1 Energy Performance Certificates (EPCs)

Apart from a few exempted buildings, a dwelling must have an EPC when constructed, sold or let. This means that over time we will have an EPC for an increasing number of properties and we should already have EPCs for all rented properties.

EPCs are not necessarily up to date. For example if a property has not been sold or let since a major upgrade, the effects of that upgrade may not be visible in the data.

2 Southampton EPCs

df <- path.expand("~/data/EW_epc/domestic-E06000045-Southampton/certificates.csv")
allEPCs_DT <- data.table::fread(df)

## Warning in require_bit64_if_needed(ans): Some columns are type 'integer64' but package bit64 is
## not installed. Those columns will print as strange looking floating point data. There is no need to
## reload the data. Simply install.packages('bit64') to obtain the integer64 print method and print the
## data again.

The EPC data file has 91833 records for Southampton and 90 variables. We’re not interested in all of these, we want:

PROPERTY_TYPE: Describes the type of property such as House, Flat, Maisonette etc. This is the type differentiator for dwellings;
BUILT_FORM: The building type of the Property e.g. Detached, Semi-Detached, Terrace etc. Together with the Property Type, the Build Form produces a structured description of the property;
ENVIRONMENT_IMPACT_CURRENT: A measure of the property’s current impact on the environment in terms of carbon dioxide (CO₂) emissions. The higher the rating the lower the CO₂ emissions. (CO₂ emissions in tonnes / year) NB this is a categorised scale calculated from CO2_EMISSIONS_CURRENT;
ENERGY_CONSUMPTION_CURRENT: Current estimated total energy consumption for the property in a 12 month period (kWh/m2). Displayed on EPC as the current primary energy use per square metre of floor area. Nb: this covers heat and hot water (and lightng?)
CO2_EMISSIONS_CURRENT: CO₂ emissions per year in tonnes/year NB: this is calculated from the modelled kWh energy input using (possibly) outdated carbon intensity values;
TENURE: Describes the tenure type of the property. One of: Owner-occupied; Rented (social); Rented (private).

We’re also going to keep:

WIND_TURBINE_COUNT: Number of wind turbines; 0 if none;
PHOTO_SUPPLY: Percentage of photovoltaic area as a percentage of total roof area. 0% indicates that a Photovoltaic Supply is not present in the property;
TOTAL_FLOOR_AREA: The total useful floor area is the total of all enclosed spaces measured to the internal face of the external walls, i.e. the gross floor area as measured in accordance with the guidance issued from time to time by the Royal Institute of Chartered Surveyors or by a body replacing that institution. (m²) - to allow for the calculation of total energy demand;
POSTCODE - to allow linkage to other datasets
LOCAL_AUTHORITY_LABEL - for checking

These may indicate ‘non-grid’ energy inputs.

If an EPC has been updated or refreshed, the EPC dataset will hold multiple EPC records for that property. We will just select the most recent.

# select just these vars
dt <- allEPCs_DT[, .(BUILDING_REFERENCE_NUMBER, LMK_KEY, LODGEMENT_DATE, PROPERTY_TYPE, BUILT_FORM,
                ENVIRONMENT_IMPACT_CURRENT, ENERGY_CONSUMPTION_CURRENT, CO2_EMISSIONS_CURRENT, TENURE,
                PHOTO_SUPPLY, WIND_TURBINE_COUNT, TOTAL_FLOOR_AREA, 
                POSTCODE, LOCAL_AUTHORITY_LABEL)]

# select most recent record within BUILDING_REFERENCE_NUMBER - how?
# better check this is doing so
setkey(dt,BUILDING_REFERENCE_NUMBER, LODGEMENT_DATE) # sort by date within reference number
sotonUniqueEPCsDT <- unique(dt, by = "BUILDING_REFERENCE_NUMBER",
                   fromLast = TRUE) # which one does it take?

test1 <- allEPCs_DT[, .(min1 = min(LODGEMENT_DATE), 
                        nRecords = .N), 
                    keyby = .(BUILDING_REFERENCE_NUMBER)]

test2 <- sotonUniqueEPCsDT[, .(min2 = min(LODGEMENT_DATE)), 
                           keyby = .(BUILDING_REFERENCE_NUMBER)]
t <- test1[test2]
t[, diff := min2 - min1]

summary(t[nRecords > 1]) # diff is always >= 0 so min2 (after unique) is always > min1

##  BUILDING_REFERENCE_NUMBER      min1               nRecords          min2                 diff     
##  Min.   : 0.000e+00        Min.   :2008-10-01   Min.   :2.000   Min.   :2008-10-10   Min.   :   0  
##  1st Qu.:1.224e-314        1st Qu.:2009-04-01   1st Qu.:2.000   1st Qu.:2014-08-22   1st Qu.: 745  
##  Median :2.473e-314        Median :2010-03-01   Median :2.000   Median :2018-02-19   Median :1960  
##  Mean   :2.463e-314        Mean   :2011-04-19   Mean   :2.179   Mean   :2017-01-05   Mean   :2088  
##  3rd Qu.:3.703e-314        3rd Qu.:2013-03-18   3rd Qu.:2.000   3rd Qu.:2019-04-30   3rd Qu.:3643  
##  Max.   :4.940e-314        Max.   :2020-06-30   Max.   :9.000   Max.   :2020-06-30   Max.   :4279

# confirms fromLast = TRUE has selected the most recent within BUILDING_REFERENCE_NUMBER

skimr::skim(sotonUniqueEPCsDT)

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling back
## to `character`.

