Final Report
ICP2 – Online Tool Modelling
Assumptions Technical Annex
This report presents the assumptions that informed the modelling that
underpins the ICP2 Online Tool
Date: May 2015
Project Code: ROT059
WRAP’s vision is a world in which
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Our mission is to accelerate the move to a
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through re-inventing how we design,
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how we use and consume products; and
re-defining what is possible through
recycling and re-use.
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Document reference (please use this reference when citing WRAP’s work):
[WRAP, 2015, Banbury, ICP2 – Online Tool Modelling Assumptions Technical Annex, Prepared by WRAP]
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Contents
1.0
2.0
3.0
4.0
5.0
About ICP2 ................................................................................................... 5
KAT and ICP2 ............................................................................................... 5
Scenarios ...................................................................................................... 7
Rurality Groups .......................................................................................... 11
Baseline Assumptions ................................................................................ 15
5.1
Waste composition................................................................................... 15
5.2
Household Numbers ................................................................................. 16
5.3
Operational Data for setting the baselines .................................................. 16
6.0 Scenario assumptions – operational .......................................................... 19
6.1
Containment ........................................................................................... 19
6.2
Vehicles .................................................................................................. 20
6.3
Material transfer ...................................................................................... 22
6.4
Travel times ............................................................................................ 23
6.5
Loading and collecting times ..................................................................... 23
6.6
Bulk density ............................................................................................ 23
7.0 Scenario Assumptions - Performance......................................................... 26
7.1
Performance Yield Differentials ................................................................. 26
7.1.1 Food Yields ................................................................................... 28
7.2
Contamination rates – dry recycling........................................................... 28
7.2.1 Co-mingled and Two Stream contamination estimates ...................... 29
7.2.2 Multi-stream contamination estimates ............................................. 30
7.3
Waste arisings ......................................................................................... 31
7.4
Set out and participation rate.................................................................... 31
8.0 Scenario assumptions – Cost...................................................................... 33
8.1
Containment ........................................................................................... 33
8.2
Vehicles .................................................................................................. 33
8.3
Crews ..................................................................................................... 34
8.4
Overheads .............................................................................................. 34
9.0 Post-collection process assumptions ......................................................... 34
9.1
Material revenue ...................................................................................... 35
9.2
Cost of sorting ......................................................................................... 35
9.3
Treatment gate fees ................................................................................ 37
Appendix 1 – LA Rurality Classification................................................................ 38
ICP2 – Online Tool Modelling Assumptions Technical Annex
3
Acknowledgements
The following environmental consultancies provided invaluable guidance on the modelling,
assumptions and validation of round sizes, and their help is gratefully acknowledged: Eco
Alternatives, AMEC, Eunomia Research and Consulting, SRI Consulting, Resource Futures,
WYG, Ricardo-AEA Ltd and Jacobs.
ICP2 – Online Tool Modelling Assumptions Technical Annex
4
1.0
About ICP2
In 2008, WRAP published Kerbside Recycling: Indicative Costs and Performance (ICP) which
provided a systematic appraisal of the characteristics of the principal kerbside recycling
collection systems looking at both their cost and effectiveness. The outputs from that
project have been widely used.
The continued use of those outputs by Local Authorities, the development of new types of
recycling services, combined with improved knowledge around scheme performance and
costs, resulted in a need to update the project (ICP2).
The aim of the update is to produce a series of benchmark costs and standard operational
data, through service modelling, that local authorities can use when evaluating their current
recycling service and considering service changes. The resultant benchmarks are based on
the performance (yields of food and dry recycling) and cost of a modelled good practice
system operated across a range of geographical areas.
The key output from this project is the ICP2 Online Tool. The online tool takes the
operational outputs and collection costs from the ICP2 modelling and allows the user to
localise the outputs by applying their own gate fees, bulking costs, disposal costs and
material revenues to produce an overall scenario cost. The localised scenarios are displayed
in a table and a graph.
This document provides the detail behind the assumptions used in the ICP2 modelling.
2.0 KAT and ICP2
The Kerbside Analysis Tool (KAT) was developed in 2004 for modelling recycling and
residual waste collection services. It has been updated over the following 10 years to
expand the number and variety of recycling collection systems that can be covered by the
model. The most recent update was in 2012 (KATv5) and it is this version that is used in
ICP2. KAT forms just one part of the ICP2 model. The other parts include a set of yield
calculations, a sorting cost tool, and an ICP2 results model ; their interactions are shown in
Figure 1. The components are shown in white boxes and the data flows in blue.
Figure 1 ICP2 components and data flow
Yield calculations
Yield data
for each
scenario
KAT – 1 for each
scenario
Collection
infrastructure
requirements
and costs
ICP Results model
Tonnes and
composition
of mixed dry
recycling
ICP sorting model
Collection
Costs
Default
revenue values
and treatment
fees
Default MRF
gate fees
ICP2 Online Tool
ICP2 – Online Tool Modelling Assumptions Technical Annex
5
The outputs from these components feed into the Online Tool. These are combined with
inputs defined by the user. The overall structure of the approach to the Online Tool is
shown below.
Figure 2 ICP Online Tool structure
User Inputs
Selects Rurality
Selects scenario
reflecting current
service
Selects scenarios
for comparison
ICP2 Inputs
Defined Inputs:
·
Bulking
·
Sorting gate
fee
·
Treatment
gate fee
·
Disposal gate
fee
·
Material
revenue
Selects results to display
·
Dry collection costs (including food
where collected)
·
Dry sorting costs
·
Dry material income
·
Food treatment
·
Residual waste collection costs
·
Residual waste disposal
Collection costs for dry recycling, food
waste collections and residual waste
collections including:
·
Vehicles (annualised capital,
standing and running costs)
·
Crews
·
Containers (including replacements)
·
Overheads
Dry and food
recycling yield
Pass Rate –
households
passed per hour of
productive time
Round Size
Pick Rate –
households
collected from per
hour of productive
time
Model Outputs
ICP2 – Online Tool Modelling Assumptions Technical Annex
6
The assumptions detailed in this annex were used in one or more components of ICP2 and
are described in the following groupings:





Baseline assumptions;
Scenario assumptions – operational;
Scenario assumptions – performance;
Scenario assumptions – cost; and
Post collection process assumptions.
3.0 Scenarios
In 2013/14, all mainland authorities in England operated a kerbside recycling. The schemes
in operation are categorised by three types:
 Single Stream Co-mingled (SS), operated by 50% of LAs in 2013/14
All materials are collected together in one compartment on the same vehicle and require
sorting at a MRF (Materials Recovery Facility).
 Two-stream (TS), operated by 34% of LAs
Materials are collected as two material streams, typically either fibres and containers, or
glass separate to other mixed material. At least one of the streams requires sorting at a
MRF.
 Multi-stream (MS), operated by 29% of LAs
Materials are separated by the householder or, on collection at the kerbside, into multiple
material streams. The material streams may include a selected mix of some materials,
typically cans and plastics, which are commonly separated using basic sorting facilities at the
operating depot or sold to reprocessors as a mixed commodity.
The percentages add up to more than 100% as some LAs will operate more than one
scheme type in their authority (e.g. multi-stream to kerbside properties and two-stream to
flats).
In 2013/14, 81% of authorities in England collected the 5 OPRL1 ‘widely recycled’ materials
(paper, card, cans, glass and plastic bottles) at the kerbside. 53% of authorities also
collected pots, tubs and trays (PTT). ICP2 focuses on good practice schemes therefore the
scenarios chosen collected all 6 of these materials at the kerbside.
Food waste collections have become more prevalent since the original ICP modelling and
research has shown that food waste collections are linked to high recycling rates2.
1
On-pack Recycling Label http://www.onpackrecyclinglabel.org.uk/
2
Regression 12/13
ICP2 – Online Tool Modelling Assumptions Technical Annex
7
Therefore, focussing again on good practice, several scenarios included a separate food
waste collection to provide an integrated service.
Whilst garden waste collections are prevalent, the services were not included in the
scenarios due to the wide variability in service collection types offered across England and
the yield derived from them. The large variations in yield and costs are a result of
differences in collection frequency, crew size, variations in garden sizes, numbers of
properties with gardens, seasonal or year round service delivery, containment approaches
and whether services are offered free or by subscription to residents.
Table 1 lists the 18 scenarios identified for modelling. The scenario code describes the
scenario:
The above example is a fortnightly two stream collection where glass is the separate stream
to the other mixed materials (paper, card, cans, plastic bottles and PTT) and collected on a
split-back RCV. Residual waste is collected fortnightly and food waste is collected weekly
with a dedicated vehicle.
