ICP2 Online Tool Modelling Assumptions Technical Annex

Transcription

1 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

2 WRAP s vision is a world in which resources are used sustainably. Our mission is to accelerate the move to a sustainable resource-efficient economy through re-inventing how we design, produce and sell products; re-thinking how we use and consume products; and re-defining what is possible through recycling and re-use. Find out more at Document reference (please use this reference when citing WRAP s work): [WRAP, 2015, Banbury, ICP2 Online Tool Modelling Assumptions Technical Annex, Prepared by WRAP] Written by: WRAP Front cover photography: Images from ICP2 Online Tool While we have tried to make sure this report is accurate, we cannot accept responsibility or be held legally responsible for any loss or damage arising out of or in connection with this information being inaccurate, incomplete or misleading. This material is copyrighted. You can copy it free of charge as long as the material is accurate and not used in a misleading context. You must identify the source of the material and acknowledge our copyright. You must not use material to endorse or suggest we have endorsed a commercial product or service. For more details please see our terms and conditions on our website at

3 Contents 1.0 About ICP KAT and ICP Scenarios Rurality Groups Baseline Assumptions Waste composition Household Numbers Operational Data for setting the baselines Scenario assumptions operational Containment Vehicles Material transfer Travel times Loading and collecting times Bulk density Scenario Assumptions - Performance Performance Yield Differentials Food Yields Contamination rates dry recycling Co-mingled and Two Stream contamination estimates Multi-stream contamination estimates Waste arisings Set out and participation rate Scenario assumptions Cost Containment Vehicles Crews Overheads Post-collection process assumptions Material revenue Cost of sorting Treatment gate fees Appendix 1 LA Rurality Classification ICP2 Online Tool Modelling Assumptions Technical Annex 3

4 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

5 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 KAT 1 for each scenario ICP Results model Tonnes and composition of mixed dry recycling ICP sorting model Yield data for each scenario Collection infrastructure requirements and costs Collection Costs Default revenue values and treatment fees Default MRF gate fees ICP2 Online Tool ICP2 Online Tool Modelling Assumptions Technical Annex 5

6 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 ICP2 Inputs Selects Rurality Selects scenario reflecting current service Selects scenarios for comparison Defined Inputs: Bulking Sorting gate fee Treatment gate fee Disposal gate fee Material revenue 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 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 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

7 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 OPRL 1 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 rates 2. 1 On-pack Recycling Label 2 Regression 12/13 ICP2 Online Tool Modelling Assumptions Technical Annex 7

8 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 SS 2W:1W Scheme type Single stream co-mingled Vehicle RCV Container and materials Recycling Frequency Residual Frequency litre wheeled bin glass, cans, paper, card, plastic bottles, PTT Fortnightly Weekly SS Single stream RCV 240 litre wheeled bin Fortnightly Fortnightly 3 All residual waste collections use wheeled bins only ICP2 Online Tool Modelling Assumptions Technical Annex 8

9 2W:2W co-mingled glass, cans, paper, card, plastic bottles, PTT SS 2W:2W pod SS 2W:2W sep TS (fb split) 2W:1W TS (fb split) 2W:2W 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 RCV with food pod for both residual and recycling RCV 7.5 tonne food waste vehicle Splitback RCV Splitback RCV Splitback RCV 240 litre wheeled bin glass, cans, paper, card, plastic bottles, PTT Kitchen and kerbside caddy with liners provided Fortnightly Fortnightly 240 litre wheeled bin glass, cans, paper, card, plastic bottles, PTT Fortnightly Fortnightly Kitchen and kerbside caddy with liners provided 240 litre wheeled bin glass, cans, plastic bottles, PTT Weekly Fortnightly Reusable sack paper and card Fortnightly Weekly 240 litre wheeled bin glass, cans, plastic bottles, PTT Reusable sack paper and card Fortnightly Fortnightly 240 litre wheeled bin glass, cans, plastic bottles, PTT TS (fb split) 2W:2W sep TS (gl split) 2W:1W TS (gl split) 2W:2W Two stream fibres separate to containers with separately collected food waste Two stream glass separate to other materials Two stream glass separate to other 7.5 tonne food waste vehicle Splitback RCV Splitback RCV Reusable sack paper and card Fortnightly Fortnightly Kitchen and kerbside caddy with liners provided 240 litre wheeled bin paper, card, cans, plastic bottles, PTT Weekly Fortnightly 55 litre box glass Fortnightly Weekly 240 litre wheeled bin paper, card, cans, plastic bottles, PTT Fortnightly Fortnightly ICP2 Online Tool Modelling Assumptions Technical Annex 9

