Exploring CSE Impact Tool data

Wider Solent region

1 Introduction

Exploration of Hampshire area data downloaded from the CSE Impact Toolkit (https://impact-tool.org.uk/about). Note that there is no obvious data re-use license on the impact-tool website. However the data is free to download so…

The data is modelled:

• Territorial emissions
• Consumption emissions

For Parishes and Local Authorities.

The methodology under-pinning the model is open and transparent.

Health warning: this is modelled data giving the ‘expected’ emissions for the area. Although some modeled estimates can be constrained to fit observed data (e.g. LSOA level total residential eletricity & gas consumption); much of it cannot. This means that specific local conditions will lead to the estimates being ‘wrong’ and in some cases these may be obviously so. This is more likely to be obvious at finer geographical (i.e. parish) levels, especially where ‘unusual’ patterns of infrastructure pertain.

As a result these estimates are best viewed as the ‘expected’ emissions “all other things being equal”. If we could actually measure the emissions we might find ‘good’ places which are doing much better than ‘expected’ (or worse). It would then be interesting to find out why…

This brief report explores the data for the Hampshire area:

• Hampshire County
• Southampton
• Portsmouth
• Isle of Wight

2 Local Authority level

f <- path.expand(paste0(rmdParams$csePath, "/local-authority-all-territorial-absolute.csv.gz"))
absTerrDT <- data.table::fread(f)
absTerrDT[, source := "Territorial absolute"]
atmDT <- melt(absTerrDT)

f <- path.expand(paste0(rmdParams$csePath, "/local-authority-all-consumption-absolute.csv.gz"))
absConsDT <- data.table::fread(f)
absConsDT[, source := "Consumption absolute"]
actmDT <- melt(absConsDT)

laAbsDT <- rbind(atmDT, actmDT)

# Local Council
# Isle of Wight
# Southampton
# Portsmouth
# Basingstoke & Deane
# East Hampshire
# Eastleigh
# Fareham
# Gosport
# Hart
# Havant
# New Forest
# Rushmoor
# Test Valley
# Winchester

solent_laAbsDT <- laAbsDT[name %like% "Hampshire" |
                            name %like% "Basingstoke" |
                            name %like% "Eastleigh" |
                            name %like% "Fareham" |
                            name %like% "Gosport" |
                            name == "Hart" |
                            name == "Havant" |
                            name %like% "New Forest" |
                            name == "Rushmoor" |
                            name %like% "Test Valley" |
                            name %like% "Winchester" |
                            name %like% "Southampton" |
                            name %like% "Portsmouth" |
                            name %like% "Isle of Wight" |
                            name %like% "Southampton"]

f <- path.expand(paste0(rmdParams$csePath, "/local-authority-all-territorial-per-household.csv.gz"))
phTerrDT <- data.table::fread(f)
phTerrDT[, source := "Territorial per household"]
phtmDT <- melt(phTerrDT)

f <- path.expand(paste0(rmdParams$csePath, "/local-authority-all-consumption-per-household.csv.gz"))
phConsDT <- data.table::fread(f)
phConsDT[, source := "Consumption per household"]
phctmDT <- melt(phConsDT)

laPhDT <- rbind(phtmDT, phctmDT)

solent_laPhDT <- laPhDT[name %like% "Hampshire" |
                            name %like% "Basingstoke" |
                            name == "Hart" |
                            name %like% "Eastleigh" |
                            name %like% "New Forest" |
                            name %like% "Test Valley" |
                            name %like% "Portsmouth" |
                            name %like% "Isle of Wight" |
                            name %like% "Southampton"]

t <- solent_laAbsDT[, .(nVariables = .N), keyby = .(name)]

ft <- flextable(t)

set_caption(ft, caption = "Local Authorities included")

Table 2.1: Local Authorities included

name	nVariables
Basingstoke and Deane	36
East Hampshire	36
Eastleigh	36
Fareham	36
Gosport	36
Hart	36
Havant	36
Isle of Wight	36
New Forest	36
Portsmouth	36
Rushmoor	36
Southampton	36
Test Valley	36
Winchester	36

Any missing?