Table 1 Scenarios for modelling
Scenario
code
Scheme type
Vehicle
RCV
SS
2W:1W
SS
3
Single stream
co-mingled
Single stream
RCV
Container and
materials
240 litre wheeled bin
– glass, cans, paper,
card, plastic bottles,
PTT
240 litre wheeled bin
Recycling
Frequency
Residual
Frequency3
Fortnightly
Fortnightly
Weekly
Fortnightly
All residual waste collections use wheeled bins only
ICP2 – Online Tool Modelling Assumptions Technical Annex
8
2W:2W
SS
2W:2W
pod
SS
2W:2W
sep
TS (fb
split)
2W:1W
TS (fb
split)
2W:2W
TS (fb
split)
2W:2W
sep
TS (gl
split)
2W:1W
TS (gl
split)
2W:2W
co-mingled
Single stream
co-mingled
with cocollected food
waste
Single stream
co-mingled
with separately
collected food
waste
Two stream –
fibres separate
to containers
Two stream –
fibres separate
to containers
Two stream –
fibres separate
to containers
with separately
collected food
waste
Two stream –
glass separate
to other
materials
Two stream –
glass separate
to other
RCV
with
food
pod for
both
residual
and
recycling
RCV
7.5
tonne
food
waste
vehicle
Splitback
RCV
– glass, cans, paper,
card, plastic bottles,
PTT
240 litre wheeled bin
– glass, cans, paper,
card, plastic bottles,
PTT
Kitchen and kerbside
caddy with liners
provided
240 litre wheeled bin
– glass, cans, paper,
card, plastic bottles,
PTT
Kitchen and kerbside
caddy with liners
provided
Splitback
RCV
Splitback
RCV
Reusable sack –
paper and card
240 litre wheeled bin
– glass, cans, plastic
bottles, PTT
Splitback
RCV
Fortnightly
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
240 litre wheeled bin
– glass, cans, plastic
bottles, PTT
Reusable sack –
paper and card
240 litre wheeled bin
– glass, cans, plastic
bottles, PTT
7.5
tonne
food
waste
vehicle
Splitback
RCV
Fortnightly
Reusable sack –
paper and card
Kitchen and kerbside
caddy with liners
provided
240 litre wheeled bin
– paper, card, cans,
plastic bottles, PTT
55 litre box – glass
240 litre wheeled bin
– paper, card, cans,
plastic bottles, PTT
ICP2 – Online Tool Modelling Assumptions Technical Annex
9
materials
Splitback
RCV
TS (gl
split)
2W:2W
sep
Two stream –
glass separate
to other
materials with
separately
collected food
waste
TS (gl
sep)
2W:1W
Two stream –
glass separate
to other
materials
TS (gl
sep)
2W:2W
Two stream –
glass separate
to other
materials
TS (gl
sep)
2W:2W
pod
Two stream –
glass separate
to other
materials with
co-collected
food waste
7.5
tonne
food
waste
vehicle
RCV
RCV
RCV
RCV
RCV
with
food
pod for
both
residual
and
recycling
RCV
Stillage4
Multi-stream
MS
2W:1W
Stillage
Multi-stream
MS
2W:2W
MS
1W:1W
Operated with
driver plus 1
55 litre box – glass
Kitchen and kerbside
caddy with liners
provided
240 litre wheeled bin
– paper, card, cans,
plastic bottles, PTT
55 litre box – glass
240 litre wheeled bin
– paper, card, cans,
plastic bottles, PTT
55 litre box – glass
240 litre wheeled bin
– cans, paper, card,
plastic bottles, PTT
Kitchen and kerbside
caddy with liners
provided
55 litre box – glass
55 litre box – glass,
card
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
Fortnightly
Weekly
Weekly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Fortnightly
Weekly
Fortnightly
Fortnightly
Weekly
Weekly
55 litre box – paper
Operated with
driver plus 2
loaders
Operated with
driver plus 2
loaders
Multi-stream
55 litre box – glass
240 litre wheeled bin
– paper, card, cans,
plastic bottles, PTT
Reusable sack – cans,
plastic bottles, PTT
55 litre box – glass,
card
55 litre box – paper
Stillage
Reusable sack – cans,
plastic bottles, PTT
55 litre box – glass,
card
55 litre box – paper
4
This stillage vehicle is typical of the new generation stillage vehicles; approx. 30m3 in volume and collects all materials on a
single pass
ICP2 – Online Tool Modelling Assumptions Technical Annex
10
loader
Stillage
MS
1W:2W
Reusable sack –
cans, plastic bottles,
PTT
55 litre box – glass,
card
Multi-stream
55 litre box – paper
Operated with
driver plus 1
loader
Reusable sack –
cans, plastic bottles,
PTT
55 litre box – glass,
card
Stillage
Weekly
Fortnightly
Weekly
Fortnightly
55 litre box – paper
MS
1W:2W
stillage
Multi-stream
with cocollected food
Reusable sack –
cans, plastic bottles,
PTT
Operated with
driver plus 1
loader
Kitchen and kerbside
caddy with liners
provided
4.0 Rurality Groups
In the ICP2 tool, the user can select one of six rurality groups to display the results. The
rurality groups represent different geographical and demographic contexts and therefore
model the scenarios in areas that are more specific than just urban/rural as was the
assumption in the original ICP study.
In developing a rurality classification scheme the 2001 Defra classification of rurality was
explored to see if it could be used. This classification of local authority area types describes
areas in terms of their rurality5 (Major Urban, Large Urban, Significant Urban, Significant
Rural, Rural-50 and Rural-80). This classification was tested against the variables ‘population
density’ and ‘proportion of rural households’ (Figure 3) to establish the extent to which it
provided a clear urban-rural gradient. The analysis showed that a number of authorities
classified as ‘Major Urban’ and ‘Large Urban’ had population densities, or proportions of
households classified as rural, that were similar to local authorities classed as ‘Significant
Rural’. This over-lapping classification meant that it was not ideal for use in ICP2.
5
https://www.gov.uk/government/statistics/2001-rural-urban-definition-la-classification-and-other-geographies
ICP2 – Online Tool Modelling Assumptions Technical Annex
11
Figure 3 Chart showing distribution of Local Authorities by population density and % rural
households; split by ONS/Defra classification of rurality
Major Urban and
Large Urban with
similar proportion
of households
classed as
Significant Rural
A simpler approach was developed where the unitary and collection authorities were split
into three groups based on the percentage of rural households (Figure 4). This classification
gave a clearer demarcation between urban authorities and those intermediate authorities
with significant rural housing. Three geographical contexts were chosen and defined as:
 Predominantly urban (less than 1.5% of households defined as ‘rural’)
 Mixed urban and rural (more than 1.5% but less than 33% defined as rural)
 Predominantly rural (more than 33% of households defined as rural)
ICP2 – Online Tool Modelling Assumptions Technical Annex
12
Figure 4 Chart showing scatterplot of population density versus ‘% households rural’, split
by 3 groups based on ‘% households rural’
Less than 1.5% rural
>1.5, less than 33% rural
>33% rural
WRAP’s work on analysing factors that affect kerbside recycling performance6 identified
deprivation as a key influence. As the relative performance of a scheme has a bearing on
workload for crews (proportion of households setting out containers and the quantity of
materials presented ) and, consequently, collection infrastructure and costs, each of the
three geography categories above were split to identify groups with higher and lower levels
of deprivation.
6
Analysis of Recycling Performance and Waste Arisings 2012/13
ICP2 – Online Tool Modelling Assumptions Technical Annex
13
Figure 5 Population density versus IMD score, split by 3-part urban-rural classification
Less than 1.5% rural
>1.5, less than 33% rural
>33% rural
Thus the following six-part Rurality classification has been used to set the local authority
context for each of the models:






Rurality 1
Rurality 2
Rurality 3
Rurality 4
Rurality 5
Rurality 6
–
–
–
–
–
–
Predominantly Urban, higher deprivation (46 LAs);
Predominantly Urban, lower deprivation (43 LAs);
Mixed Urban/Rural, higher deprivation (53 LAs);
Mixed Urban/Rural, lower deprivation (49 LAs);
Predominantly Rural, higher deprivation (67 LAs); and
Predominantly Rural, lower deprivation (68 LAs).