10 TS (gl split) 2W:2W sep TS (gl sep) 2W:1W TS (gl sep) 2W:2W TS (gl sep) 2W:2W pod MS 2W:1W MS 2W:2W MS 1W:1W materials Two stream glass separate to other materials with separately collected food waste Two stream glass separate to other materials Two stream glass separate to other materials Two stream glass separate to other materials with co-collected food waste Multi-stream Operated with driver plus 2 loaders Multi-stream Operated with driver plus 2 loaders Multi-stream Operated with driver plus 1 Splitback RCV 7.5 tonne food waste vehicle RCV 55 litre box glass 240 litre wheeled bin paper, card, cans, plastic bottles, PTT 55 litre box glass Fortnightly Fortnightly Kitchen and kerbside caddy with liners provided Weekly Fortnightly 240 litre wheeled bin paper, card, cans, plastic bottles, PTT Fortnightly Weekly RCV 55 litre box glass Fortnightly Weekly RCV 240 litre wheeled bin paper, card, cans, plastic bottles, PTT Fortnightly Fortnightly RCV 55 litre box glass Fortnightly Fortnightly RCV with food pod for both residual and recycling 240 litre wheeled bin cans, paper, card, plastic bottles, PTT Kitchen and kerbside caddy with liners provided Fortnightly Fortnightly RCV 55 litre box glass Fortnightly Fortnightly Stillage 4 55 litre box glass, card Stillage Stillage 55 litre box paper Reusable sack cans, plastic bottles, PTT Fortnightly Weekly 55 litre box glass, card 55 litre box paper Reusable sack cans, plastic bottles, PTT Fortnightly Fortnightly 55 litre box glass, card 55 litre box paper Weekly Weekly 4 This stillage vehicle is typical of the new generation stillage vehicles; approx. 30m 3 in volume and collects all materials on a single pass ICP2 Online Tool Modelling Assumptions Technical Annex 10

11 loader Stillage Reusable sack cans, plastic bottles, PTT 55 litre box glass, card Multi-stream 55 litre box paper MS 1W:2W Operated with driver plus 1 loader Stillage Reusable sack cans, plastic bottles, PTT Weekly Fortnightly 55 litre box glass, card 55 litre box paper Multi-stream with cocollected food Reusable sack cans, plastic bottles, PTT MS 1W:2W stillage Operated with driver plus 1 loader Kitchen and kerbside caddy with liners provided Weekly Fortnightly 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 rurality 5 (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 ICP ICP2 Online Tool Modelling Assumptions Technical Annex 11

12 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

13 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 performance 6 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

14 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 Predominantly Urban, higher deprivation (46 LAs); Rurality 2 Predominantly Urban, lower deprivation (43 LAs); Rurality 3 Mixed Urban/Rural, higher deprivation (53 LAs); Rurality 4 Mixed Urban/Rural, lower deprivation (49 LAs); Rurality 5 Predominantly Rural, higher deprivation (67 LAs); and Rurality 6 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

15 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, 2010/11 7. 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 ICP2 kerbside KAT material categories waste composition Newspapers and Magazines 10.95% Other paper 8.63% Corrugated card 2.16% Non-corrugated card 3.41% Plastic film 5.17% Plastic bottles 2.50% Plastic - other dense 3.54% Glass flint 4.54% Glass brown 0.68% Glass green 2.32% Steel cans 1.64% Aluminium cans 0.33% Foil containers 0.49% Textiles 3.05% Soil and other organic 4.25% Food waste 24.64% Compostable garden waste 4.00% 7 Documents ICP2 Online Tool Modelling Assumptions Technical Annex 15

16 Other 17.72% Total 100.0% 5.2 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 Average dwelling stock Average flats proportion Non-flat households Modelled households 1) Predominantly urban, higher deprivation % ) Predominantly urban, lower deprivation % ) Mixed urban/rural, higher deprivation % ) Mixed urban/rural, lower deprivation % ) Predominantly rural, higher deprivation % ) Predominantly rural, lower deprivation % 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

17 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

18 Table 4 below. ICP2 Online Tool Modelling Assumptions Technical Annex 18

19 Table 4 Key assumptions for residual waste collections Rurality 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) Frequency of collection in baseline Average Round Size for frequency Number of vehicles for weekly residual waste determined from round size and modelled households 1) Predominantly urban, higher deprivation weekly ) Predominantly urban, lower deprivation weekly ) Mixed urban/rural, higher deprivation fortnightly ) Mixed urban/rural, lower deprivation fortnightly ) Predominantly rural, higher deprivation fortnightly ) Predominantly rural, lower deprivation fortnightly Number of vehicles for fortnightly residual waste determined from round size and modelled households ICP2 Online Tool Modelling Assumptions Technical Annex 19

20 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 2W:1W SS 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 TS (gl split) 2W:2W sep TS (gl sep) 2W:1W TS (gl sep) 2W:2W TS (gl sep) 2W:2W pod Dry container 240 litre wheeled bin 240 litre wheeled bin 90 litre reusable sack 240 litre wheeled bin 55 litre box glass Materials collected glass, cans, paper, card, plastic bottles, PTT glass, cans, plastic bottles, PTT paper and card paper, card, cans, plastic bottles, PTT Effective weekly dry containment capacity 120 litres 165 litres combined litres combined ICP2 Online Tool Modelling Assumptions Technical Annex 20