2.1 Territorial emissions

Figure 2.1 shows the absolute values for territorial emissions. The plot is visually dominated by ‘power generation’ in the New Forest.

dt <- solent_laAbsDT[source == "Territorial absolute", 
                     .(tCO2e = mean(value)),
                     keyby = .(variable, name)] 

t <- dcast(dt,  variable ~  name)

## Using 'tCO2e' as value column. Use 'value.var' to override

ft <- flextable::flextable(t)
ft <- set_caption(ft,
            caption = "Territorial emissions: absolute T CO2e")
ft <- colformat_num(ft, 2:10, digits = 2)
flextable::autofit(ft)

Table 2.2: Territorial emissions: absolute T CO2e

variable	Basingstoke and Deane	East Hampshire	Eastleigh	Fareham	Gosport	Hart	Havant	Isle of Wight	New Forest	Portsmouth	Rushmoor	Southampton	Test Valley	Winchester
Housing - Mains gas (t CO2e)	147,023.80	117,246.69	117,495.14	105,583.15	61,982.08	105,821.21	112,952.56	119,302.02	169,568.25	155539.9380	84419.4500	164399.3360	91074.957	105049.9160
Housing - Electricity (t CO2e)	78,059.00	59,941.29	51,081.78	46,645.44	33,380.57	43,065.63	51,269.58	67,417.29	82,569.40	76323.2150	35761.9350	98010.1210	60124.250	57665.2490
Housing - Oil (t CO2e)	56,696.59	39,414.71	1,776.53	2,433.82	76.44	9,516.95	704.05	18,384.06	40,393.22	17.4950	61.1450	102.5000	73274.436	51180.0210
Housing - LPG (t CO2e)	3,591.26	5,081.61	1,106.17	918.83	175.20	1,776.58	697.82	4,783.99	5,782.02	472.5850	226.9520	349.2860	6643.444	6365.5550
Housing - Biomass (t CO2e)	848.64	1,047.42	302.08	307.19	69.87	478.90	253.71	744.80	1,419.59	152.9540	147.3880	243.3950	876.773	1112.8430
Housing - Coal (t CO2e)	1,179.32	1,122.30	313.84	409.54	259.27	278.47	514.10	1,058.53	2,003.98	531.2630	332.4920	794.9130	1146.400	1085.9000
Industrial and commercial - Electricity (t CO2e)	119,331.98	48,625.99	76,113.93	57,726.91	33,819.21	63,174.50	47,968.54	65,308.33	102,653.75	143882.5330	58329.6941	128052.3538	76714.128	88283.7207
Industrial and commercial - Mains gas (t CO2e)	80,817.89	24,563.25	31,501.54	49,596.39	17,550.20	26,108.21	19,749.00	82,866.21	45,729.95	78973.9296	39336.4675	97616.2671	61728.728	46393.9226
Industrial and commercial - Other Fuels (t CO2e)	40,751.48	46,692.26	23,806.63	15,936.83	7,686.69	21,163.15	20,065.98	32,396.15	62,564.65	37391.4692	14690.5685	31532.0174	50945.509	37812.6888
Industrial and commercial - Large industrial consumers (t CO2e)	553.51	0.00	0.00	3,537.65	126.87	81.32	1,896.31	1,468.57	70,203.44	2103.2965	0.0000	349.0314	1142.551	0.0000
Power generation (t CO2e)	6,086.44	1,426.74	1.48	1,736.98	0.23	368.38	1,709.73	4,079.53	1,734,141.06	6551.7534	13778.4803	400428.3295	1453.678	748.7915
Agriculture - Fuel (t CO2e)	16,773.77	13,180.75	1,693.42	1,603.40	304.60	4,493.53	933.17	11,325.71	14,651.75	509.0324	291.1050	423.4139	20220.507	20344.4235
Agriculture - Livestock and crop-related emissions (t CO2e)	60,498.69	52,425.93	5,393.35	3,843.98	1,434.72	15,119.49	2,038.80	92,296.64	64,998.18	1441.3468	264.4438	353.0500	70706.621	86220.2738
Aviation (t CO2e)	106,423.41	73,085.74	79,831.03	70,456.17	51,648.32	58,316.10	76,193.73	85,716.96	108,860.39	130286.9021	57619.0377	153096.0276	75803.718	75274.4140
Shipping (t CO2e)	42,387.47	29,109.38	31,795.97	28,062.05	20,571.05	23,226.77	30,347.27	34,140.29	43,358.10	51892.0857	22949.1376	60976.7525	30191.930	29981.1130
Diesel fuelled railways (t CO2e)	13,873.00	509.72	9,247.69	883.37	0.00	1,729.88	120.36	0.00	3,059.11	1986.6946	1459.7709	742.1455	9856.606	1881.1531
F-gases (t CO2e)	40,218.79	16,388.56	25,652.89	19,455.86	11,398.18	21,291.88	16,166.97	22,011.05	34,597.68	48493.1374	19659.0219	43157.8473	25855.180	29754.5123
Road Transport (t CO2e)	534,643.62	342,258.55	277,475.81	216,288.69	63,699.65	235,161.67	171,377.80	120,395.01	479,560.55	291295.4311	137326.5835	233518.2229	469127.543	482441.6985
Other Transport (t CO2e)	2,591.27	2,694.53	7,638.19	1,088.53	331.37	1,378.14	917.36	726.13	2,495.40	1414.3609	776.2174	4424.7332	2137.957	2616.7733
Waste management (t CO2e)	83,856.70	44,936.93	13,969.24	20,941.69	2,803.84	26,671.42	7,061.60	70,268.61	187,091.53	97437.0779	6051.8316	27411.1872	85509.424	29717.7077
Land use, land-use change, and forestry (t CO2e)	-61,675.28	-51,060.49	-5,740.53	-5,665.84	-1,839.16	-25,929.23	-3,992.87	-30,546.22	-117,206.95	-2293.1372	-7577.8106	-6015.5179	-56599.544	-49083.6279