Figure 5 above shows that a number of authorities lie very close to the classification
boundaries. In these cases, it may be useful for some authorities to also analyse the ICP2
results from a neighbouring rurality category. Some authorities may also have wide
variation in rurality across the authority area and may find it useful to apply the results from
different ruralities to each of those areas. Local Authorities can review which category they
have been allocated in Appendix 1.
ICP2 – Online Tool Modelling Assumptions Technical Annex
14
5.0
Baseline Assumptions
Typically a baseline is set up to reflect the current recycling and residual collection in an
authority so that all scenarios can be compared against it. In ICP2, the baseline model is
used to set up the context (geographical and operational) of the rurality and, to do this,
data for the residual waste collection is used. It ensures that all scenarios are modelled
against the same authority background.
5.1
Waste composition
The waste composition used in the KAT models is based on Defra’s Updated compositional
estimates for local authority collected waste and recycling in England, 2010/117. The
kerbside recycling and residual waste composition was translated across into the material
categories present in KAT.
Each of the scenarios assumes that a free garden waste collection is present though is not
modelled for costs. Since capture of garden waste is typically high it is assumed therefore
that the 15.5% of the kerbside residual and recycling waste composition that represents
garden waste is reduced to 4% (representing the amount in residual), and the other
materials adjusted accordingly to bring the total back to 100%.
Table 2 Modelled waste composition
KAT material categories
Newspapers and Magazines
Other paper
Corrugated card
Non-corrugated card
Plastic film
Plastic bottles
Plastic - other dense
Glass flint
Glass brown
Glass green
Steel cans
Aluminium cans
Foil containers
Textiles
Soil and other organic
Food waste
Compostable garden waste
ICP2 kerbside
waste
composition
10.95%
8.63%
2.16%
3.41%
5.17%
2.50%
3.54%
4.54%
0.68%
2.32%
1.64%
0.33%
0.49%
3.05%
4.25%
24.64%
4.00%
7
http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=18237#Related
Documents
ICP2 – Online Tool Modelling Assumptions Technical Annex
15
Other
Total
5.2
17.72%
100.0%
Household Numbers
This study focussed on collections from kerbside rounds and does not include collections
from flats. This is because both the logistics of collecting from flats, and performance
achieved from them, tends to be quite different to kerbside operations.
The average dwelling stock for each rurality was calculated using the 2008/09 dwelling stock
figures for each authority in the particular group. Operational residual waste collection data
was gathered from a sample of authorities from each rurality group and the percentage of
housing stock that were flats were calculated via 2001 census data for those authorities.
The flats percentage was then removed from the dwelling stock, and the remaining figure
rounded to provide the number of kerbside households to use in the modelling for each
rurality.
Table 3 Modelled household numbers
Rurality
1) Predominantly urban,
higher deprivation
2) Predominantly urban,
lower deprivation
3) Mixed urban/rural,
higher deprivation
4) Mixed urban/rural,
lower deprivation
5) Predominantly rural,
higher deprivation
6) Predominantly rural,
lower deprivation
Average
dwelling
stock
Average
flats
proportion
Non-flat
households
Modelled
households
112720
22%
88328
88000
67355
21%
53538
54000
84350
10%
75502
76000
55400
14%
47606
48000
44093
7%
41111
41000
46013
8%
42425
42000
5.3
Operational Data for setting the baselines
The baseline set up in KAT for each rurality takes account of the average geography,
housing density and travel distances within the authority, as well as work rates of crews, in
order to calculate a baseline rate of round productivity. The baseline rate of productivity is
projected into all the modelling scenarios to allow for like-for-like comparisons. It is
therefore important to make sure that the baseline matches up closely with what actually
happens in the local authority being modelled.
As ICP2 uses 6 fictional authorities to represent the 6 rurality groups, it was vital to get
robust data from actual authorities within those rurality groups to inform the baseline. The
following data on residual collections was gathered from over 90 authorities:
· Frequency;
· Containment;
· Number and type of vehicles;
ICP2 – Online Tool Modelling Assumptions Technical Annex
16
·
·
·
·
·
·
·
·
Crew sizes;
Working hours;
Time to travel from depot to start of round;
Time to travel from round to unload;
Unloading Time;
Time to travel from unloading to depot;
Number of loads per day; and
Round sizes.
82% of English local authorities used a wheeled bin for collecting residual waste in 2013/14.
Wheeled bins were therefore used in the modelling. The operational data collected was
filtered to remove sack collections and any collections using split vehicles. It was also
filtered to focus on collections operating over five days as five-day working was more
prevalent than four-day working when the data was collected.
Weekly residual waste collections were more prevalent than fortnightly collections in
ruralities 1 and 2 so the baseline data was set up to reflect this. Fortnightly residual waste
collections were modelled in the baselines for ruralities 3-6.
The key average values taken from the gathered data and fed into the baselines are listed in
ICP2 – Online Tool Modelling Assumptions Technical Annex
17
Table 4 below.
ICP2 – Online Tool Modelling Assumptions Technical Annex
18
Table 4 Key assumptions for residual waste collections
Rurality
1) Predominantly
urban, higher
deprivation
2) Predominantly
urban, lower
deprivation
3) Mixed
urban/rural,
higher
deprivation
4) Mixed
urban/rural,
lower deprivation
5) Predominantly
rural, higher
deprivation
6) Predominantly
rural, lower
deprivation
Average
Round Size for
frequency
Number of
vehicles for
weekly
residual waste
determined
from round
size and
modelled
households
weekly
1501
11.7
21
weekly
1283
8.4
21
16
fortnightly
1200
6.3
16
24
19
fortnightly
1135
4.2
19
23
17
fortnightly
1148
3.6
17
24
19
fortnightly
1044
4.0
Av. time to
drive from
starting
depot to
beginning
of round
(mins)
Av. time to
drive from
round to
unload
(mins)
Av. time to
drive from
unloading to
the finish
depot (mins)
16
21
11
14
24
14
Frequency of
collection in
baseline
Number of
vehicles for
fortnightly
residual waste
determined
from round size
and modelled
households
ICP2 – Online Tool Modelling Assumptions Technical Annex
19
The average unloading time of 18 minutes was used across all ruralities. The most common
working day length was 6.5 hours. This is the time from leaving the depot to returning at
the end of the shift but excludes breaks.
The numbers of households in the baselines were divided by the average round size to
determine the number of vehicles required. The data for existing services also showed that
a driver plus 2 loaders was the most common crew levels for wheeled bin residual waste
collections.
6.0
Scenario assumptions – operational
6.1
Containment
Residual waste collections are modelled with a 180 litre wheeled bin when collected weekly,
and a 240 litre wheeled bin when collected fortnightly. For each scenario all households are
modelled with the same containment and it is assumed that each household can
accommodate a wheeled bin.
Where food waste is collected, the householder is provided with a 5 litre indoor caddy with
liners and a 23 litre outdoor caddy.
The containment for the dry recycling collections depends on the type of collection. The
containers for each scheme type are listed in Table 5. The effective weekly dry containment
capacity shows how much volume is available to the householder on a per week basis and
ranges from 100 to 200 litres.
Table 5 Dry recycling containers
Scheme
type
Single stream
co-mingled
Two stream –
fibres
separate to
containers
Two stream –
glass
separate to
other
materials
Scenario codes
SS
SS
SS
SS
2W:1W
2W:2W
2W:2W pod
2W:2W sep
TS (fb split) 2W:1W
TS (fb split) 2W:2W
TS (fb split) 2W:2W
sep
TS (gl
TS (gl
TS (gl
sep
TS (gl
TS (gl
TS (gl
pod
Dry
container
240 litre
wheeled bin
240 litre
wheeled bin
Materials
collected
glass, cans,
paper, card,
plastic bottles,
PTT
glass, cans,
plastic bottles,
PTT
90 litre
reusable sack
paper and card
split) 2W:1W
split) 2W:2W
split) 2W:2W
240 litre
wheeled bin
paper, card,
cans, plastic
bottles, PTT
sep) 2W:1W
sep) 2W:2W
sep) 2W:2W
55 litre box
Effective
weekly dry
containment
capacity
120 litres
165 litres
combined
147.5 litres
combined
glass
ICP2 – Online Tool Modelling Assumptions Technical Annex
20
55 litre box
Fortnightly
Multi-stream
MS 2W:1W
MS 2W:2W
Weekly Multistream
MS 1W:1W
MS 1W:2W
MS 1W:2W stillage
55 litre box
90 litre
Reusable sack
55 litre box
55 litre box
90 litre
Reusable sack
glass, card
paper
cans, plastic
bottles, PTT
glass, card
paper
cans, plastic
bottles, PTT
100 litres
combined
200 litres
combined
6.2
Vehicles
The specifications, for vehicles used in the modelling, are provided in Table 6.