21 Fortnightly Multi-stream Weekly Multistream MS 2W:1W MS 2W:2W MS 1W:1W MS 1W:2W MS 1W:2W stillage 55 litre box glass, card 55 litre box paper 90 litre Reusable sack cans, plastic bottles, PTT 55 litre box glass, card 55 litre box paper 90 litre Reusable sack 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 Scenarios Residual waste collections for all scenarios where food is not cocollected Volume capacity Maximum payload Vehicle unloading time RCV Single stream co-mingled collections SS 2W:1W SS 2W:2W SS 2W:2W sep 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 Crewed with a driver plus 2 loaders 22m 3 11 tonnes 20 minutes 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 22m 3 total (17m 3 for residual waste) 10 tonnes overall (8 tonnes for residual waste) 30 minutes ICP2 Online Tool Modelling Assumptions Technical Annex 21

22 Splitbodied RCV (50:50) Splitbodied RCV (70:30) Compartm entalised vehicle 8 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) 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 Crewed with a driver plus 2 loaders 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 All multi-stream collections. The compartments were arranged 9 : 1) Plastics and cans 2) Paper 3) Card 4) Clear glass 5) Green/brown glass 6) Food (where applicable) MS 2W:1W MS 2W:2W MS 1W:1W MS 1W:2W MS 1W:2W stillage 21m 3 10 tonnes 25 minutes 21m 3 10 tonnes 25 minutes 30m tonnes 30 minutes 8 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

23 Dedicated food waste vehicle Dedicated glass RCV 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 SS 2W:2W sep TS (fb split) 2W:2W sep TS (gl split) 2W:2W sep Crewed with a driver plus 1 loader Two stream scenarios where glass is collected separately by a dedicated fleet TS (gl sep) 2W:1W TS (gl sep) 2W:2W TS (gl sep) 2W:2W pod Crewed with a driver plus 1 loader 7.5m 3 3 tonnes 20 minutes 12m 3 8 tonnes 20 minutes 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 Destination after transfer station All SS scenarios MRF Yes TS (gl split) and TS (gl sep) scenarios TS (gl split) and TS (gl sep) scenarios MRF Reprocessor Bulking and haulage applied? Yes Factored into revenue ICP2 Online Tool Modelling Assumptions Technical Annex 23

24 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) Mixed plastics and cans TS (fb split) scenarios MRF Yes TS (fb split) scenarios All MS scenarios All MS scenarios Reprocessor Reprocessor Sorting and transfer station Food waste All food scenarios AD facility Yes Factored into revenue Factored into revenue Factored into revenue post sorting 6.4 Travel times The travel times provided in ICP2 Online Tool Modelling Assumptions Technical Annex 24

25 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

26 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 News and mags Bulk Density (kg/m 3 ) 300 Other paper 250 Corrugated card Noncorrugated card References used to derive estimate 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, 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)

27 Plastic film 20 Plastic bottles Plastics - other dense 43 Glass 300 Steel cans 86 Aluminium cans Foil containers Textiles 200 Soil and other organic 375 Food waste 500 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 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 NWS work, "Characterisation of source-separated household waste intended for composting" in Bioresource Technology 2011 February; 102(3): , Zero Waste Italy - "Implementing and optimising separate collections of biowaste: the Italian way to tackle operational and economic issues" 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 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

28 Compostable garden waste 250 work, "Characterisation of source-separated household waste intended for composting" in Bioresource Technology 2011 February; 102(3): , Zero Waste Italy - "Implementing and optimising separate collections of biowaste: the Italian way to tackle operational and economic issues" 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 NWS work, "Characterisation of source-separated household waste intended for composting" in Bioresource Technology 2011 February; 102(3): 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

29 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 midyear, 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

30 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

31 Figure 7 Modelled dry recycling yields for each scheme/rurality combination Food Yields For the scenarios collecting food waste, the expected yield of food was derived from WRAP s food waste ready reckoner 11. 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 Food yield (kg/hh/yr) R1 65 R2 84 R3 74 R4 92 R5 83 R6 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 ICP2 Online Tool Modelling Assumptions Technical Annex 31

32 collected at the kerbside was analysed. Contamination was defined as non-targeted material 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

33 Contamination (kg/household/year) Figure 8 Model to predict contamination across all single stream and two stream systems 70 Contamination yield as a function of collected dry recycling yield y = e x R² = Kerbside yield (kg/household/year) 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 Contamination All co-mingled and two stream *EXP(0.0077*total yield) Multi-stream 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

34 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 collections 12. 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 Kerbside arisings yield weekly residual (kg/hh/yr) Kerbside arisings yield fortnightly residual (kg/hh/yr) 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) Predominantly Rural, lower deprivation 90% 4) Mixed Urban/Rural, lower deprivation 88% 2) Predominantly Urban, lower deprivation 86% 5) Predominantly Rural, higher deprivation 84% 3) 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