Table 2.1: Data summary

Name	sotonUniqueEPCsDT
Number of rows	71600
Number of columns	14
_______________________
Column type frequency:
character	7
Date	1
numeric	6
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	empty	n_unique
BUILDING_REFERENCE_NUMBER	1	17	21	0	71600
LMK_KEY	1	29	34	0	71600
PROPERTY_TYPE	1	4	10	0	5
BUILT_FORM	1	8	20	0	7
TENURE	1	0	16	1988	6
POSTCODE	1	8	8	0	5107
LOCAL_AUTHORITY_LABEL	1	11	11	0	1

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
LODGEMENT_DATE	0	1	2008-10-01	2020-06-30	2014-10-20	4132

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ENVIRONMENT_IMPACT_CURRENT	0	1.00	62.56	15.73	1.0	52.0	63.0	73	115.00	▁▂▇▅▁
ENERGY_CONSUMPTION_CURRENT	0	1.00	262.90	140.55	-184.0	173.0	233.0	327	1597.00	▃▇▁▁▁
CO2_EMISSIONS_CURRENT	0	1.00	3.16	1.94	-1.8	1.8	2.8	4	77.00	▇▁▁▁▁
PHOTO_SUPPLY	38595	0.46	0.59	5.12	0.0	0.0	0.0	0	100.00	▇▁▁▁▁
WIND_TURBINE_COUNT	5556	0.92	0.00	0.03	-1.0	0.0	0.0	0	1.00	▁▁▇▁▁
TOTAL_FLOOR_AREA	0	1.00	72.97	34.93	0.0	49.0	69.0	87	1353.68	▇▁▁▁▁

As we can see that we have 71600 unique property reference numbers. We can also see some strangeness. In some cases we seem to have:

negative energy consumption;
negative emissions;
0 floor area

This is not surprising since the kWh/y and TCO2/y values are estimated using a model but before we go any further we’d better check if these are significant in number.

2.1 Check ‘missing’ EPC rates

We will do this mostly at MSOA level as it allows us to link to other MSOA level datasets. Arguably it would be better to do this at LSOA level but…

First we’ll use the BEIS 2018 MSOA level annual electricity data to estimate the number of meters (not properties) - some addresses can have 2 meters (e.g. standard & economy 7). This is more useful than the number of gas meters since not all dwellings have mains gas but all have an electricity meter.

beisElecDT <- data.table::fread("~/data/beis/MSOA_DOM_ELEC_csv/MSOA_ELEC_2018.csv")
sotonElecDT <- beisElecDT[LAName %like% "Southampton", .(nElecMeters = METERS,  
                                                         beisElecMWh = KWH/1000, 
                                                         MSOACode, LAName)
                          ]


beisGasDT <- data.table::fread("~/data/beis/MSOA_DOM_GAS_csv/MSOA_GAS_2018.csv")
sotonGasDT <- beisGasDT[LAName %like% "Southampton", .(nGasMeters = METERS,  
                                                         beisGasMWh = KWH/1000, 
                                                         MSOACode)]

setkey(sotonElecDT, MSOACode)
setkey(sotonGasDT, MSOACode)
sotonEnergyDT <- sotonGasDT[sotonElecDT]
sotonEnergyDT[, beisEnergyMWh := beisElecMWh + beisGasMWh]
#head(sotonEnergyDT)

Next we’ll check for the number of households reported by the 2011 Census.

would be better to use dwellings but this gives us tenure

#censusDT <- data.table::fread(path.expand("~/data/"))
# IMD ----
deprivationDT <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_deprivation.csv"))
deprivationDT[, totalHouseholds := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]
deprivationDT[, MSOACode := `geography code`]
setkey(deprivationDT, MSOACode)
setkey(sotonElecDT, MSOACode)
# link LA name from Soton elec for now
sotonDep_DT <- deprivationDT[sotonElecDT[, .(MSOACode, LAName)]]
sotonDep_DT[, nHHs_deprivation := `Household Deprivation: All categories: Classification of household deprivation; measures: Value`]

#sotonDep_DT[, .(nHouseholds = sum(totalHouseholds)), keyby = .(LAName)]

# census tenure ----
sotonTenureDT <- data.table::fread(path.expand("~/data/census2011/2011_MSOA_householdTenure_Soton.csv"))

sotonTenureDT[, census2011_socialRent := `Tenure: Social rented; measures: Value`]
sotonTenureDT[, census2011_privateRent := `Tenure: Private rented; measures: Value`]
sotonTenureDT[, census2011_ownerOccupy := `Tenure: Owned; measures: Value`]
sotonTenureDT[, census2011_other := `Tenure: Living rent free; measures: Value`]
sotonTenureDT[, MSOACode := `geography code`]

sotonTenureDT[, hhCheck := census2011_socialRent + census2011_privateRent + census2011_ownerOccupy + census2011_other]
sotonTenureDT[, nHHs_tenure := `Tenure: All households; measures: Value`]

# summary(sotonTenureDT[, .(hhCheck, nHHs_tenure)])
# might not quite match due to cell perturbation etc?

# join em ----
setkey(sotonDep_DT, MSOACode)
setkey(sotonTenureDT, MSOACode)

sotonCensus2011_DT <- sotonTenureDT[sotonDep_DT]

t <- sotonCensus2011_DT[, .(sum_Deprivation = sum(nHHs_deprivation),
                            sum_Tenure = sum(nHHs_tenure)), keyby = .(LAName)]
kableExtra::kable(t, caption = "Census derived household counts")

Table 2.2: Census derived household counts
LAName	sum_Deprivation	sum_Tenure
Southampton	98254	98254

That’s lower (as expected) but doesn’t allow for dwellings that were empty on census night.

# Postcodes don't help - no count of addresses in the data (there used to be??)
# but we can use it to check which Soton postcodes are missing from the EPC file
soPostcodesDT <- data.table::fread(path.expand("~/data/UK_postcodes/NSPL_AUG_2020_UK/Data/multi_csv/NSPL_AUG_2020_UK_SO.csv"))

soPostcodesDT <- soPostcodesDT[is.na(doterm)] # keep current

sotonPostcodesDT <- soPostcodesDT[laua == "E06000045"] # keep Southampton City

sotonPostcodesReducedDT <- sotonPostcodesDT[, .(pcd, pcd2, pcds, laua, msoa11, lsoa11)]

sotonPostcodesReducedDT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(pcds, 
                                                                   split = " "
                                                                   )
                        ]
sotonPostcodesReducedDT[, .(nEPCs = .N), keyby = .(pc_chunk1)]

##    pc_chunk1 nEPCs
## 1:      SO14   849
## 2:      SO15  1176
## 3:      SO16  1328
## 4:      SO17   443
## 5:      SO18   859
## 6:      SO19  1164

We should not have single digit postcodes in the postcode data - i.e. S01 should not be there (since 1993). Southampton City is unusual in only having double digit postcodes.