The vehicle unloading time is the time between entering and leaving the transfer
station/depot and therefore includes queueing time.
Table 6 Vehicle specifications
Vehicle
type
Volume
capacity
Scenarios
Maximum
payload
Vehicle
unloading
time
Residual waste collections for all
scenarios where food is not cocollected
Single stream co-mingled
collections
SS 2W:1W
SS 2W:2W
SS 2W:2W sep
RCV
Two stream collections – the
mixed material fraction when
glass is collected on a separate
vehicle
TS (gl sep) 2W:1W
TS (gl sep) 2W:2W
22m3
11 tonnes
20 minutes
22m3 total
(17m3 for
residual
waste)
10 tonnes
overall (8
tonnes for
residual
waste)
30 minutes
Crewed with a driver plus 2
loaders
RCV with
food pod
Co-collection of food waste with
single stream co-mingled (one
week) and residual (second
week)
SS 2W:2W pod
TS (gl sep) 2W:2W pod
ICP2 – Online Tool Modelling Assumptions Technical Annex
21
Crewed with a driver plus 3
loaders in urban areas (Rurality
1-4) and a driver plus 2 loaders
in rural areas (Rurality 5+6)
Splitbodied
RCV
(50:50)
Two stream collections where
fibres are in a separate stream to
containers (plastics, glass and
cans). The 50:50 arrangement
was optimal for vehicle filling
TS (fb split) 2W:1W
TS (fb split) 2W:2W
TS (fb split) 2W:2W sep
21m3
10 tonnes
25 minutes
21m3
10 tonnes
25 minutes
4.5 tonnes
30 minutes
Crewed with a driver plus 2
loaders
Splitbodied
RCV
(70:30)
Two stream collections where
glass is in a separate stream to
the other mixed materials
(paper, card, plastics and cans).
The 70:30 arrangement with
glass in the smaller compartment
was optimal for vehicle filling
TS (gl split) 2W:1W
TS (gl split) 2W:2W
TS (gl split) 2W:2W sep
Crewed with a driver plus 2
loaders
Compartm
entalised
vehicle8
All multi-stream collections. The
compartments were arranged9:
1) Plastics and cans
2) Paper
3) Card
4) Clear glass
5) Green/brown glass
30m3
6) Food (where applicable)
MS
MS
MS
MS
MS
8
2W:1W
2W:2W
1W:1W
1W:2W
1W:2W stillage
One example of this type of a vehicle is a Resource Recovery Vehicle (RRV)
9
KAT assumes efficient filling of the whole vehicle. To take account of some compartments filling before others and therefore
forcing the vehicle to return to tip before the vehicle is full, the overall filling of the vehicle is set to 80%
ICP2 – Online Tool Modelling Assumptions Technical Annex
22
Crewed with a driver plus 2
loaders in heavily urban areas
(R1+R2) and a driver plus 1
loader in all other areas (R3-R6)
All scenarios where food waste is
collected by a dedicated fleet
Dedicated
food waste
vehicle
SS 2W:2W sep
TS (fb split) 2W:2W sep
TS (gl split) 2W:2W sep
7.5m3
3 tonnes
20 minutes
12m3
8 tonnes
20 minutes
Crewed with a driver plus 1
loader
Two stream scenarios where
glass is collected separately by a
dedicated fleet
Dedicated
glass RCV
TS (gl sep) 2W:1W
TS (gl sep) 2W:2W
TS (gl sep) 2W:2W pod
Crewed with a driver plus 1
loader
The unloading times are derived from KAT default values and operational knowledge. The
additional time required for tipping of the pod vehicle compared to the standard RCV is due
to tipping the food waste into a secure area compliant with animal by-products regulations.
6.3
Material transfer
Residual waste, once collected at kerbside, is taken straight to disposal. All other material
streams are taken to the transfer station/depot. The onward destination of the material,
and whether costs for bulking and haulage are applied, are provided in Table 7.
£10/t is the default value for bulking and haulage in the online tool however this can be
overwritten by the user.
Table 7 Material stream destinations
Material Stream
All dry materials mixed
(paper, card, cans, plastic
bottles, PTT and glass
All dry materials mixed
excluding glass
Separate glass stream
Scenarios
All SS scenarios
Destination after
transfer station
MRF
TS (gl split) and TS (gl
MRF
sep) scenarios
TS (gl split) and TS (gl
Reprocessor
sep) scenarios
Bulking and
haulage
applied?
Yes
Yes
Factored into
revenue
ICP2 – Online Tool Modelling Assumptions Technical Annex
23
Mixed containers (glass,
cans, plastic bottles, PTT)
Separate fibres stream
(paper and card)
Individual kerbside sorted
dry materials (paper, card,
clear glass, brown/green
glass)
TS (fb split) scenarios
MRF
Yes
TS (fb split) scenarios
Reprocessor
Factored into
revenue
All MS scenarios
Reprocessor
Factored into
revenue
Mixed plastics and cans
All MS scenarios
Food waste
All food scenarios
Factored into
Sorting and transfer
revenue post
station
sorting
AD facility
Yes
6.4
Travel times
The travel times provided in
ICP2 – Online Tool Modelling Assumptions Technical Annex
24
Table 4 and used in the baseline are based on the residual waste being tipped at the landfill
site or at a transfer station that is not at the depot. Section 6.3 states that all dry recycling
and food waste is initially tipped at a transfer station before onward transport and therefore
the travel times from
ICP2 – Online Tool Modelling Assumptions Technical Annex
25
Table 4 are applied to all the scenarios.
6.5
Loading and collecting times
The time taken to collect a container and to load the contents onto the vehicle is critical to
the outputs of the modelling process. Collection time is defined as the time taken to pick
up/gather the container and be ready to take it back to the vehicle. In other words, the
time taken to grasp the handle of the bin or for box collections the time to gather the
box/sack combination. Loading time is defined as the time taken to load the contents of the
container onto the vehicle and be ready to return the container to the set out point. For
wheeled bins, this is the time it takes to hook the bin onto the vehicle, for the emptying
cycle to complete, and to unhook the bin (total of 10 seconds). The time taken to tip
materials from a specific container tends to be standard regardless of the mix of materials in
the container.
The time taken to sort materials at the kerbside depends on the mix of the materials in the
container, and the number of materials present. The loading time for the multi-stream
collection is therefore calculated individually for each specific multi-stream scenario (as the
yeild would impact on the number of materials to sort).
The collection and loading times, plus the time to go from the vehicle to the set out have
been obtained from the filming of recycling collections and analysing the actions of the
crews.
6.6
Bulk density
The bulk density of materials collected dictates when a vehicle is full, with the determinant
being either its volume or weight limit. The assumed individual material bulk densities
represent uncompacted material on a vehicle (as compaction is applied subsequently).