35 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 Predominantly Urban More Less deprived deprived Rurality Mixed Urban/Rural More deprived Less deprived Predominantly Rural More Less deprived deprived Scenarios R1 R2 R3 R4 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

36 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 Unit cost plus Unit cost plus Replacement Container urban delivery rural delivery rate 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 % 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 Capital cost including bin lift where required Annual standing costs Annual running costs RCV (22m 3 ) 142,000 7,750 14,200 RCV with food pod 162,000 8,750 16,200 Split-bodied RCV (50:50) 152,000 8,250 15,200 Split-bodied RCV (70:30) 152,000 8,250 15,200 Compartmentalised vehicle 92,000 4,800 9,200 Dedicated food waste vehicle 65,000 3,450 4,875 Dedicated glass RCV (12m 3 ) 120,000 6,650 12,000 ICP2 Online Tool Modelling Assumptions Technical Annex 36

37 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) Direct Overheads per household ( ) 20-30, , , , >100, 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 ICP2 Online Tool Modelling Assumptions Technical Annex 37

38 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 ICP2 Scenario Average 2012 MPR value ( /t) Mixed paper and cardboard TS (fb split) 59 News and Pams MS 92 OCC 16 MS 74 Clear glass MS 19 Green/brown glass 17 MS 4 Mixed glass TS (gl split) TS (gl sep) 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 ICP2 scenarios applicable SS 2W:1W SS 2W:2W SS 2W:2W sep SS 2W:2W pod TS (gl split) 2W:1W TS (gl split) 2W:2W TS (gl split) 2W:2W sep TS (gl sep) 2W:1W TS (gl sep) 2W:2W TS (gl sep) 2W:2W pod Mixed materials Paper, card, glass, cans, plastic bottles and PTT Paper, card, cans, plastic bottles and PTT 18 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%. 3 TS (fb split) 2W:1W TS (fb split) 2W:2W Glass, cans, plastic bottles Mix of containers sent to dedicated processing facility with 40ktpa of Old Corrugated Cardboard 17 Value for green glass as majority of mix is green 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

39 TS (fb split) 2W:2W sep and PTT capacity for containers (Fibres (paper and card) are sent directly to Reprocessor loose from the authority transfer station) 4 MS 2W:1W MS 2W:2W MS 1W:1W MS 1W:2W MS 1W:2W stillage Cans, plastic bottles and PTT 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 percentage 19 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 6 bottles and PTT Paper, card, cans, plastic bottles and - 6 PTT % 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

40 3 Glass, cans, plastic bottles and PTT 6 4 Cans, plastic bottles and PTT 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 report 21 and the 2012/13 landfill tax value was applied ( 64) for disposal. Table 18 Gate fees Material Stream Destination Gate Fee ( /t) Food waste ABPR compliant facility Anaerobic Digestion Residual waste Landfill 85 ( 21 gate fee plus 64 landfill tax) Gate fee for anaerobic digestion assumed, however In-vessel composting would be an alternative ICP2 Online Tool Modelling Assumptions Technical Annex 40

41 Appendix 1 LA Rurality Classification Local Authority Region Rurality Group Adur & Worthing South East 2) Predominantly urban, lower deprivation Allerdale North West 5) Predominantly Rural, higher deprivation Amber Valley East Midlands 5) Predominantly Rural, higher deprivation Arun South East 4) Mixed urban/rural, lower deprivation Ashfield East Midlands 1) Predominantly urban, higher deprivation Ashford South East 5) Predominantly Rural, higher deprivation Aylesbury Vale South East 6) Predominantly Rural, lower deprivation Babergh East of England 6) Predominantly Rural, lower deprivation Barking and Dagenham London 1) Predominantly urban, higher deprivation Barnet London 2) Predominantly urban, lower deprivation Barnsley Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Barrow-in-Furness North West 3) Mixed urban/rural, higher deprivation Basildon East of England 2) Predominantly urban, lower deprivation Basingstoke and Deane South East 4) Mixed urban/rural, lower deprivation Bassetlaw East Midlands 5) Predominantly Rural, higher deprivation Bath and North East Somerset South West 6) Predominantly Rural, lower deprivation Bedford East of England 4) Mixed urban/rural, lower deprivation Bexley London 2) Predominantly urban, lower deprivation Birmingham West Midlands 1) Predominantly urban, higher deprivation Blaby East Midlands 4) Mixed urban/rural, lower deprivation Blackburn with Darwen North West 3) Mixed urban/rural, higher deprivation Blackpool North West 1) Predominantly urban, higher deprivation Bolsover East Midlands 5) Predominantly Rural, higher deprivation Bolton North West 3) Mixed urban/rural, higher deprivation Boston East Midlands 5) Predominantly Rural, higher deprivation Bournemouth South West 2) Predominantly urban, lower deprivation Bracknell Forest South East 4) Mixed urban/rural, lower deprivation Bradford Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Braintree East of England 5) Predominantly Rural, higher deprivation Breckland East of England 5) Predominantly Rural, higher deprivation Brent London 1) Predominantly urban, higher deprivation Brentwood East of England 4) Mixed urban/rural, lower deprivation Brighton and Hove South East 1) Predominantly urban, higher deprivation Bristol South West 1) Predominantly urban, higher deprivation Broadland East of England 6) Predominantly Rural, lower deprivation Bromley London 2) Predominantly urban, lower deprivation Bromsgrove West Midlands 4) Mixed urban/rural, lower deprivation Broxbourne East of England 4) Mixed urban/rural, lower deprivation Broxtowe East Midlands 4) Mixed urban/rural, lower deprivation Burnley North West 3) Mixed urban/rural, higher deprivation Bury North West 3) Mixed urban/rural, higher deprivation Calderdale Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Cambridge City Council East of England 2) Predominantly urban, lower deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 41

