 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSgas_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 4.6 uses the same plotting method to show emissions per dwelling due to electricity use. This is mnuch more random… although note the LSOAs with higher % electric heating.

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

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_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 4.7 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 4.7: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits? 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation test (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSelec_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
##                  RUC11 mean_gas_kgco2e mean_elec_kgco2e
## 1: Urban city and town        1758.058         1004.407
##    mean_other_energy_kgco2e
## 1:                 117.6261

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

## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSmeasuredHomeEnergy_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'

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

Strong correlkation. 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:  credsLsoaDT$CREDStotal_kgco2e_pdw and credsLsoaDT$CREDSallHomeEnergy_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'

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

Slightly weaker correlation…

4.2.1.3 Transport

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

Figure 4.8 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 4.8: Scatter of LSOA level car use per dwelling emissions against IMD score 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDScar_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
##                  RUC11 mean_car_kgco2e mean_van_kgco2e
## 1: Urban city and town        1848.645        266.0683

Figure 4.9 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 4.9: Scatter of LSOA level van use per dwelling emissions against IMD score 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Correlation with IMD score (pwcorr)
## 
##  Pearson's product-moment correlation
## 
## data:  credsLsoaDT$IMDScore and credsLsoaDT$CREDSvan_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

4.2.2 Impute EPC counts

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. 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…

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

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'

4.2.3 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!)

4.2.3.1 Scenario 1: central cost

The table below shows the overall £ GBP total for the case study area in £M.

##    beis_GBPtotal_c beis_total_c_gas beis_GBPtotal_c_elec
## 1:          435.34            44.78                26.66

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

##    beis_GBPtotal_c_perdw beis_GBPtotal_c_gas_perdw beis_GBPtotal_c_elec_perdw
## 1:                  4200                       430                        250
##    beis_GBPtotal_c_energy_perdw
## 1:                          680

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 4.10: £k per LSOA revenue using BEIS central carbon price 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA revenue using BEIS central carbon price

Figure 4.11: £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
##     LSOA11CD     WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017249    Shirley              44958.42             11014.812
## 2: E01017148    Bassett              43544.19             10668.326
## 3: E01017197 Freemantle              41429.10             10150.129
## 4: E01017224   Peartree              31226.09              7650.391
## 5: E01017180    Coxford              30703.76              7522.422
## 6: E01017214  Millbrook              30163.70              7390.107
##     LSOA11CD    WD18NM CREDStotal_kgco2e_pdw beis_GBPtotal_c_perdw
## 1: E01017245 Redbridge              7967.564              1952.053
## 2: E01017241 Redbridge              7871.967              1928.632
## 3: E01032738    Bevois              7870.684              1928.318
## 4: E01017182   Coxford              7344.557              1799.416
## 5: E01017139   Bargate              7015.385              1718.769
## 6: E01017140   Bargate              5845.638              1432.181

Figure ?? repeats the analysis but just for gas.

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

Figure 4.12: £k per LSOA incurred via gas using BEIS central carbon price 

## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via gas using BEIS central carbon price

Figure 4.13: £k per LSOA incurred via 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. 
##    3.14  332.71  423.12  430.72  530.76  896.55
##     LSOA11CD     WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017249    Shirley     3.659406                  896.5545
## 2: E01017148    Bassett     3.633488                  890.2047
## 3: E01017197 Freemantle     2.998158                  734.5488
## 4: E01032753  Portswood     2.945786                  721.7175
## 5: E01017252    Shirley     2.924247                  716.4405
## 6: E01017145    Bassett     2.903698                  711.4061
##     LSOA11CD   WD18NM gasTCO2e_pdw beis_GBPtotal_c_gas_perdw
## 1: E01017142  Bargate    0.6995069                171.379188
## 2: E01032748  Bargate    0.6532194                160.038752
## 3: E01017140  Bargate    0.5874720                143.930649
## 4: E01017281 Woolston    0.3330864                 81.606173
## 5: E01032755  Bargate    0.2409302                 59.027907
## 6: E01032746  Bargate    0.0128181                  3.140433

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

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

Figure 4.15: £k per LSOA incurred via electricity 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. 
##   160.6   230.0   242.3   246.1   255.8   357.7
##     LSOA11CD     WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01032746    Bargate      1.459952                   357.6883
## 2: E01017202  Harefield      1.299459                   318.3676
## 3: E01017270 Swaythling      1.284754                   314.7646
## 4: E01017170   Bitterne      1.265758                   310.1107
## 5: E01032748    Bargate      1.265183                   309.9698
## 6: E01017142    Bargate      1.262154                   309.2277
##     LSOA11CD     WD18NM elecTCO2e_pdw beis_GBPtotal_c_elec_perdw
## 1: E01017138    Bargate     0.8657028                   212.0972
## 2: E01017160     Bevois     0.8092219                   198.2594
## 3: E01017281   Woolston     0.7904938                   193.6710
## 4: E01017250    Shirley     0.7889987                   193.3047
## 5: E01017196 Freemantle     0.7812467                   191.4054
## 6: E01017278   Woolston     0.6554957                   160.5964

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 4.16: £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'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
£k per LSOA incurred via electricity and gas using BEIS central carbon price

Figure 4.17: £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

4.2.3.2 Scenario 2: Rising block tariff

Applied at to per dwelling values (not LSOA total)

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

## Cuts for total per dw
##        0%       25%       50%       75%      100% 
##  5845.638 11905.745 15464.936 21050.667 44958.416
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