# EPC
# set up counters
sotonUniqueEPCsDT[, epcIsSocialRent := ifelse(TENURE == "rental (social)", 1, 0)]
sotonUniqueEPCsDT[, epcIsPrivateRent := ifelse(TENURE == "rental (private)", 1, 0)]
sotonUniqueEPCsDT[, epcIsOwnerOcc := ifelse(TENURE == "owner-occupied", 1, 0)]
sotonUniqueEPCsDT[, epcIsUnknownTenure := ifelse(TENURE == "NO DATA!" |
                                          TENURE == "" , 1, 0)]
# aggregate EPCs to postcodes
sotonEpcPostcodes_DT <- sotonUniqueEPCsDT[, .(nEPCs = .N,
                                              sumEPC_tCO2 = sum(CO2_EMISSIONS_CURRENT, na.rm = TRUE),
                                     n_epcIsSocialRent = sum(epcIsSocialRent, na.rm = TRUE),
                                     n_epcIsPrivateRent = sum(epcIsPrivateRent, na.rm = TRUE),
                                     n_epcIsOwnerOcc = sum(epcIsOwnerOcc, na.rm = TRUE),
                                     n_epcIsUnknownTenure = sum(epcIsUnknownTenure, na.rm = TRUE),
                               sumEpcMWh = sum(ENERGY_CONSUMPTION_CURRENT* TOTAL_FLOOR_AREA)/1000), # crucial as ENERGY_CONSUMPTION_CURRENT = kWh/m2
                           keyby = .(POSTCODE, LOCAL_AUTHORITY_LABEL)]

sotonEpcPostcodes_DT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(POSTCODE, 
                                                                   split = " "
                                                                   )
                        ]
sotonEpcPostcodes_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]

##    pc_chunk1 nEPCs
## 1:      SO14   601
## 2:      SO15   960
## 3:      SO16  1245
## 4:      SO17   403
## 5:      SO18   776
## 6:      SO19  1122

# check original EPC data for Soton - which postcodes are covered?
allEPCs_DT[, c("pc_chunk1","pc_chunk2" ) := tstrsplit(POSTCODE, 
                                                                   split = " "
                                                                   )
                        ]
allEPCs_DT[, .(nEPCs = .N), keyby = .(pc_chunk1)]

##    pc_chunk1 nEPCs
## 1:      SO14 14213
## 2:      SO15 17855
## 3:      SO16 20270
## 4:      SO17  8446
## 5:      SO18 10661
## 6:      SO19 20388

It looks like we have EPCs for each postcode sector which is good.

# match the EPC postcode summaries to the postcode extract
sotonPostcodesReducedDT[, POSTCODE_s := stringr::str_remove(pcds, " ")]
setkey(sotonPostcodesReducedDT, POSTCODE_s)
sotonPostcodesReducedDT[, MSOACode := msoa11]
message("Number of postcodes: ",uniqueN(sotonPostcodesReducedDT$POSTCODE_s))

## Number of postcodes: 5819

sotonEpcPostcodes_DT[, POSTCODE_s := stringr::str_remove(POSTCODE, " ")]
setkey(sotonEpcPostcodes_DT, POSTCODE_s)
message("Number of postcodes with EPCs: ",uniqueN(sotonEpcPostcodes_DT$POSTCODE_s))

## Number of postcodes with EPCs: 5107

dt <- sotonEpcPostcodes_DT[sotonPostcodesReducedDT]

# aggregate to MSOA - watch for NAs where no EPCs in a given postcode
sotonEpcMSOA_DT <- dt[, .(nEPCs = sum(nEPCs, na.rm = TRUE), 
                          sumEPC_tCO2 = sum(sumEPC_tCO2, na.rm = TRUE),
                                        n_epcIsSocialRent = sum(n_epcIsSocialRent, na.rm = TRUE),
                                        n_epcIsPrivateRent = sum(n_epcIsPrivateRent, na.rm = TRUE),
                                        n_epcIsOwnerOcc = sum(n_epcIsOwnerOcc, na.rm = TRUE),
                                        n_epcIsUnknownTenure = sum(n_epcIsUnknownTenure, na.rm = TRUE),
                                        sumEpcMWh = sum(sumEpcMWh, na.rm = TRUE)
                                        ),
                                    keyby = .(MSOACode) # change name on the fly for easier matching
                                    ] 

#summary(sotonEpcMSOA_DT)

So we have some postcodes with no EPCs.

Join the estimates together at MSOA level for comparison. There are 32 MSOAs in Southampton.

# 32 LSOAs in Soton
# add deprivation
setkey(sotonEnergyDT, MSOACode)
setkey(sotonCensus2011_DT, MSOACode)
setkey(sotonEpcMSOA_DT, MSOACode)

sotonMSOA_DT <- sotonCensus2011_DT[sotonEnergyDT]
#names(sotonMSOA_DT)
sotonMSOA_DT <- sotonEpcMSOA_DT[sotonMSOA_DT]
#names(sotonMSOA_DT)

# add MSOA names from the postcode LUT

msoaNamesDT <- data.table::as.data.table(readxl::read_xlsx(path.expand("~/data/UK_postcodes/NSPL_AUG_2020_UK/Documents/MSOA (2011) names and codes UK as at 12_12.xlsx")))
msoaNamesDT[, MSOACode := MSOA11CD]
msoaNamesDT[, MSOAName := MSOA11NM]
setkey(msoaNamesDT, MSOACode)

sotonMSOA_DT <- msoaNamesDT[sotonMSOA_DT]

#names(sotonMSOA_DT)

t <- sotonMSOA_DT[, .(nHouseholds_2011 = sum(nHHs_tenure),
                      nElecMeters_2018 = sum(nElecMeters),
                      nEPCs_2020 = sum(nEPCs),
                      sumEPCMWh = sum(sumEpcMWh),
                  sumBEISMWh = sum(beisEnergyMWh),
                  sumEPC_tCO2 = sum(sumEPC_tCO2)
                  )]

kableExtra::kable(t, caption = "Comparison of different estimates of the number of dwellings and energy demand") %>%
  kable_styling()