42 Camden London 1) Predominantly urban, higher deprivation Cannock Chase West Midlands 5) Predominantly Rural, higher deprivation Canterbury South East 4) Mixed urban/rural, lower deprivation Carlisle North West 3) Mixed urban/rural, higher deprivation Castle Point East of England 2) Predominantly urban, lower deprivation Central Bedfordshire East of England 4) Mixed urban/rural, lower deprivation Charnwood East Midlands 4) Mixed urban/rural, lower deprivation Chelmsford East of England 4) Mixed urban/rural, lower deprivation Cheltenham South West 2) Predominantly urban, lower deprivation Cherwell South East 6) Predominantly Rural, lower deprivation Cheshire East North West 5) Predominantly Rural, higher deprivation Cheshire West and Chester North West 4) Mixed urban/rural, lower deprivation Chesterfield East Midlands 3) Mixed urban/rural, higher deprivation Chichester South East 6) Predominantly Rural, lower deprivation Chiltern South East 6) Predominantly Rural, lower deprivation Chorley North West 5) Predominantly Rural, higher deprivation Christchurch South West 2) Predominantly urban, lower deprivation City of London London 2) Predominantly urban, lower deprivation Colchester East of England 5) Predominantly Rural, higher deprivation Copeland North West 5) Predominantly Rural, higher deprivation Corby East Midlands 3) Mixed urban/rural, higher deprivation Cornwall South West 5) Predominantly Rural, higher deprivation Cotswold South West 6) Predominantly Rural, lower deprivation Coventry West Midlands 1) Predominantly urban, higher deprivation Craven Yorkshire and The Humber 6) Predominantly Rural, lower deprivation Crawley South East 2) Predominantly urban, lower deprivation Croydon London 2) Predominantly urban, lower deprivation Dacorum East of England 6) Predominantly Rural, lower deprivation Darlington North East 3) Mixed urban/rural, higher deprivation Dartford South East 4) Mixed urban/rural, lower deprivation Daventry East Midlands 6) Predominantly Rural, lower deprivation Derby East Midlands 1) Predominantly urban, higher deprivation Derbyshire Dales East Midlands 6) Predominantly Rural, lower deprivation Doncaster Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Dover South East 5) Predominantly Rural, higher deprivation Dudley West Midlands 2) Predominantly urban, lower deprivation Durham North East 5) Predominantly Rural, higher deprivation Ealing London 1) Predominantly urban, higher deprivation East Cambridgeshire East of England 6) Predominantly Rural, lower deprivation East Devon South West 5) Predominantly Rural, higher deprivation East Dorset South West 6) Predominantly Rural, lower deprivation East Hampshire South East 6) Predominantly Rural, lower deprivation East Hertfordshire East of England 4) Mixed urban/rural, lower deprivation East Lindsey East Midlands 5) Predominantly Rural, higher deprivation East Northamptonshire East Midlands 6) Predominantly Rural, lower deprivation East Riding of Yorkshire Yorkshire and The Humber 5) Predominantly Rural, higher deprivation East Staffordshire West Midlands 5) Predominantly Rural, higher deprivation Eastbourne South East 2) Predominantly urban, lower deprivation Eastleigh South East 4) Mixed urban/rural, lower deprivation Eden North West 5) Predominantly Rural, higher deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 42