Table 8 Bulk densities to represent uncompacted materials in back of vehicle
Material
Bulk Density
(kg/m3)
References used to derive estimate
WRAP & Resource Futures bulk density report phase 1
News and
mags
300
Other paper
250
Corrugated
card
50
Noncorrugated
card
85
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP & Resource Futures bulk
density report phase 2, WRAP FRA0035 "Bulk Density Study:
Phase 2" (2009), Resource Futures local authority waste audit,
independent industry expert, Ontario government "A Guide to
Waste Audits and Waste Reduction Work Plans for Industrial,
Commercial and Institutional Sectors" - Appendix C (2008)
Resource Futures data derived from multiple waste audits for
360l bin (2009)
Independent industry expert, WRAP & Resource Futures bulk
density report phase 1 "Review of Bulk Densities of Various
Materials in Different Containment Systems" (2007), WRAP
Hospitality Sector Study
Independent industry expert, Ontario government "A Guide to
Waste Audits and Waste Reduction Work Plans for Industrial,
Commercial and Institutional Sectors" - Appendix C (2008),
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007)
Plastic film
20
Plastic
bottles10
22
Plastics other dense
43
Glass
300
Steel cans
86
Aluminium
cans
36
Foil
containers
15
Textiles
200
Soil and
other organic
375
Food waste
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP Hospitality Sector Study,
Resource Futures local authority waste audit, European plastic
recycler
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP & Resource Futures bulk
density report phase 2, WRAP FRA0035 "Bulk Density Study:
Phase 2" (2009), Ontario government "A Guide to Waste
Audits and Waste Reduction Work Plans for Industrial,
Commercial and Institutional Sectors" - Appendix C (2008)
Resource Futures local authority waste audit, European
manufacturer of plastics sorting technologies
Ontario government "A Guide to Waste Audits and Waste
Reduction Work Plans for Industrial, Commercial and
Institutional Sectors" - Appendix C (2008), previous WRAP
references
Ontario government "A Guide to Waste Audits and Waste
Reduction Work Plans for Industrial, Commercial and
Institutional Sectors" - Appendix C (2008)
Ontario government "A Guide to Waste Audits and Waste
Reduction Work Plans for Industrial, Commercial and
Institutional Sectors" - Appendix C (2008)
Resource Futures local authority waste audit
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), Resource Futures local
authority waste audit, Resource Futures conversion factors for
use in 2002-03 NWS work, independent industry expert,
textile reuse company, Ontario government "A Guide to Waste
Audits and Waste Reduction Work Plans for Industrial,
Commercial and Institutional Sectors" - Appendix C (2008)
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP & Resource Futures bulk
density report phase 2, WRAP FRA0035 "Bulk Density Study:
Phase 2" (2009), Resource Futures local authority waste audit,
Resource Futures conversion factors for use in 2002-03 NWS
work, "Characterisation of source-separated household waste
intended for composting" in Bioresource Technology 2011
February; 102(3): 2859–2867, Zero Waste Italy "Implementing and optimising separate collections of
biowaste: the Italian way to tackle operational and economic
issues"
500
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP & Resource Futures bulk
density report phase 2, WRAP FRA0035 "Bulk Density Study:
Phase 2" (2009), Resource Futures local authority waste audit,
Resource Futures conversion factors for use in 2002-03 NWS
10
Bulk density for plastics and cans in multi-stream scenarios is increased to represent the compaction of these materials in the
vehicle compartment
ICP2 – Online Tool Modelling Assumptions Technical Annex
27
work, "Characterisation of source-separated household waste
intended for composting" in Bioresource Technology 2011
February; 102(3): 2859–2867, Zero Waste Italy "Implementing and optimising separate collections of
biowaste: the Italian way to tackle operational and economic
issues"
Compostable
garden waste
250
WRAP & Resource Futures bulk density report phase 1
"Review of Bulk Densities of Various Materials in Different
Containment Systems" (2007), WRAP & Resource Futures bulk
density report phase 2, WRAP FRA0035 "Bulk Density Study:
Phase 2" (2009), Resource Futures local authority waste audit,
Resource Futures conversion factors for use in 2002-03 NWS
work, "Characterisation of source-separated household waste
intended for composting" in Bioresource Technology 2011
February; 102(3): 2859–2867
Full compaction (at a ratio of 3:1) is assumed for the residual waste collections and partial
compaction (at a ratio of 2:1) is assumed for co-mingled dry recycling collections.
ICP2 – Online Tool Modelling Assumptions Technical Annex
28
7.0
Scenario Assumptions - Performance
7.1
Performance Yield Differentials
Kerbside dry yields were analysed from local authorities that met the following thresholds:
·
80% or more households in the authority that are offered a recycling scheme are all
given the same scheme. This means that authorities that had a scheme change mid-
year, or have two schemes in operation in large areas within the authority were
omitted from the analysis. The 80% threshold was important so that performance
could be assigned to a single scheme type.
·
Collect paper, card, cans, plastic bottles and glass from the kerbside. The combined
yield of ‘all 5’ materials was used in the performance differential analysis.
The ‘all 5’ yields were adjusted for contamination (see section 6.2) and analysed for each
rurality group. The upper quartile yields for sample authorities in each rurality are given in
Figure 6.
Figure 6 Step-wise progression of upper quartile performance across ruralities
The trend in Figure 6 showed a step-change in the following sequence:
R6 – predominantly rural, lower deprivation (highest yield)
R4 – mixed urban and rural, lower deprivation
R2 – predominantly urban, lower deprivation
R5 – predominantly rural, higher deprivation
R3 – mixed urban and rural, higher deprivation
R1 – predominantly urban, higher deprivation (lowest yield)
ICP2 – Online Tool Modelling Assumptions Technical Annex
29
A 16 kg/hh/yr increase in yield was applied to each of the ruralities which gave an 80
kg/hh/yr overall difference between R6 and R1, matching well with the difference in Figure
6.
To understand the difference in kerbside yields between difference scheme types, yields
were analysed, looking at confidence intervals around the mean, to understand the variation
across authorities within each Rurality group. Combining this with the step-change for
rurality enabled the calculation of a set of performance yield differentials.
From the monitoring of performance benchmarks of actual schemes in operation between
2010/11 and 2012/13, it was felt that the performance differentials developed using
2010/11 data were still applicable in 2012/13. The differentials were therefore applied to
2012/13 upper quartile yields for each rurality group to give the total ‘all 5’ yields for each
rurality-scenario combination.
This ‘all 5’ total yield was split to give yields for the individual material streams. The split
applied for each rurality was calculated from the distribution of material yields of the
authorities in the top quartile for each rurality group. For example, in rurality 1, the split of
materials for authorities in the top quartile is as follows:





45% Paper
16% Card
5% Cans
27% Glass
7% Plastic bottles
Since all the scenarios included plastic pots, tubs and trays (PTT), a yield for PTT needed to
be added. Reviewing the available waste composition studies of the authorities included in
the analysis showed that where PTT were collected, the ratio between bottles and PTT was
55:45. The plastic bottle yield derived from the ‘all 5’ was therefore scaled to produce the
PTT yield.
The total yields excluding contamination for each rurality-scenario combination are provided
in Figure 7.
ICP2 – Online Tool Modelling Assumptions Technical Annex
30
Figure 7 Modelled dry recycling yields for each scheme/rurality combination
7.1.1 Food Yields
For the scenarios collecting food waste, the expected yield of food was derived from WRAP’s
food waste ready reckoner11. This calculation links the yield of food waste collected to the
level of deprivation and frequency of residual waste collection. It provided the following
yields:
Rurality
R1
R2
R3
R4
R5
R6
Food yield
(kg/hh/yr)
65
84
74
92
83
94
These yields are indicative and dependent on the provision of liners and the use of clear and
regular communications. The cost of food liners has been included in the model but the
cost of communications has not.
7.2
Contamination rates – dry recycling
Where waste composition studies were available (between 2008 and 2012) for the
authorities meeting the selection thresholds, the level of non-targeted material that gets
11
http://www.wrap.org.uk/sites/files/wrap/Evaluation_of_the_WRAP_FW_Collection_Trials_Update_June_2009.pdf
ICP2 – Online Tool Modelling Assumptions Technical Annex
31
collected at the kerbside was analysed. Contamination was defined as non-targeted
material.
7.2.1 Co-mingled and Two Stream contamination estimates
The sample size was sufficient to analyse fortnightly co-mingled collections with fortnightly
residual (SS 2W:2W), but not sufficient to look at two stream collections, or co-mingled
collections with weekly residual, in isolation (especially as the associated yields clustered
around lower performing authorities rather than giving a range of performance).
A different approach was therefore developed, combining all the co-mingled and two stream
data points into one dataset. This dataset was split into different recycling/ residual waste
frequency combinations:
· weekly recycling with weekly residual waste;
· fortnightly recycling with weekly residual waste; and
· fortnightly recycling with fortnightly residual waste.
The groups were compared and there was no statistically significant difference between the
mean contamination rates of these groups.
The relatively low yields for the two-stream collections and other co-mingled collection
frequencies were compared with the lower yields of the SS 2W:2W dataset. Again there was
no statistically significant difference in mean contamination rates between these two groups.
Since no significant differences were found between co-mingled and two stream collections
at low yields, and between different recycling and residual waste frequency combinations,
an exponential function for contamination was determined from the combined dataset. This
function was applied to all co-mingled and two-stream collections. Linear and polynomial
models were considered however the best fit of the data was achieved with the exponential
function.
The function is valid for the sample and the range of yields used to construct it. Caution
should be applied in using the function on yields that exceed the modelled range.