Table 2.3: Comparison of different estimates of the number of dwellings and energy demand
nHouseholds_2011	nElecMeters_2018	nEPCs_2020	sumEPCMWh	sumBEISMWh	sumEPC_tCO2
98254	108333	71276	1286499	1276983	225568

nHouseholds_2011f <- sum(sotonMSOA_DT$nHHs_tenure)
nElecMeters_2018f <- sum(sotonMSOA_DT$elecMeters)
nEPCs_2020f <- sum(sotonMSOA_DT$nEPCs)

makePC <- function(x,y,r){
  # make a percent of x/y and round it to r decimal places
  pc <- round(100*(x/y),r)
  return(pc)
}

We can see that the number of EPCs we have is:

72.5% of Census 2011 households
- % of the recorded 2018 electricity meters

We can also see that despite having ‘missing’ EPCs, the estimated total EPC-derived energy demand is marginally higher than the BEIS-derived weather corrected energy demand data. Given that the BEIS data accounts for all heating, cooking, hot water, lighting and appliance use we would expect the EPC data to be lower even if no EPCs were missing…

sotonMSOA_DT[, dep0_pc := 100*(`Household Deprivation: Household is not deprived in any dimension; measures: Value`/nHHs_deprivation)]
sotonMSOA_DT[, socRent_pc := 100*(census2011_socialRent/nHHs_tenure)]
sotonMSOA_DT[, privRent_pc := 100*(census2011_privateRent/nHHs_tenure)]
sotonMSOA_DT[, ownerOcc_pc := 100*(census2011_ownerOccupy/nHHs_tenure)]

t <- sotonMSOA_DT[, .(MSOAName, MSOACode, nHHs_tenure,nElecMeters,nEPCs,
                      dep0_pc, socRent_pc, privRent_pc, ownerOcc_pc,sumEpcMWh, beisEnergyMWh )]

t[, pc_missingHH := makePC(nEPCs,nHHs_tenure,1)]
t[, pc_missingMeters := makePC(nEPCs,nElecMeters,1)]
t[, pc_energyBEIS := makePC(sumEpcMWh,beisEnergyMWh,1)]

kableExtra::kable(t[order(-pc_missingHH)], digits = 2, caption = "EPC records as a % of n census households and n meters per MSOA") %>%
  kable_styling()

Table 2.4: EPC records as a % of n census households and n meters per MSOA
MSOAName	MSOACode	nHHs_tenure	nElecMeters	nEPCs	dep0_pc	socRent_pc	privRent_pc	ownerOcc_pc	sumEpcMWh	beisEnergyMWh	pc_missingHH	pc_missingMeters	pc_energyBEIS
Southampton 029	E02003577	4908	6734	5902	37.92	27.61	43.09	24.67	77239.37	47461.33	120.3	87.6	162.7
Southampton 023	E02003571	3040	3530	2958	47.96	20.23	56.97	21.48	47763.66	33485.69	97.3	83.8	142.6
Southampton 022	E02003570	3635	4142	3426	25.56	29.82	45.69	22.59	58314.50	49449.97	94.3	82.7	117.9
Southampton 017	E02003565	2563	2840	2403	48.81	11.63	57.24	28.95	42564.50	35191.63	93.8	84.6	121.0
Southampton 031	E02003579	3357	4460	3112	44.92	11.56	23.80	63.09	46142.79	47913.06	92.7	69.8	96.3
Southampton 013	E02003561	3181	3489	2663	39.80	19.68	44.73	33.98	49213.04	38430.85	83.7	76.3	128.1
Southampton 009	E02003557	2753	3103	2137	50.78	7.37	38.98	52.49	47035.21	42842.23	77.6	68.9	109.8
Southampton 020	E02003568	3820	3900	2959	50.08	4.53	50.92	42.93	53455.26	47024.09	77.5	75.9	113.7
Southampton 010	E02003558	2924	3222	2223	33.96	32.32	25.82	39.81	38646.22	34421.99	76.0	69.0	112.3
Southampton 021	E02003569	3527	3999	2671	40.71	15.00	38.28	44.32	46809.22	41380.67	75.7	66.8	113.1
Southampton 015	E02003563	3483	3818	2551	37.81	21.79	24.17	51.79	46440.94	39920.85	73.2	66.8	116.3
Southampton 027	E02003575	2808	2987	2028	29.59	51.14	8.23	37.64	36556.34	28941.46	72.2	67.9	126.3
Southampton 007	E02003555	3140	3763	2261	34.59	30.96	11.15	56.15	35182.76	40416.83	72.0	60.1	87.0
Southampton 005	E02003553	2394	2464	1686	39.01	25.44	40.27	32.00	31931.92	33476.91	70.4	68.4	95.4
Southampton 014	E02003562	3636	3921	2513	45.68	9.13	29.24	59.19	47112.87	51289.66	69.1	64.1	91.9
Southampton 032	E02003580	2617	2825	1786	27.21	55.48	6.65	35.69	30591.72	24488.16	68.2	63.2	124.9
Southampton 025	E02003573	3236	3470	2106	29.57	43.54	6.12	47.84	38983.67	41714.91	65.1	60.7	93.5
Southampton 003	E02003551	2256	2446	1456	33.69	38.96	15.29	42.95	27406.13	26316.61	64.5	59.5	104.1
Southampton 012	E02003560	3040	3191	1952	26.97	53.52	8.75	36.12	33850.25	34252.94	64.2	61.2	98.8
Southampton 006	E02003554	2646	2873	1684	46.49	14.55	21.05	63.00	35551.16	39712.77	63.6	58.6	89.5
Southampton 004	E02003552	2646	2809	1653	28.12	47.47	9.26	40.97	30037.47	28051.01	62.5	58.8	107.1
Southampton 028	E02003576	3434	3614	2121	38.99	22.83	18.58	56.41	39567.93	44100.48	61.8	58.7	89.7
Southampton 018	E02003566	2607	2831	1604	35.21	29.84	8.36	59.42	27084.70	36047.76	61.5	56.7	75.1
Southampton 016	E02003564	3474	3563	2124	39.38	22.54	12.09	63.39	42670.04	43718.49	61.1	59.6	97.6
Southampton 001	E02003549	2849	2832	1737	52.37	11.23	25.06	62.06	41456.28	49676.93	61.0	61.3	83.5
Southampton 002	E02003550	3216	3527	1923	43.10	21.05	11.04	66.08	36473.86	41124.17	59.8	54.5	88.7
Southampton 019	E02003567	2991	3200	1780	39.18	27.28	14.11	56.80	33242.61	43448.21	59.5	55.6	76.5
Southampton 026	E02003574	3412	3599	1972	40.77	11.78	13.66	71.72	37590.51	45562.64	57.8	54.8	82.5
Southampton 030	E02003578	2641	2830	1519	44.07	10.64	15.68	72.13	27635.37	36572.30	57.5	53.7	75.6
Southampton 024	E02003572	2484	2597	1367	45.61	8.13	15.46	75.28	30084.88	39680.38	55.0	52.6	75.8
Southampton 011	E02003559	3065	3165	1678	53.38	5.97	15.14	76.70	42375.46	55256.20	54.7	53.0	76.7
Southampton 008	E02003556	2471	2589	1321	42.57	12.51	16.15	70.42	27488.33	35612.03	53.5	51.0	77.2