43 Elmbridge South East 4) Mixed urban/rural, lower deprivation Enfield London 1) Predominantly urban, higher deprivation Epping Forest East of England 5) Predominantly Rural, higher deprivation Epsom and Ewell South East 4) Mixed urban/rural, lower deprivation Erewash East Midlands 4) Mixed urban/rural, lower deprivation Exeter South West 3) Mixed urban/rural, higher deprivation Fareham South East 4) Mixed urban/rural, lower deprivation Fenland East of England 5) Predominantly Rural, higher deprivation Forest Heath East of England 6) Predominantly Rural, lower deprivation Forest of Dean South West 5) Predominantly Rural, higher deprivation Fylde North West 6) Predominantly Rural, lower deprivation Gateshead North East 3) Mixed urban/rural, higher deprivation Gedling East Midlands 4) Mixed urban/rural, lower deprivation Gloucester South West 2) Predominantly urban, lower deprivation Gosport South East 2) Predominantly urban, lower deprivation Gravesham South East 3) Mixed urban/rural, higher deprivation Great Yarmouth East of England 3) Mixed urban/rural, higher deprivation Greenwich London 1) Predominantly urban, higher deprivation Guildford South East 4) Mixed urban/rural, lower deprivation Hackney London 1) Predominantly urban, higher deprivation Halton North West 3) Mixed urban/rural, higher deprivation Hambleton Yorkshire and The Humber 6) Predominantly Rural, lower deprivation Hammersmith and Fulham London 1) Predominantly urban, higher deprivation Harborough East Midlands 6) Predominantly Rural, lower deprivation Haringey London 1) Predominantly urban, higher deprivation Harlow East of England 2) Predominantly urban, lower deprivation Harrogate Yorkshire and The Humber 6) Predominantly Rural, lower deprivation Harrow London 2) Predominantly urban, lower deprivation Hart South East 4) Mixed urban/rural, lower deprivation Hartlepool North East 3) Mixed urban/rural, higher deprivation Hastings South East 1) Predominantly urban, higher deprivation Havant South East 3) Mixed urban/rural, higher deprivation Havering London 2) Predominantly urban, lower deprivation Herefordshire West Midlands 5) Predominantly Rural, higher deprivation Hertsmere East of England 6) Predominantly Rural, lower deprivation High Peak East Midlands 5) Predominantly Rural, higher deprivation Hillingdon London 3) Mixed urban/rural, higher deprivation Hinckley and Bosworth East Midlands 4) Mixed urban/rural, lower deprivation Horsham South East 6) Predominantly Rural, lower deprivation Hounslow London 2) Predominantly urban, lower deprivation Huntingdonshire East of England 6) Predominantly Rural, lower deprivation Hyndburn North West 3) Mixed urban/rural, higher deprivation Ipswich East of England 2) Predominantly urban, lower deprivation Isle of Wight South East 5) Predominantly Rural, higher deprivation Isles of Scilly South West 5) Predominantly Rural, higher deprivation Islington London 1) Predominantly urban, higher deprivation Kensington and Chelsea London 2) Predominantly urban, lower deprivation Kettering East Midlands 4) Mixed urban/rural, lower deprivation Kings Lynn and West Norfolk East of England 5) Predominantly Rural, higher deprivation Kingston upon Thames London 2) Predominantly urban, lower deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 43

44 Kingston-upon-Hull Yorkshire and The Humber 1) Predominantly urban, higher deprivation Kirklees Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Knowsley North West 1) Predominantly urban, higher deprivation Lambeth London 1) Predominantly urban, higher deprivation Lancaster North West 3) Mixed urban/rural, higher deprivation Leeds Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Leicester East Midlands 1) Predominantly urban, higher deprivation Lewes South East 5) Predominantly Rural, higher deprivation Lewisham London 1) Predominantly urban, higher deprivation Lichfield West Midlands 6) Predominantly Rural, lower deprivation Lincoln East Midlands 1) Predominantly urban, higher deprivation Liverpool North West 1) Predominantly urban, higher deprivation Luton East of England 1) Predominantly urban, higher deprivation Maidstone South East 4) Mixed urban/rural, lower deprivation Maldon East of England 6) Predominantly Rural, lower deprivation Malvern Hills West Midlands 6) Predominantly Rural, lower deprivation Manchester North West 1) Predominantly urban, higher deprivation Mansfield East Midlands 3) Mixed urban/rural, higher deprivation Medway South East 3) Mixed urban/rural, higher deprivation Melton East Midlands 6) Predominantly Rural, lower deprivation Mendip South West 5) Predominantly Rural, higher deprivation Merton London 2) Predominantly urban, lower deprivation Mid Devon South West 5) Predominantly Rural, higher deprivation Mid Suffolk East of England 6) Predominantly Rural, lower deprivation Mid Sussex South East 6) Predominantly Rural, lower deprivation Middlesbrough North East 1) Predominantly urban, higher deprivation Milton Keynes South East 4) Mixed urban/rural, lower deprivation Mole Valley South East 6) Predominantly Rural, lower deprivation New Forest South East 6) Predominantly Rural, lower deprivation Newark and Sherwood East Midlands 5) Predominantly Rural, higher deprivation Newcastle upon Tyne North East 3) Mixed urban/rural, higher deprivation Newcastle-under-Lyme West Midlands 3) Mixed urban/rural, higher deprivation Newham London 1) Predominantly urban, higher deprivation North Devon South West 5) Predominantly Rural, higher deprivation North Dorset South West 5) Predominantly Rural, higher deprivation North East Derbyshire East Midlands 5) Predominantly Rural, higher deprivation North East Lincolnshire Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation North Hertfordshire East of England 4) Mixed urban/rural, lower deprivation North Kesteven East Midlands 6) Predominantly Rural, lower deprivation North Lincolnshire Yorkshire and The Humber 5) Predominantly Rural, higher deprivation North Norfolk East of England 5) Predominantly Rural, higher deprivation North Somerset South West 5) Predominantly Rural, higher deprivation North Tyneside North East 3) Mixed urban/rural, higher deprivation North Warwickshire West Midlands 5) Predominantly Rural, higher deprivation North West Leicestershire East Midlands 5) Predominantly Rural, higher deprivation Northampton East Midlands 2) Predominantly urban, lower deprivation Northumberland North East 5) Predominantly Rural, higher deprivation Norwich East of England 1) Predominantly urban, higher deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 44