ICP2 – Online Tool Modelling Assumptions Technical Annex
32
Figure 8 Model to predict contamination across all single stream and two stream systems
Contamination yield as a function of collected dry recycling yield
Contamination (kg/household/year)
70
60
50
y = 3.6434e0.0077x
R² = 0.2282
40
30
20
10
0
100
150
200
250
300
350
Kerbside yield (kg/household/year)
7.2.2 Multi-stream contamination estimates
Using contamination estimates from waste composition studies for multi-stream collections
do not take into account the reduction of contamination that occurs through crews leaving
non-targeted material in the container for the householder, instead of loading them on the
vehicle, because the samples for waste composition studies are taken prior to the crews
sorting the material at the kerbside. Reviews of pre-2008 composition data, applying an
assumption that half the contamination at the kerbside would be eliminated by operatives,
produced a 2% contamination rate. The 2% figure was applied to the yields calculated from
WDF but it should be noted that the tonnages within WDF for multi-stream authorities are
sometimes net of contamination identified at the transfer station as the weighing of the
material occurs after bulking for onward transport to the next destination for that material.
The contamination rates derived above were applied to collected yields at the kerbside to
give an indication of ‘adjusted’ yield.
Table 9 Contamination rates applied to kerbside dry yields
Scheme Type
All co-mingled and two stream
Multi-stream
Contamination
3.6434*EXP(0.0077*total yield)
2%
Applying this formula gives a contamination rate of 8-11% for the fully co-mingled
collections.
ICP2 – Online Tool Modelling Assumptions Technical Annex
33
7.3
Waste arisings
Analysis of the kerbside arisings (residual, dry and food), for the authorities used to
generate the performance differentials (Section 7.1), showed an average difference in
kerbside arisings of 55 kg/hh/yr between authorities with weekly residual and those with
fortnightly residual collections12. Combining this with the different dry performance
differentials and food waste yields across the ruralities, the following kerbside arisings yields
were calculated:
Table 10 Kerbside Arisings Yields (dry, food and residual)
Rurality
1
2
3
4
5
6
Kerbside arisings yield –
weekly residual (kg/hh/yr)
720
768
736
784
752
800
Kerbside arisings yield –
fortnightly residual (kg/hh/yr)
665
713
681
729
697
745
These yields were multiplied by the household numbers and then used as the kerbside
waste arisings for the models.
WRAP’s study ‘Analysis of recycling performance and waste arisings in the UK 2012/13 13’
shows no statistically significant difference between the effective weekly residual
containment capacity (a pseudo measure of frequency) and overall waste arisings.
Therefore even though authorities often notice changes in kerbside arisings upon changing
residual waste frequency (as assumed above), the material is likely to be diverted into other
streams (such as HWRCs).
7.4
Set out and participation rate
The study looks at good practice schemes (which assumes effective communications) so it
was assumed that participation levels would be high. Considering the difference in
performance between the 6 ruralities it was assumed that they would not all have the same
participation rate. The maximum participation rate for the highest performing rurality group
(R6 - Predominantly rural, lower deprivation) was set to 90% and the maximum rates for
the other groups reduced by 2 percentage points based on their relative performances. This
gave the following maximum participation rates:
6)
4)
2)
5)
3)
Predominantly Rural, lower deprivation – 90%
Mixed Urban/Rural, lower deprivation – 88%
Predominantly Urban, lower deprivation – 86%
Predominantly Rural, higher deprivation – 84%
Mixed Urban/Rural, higher deprivation – 82%
12
The 55 kg/hh/yr figure was derived from the 2010/11 dataset. A subsequent analysis of 2012/13 data shows the difference
beween median kerbside arisings in England to be 49 kg/hh/yr and the difference at upper quartile level to be 57 kg/hh/yr.
13
Analysis of Recycling Performance and Waste Arisings 2012/13
ICP2 – Online Tool Modelling Assumptions Technical Annex
34
1) Predominantly Urban, higher deprivation – 80%
Due to the lack of generalised participation rate studies, it was assumed that providing there
is sufficient containment capacity, the type of scheme (multi-stream, single stream comingled, two stream co-mingled) was less of an influence on participation than the
frequency of the recycling and residual waste collection. Fortnightly multi-stream has the
lowest effective weekly recycling containment capacity (100 litres per week) so it was
assumed that it too would have lower participation compared to weekly multi-stream,
fortnightly co-mingled and fortnightly two-stream.
To reflect the differences in participation due to frequency of collection of recycling and
residual waste, the following reductions in maximum participation rates were applied:
·
·
·
Difference between fortnightly residual scheme and weekly residual waste scheme
(for co-mingled, two stream and weekly multi-stream) - 8 percentage points
Difference between multi-stream fortnightly residual waste with fortnightly recycling
from multi-stream fortnightly residual waste with weekly recycling – 5 percentage
points
Difference in multi-stream weekly residual waste with fortnightly recycling and multistream weekly residual waste with weekly recycling– 2 percentage points.
Table 11 below shows the final participation rates for each scenario modelled.
Table 11 Participation rates for each scenario/rurality combination
Scenarios
Predominantly
Urban
More
Less
deprived deprived
R1
R2
Rurality
Mixed
Urban/Rural
More
Less
deprived deprived
R3
R4
Predominantly
Rural
More
Less
deprived
deprived
R5
R6
SS 2W:1W
72%
78%
74%
80%
76%
82%
SS 2W:2W
80%
86%
82%
88%
84%
90%
TS 2W:1W
72%
78%
74%
80%
76%
82%
TS 2W:2W
80%
86%
82%
88%
84%
90%
MS 2W:1W
70%
76%
72%
78%
74%
80%
MS 2W:2W
75%
81%
77%
83%
79%
85%
MS 1W:1W
72%
78%
74%
80%
76%
82%
MS 1W:2W
80%
86%
82%
88%
84%
90%
The set out rate applied was dependent on the frequency of the recycling collection. From
analysing the limited data available on set out and participation, set out was 8 percentage
points lower than participation when recycling was collected fortnightly, and 20 percentage
points lower when collected weekly. This function was applied to the participation rates in
Table 11 and then rounded to the nearest 5% to produce the set out rates (KAT models set
out in intervals of 5%).
ICP2 – Online Tool Modelling Assumptions Technical Annex
35
8.0
Scenario assumptions – Cost
8.1
Containment
The capital cost of containers and their annual replacement rates are provided in Table 12.
For each scenario, the capital cost of container purchase is annualised over a 7 year period
at a financing rate of 2%.
Table 12 Container costs and replacement rates
Container
55 litre box with lid
90 litre reusable sack
180 litre wheeled bin
240 litre wheeled bin
23 litre kerbside caddy and
5 litre kitchen caddy
Unit cost plus
urban delivery
£3.90
£1.50
£17.85
£18.20
Unit cost plus
rural delivery
£4.05
£1.60
£18.25
£18.60
Replacement
rate
4%
25%
2%
2%
£4.06
£4.28
4%
Food waste liners for the 5 litre kitchen caddies were 2 pence per liner with 2.5 liners per
participating household per week.
8.2
Vehicles
The vehicle capital costs reflect the technology required to meet Euro VI regulations for
emissions. In the modelling, the capital cost is annualised over a 7 year period at a
financing rate of 2%.
The standing cost includes insurance, tax and licensing for the vehicles and is calculated as
5% of the capital plus road tax. The running costs cover maintenance, tyres and oil as is
calculated as 10% of the capital for all vehicles except the 7.5 tonne dedicated food waste
vehicle where running costs are 7.5% of the capital.
Table 13 Vehicle capital, annual standing and annual running costs
Vehicle
RCV (22m3)
RCV with food pod
Split-bodied RCV (50:50)
Split-bodied RCV (70:30)
Compartmentalised vehicle
Dedicated food waste
vehicle
Dedicated glass RCV (12m3)
Capital cost
including bin lift
where required
£142,000
£162,000
£152,000
£152,000
£92,000
Annual standing Annual running
costs
costs
£7,750
£8,750
£8,250
£8,250
£4,800
£14,200
£16,200
£15,200
£15,200
£9,200
£65,000
£3,450
£4,875
£120,000
£6,650
£12,000
ICP2 – Online Tool Modelling Assumptions Technical Annex
36
Fuel is assumed to be £1.29 per litre. This is based on the price for diesel (28/06/13)14
excluding VAT. The fuel price is then increased by 11% to account for the reduced fuel
efficiency resulting from the technology required for the Euro VI regulations.
8.3
Crews
The annual cost for drivers and loaders included salary, on-costs, holiday and sickness cover
(16.5% of salary and on-costs) and pension (12% of salary and on-cost). The total cost per
driver and loader were therefore:
 Driver £30,816
 Loader £23,966
Supervision costs are included as 9% of the total crew costs.