ggplot2::ggplot(dt, aes(x = name, y = variable, fill = tCO2e)) +
  geom_tile() +
  scale_fill_continuous(low = "green", high = "red") +
  theme(axis.text.x = element_text(angle = 90)) +
  theme(legend.position="bottom") +
  labs(caption = rmdParams$dataSource,
         x = "Local Authority",
         y = "Emissions category")

Figure 2.1: Wider Hampshire area total territorial emissions (T CO2e 2019-20)

2.2 Consumption emissions

Consumption emissions are most likely to be driven by differences in socio-economic context and numbers of households within a local authority.

Figure 2.2 shows the absolute values while figure 2.3 shows per household values.

dt <- solent_laAbsDT[source == "Consumption absolute", 
                     .(tCO2e = mean(value)),
                     keyby = .(variable, name)] 

t <- dcast(dt,  variable ~  name)

## Using 'tCO2e' as value column. Use 'value.var' to override

ft <- flextable::flextable(t)
ft <- set_caption(ft,
            caption = "Consumption emissions: absolute T CO2e")
ft <- colformat_num(ft, 2:10, digits = 2)
flextable::autofit(ft)

Table 2.3: Consumption emissions: absolute T CO2e

variable	Basingstoke and Deane	East Hampshire	Eastleigh	Fareham	Gosport	Hart	Havant	Isle of Wight	New Forest	Portsmouth	Rushmoor	Southampton	Test Valley	Winchester
Housing - Mains gas (t CO2e)	147,023.80	117,246.69	117,495.14	105,583.15	61,982.08	105,821.21	112,952.56	119,302.02	169,568.25	155539.938	84419.450	164399.336	91074.957	105049.916
Housing - Electricity (t CO2e)	78,059.00	59,941.29	51,081.78	46,645.44	33,380.57	43,065.63	51,269.58	67,417.29	82,569.40	76323.215	35761.935	98010.121	60124.250	57665.249
Housing - Oil (t CO2e)	56,696.59	39,414.71	1,776.53	2,433.82	76.44	9,516.95	704.05	18,384.06	40,393.22	17.495	61.145	102.500	73274.436	51180.021
Housing - LPG (t CO2e)	3,591.26	5,081.61	1,106.17	918.83	175.20	1,776.58	697.82	4,783.99	5,782.02	472.585	226.952	349.286	6643.444	6365.555
Housing - Biomass (t CO2e)	848.64	1,047.42	302.08	307.19	69.87	478.90	253.71	744.80	1,419.59	152.954	147.388	243.395	876.773	1112.843
Housing - Coal (t CO2e)	1,179.32	1,122.30	313.84	409.54	259.27	278.47	514.10	1,058.53	2,003.98	531.263	332.492	794.913	1146.400	1085.900
Consumption of goods and services - Purchase of goods (t CO2e)	249,350.07	188,326.16	181,326.08	162,899.47	97,039.92	154,756.47	151,830.73	181,236.46	257,279.44	229667.315	124249.039	269696.885	184028.387	191726.576
Consumption of goods and services - Use of services (t CO2e)	110,434.88	82,872.61	82,839.36	73,511.83	46,218.64	68,576.19	71,057.01	84,527.35	114,382.80	108708.192	57446.689	129434.285	80585.313	84128.660
Consumption of goods and services - Other consumption related emissions (t CO2e)	93,605.82	70,786.69	71,921.24	62,406.62	39,766.57	55,210.40	60,373.11	79,263.58	102,018.90	99150.544	47243.589	112449.240	67193.545	72751.843
Food and diet - Meat and fish (t CO2e)	159,118.58	113,845.35	116,788.50	103,375.66	69,666.46	91,210.36	105,606.04	128,237.36	163,084.59	165612.355	83281.746	194248.368	114742.400	113203.537
Food and diet - Other food and drink (t CO2e)	133,991.58	99,027.06	96,869.95	86,097.03	53,874.23	81,037.38	83,172.54	99,024.43	135,907.32	128713.531	68200.721	150160.752	97496.081	100080.657
Travel - Flights (t CO2e)	95,652.30	83,698.26	62,908.89	58,092.47	28,768.08	68,920.66	50,350.72	66,245.48	102,737.79	68645.586	42108.317	74771.654	77584.689	89310.739
Travel - Public transport (t CO2e)	37,843.81	27,154.95	25,543.66	21,526.11	15,460.93	20,999.78	22,915.82	31,072.10	37,860.87	37642.193	18023.989	45897.271	27672.811	29546.984
Travel - Private transport (t CO2e)	138,387.53	102,371.73	89,485.90	73,448.74	48,745.25	79,199.85	71,825.77	105,165.10	134,373.99	118462.281	61475.681	140611.780	101444.647	113175.199
Waste - Waste (t CO2e)	3,587.91	2,437.44	2,582.18	2,238.30	1,650.32	1,920.66	2,478.57	13,361.90	3,499.02	2808.465	1961.597	5027.028	2523.449	2360.321