45 Nottingham City East Midlands 1) Predominantly urban, higher deprivation Nuneaton and Bedworth West Midlands 3) Mixed urban/rural, higher deprivation Oadby and Wigston East Midlands 2) Predominantly urban, lower deprivation Oldham North West 3) Mixed urban/rural, higher deprivation Oxford South East 2) Predominantly urban, lower deprivation Pendle North West 3) Mixed urban/rural, higher deprivation Peterborough East of England 3) Mixed urban/rural, higher deprivation Plymouth South West 1) Predominantly urban, higher deprivation Poole South West 4) Mixed urban/rural, lower deprivation Portsmouth South East 1) Predominantly urban, higher deprivation Preston North West 3) Mixed urban/rural, higher deprivation Purbeck South West 5) Predominantly Rural, higher deprivation Reading South East 2) Predominantly urban, lower deprivation Redbridge London 2) Predominantly urban, lower deprivation Redcar and Cleveland North East 5) Predominantly Rural, higher deprivation Redditch West Midlands 3) Mixed urban/rural, higher deprivation Reigate and Banstead South East 4) Mixed urban/rural, lower deprivation Ribble Valley North West 6) Predominantly Rural, lower deprivation Richmond upon Thames London 2) Predominantly urban, lower deprivation Richmondshire Yorkshire and The Humber 6) Predominantly Rural, lower deprivation Rochdale North West 3) Mixed urban/rural, higher deprivation Rochford East of England 4) Mixed urban/rural, lower deprivation Rossendale North West 3) Mixed urban/rural, higher deprivation Rother South East 5) Predominantly Rural, higher deprivation Rotherham Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Rugby West Midlands 4) Mixed urban/rural, lower deprivation Runnymede South East 4) Mixed urban/rural, lower deprivation Rushcliffe East Midlands 6) Predominantly Rural, lower deprivation Rushmoor South East 2) Predominantly urban, lower deprivation Rutland East Midlands 6) Predominantly Rural, lower deprivation Ryedale Yorkshire and The Humber 5) Predominantly Rural, higher deprivation Salford North West 1) Predominantly urban, higher deprivation Sandwell West Midlands 1) Predominantly urban, higher deprivation Scarborough Yorkshire and The Humber 5) Predominantly Rural, higher deprivation Sedgemoor South West 5) Predominantly Rural, higher deprivation Sefton North West 3) Mixed urban/rural, higher deprivation Selby Yorkshire and The Humber 6) Predominantly Rural, lower deprivation Sevenoaks South East 6) Predominantly Rural, lower deprivation Sheffield Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Shepway South East 5) Predominantly Rural, higher deprivation Shropshire West Midlands 5) Predominantly Rural, higher deprivation Slough South East 2) Predominantly urban, lower deprivation Solihull West Midlands 4) Mixed urban/rural, lower deprivation South Bucks South East 6) Predominantly Rural, lower deprivation South Cambridgeshire East of England 6) Predominantly Rural, lower deprivation South Derbyshire East Midlands 6) Predominantly Rural, lower deprivation South Gloucestershire South West 4) Mixed urban/rural, lower deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 45