8.4
Overheads
To account for a contribution towards depot cost and local overheads, a cost per household
was assumed that was graded depending on household size:
Table 14 Direct overheads
Authority size (households)
20-30,000
40-50,000
50-60,000
60-70,000
>100,000
Direct Overheads per
household (£)
4.00
3.50
3.00
2.50
2.50
Direct (or Local) overheads are individual to each authority therefore the values assumed
above just provide a contribution towards overall local overheads for the waste service.
Corporate overheads are additional to the local overheads and are assumed to be 6% of the
total collection costs for each service.
9.0 Post-collection process assumptions
The KAT models provide the costs and tonnages associated with the collection of recycling
and residual waste from the household and taking it to the transfer station/disposal site.
These costs are the collection costs used in the Online Tool.
The user can then define their own bulking and haulage, sorting fees, material revenue,
treatment and disposal costs. ICP2 does provide default values which are explained in this
section.
14
https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/253438/weekly_fuel_prices_281013.xls
ICP2 – Online Tool Modelling Assumptions Technical Annex
37
9.1
Material revenue
Where an individual material is separately collected and sold to the reprocessor from the
transfer station, the revenue value received for that material is the 2012 average taken from
WRAP’s Material Pricing Report (MPR)15. All material values except glass taken from the
MPR are ‘ex-works’ which means that the cost of bulking and hauling the material is
accounted for within the price. For glass, a £10/tonne bulking and haulage fee has been
subtracted from the reported MPR price to make it equivalent to the other materials.
Table 15 Material revenues – separately collected materials
Material Grade
Mixed paper and cardboard
News and Pams
OCC16
Clear glass
Green/brown glass17
Mixed glass
ICP2 Scenario
TS (fb split)
MS
MS
MS
MS
TS (gl split)
TS (gl sep)
Average 2012 MPR
value (£/t)
£59
£92
£74
£19
£4
-£2
9.2
Cost of sorting
The ICP2 sorting cost model calculates the cost of sorting 4 different mixes of dry recyclate.
The assumptions about the facilities used for each mix are detailed in Table 16.
Table 16 Vehicle capital, annual standing and annual running costs
Sorting
Scenario
1
2
3
SS
SS
SS
SS
ICP2 scenarios
applicable
2W:1W
2W:2W
2W:2W sep
2W:2W pod
TS
TS
TS
TS
TS
TS
(gl
(gl
(gl
(gl
(gl
(gl
split) 2W:1W
split) 2W:2W
split) 2W:2W sep
sep) 2W:1W
sep) 2W:2W
sep) 2W:2W pod
TS (fb split) 2W:1W
TS (fb split) 2W:2W
Mixed
materials
Paper, card,
glass, cans,
plastic bottles
and PTT
Paper, card,
cans, plastic
bottles and
PTT18
Glass, cans,
plastic bottles
15
http://www.wrap.org.uk/content/materials-pricing-report
16
Old Corrugated Cardboard
17
Value for green glass as majority of mix is green
Facility assumptions
MRF designed to have a capacity of
120ktpa operating at 80% of
overall capacity. Shifts covering 96
hours per week. Shift time utilised
by 90%.
Mix of containers sent to dedicated
processing facility with 40ktpa of
18
The facility for sorting scenario 1 and 2 is the same and accepts glass. Scenario 2 does not include glass in the mix but is
assumed to take the material to a glass accepting facility as this appears to be an increasingly common practice.
ICP2 – Online Tool Modelling Assumptions Technical Annex
38
TS (fb split) 2W:2W sep
4
MS
MS
MS
MS
MS
and PTT
2W:1W
2W:2W
1W:1W
1W:2W
1W:2W stillage
Cans, plastic
bottles and
PTT
capacity for containers (Fibres
(paper and card) are sent directly
to Reprocessor loose from the
authority transfer station)
Depot based sorting line. Designed
to accept 2.5ktpa of mixed plastic
packaging and cans. Assume 80%
utilisation of capacity. One
operative pre-screening, Overband
magnet and eddy current
separator with the remaining
material straight to baler.
Input composition and tonnage was determined by the ICP2 performance differentials and
the KAT modelling. This was slightly different for each scenario/rurality combination. The
distribution of the input tonnages into the MRF material output streams was based on MRF
output compositions and confidential industry data. This provided a mass flow of tonnages
in and out of the sorting facility.
The capital and operating costs of the sorting facility were based, as far as possible, on
financial outturns from different facilities. The operating costs and the annualised capital
costs are divided by the tonnage throughput to give a processing cost per tonne. The cost
of disposing of reject material is added to the processing cost and corporate overhead and
profit is added as a percentage19 of the total. The total of these components gives the
sorting cost per tonne of material input. The outputs of the sorting cost tool were validated
by industry experts.
The basket price for each sorting scenario was based on the average 2012 values from the
MPR but a MPR to MRF conversion factor has been applied to specific materials to account
for market conditions and material qualities. The adjustment factor was determined by
comparing confidential real MRF sales values to the MPR values for the same period.
Combining the sorting cost and the basket price gave a “gate fee” for each sorting scenario
and has been averaged across all ruralites.
Table 17 Median “gate fee” for dry sorting used in the Online Tool
Sorting
Scenario
1
2
Mixed materials
“Gate Fee”
Paper, card, glass, cans, plastic
bottles and PTT
Paper, card, cans, plastic bottles and
PTT20
£6
-£6
19
20% for sorting scenarios 1-3 which use MRFs, 0% for scenario 4 as it is assumed to be at the transfer station for the
authority
20
The facility for sorting scenario 1 and 2 is the same and accepts glass. Scenario 2 does not include glass in the mix but is
assumed to take the material to a glass accepting facility as this appears to be an increasingly common practice.
ICP2 – Online Tool Modelling Assumptions Technical Annex
39
3
4
£6
-£19
Glass, cans, plastic bottles and PTT
Cans, plastic bottles and PTT
9.3
Treatment gate fees
The treatment costs for food waste and the disposal costs for residual waste were
accounted for using gate fees. The gate fees were taken from the 2012 gate fees report21
and the 2012/13 landfill tax value was applied (£64) for disposal.
Table 18 Gate fees
Material Stream
Food waste
Residual waste
Destination
Gate Fee (£/t)
ABPR compliant facility22
Anaerobic Digestion
Landfill
£41
£85 (£21 gate fee plus £64 landfill tax)
21
http://www.wrap.org.uk/sites/files/wrap/Gate%20Fees%20Report%202012.pdf
22
Gate fee for anaerobic digestion assumed, however In-vessel composting would be an alternative
ICP2 – Online Tool Modelling Assumptions Technical Annex
40
Appendix 1 – LA Rurality Classification
Local Authority
Region
Rurality Group
Adur & Worthing
Allerdale
Amber Valley
Arun
Ashfield
Ashford
Aylesbury Vale
Babergh
Barking and Dagenham
Barnet
South East
North West
East Midlands
South East
East Midlands
South East
South East
East of England
London
London
Yorkshire and The
Humber
North West
East of England
South East
East Midlands
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
South West
East of England
London
West Midlands
East Midlands
North West
North West
East Midlands
North West
East Midlands
South West
South East
Yorkshire and The
Humber
East of England
East of England
London
East of England
South East
South West
East of England
London
West Midlands
East of England
East Midlands
North West
North West
Yorkshire and The
Humber
East of England
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
2) Predominantly urban, lower deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
Barnsley
Barrow-in-Furness
Basildon
Basingstoke and Deane
Bassetlaw
Bath and North East
Somerset
Bedford
Bexley
Birmingham
Blaby
Blackburn with Darwen
Blackpool
Bolsover
Bolton
Boston
Bournemouth
Bracknell Forest
Bradford
Braintree
Breckland
Brent
Brentwood
Brighton and Hove
Bristol
Broadland
Bromley
Bromsgrove
Broxbourne
Broxtowe
Burnley
Bury
Calderdale
Cambridge City Council
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
41
Camden
Cannock Chase
Canterbury
Carlisle
Castle Point
Central Bedfordshire
Charnwood
Chelmsford
Cheltenham
Cherwell
Cheshire East
Cheshire West and Chester
Chesterfield
Chichester
Chiltern
Chorley
Christchurch
City of London
Colchester
Copeland
Corby
Cornwall
Cotswold
Coventry
Craven
Crawley
Croydon
Dacorum
Darlington
Dartford
Daventry
Derby
Derbyshire Dales
Doncaster
Dover
Dudley
Durham
Ealing
East Cambridgeshire
East Devon
East Dorset
East Hampshire
East Hertfordshire
East Lindsey
East Northamptonshire
East Riding of Yorkshire
East Staffordshire