ggplot2::ggplot(dt, aes(x = name, y = variable, fill = tCO2e)) +
  geom_tile() +
  scale_fill_continuous(low = "green", high = "red") +
  theme(axis.text.x = element_text(angle = 90)) +
  theme(legend.position="bottom") +
  labs(caption = rmdParams$dataSource,
         x = "Local Authority",
         y = "Emissions category")

Figure 2.2: Wider Hampshire area total consumption emissions (T CO2e 2019-20)

dt <- solent_laPhDT[source %like% "Consumption", 
                     .(tCO2e = mean(value)),
                     keyby = .(variable, name)] 

t <- dcast(dt,  variable ~  name)

## Using 'tCO2e' as value column. Use 'value.var' to override

ft <- flextable::flextable(t)
ft <- set_caption(ft,
            caption = "Consumption emissions: per household T CO2e")
ft <- colformat_num(ft, 2:10, digits = 2)
flextable::autofit(ft)

Table 2.4: Consumption emissions: per household T CO2e

variable	Basingstoke and Deane	East Hampshire	Eastleigh	Hart	Isle of Wight	New Forest	Portsmouth	Southampton	Test Valley
Housing - Mains gas (t CO2e)	1.94	2.22	2.09	2.65	1.69	2.08	1.76	1.54	1.66
Housing - Electricity (t CO2e)	1.03	1.14	0.91	1.08	0.95	1.01	0.86	0.92	1.10
Housing - Oil (t CO2e)	0.75	0.75	0.03	0.24	0.26	0.49	0.00	0.00	1.34
Housing - LPG (t CO2e)	0.05	0.10	0.02	0.04	0.07	0.07	0.01	0.00	0.12
Housing - Biomass (t CO2e)	0.01	0.02	0.01	0.01	0.01	0.02	0.00	0.00	0.02
Housing - Coal (t CO2e)	0.02	0.02	0.01	0.01	0.01	0.03	0.01	0.01	0.02
Consumption of goods and services - Purchase of goods (t CO2e)	3.29	3.57	3.23	3.88	2.56	3.15	2.59	2.53	3.36
Consumption of goods and services - Use of services (t CO2e)	1.46	1.57	1.47	1.72	1.20	1.40	1.23	1.22	1.47
Consumption of goods and services - Other consumption related emissions (t CO2e)	1.23	1.34	1.28	1.38	1.12	1.25	1.12	1.06	1.23
Food and diet - Meat and fish (t CO2e)	2.10	2.16	2.08	2.29	1.81	2.00	1.87	1.82	2.10
Food and diet - Other food and drink (t CO2e)	1.77	1.88	1.72	2.03	1.40	1.66	1.45	1.41	1.78
Travel - Flights (t CO2e)	1.26	1.59	1.12	1.73	0.94	1.26	0.78	0.70	1.42
Travel - Public transport (t CO2e)	0.50	0.51	0.45	0.53	0.44	0.46	0.42	0.43	0.51
Travel - Private transport (t CO2e)	1.82	1.94	1.59	1.99	1.49	1.64	1.34	1.32	1.85
Waste - Waste (t CO2e)	0.05	0.05	0.05	0.05	0.19	0.04	0.03	0.05	0.05

ggplot2::ggplot(dt, aes(x = name, y = variable, fill = tCO2e)) +
  geom_tile() +
  scale_fill_continuous(low = "green", high = "red") +
  theme(axis.text.x = element_text(angle = 90)) +
  theme(legend.position="bottom") +
  labs(caption = rmdParams$dataSource,
         x = "Local Authority",
         y = "Emissions category")

Figure 2.3: Wider Hampshire area per household consumption emissions (T CO2e 2019-20)

3 Parish level

tbc

4 R Environment

Packages used:

• data.table (Dowle et al. 2015)
• ggplot2 (Wickham 2009)
• flextable (Gohel 2020)

5 Annexes

References

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.

Gohel, David. 2020. Flextable: Functions for Tabular Reporting. https://CRAN.R-project.org/package=flextable.

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