##            V1 beis_GBPtotal_sc2_l_perdw beis_GBPtotal_sc2_c_perdw
##  1: 14.056160                 1452.5009                  526.8518
##  2: 18.324152                 1452.5009                  872.0019
##  3:  9.203213                 1122.7920                    0.0000
##  4:  7.015385                  855.8769                    0.0000
##  5:  5.845638                  713.1678                    0.0000
##  6: 14.007034                 1452.5009                  514.8159
##  7: 26.572009                 1452.5009                  872.0019
##  8: 25.334282                 1452.5009                  872.0019
##  9: 21.013503                 1452.5009                  872.0019
## 10: 25.055866                 1452.5009                  872.0019
##     beis_GBPtotal_sc2_h_perdw beis_GBPtotal_sc2_perdw
##  1:                     0.000               1979.3527
##  2:                  1049.332               3373.8348
##  3:                     0.000               1122.7920
##  4:                     0.000                855.8769
##  5:                     0.000                713.1678
##  6:                     0.000               1967.3167
##  7:                  4076.296               6400.7985
##  8:                  3622.050               5946.5525
##  9:                  2036.324               4360.8268
## 10:                  3519.871               5844.3740
Table 4.3: 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 ▇▅▂▁▁ 
##    nLSOAs sum_total_sc1 sum_total_sc2
## 1:    148      435.3365      323.2344

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##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw
## 1:               1900.7450               165.67756
## 2:               2388.2635               165.67756
## 3:               1032.2892               125.93928
## 4:                870.0000               106.14000
## 5:                587.4720                71.67159
## 6:                699.5069                85.33984
##    CREDSgas_kgco2e2018_pdw beis_GBPgas_sc2_l_perdw beis_GBPgas_sc2_c_perdw
## 1:               1900.7450               165.67756                90.40898
## 2:               2388.2635               165.67756                90.40898
## 3:               1032.2892               125.93928                 0.00000
## 4:                870.0000               106.14000                 0.00000
## 5:                587.4720                71.67159                 0.00000
## 6:                699.5069                85.33984                 0.00000
##    beis_GBPgas_sc2_h_perdw beis_GBPgas_sc2_perdw
## 1:                63.75374             319.84029
## 2:               242.67303             498.75957
## 3:                 0.00000             125.93928
## 4:                 0.00000             106.14000
## 5:                 0.00000              71.67159
## 6:                 0.00000              85.33984
## [1] 31.10013

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## [1] 16.02575

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##    nLSOAs sumAllConsEmissions_GBP sumGasEmissions_GBP sumElecEmissions_GBP
## 1:    148                323.2344            31.10013             16.02575

4.2.4 Estimate retofit costs

  • from A-E <- 13300
  • from F-G <- 26800

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

## 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 £k)
## [1] 14.41774
##    meanRetrofitPerLSOA totalRetrofit
## 1:            6135.936     908118528

Plot retrofit costs

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

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

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

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

Map retrofit costs

Figure 4.18: LSOA retrofit costs (upgrade EPC C to F) 

##     LSOA11CD     WD18NM epc_pc_A_C retrofitSum
## 1: E01032746    Bargate   83.07087     1906205
## 2: E01032745    Bargate   85.42274     2083370
## 3: E01017264 Swaythling   62.43243     2825518
## 4: E01032748    Bargate   81.94690     3111156
## 5: E01032751    Bargate   72.26776     3461155
## 6: E01017262    Sholing   52.91829     3569217
##     LSOA11CD     WD18NM epc_pc_A_C retrofitSum
## 1: E01017154     Bevois   25.51020    14171398
## 2: E01017202  Harefield   20.62615    11080907
## 3: E01017192 Freemantle   25.22523    10179160
## 4: E01017185    Coxford   20.97130     9724814
## 5: E01017260    Sholing   14.62766     9723096
## 6: E01032753  Portswood   25.93284     9423102

4.2.5 Compare levy with costs

4.2.5.1 Scenario 1

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

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

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

Repeat per dwelling

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

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

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

4.2.5.2 Scenario 2

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

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

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

Repeat per dwelling

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

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

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

4.2.6 Years to pay…

4.2.6.1 Scenario 1

##    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'

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

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

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##     LSOA11CD WD18NM retrofitSum  epc_D_pc  epc_E_pc   epc_F_pc    epc_G_pc
## 1: E01017154 Bevois    14171398 0.1505102 0.2806122 0.19132653 0.122448980
## 2: E01017158 Bevois     6302740 0.3222506 0.1202046 0.01662404 0.002557545
## 3: E01017160 Bevois     5743744 0.4450172 0.1391753 0.01374570 0.005154639

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

4.2.6.2 Scenario 2

##    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'

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

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

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##     LSOA11CD WD18NM retrofitSum  epc_D_pc  epc_E_pc   epc_F_pc    epc_G_pc
## 1: E01017154 Bevois    14171398 0.1505102 0.2806122 0.19132653 0.122448980
## 2: E01017158 Bevois     6302740 0.3222506 0.1202046 0.01662404 0.002557545
## 3: E01017160 Bevois     5743744 0.4450172 0.1391753 0.01374570 0.005154639

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

4.2.6.3 Compare scenarios

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5 R environment

5.1 R packages used

  • base R (R Core Team 2016)
  • bookdown (Xie 2016a)
  • data.table (Dowle et al. 2015)
  • ggplot2 (Wickham 2009)
  • kableExtra (Zhu 2018)
  • knitr (Xie 2016b)
  • rmarkdown (Allaire et al. 2018)
  • skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

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.
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.
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.
Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.