ggplot2::ggplot(t, aes(x = pc_missingHH, 
                       y = pc_missingMeters,
                       colour = round(ownerOcc_pc))) +
  geom_abline(alpha = 0.2, slope=1, intercept=0) +
  geom_point() +
  scale_color_continuous(name = "% owner occupiers \n(Census 2011)", high = "red", low = "green") +
  #theme(legend.position = "bottom") +
  labs(x = "EPCs 2020 as % of Census 2011 households",
       y = "EPCs 2020 as % of electricity meters 2018",
       caption = "x = y line included for clarity")

Figure 2.1: % ‘missing’ rates comparison

outlierMSOA <- t[pc_missingHH > 100]

Figure 2.4 suggests that rates vary considerably by MSOA but are relatively consistent across the two baseline ‘truth’ estimates with the exception of E02003577 which appears to have many more EPCs than Census 2011 households. It is worth noting that this MSOA covers the city centre and dock areas which have had substantial new build since 2011 and so may have households inhabiting dwellings that did not exist at Census 2011. This is also supported by the considerably higher EPC derived energy demand data compared to BEIS’s 2018 data - although it suggests the dwellings are either very new (since 2018) or are yet to be occupied.

As we would expect those MSOAs with the lowest EPC coverage on both baseline measures tend to have higher proportions of owner occupiers.

We can use the same approach to compare estimates of total energy demand at the MSOA level. To do this we compare:

estimated total energy demand in MWh/year derived from the EPC estimates. This energy only relates to current primary energy (space heating, hot water and lighting) and of course also suffers from missing EPCs (see above)
observed electricity and gas demand collated by BEIS for their sub-national statistical series. This applies to all domestic energy demand but the most recent data is for 2018 so will suffer from the absence of dwellings that are present in the most recent EPC data (see above).

We should therefore not expect the values to match but we might reasonably expect a correlation.

ggplot2::ggplot(t, aes(x = sumEpcMWh, 
                       y = beisEnergyMWh,
                       colour = round(ownerOcc_pc))) +
  geom_abline(alpha = 0.2, slope=1, intercept=0) +
  geom_point() +
  scale_color_continuous(name = "% owner occupiers \n(Census 2011)", high = "red", low = "green") +
  #theme(legend.position = "bottom") +
  labs(x = "EPC 2020 derived total MWh/year",
       y = "BEIS 2018 derived total MWh/year",
       caption = "x = y line included for clarity")

Figure 2.2: Energy demand comparison

outlier <- t[sumEpcMWh > 70000]

2.2 shows that both of these are true. MSOAs with a high proportion of owner occupiers (and therefore more likely to have missing EPCs) tend to have higher observed energy demand than the EOC data suggests - they are above the reference line. MSOAs with a lower proportion of owner occupiers (and therefore more likely to have more complete EPC coverage) tend to be on or below the line. As before we have the same notable outlier (E02003577) and for the same reasons… In this case this produces a much higher energy demand estimate than the BEIS 2018 data records

2.2 Check ENERGY_CONSUMPTION_CURRENT

We recode the current energy consumption into categories for comparison with other low values and the presence of wind turbines/PV. We use -ve, 0 and 1 kWh as the thresholds of interest.

ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = ENERGY_CONSUMPTION_CURRENT)) +
  geom_histogram(binwidth = 5) + 
  facet_wrap(~TENURE) +
  geom_vline(xintercept = 0)

Figure 2.3: Histogram of ENERGY_CONSUMPTION_CURRENT

underZero <- nrow(sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT < 0])

t <- with(sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT < 0],
     table(BUILT_FORM,TENURE))


kableExtra::kable(t, caption = "Properties with ENERGY_CONSUMPTION_CURRENT < 0")

Table 2.5: Properties with ENERGY_CONSUMPTION_CURRENT < 0
		owner-occupied	rental (social)	unknown
Detached	0	2	0	0
End-Terrace	2	0	2	0
Mid-Terrace	3	1	1	0
NO DATA!	0	0	0	2
Semi-Detached	6	0	0	1

# do we think this is caused by solar/wind?
sotonUniqueEPCsDT[, hasWind := ifelse(WIND_TURBINE_COUNT > 0, "Yes", "No")]
#table(sotonUniqueEPCsDT$hasWind)
sotonUniqueEPCsDT[, hasPV := ifelse(PHOTO_SUPPLY >0, "Yes", "No")]
#table(sotonUniqueEPCsDT$hasPV)
sotonUniqueEPCsDT[, consFlag := ifelse(ENERGY_CONSUMPTION_CURRENT < 0, "-ve kWh/y", NA)]
sotonUniqueEPCsDT[, consFlag := ifelse(ENERGY_CONSUMPTION_CURRENT == 0, "0 kWh/y", consFlag)]
sotonUniqueEPCsDT[, consFlag := ifelse(ENERGY_CONSUMPTION_CURRENT > 0 & 
                                     ENERGY_CONSUMPTION_CURRENT <= 1, "0-1 kWh/y", consFlag)]
sotonUniqueEPCsDT[, consFlag := ifelse(ENERGY_CONSUMPTION_CURRENT > 1, "1+ kWh/y", consFlag)]

t <- sotonUniqueEPCsDT[, .(nObs = .N), keyby = .(consFlag, hasWind, hasPV)]

kableExtra::kable(t, caption = "Properties in ENERGY_CONSUMPTION_CURRENT category by presence of microgeneration")

Table 2.5: Properties in ENERGY_CONSUMPTION_CURRENT category by presence of microgeneration
consFlag	hasWind	hasPV	nObs
-ve kWh/y	NA	NA	5
-ve kWh/y	No	NA	15
0 kWh/y	NA	NA	1
0 kWh/y	No	No	1
1+ kWh/y	NA	NA	5550
1+ kWh/y	No	NA	33018
1+ kWh/y	No	No	32529
1+ kWh/y	No	Yes	447
1+ kWh/y	Yes	NA	6
1+ kWh/y	Yes	No	28

There are only 20 dwellings where ENERGY_CONSUMPTION_CURRENT < 0 and none of them seem to have PV or a wind turbine so we can probably ignore them.