Eastbourne
Eastleigh
Eden
London
West Midlands
South East
North West
East of England
East of England
East Midlands
East of England
South West
South East
North West
North West
East Midlands
South East
South East
North West
South West
London
East of England
North West
East Midlands
South West
South West
West Midlands
Yorkshire and The
Humber
South East
London
East of England
North East
South East
East Midlands
East Midlands
East Midlands
Yorkshire and The
Humber
South East
West Midlands
North East
London
East of England
South West
South West
South East
East of England
East Midlands
East Midlands
Yorkshire and The
Humber
West Midlands
South East
South East
North West
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
5) Predominantly Rural, higher deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
42
Elmbridge
Enfield
Epping Forest
Epsom and Ewell
Erewash
Exeter
Fareham
Fenland
Forest Heath
Forest of Dean
Fylde
Gateshead
Gedling
Gloucester
Gosport
Gravesham
Great Yarmouth
Greenwich
Guildford
Hackney
Halton
Hambleton
Hammersmith and Fulham
Harborough
Haringey
Harlow
Harrogate
Harrow
Hart
Hartlepool
Hastings
Havant
Havering
Herefordshire
Hertsmere
High Peak
Hillingdon
Hinckley and Bosworth
Horsham
Hounslow
Huntingdonshire
Hyndburn
Ipswich
Isle of Wight
Isles of Scilly
Islington
Kensington and Chelsea
Kettering
Kings Lynn and West
Norfolk
Kingston upon Thames
South East
London
East of England
South East
East Midlands
South West
South East
East of England
East of England
South West
North West
North East
East Midlands
South West
South East
South East
East of England
London
South East
London
North West
Yorkshire and The
Humber
London
East Midlands
London
East of England
Yorkshire and The
Humber
London
South East
North East
South East
South East
London
West Midlands
East of England
East Midlands
London
East Midlands
South East
London
East of England
North West
East of England
South East
South West
London
London
East Midlands
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
2) Predominantly urban, lower deprivation
2) Predominantly urban, lower deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
East of England
London
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
43
Kingston-upon-Hull
Kirklees
Knowsley
Lambeth
Lancaster
Leeds
Leicester
Lewes
Lewisham
Lichfield
Lincoln
Liverpool
Luton
Maidstone
Maldon
Malvern Hills
Manchester
Mansfield
Medway
Melton
Mendip
Merton
Mid Devon
Mid Suffolk
Mid Sussex
Middlesbrough
Milton Keynes
Mole Valley
New Forest
Newark and Sherwood
Newcastle upon Tyne
Newcastle-under-Lyme
Newham
North Devon
North Dorset
North East Derbyshire
North East Lincolnshire
North Hertfordshire
North Kesteven
North Lincolnshire
North Norfolk
North Somerset
North Tyneside
North Warwickshire
North West Leicestershire
Northampton
Northumberland
Norwich
Yorkshire and The
Humber
Yorkshire and The
Humber
North West
London
North West
Yorkshire and The
Humber
East Midlands
South East
London
West Midlands
East Midlands
North West
East of England
South East
East of England
West Midlands
North West
East Midlands
South East
East Midlands
South West
London
South West
East of England
South East
North East
South East
South East
South East
East Midlands
North East
West Midlands
London
South West
South West
East Midlands
Yorkshire and The
Humber
East of England
East Midlands
Yorkshire and The
Humber
East of England
South West
North East
West Midlands
East Midlands
East Midlands
North East
East of England
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
44
Nottingham City
Nuneaton and Bedworth
Oadby and Wigston
Oldham
Oxford
Pendle
Peterborough
Plymouth
Poole
Portsmouth
Preston
Purbeck
Reading
Redbridge
Redcar and Cleveland
Redditch
Reigate and Banstead
Ribble Valley
Richmond upon Thames
Richmondshire
Rochdale
Rochford
Rossendale
Rother
Rotherham
Rugby
Runnymede
Rushcliffe
Rushmoor
Rutland
Ryedale
Salford
Sandwell
Scarborough
Sedgemoor
Sefton
Selby
Sevenoaks
Sheffield
Shepway
Shropshire
Slough
Solihull
South Bucks
South Cambridgeshire
South Derbyshire
South Gloucestershire
East Midlands
West Midlands
East Midlands
North West
South East
North West
East of England
South West
South West
South East
North West
South West
South East
London
North East
West Midlands
South East
North West
London
Yorkshire and The
Humber
North West
East of England
North West
South East
Yorkshire and The
Humber
West Midlands
South East
East Midlands
South East
East Midlands
Yorkshire and The
Humber
North West
West Midlands
Yorkshire and The
Humber
South West
North West
Yorkshire and The
Humber
South East
Yorkshire and The
Humber
South East
West Midlands
South East
West Midlands
South East
East of England
East Midlands
South West
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
3) Mixed urban/rural, higher deprivation
2) Predominantly urban, lower deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
45
South Hams
South Holland
South Kesteven
South Lakeland
South Norfolk
South Northamptonshire
South Oxfordshire
South Ribble
South Somerset
South Staffordshire
South Tyneside
Southampton
Southend-on-Sea
Southwark
Spelthorne
St Albans
St Edmundsbury
St Helens
Stafford
Staffordshire Moorlands
Stevenage
Stockport
Stockton-on-Tees
Stoke-on-Trent
Stratford-on-Avon
Stroud
Suffolk Coastal
Sunderland
Surrey Heath
Sutton
Swale
Swindon
Tameside
Tamworth
Tandridge
Taunton Deane
Teignbridge
Telford and Wrekin
Tendring
Test Valley
Tewkesbury
Thanet
Three Rivers
Thurrock
Tonbridge and Malling
Torbay
Torridge
Tower Hamlets
Trafford
Tunbridge Wells
Uttlesford
Vale of White Horse
South West
East Midlands
East Midlands
North West
East of England
East Midlands
South East
North West
South West
West Midlands
North East
South East
East of England
London
South East
East of England
East of England
North West
West Midlands
West Midlands
East of England
North West
North East
West Midlands
West Midlands
South West
East of England
North East
South East
London
South East
South West
North West
West Midlands
South East
South West
South West
West Midlands
East of England
South East
South West
South East
East of England
East of England
South East
South West
South West
London
North West
South East
East of England
South East
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
2) Predominantly urban, lower deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
1) Predominantly urban, higher deprivation
4) Mixed urban/rural, lower deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
3) Mixed urban/rural, higher deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
46
Wakefield
Walsall
Waltham Forest
Wandsworth
Warrington
Warwick
Watford
Waveney
Waverley
Wealden
Wellingborough
Welwyn Hatfield
West Berkshire
West Devon
West Dorset
West Lancashire
West Lindsey
West Oxfordshire
West Somerset
Westminster
Weymouth and Portland
Wigan
Wiltshire
Winchester
Windsor and Maidenhead
Wirral
Woking
Wokingham
Yorkshire and The
Humber
West Midlands
London
London
North West
West Midlands
East of England
East of England
South East
South East
East Midlands
East of England
South East
South West
South West
North West
East Midlands
South East
South West
London
South West
North West
South East
South East
North West
South East
South East
3) Mixed urban/rural, higher deprivation
1) Predominantly urban, higher deprivation
1) Predominantly urban, higher deprivation
2) Predominantly urban, lower deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
2) Predominantly urban, lower deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
4) Mixed urban/rural, lower deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
5) Predominantly Rural, higher deprivation
6) Predominantly Rural, lower deprivation
5) Predominantly Rural, higher deprivation
1) Predominantly urban, higher deprivation
3) Mixed urban/rural, higher deprivation
3) Mixed urban/rural, higher deprivation
6) Predominantly Rural, lower deprivation
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
3) Mixed urban/rural, higher deprivation
4) Mixed urban/rural, lower deprivation
4) Mixed urban/rural, lower deprivation
Wolverhampton
West Midlands
1) Predominantly urban, higher deprivation
Worcester
West Midlands
2) Predominantly urban, lower deprivation
Worthing (see Adur)
South East
2) Predominantly urban, lower deprivation
Wychavon
West Midlands
6) Predominantly Rural, lower deprivation
Wycombe
Wyre
South East
North West
6) Predominantly Rural, lower deprivation
4) Mixed urban/rural, lower deprivation
Wyre Forest
West Midlands
Yorkshire and The
Humber
5) Predominantly Rural, higher deprivation
York
South West
4) Mixed urban/rural, lower deprivation
ICP2 – Online Tool Modelling Assumptions Technical Annex
47
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