46 South Hams South West 5) Predominantly Rural, higher deprivation South Holland East Midlands 5) Predominantly Rural, higher deprivation South Kesteven East Midlands 6) Predominantly Rural, lower deprivation South Lakeland North West 6) Predominantly Rural, lower deprivation South Norfolk East of England 6) Predominantly Rural, lower deprivation South Northamptonshire East Midlands 6) Predominantly Rural, lower deprivation South Oxfordshire South East 6) Predominantly Rural, lower deprivation South Ribble North West 4) Mixed urban/rural, lower deprivation South Somerset South West 5) Predominantly Rural, higher deprivation South Staffordshire West Midlands 6) Predominantly Rural, lower deprivation South Tyneside North East 1) Predominantly urban, higher deprivation Southampton South East 1) Predominantly urban, higher deprivation Southend-on-Sea East of England 2) Predominantly urban, lower deprivation Southwark London 1) Predominantly urban, higher deprivation Spelthorne South East 2) Predominantly urban, lower deprivation St Albans East of England 4) Mixed urban/rural, lower deprivation St Edmundsbury East of England 6) Predominantly Rural, lower deprivation St Helens North West 3) Mixed urban/rural, higher deprivation Stafford West Midlands 6) Predominantly Rural, lower deprivation Staffordshire Moorlands West Midlands 5) Predominantly Rural, higher deprivation Stevenage East of England 2) Predominantly urban, lower deprivation Stockport North West 3) Mixed urban/rural, higher deprivation Stockton-on-Tees North East 3) Mixed urban/rural, higher deprivation Stoke-on-Trent West Midlands 1) Predominantly urban, higher deprivation Stratford-on-Avon West Midlands 6) Predominantly Rural, lower deprivation Stroud South West 6) Predominantly Rural, lower deprivation Suffolk Coastal East of England 6) Predominantly Rural, lower deprivation Sunderland North East 1) Predominantly urban, higher deprivation Surrey Heath South East 4) Mixed urban/rural, lower deprivation Sutton London 2) Predominantly urban, lower deprivation Swale South East 5) Predominantly Rural, higher deprivation Swindon South West 4) Mixed urban/rural, lower deprivation Tameside North West 1) Predominantly urban, higher deprivation Tamworth West Midlands 2) Predominantly urban, lower deprivation Tandridge South East 6) Predominantly Rural, lower deprivation Taunton Deane South West 5) Predominantly Rural, higher deprivation Teignbridge South West 5) Predominantly Rural, higher deprivation Telford and Wrekin West Midlands 3) Mixed urban/rural, higher deprivation Tendring East of England 5) Predominantly Rural, higher deprivation Test Valley South East 6) Predominantly Rural, lower deprivation Tewkesbury South West 6) Predominantly Rural, lower deprivation Thanet South East 3) Mixed urban/rural, higher deprivation Three Rivers East of England 4) Mixed urban/rural, lower deprivation Thurrock East of England 3) Mixed urban/rural, higher deprivation Tonbridge and Malling South East 6) Predominantly Rural, lower deprivation Torbay South West 3) Mixed urban/rural, higher deprivation Torridge South West 5) Predominantly Rural, higher deprivation Tower Hamlets London 1) Predominantly urban, higher deprivation Trafford North West 2) Predominantly urban, lower deprivation Tunbridge Wells South East 6) Predominantly Rural, lower deprivation Uttlesford East of England 6) Predominantly Rural, lower deprivation Vale of White Horse South East 6) Predominantly Rural, lower deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 46

47 Wakefield Yorkshire and The Humber 3) Mixed urban/rural, higher deprivation Walsall West Midlands 1) Predominantly urban, higher deprivation Waltham Forest London 1) Predominantly urban, higher deprivation Wandsworth London 2) Predominantly urban, lower deprivation Warrington North West 4) Mixed urban/rural, lower deprivation Warwick West Midlands 4) Mixed urban/rural, lower deprivation Watford East of England 2) Predominantly urban, lower deprivation Waveney East of England 5) Predominantly Rural, higher deprivation Waverley South East 6) Predominantly Rural, lower deprivation Wealden South East 6) Predominantly Rural, lower deprivation Wellingborough East Midlands 5) Predominantly Rural, higher deprivation Welwyn Hatfield East of England 4) Mixed urban/rural, lower deprivation West Berkshire South East 6) Predominantly Rural, lower deprivation West Devon South West 5) Predominantly Rural, higher deprivation West Dorset South West 5) Predominantly Rural, higher deprivation West Lancashire North West 5) Predominantly Rural, higher deprivation West Lindsey East Midlands 5) Predominantly Rural, higher deprivation West Oxfordshire South East 6) Predominantly Rural, lower deprivation West Somerset South West 5) Predominantly Rural, higher deprivation Westminster London 1) Predominantly urban, higher deprivation Weymouth and Portland South West 3) Mixed urban/rural, higher deprivation Wigan North West 3) Mixed urban/rural, higher deprivation Wiltshire South West 6) Predominantly Rural, lower deprivation Winchester South East 6) Predominantly Rural, lower deprivation Windsor and Maidenhead South East 4) Mixed urban/rural, lower deprivation Wirral North West 3) Mixed urban/rural, higher deprivation Woking South East 4) Mixed urban/rural, lower deprivation Wokingham South East 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 South East 6) Predominantly Rural, lower deprivation Wyre North West 4) Mixed urban/rural, lower deprivation Wyre Forest West Midlands 5) Predominantly Rural, higher deprivation Yorkshire and The York Humber 4) Mixed urban/rural, lower deprivation ICP2 Online Tool Modelling Assumptions Technical Annex 47

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