# repeat with a density plot to allow easy overlap 
# exclude those with no data
ggplot2::ggplot(sotonUniqueEPCsDT[TENURE != "NO DATA!" &
                           TENURE != "unknown" &
                           TENURE != ""], aes(x = ENERGY_CONSUMPTION_CURRENT, 
                                              fill = TENURE, alpha = 0.2)) +
  geom_density() +
  facet_wrap(~BUILT_FORM) +
  guides(alpha = FALSE) +
  theme(legend.position = "bottom")

Figure 2.4: Comparing distributions of ENERGY_CONSUMPTION_CURRENT by tenure and built form

2.3 Check CO2_EMISSIONS_CURRENT

Next we do the same for current emissions. Repeat the coding for total floor area using 0 and 1 TCO2/y as the threshold of interest.

ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = CO2_EMISSIONS_CURRENT)) +
  geom_histogram(binwidth = 1)

Figure 2.5: Histogram of CO2_EMISSIONS_CURRENT

nZeroEmissions <- nrow(sotonUniqueEPCsDT[CO2_EMISSIONS_CURRENT < 0])

sotonUniqueEPCsDT[, emissionsFlag := ifelse(CO2_EMISSIONS_CURRENT < 0, "-ve CO2/y", NA)]
sotonUniqueEPCsDT[, emissionsFlag := ifelse(CO2_EMISSIONS_CURRENT == 0, "0 CO2/y", emissionsFlag)]
sotonUniqueEPCsDT[, emissionsFlag := ifelse(CO2_EMISSIONS_CURRENT > 0 & 
                                     CO2_EMISSIONS_CURRENT <= 1, "0-1 TCO2/y", emissionsFlag)]
sotonUniqueEPCsDT[, emissionsFlag := ifelse(CO2_EMISSIONS_CURRENT > 1, "1+ TCO2/y", emissionsFlag)]

t <- sotonUniqueEPCsDT[, .(nObs = .N), keyby = .(emissionsFlag, hasWind, hasPV)]

kableExtra::kable(t, caption = "Properties with CO2_EMISSIONS_CURRENT < 0 by presence of microgeneration")

Table 2.6: Properties with CO2_EMISSIONS_CURRENT < 0 by presence of microgeneration
emissionsFlag	hasWind	hasPV	nObs
-ve CO2/y	NA	NA	4
-ve CO2/y	No	NA	16
-ve CO2/y	No	No	2
0 CO2/y	NA	NA	5
0 CO2/y	No	No	1
0-1 TCO2/y	NA	NA	3446
0-1 TCO2/y	No	NA	1916
0-1 TCO2/y	No	No	533
0-1 TCO2/y	No	Yes	19
0-1 TCO2/y	Yes	NA	1
0-1 TCO2/y	Yes	No	1
1+ TCO2/y	NA	NA	2101
1+ TCO2/y	No	NA	31101
1+ TCO2/y	No	No	31994
1+ TCO2/y	No	Yes	428
1+ TCO2/y	Yes	NA	5
1+ TCO2/y	Yes	No	27

kableExtra::kable(round(100*(prop.table(table(sotonUniqueEPCsDT$emissionsFlag, 
                                              sotonUniqueEPCsDT$consFlag, 
                                              useNA = "always")
                                        )
                             )
                        ,2)
                  , caption = "% properties in CO2_EMISSIONS_CURRENT categories by ENERGY_CONSUMPTION_CURRENT categories")

Table 2.6: % properties in CO2_EMISSIONS_CURRENT categories by ENERGY_CONSUMPTION_CURRENT categories
	-ve kWh/y	1+ kWh/y
-ve CO2/y	0.03	0.00
0 CO2/y	0.00	0.00
0-1 TCO2/y	0.00	8.26
1+ TCO2/y	0.00	91.70
NA	0.00	0.00

There are 22 properties with 0 or negative emissions. It looks like they are also the properties with -ve kWh as we might expect. So we can safely ignore them.

2.4 Check ENVIRONMENT_IMPACT_CURRENT

Environmental impact should decrease as emissions increase.

ggplot2::ggplot(allEPCs_DT, aes(x = ENVIRONMENT_IMPACT_CURRENT)) +
  geom_histogram()

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

Figure 2.6: Histogram of ENVIRONMENT_IMPACT_CURRENT

So what is the relationship between ENVIRONMENT_IMPACT_CURRENT and CO2_EMISSIONS_CURRENT? It is not linear… (Figure 2.7) and there are some interesting outliers.

ggplot2::ggplot(allEPCs_DT, aes(x = CO2_EMISSIONS_CURRENT, 
                           y = ENVIRONMENT_IMPACT_CURRENT,
                           colour = TENURE)) +
  geom_point() +
  facet_wrap(TENURE~.) +
  theme(legend.position = "bottom")

Figure 2.7: PLot of ENVIRONMENT_IMPACT_CURRENT vs CO2_EMISSIONS_CURRENT

2.5 Check TOTAL_FLOOR_AREA

Repeat the coding for total floor area using 5 m2 as the threshold of interest.

ggplot2::ggplot(sotonUniqueEPCsDT, aes(x = TOTAL_FLOOR_AREA)) +
  geom_histogram(binwidth = 1)

Figure 2.8: Histogram of TOTAL_FLOOR_AREA

nZeroFloorArea <- nrow(sotonUniqueEPCsDT[TOTAL_FLOOR_AREA < 0])

sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA == 0, "0 m2", NA)]
sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA > 0 & 
                                     TOTAL_FLOOR_AREA <= 10, "0-5 m2", floorFlag)]
sotonUniqueEPCsDT[, floorFlag := ifelse(TOTAL_FLOOR_AREA > 10, "5+ m2", floorFlag)]

t <- with(sotonUniqueEPCsDT, table(floorFlag, consFlag))

kableExtra::kable(round(100*prop.table(t),2), caption = "% properties with TOTAL_FLOOR_AREA category by ENERGY_CONSUMPTION_CURRENT category")

Table 2.7: % properties with TOTAL_FLOOR_AREA category by ENERGY_CONSUMPTION_CURRENT category
	-ve kWh/y	1+ kWh/y
0 m2	0.00	0.10
0-5 m2	0.00	0.02
5+ m2	0.03	99.85

kableExtra::kable(head(sotonUniqueEPCsDT[, .(BUILDING_REFERENCE_NUMBER, PROPERTY_TYPE, TOTAL_FLOOR_AREA, 
                                    ENERGY_CONSUMPTION_CURRENT)][order(-TOTAL_FLOOR_AREA)], 10), 
                  caption = "Top 10 by floor area (largest)")

Table 2.7: Top 10 by floor area (largest)
BUILDING_REFERENCE_NUMBER	PROPERTY_TYPE	TOTAL_FLOOR_AREA	ENERGY_CONSUMPTION_CURRENT
4.697565e-314	House	1353.680	140
1.894551e-314	House	1123.000	120
4.846111e-314	House	973.210	522
2.559778e-314	House	861.360	279
8.172097e-315	House	855.000	170
4.440838e-315	House	846.421	161
1.933817e-314	House	833.000	206
4.076249e-314	House	800.000	185
1.280057e-314	House	714.000	224
2.444460e-314	Flat	694.000	88

kableExtra::kable(head(sotonUniqueEPCsDT[, .(BUILDING_REFERENCE_NUMBER, PROPERTY_TYPE, TOTAL_FLOOR_AREA,
                                    ENERGY_CONSUMPTION_CURRENT)][order(TOTAL_FLOOR_AREA)], 10), 
                  caption = "Bottom 10 by floor area (smallest)")

Table 2.7: Bottom 10 by floor area (smallest)
BUILDING_REFERENCE_NUMBER	PROPERTY_TYPE	ENERGY_CONSUMPTION_CURRENT
9.111592e-316	Flat	104
3.102124e-315	Flat	58
3.294384e-315	Flat	110
3.695003e-315	Flat	70
4.369619e-315	Flat	119
4.371685e-315	Flat	71
7.302298e-315	Flat	115
9.515727e-315	Flat	144
9.562249e-315	Flat	103
1.059979e-314	Flat	117

kableExtra::kable(round(100*prop.table(t),2), caption = "% properties with TOTAL_FLOOR_AREA category by ENERGY_CONSUMPTION_CURRENT category")

Table 2.7: % properties with TOTAL_FLOOR_AREA category by ENERGY_CONSUMPTION_CURRENT category
	-ve kWh/y	1+ kWh/y
0 m2	0.00	0.10
0-5 m2	0.00	0.02
5+ m2	0.03	99.85

2.6 shows that the properties with floor area of < 10m2 are not necessarily the ones with 0 or negative kWh values. Nevertheless they represent a small proportion of all properties.

The scale of the x axis also suggests a few very large properties.

2.6 Data summary

We have identified some issues with a small number of the properties in the EPC dataset. These are not unexpected given that much of the estimates rely on partial or presumed data. Data entry errors are also quite likely. As a result we exclude:

any property where ENERGY_CONSUMPTION_CURRENT <= 0
any property where TOTAL_FLOOR_AREA <= 5
any property where CO2_EMISSIONS_CURRENT <= 0

finalDT <- sotonUniqueEPCsDT[ENERGY_CONSUMPTION_CURRENT > 0 &
                      TOTAL_FLOOR_AREA > 5 &
                      CO2_EMISSIONS_CURRENT > 0]

skimr::skim(finalDT)

## Warning: Couldn't find skimmers for class: integer64; No user-defined `sfl` provided. Falling back
## to `character`.

Table 2.8: Data summary

Name	finalDT
Number of rows	71501
Number of columns	23
_______________________
Column type frequency:
character	12
Date	1
numeric	10
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique
BUILDING_REFERENCE_NUMBER	0	1.00	17	21	0	71501
LMK_KEY	0	1.00	29	34	0	71501
PROPERTY_TYPE	0	1.00	4	10	0	5
BUILT_FORM	0	1.00	8	20	0	7
TENURE	0	1.00	0	16	1906	6
POSTCODE	0	1.00	8	8	0	5105
LOCAL_AUTHORITY_LABEL	0	1.00	11	11	0	1
hasWind	5547	0.92	2	3	0	2
hasPV	38499	0.46	2	3	0	2
consFlag	0	1.00	8	8	0	1
emissionsFlag	0	1.00	9	10	0	2
floorFlag	0	1.00	5	6	0	2

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
LODGEMENT_DATE	0	1	2008-10-01	2020-06-30	2014-10-22	4132

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ENVIRONMENT_IMPACT_CURRENT	0	1.00	62.52	15.70	1.00	52.0	63.0	73	100.00	▁▂▆▇▂
ENERGY_CONSUMPTION_CURRENT	0	1.00	263.17	140.44	4.00	174.0	233.0	327	1597.00	▇▂▁▁▁
CO2_EMISSIONS_CURRENT	0	1.00	3.16	1.94	0.10	1.8	2.8	4	77.00	▇▁▁▁▁
PHOTO_SUPPLY	38499	0.46	0.59	5.12	0.00	0.0	0.0	0	100.00	▇▁▁▁▁
WIND_TURBINE_COUNT	5547	0.92	0.00	0.02	-1.00	0.0	0.0	0	1.00	▁▁▇▁▁
TOTAL_FLOOR_AREA	0	1.00	73.05	34.87	5.85	49.0	69.0	87	1353.68	▇▁▁▁▁
epcIsSocialRent	0	1.00	0.21	0.40	0.00	0.0	0.0	0	1.00	▇▁▁▁▂
epcIsPrivateRent	0	1.00	0.27	0.44	0.00	0.0	0.0	1	1.00	▇▁▁▁▃
epcIsOwnerOcc	0	1.00	0.41	0.49	0.00	0.0	0.0	1	1.00	▇▁▁▁▆
epcIsUnknownTenure	0	1.00	0.04	0.19	0.00	0.0	0.0	0	1.00	▇▁▁▁▁

This leaves us with a total of 71,501 properties. ` # Current estimated annual CO2 emmisions

We can now use the cleaned data to estimated the annual CO2 emissions at:

MSOA level for Southampton using
- BEIS observed data
- aggregated EPC data
Dwelling level for Southampton using
- aggregated EPC data

Obviously the EPC-derived totals will not be the total CO2 emissions for all Southampton properties since we know not all dwellings are represented in the EPC data (see above).

2.7 MSOA estimates

Method:

elecCF <- 200 # CO2e/kWh https://www.icax.co.uk/Grid_Carbon_Factors.html
gasCF <- 215 # https://www.icax.co.uk/Carbon_Emissions_Calculator.html

BEIS: apply 2019 mean grid carbon intensity for: * electricity: 200 g * gas: 215 g CO2e/kWh EPC: use estimated CO2 values - note based on ‘old’ electricity grid carbon intensity values ()

sotonMSOA_DT[, sumBEIS_tCO2 := (beisElecMWh/1000)*elecCF + (beisGasMWh/1000)*gasCF]

ggplot2::ggplot(sotonMSOA_DT, aes(x = sumBEIS_tCO2, 
                       y = sumEPC_tCO2,
                       colour = round(ownerOcc_pc))) +
  geom_abline(alpha = 0.2, slope=1, intercept=0) +
  geom_point() +
  scale_color_continuous(name = "% owner occupiers \n(Census 2011)", high = "red", low = "green") +
  #theme(legend.position = "bottom") +
  labs(x = "EPC 2020 derived total T CO2/year",
       y = "BEIS 2018 derived total T CO2/year",
       caption = "x = y line included for clarity")

Figure 2.9: Energy demand comparison

#outlier <- t[sumEpcMWh > 70000]

2.2 shows that

3 Carbon Tax Scenarios

3.1 No change to carbon intensity

no emissions allowances (unlike the ETS and also current UK government proposals)
Carbon tax rate of £16/T (currently only proposed for businesses)

Applying these rates enables us to calculate the Southampton and MSOA level Carbon Tax liability of households via the EPC and BEIS observed energy consumption methods.

sotonMSOA_DT[, ct_BEIS := sumBEIS_tCO2 * 16]
sotonMSOA_DT[, ct_EPCs := sumEPC_tCO2 * 16]

t <- sotonMSOA_DT[, .(CarbonTaxBEIS_GBP = prettyNum(sum(ct_BEIS), big.mark = ","),
                      CarbonTaxEPCs_GBP = prettyNum(sum(ct_EPCs), big.mark = ",")),
                  keyby = .(LAName)]

kableExtra::kable(t, caption = "Estimated Carbon tax liability for Southampton households/properties under Scenario 1") %>%
  kable_styling()

Table 3.1: Estimated Carbon tax liability for Southampton households/properties under Scenario 1
LAName	CarbonTaxBEIS_GBP	CarbonTaxEPCs_GBP
Southampton	4,300,808	3,609,088

As we would expect the values are relatively close due to the similar total emissions values estimated above.

If we look at the values by MSOA (3.1), we find that values differ quite substantially between the methods depending on the levels of EPC records (or missing households - see above) that we are likely to have.

ggplot2::ggplot(sotonMSOA_DT, aes(x = ct_BEIS/1000, 
                       y = ct_EPCs/1000,
                       colour = round(ownerOcc_pc))) +
  geom_abline(alpha = 0.2, slope=1, intercept=0) +
  geom_point() +
  scale_color_continuous(name = "% owner occupiers \n(Census 2011)", high = "red", low = "green") +
  #theme(legend.position = "bottom") +
  labs(x = "EPC 2020 derived total Carbon Tax £k/year",
       y = "BEIS 2018 derived total Carbon Tax £k/year",
       caption = "x = y line included for clarity")

Figure 3.1: Energy demand comparison

#outlier <- t[sumEpcMWh > 70000]

Perhaps of more interest however is the relationship between estimated Carbon Tax £ and levels of deprivation. Figure 3.2 shows the estimated total Carbon Tax (in £k per year) per MSOA against the proportion of households in the MSOA who do not suffer from any dimension of deprivation as defined by the English Indices of Multiple Deprivation. As we can see the higher the proportion of households with no deprivation, the higher the total MSOA Carbon Tax. This suggests that a Carbon Tax will be regressive - those who pay the most are likely to be those who use more energy and thus are likely to be those who can afford to do so.

But we need to be very careful. Some deprived households might well spend a high proportion of their income on energy in order to heat very energy efficient homes. For them, a Carbon Tax would be similar to VAT - an additional burden that might be relatively small in £ terms (compared to a well-off high energy-using household) but high in terms of the % of their income (or expenditure). This is a well known issue highlighted by recent ONS data on family energy expenditures.

t1 <- sotonMSOA_DT[, .(MSOACode, ctSum = ct_EPCs, dep0_pc)]
t1[, source := "BEIS 2018"]
t2 <- sotonMSOA_DT[, .(MSOACode, ctSum = ct_BEIS, dep0_pc)]
t2[, source := "EPC 2020"]

plotDT <- rbind(t1,t2)

ggplot2::ggplot(plotDT, aes(x = dep0_pc, y = ctSum, colour = source)) +
  geom_point() +
  geom_smooth() +
  #theme(legend.position = "bottom") +
  labs(x = "% with no deprivation dimensions \n(Census 2011)",
       y = "Carbon Tax £k/year",
       caption = "x = y line included for clarity")

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

Figure 3.2: Energy demand comparison

#outlier <- t[sumEpcMWh > 70000]

3.2 National Grid’s Future Energy Scenarios:

2030 emissions level for electricity of 0.102 kgCO2/kWh
gas unchanged

4 R packages used

rmarkdown (Allaire et al. 2018)
bookdown (Xie 2016a)
knitr (Xie 2016b)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
readxl (Wickham and Bryan 2017)

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.

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.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Wickham, Hadley, and Jennifer Bryan. 2017. Readxl: Read Excel Files. https://CRAN.R-project.org/package=readxl.

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.

What can EPC data tell us about the domestic cost of carbon?

Ben Anderson

Last run at: 2020-11-06 18:58:46