Project code: RBM200-009 Research date: Dec 2012 to Mar 2013

Final Report

Economic Impacts of Resource

Efficient Business Models

This report analyses a selection of Resource Efficient Business Models in four key sectors across the UK to assess their potential impacts on the UK economy.

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Written by: Victoria Cox (Ricardo-AEA), Stephanie Boulos (Ricardo-AEA), Jackie Fitzgerald (Ricardo-AEA), Maria Vinogradova (Ricardo-AEA), Thomas Buckland (Ricardo-AEA), Chris Thoung (Cambridge Econometrics)

Front cover photography: Woman flicking through some clothes on hangers

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Economic Impacts of Resource Efficient Business Models 1

Executive summary

There is a growing body of evidence and research into the goal of the Circular Economy,

illustrated by the work of the Ellen MacArthur Foundation, and increasingly gathering interest

around the world. Transitioning towards business models which recirculate products and

reduce demand for raw materials is argued to stimulate economic activity in the areas of

product and service innovation, remanufacturing and refurbishment, and in turn generate

employment. This is a potential win-win from the perspective of the UK economy, which is of

crucial interest in time of economic recovery but also in the face of increasing resource

scarcity considerations.

This report analyses a selection of Resource Efficient Business Models (REBMs) in four key

product sectors (white goods, clothing, electronics and furniture), to assess their potential

impacts on the UK economy. The formulation and macro-economic assessment of these

models naturally involves making assumptions which are clearly explained in the report.

Radical examples were purposefully modelled to demonstrate the “maximum impact”

scenario. This is to help policymakers and delivery bodies understand the consequences of

these models becoming successful.

The REBMs investigated include take-back for re-sale schemes, leasing and refurbishment

arrangements, and extended product lifetimes, using four example products: washing

machines, clothing, TVs and business-to-business office furniture.

In all cases a scenario versus baseline approach was followed, which compared two (simulated) states of the world:

The Baseline: a ‘business-as-usual’ projection of future UK economic performance in which REBMs have not been implemented. The same baseline is used throughout the analysis.

The Scenario, one for each REBM, with the business model introduced from 2013 onwards.

The table below summarises the differences between the scenario and baseline for three key

indicators (Gross Domestic Product (GDP), Net Trade and Employment) alongside an

assessment as to whether the impact is large enough to be considered meaningful at the

macroeconomic level.

In general the results indicate that, while using fewer raw materials, none of the REBMs

would adversely affect economic activity, and some of them would actually increase GDP (a

key variable used to demonstrate economic success).

In particular, the take-back for re-sale business models for TVs and clothing deliver the most

significant beneficial economic impact, in terms of increased GDP. For TVs, the prevalence of

the business model leads to an additional increase in GDP of over £750m in 2020 (compared

to an alternative scenario). For clothing, the impact is even greater at over £1bn additional

GDP in 2020. For TVs, the model is specified for TVs only, but if the model were scaled up

to the whole electronics sector (for which TVs compose only 13%), this could leverage over

seven times the amount of additional GDP. The take-back models also create an even more

significant improvement in the UK balance of trade due to lower reliance on imports. This

implies that widening take-back for re-sale schemes beyond TVs and clothing might deliver a

noticeable improvement in both GDP and trade.

Table: Impacts of REBMs in 2020 compared to baseline

Sector REBM GDP

(£2012m) Net trade (£2012m)

Employment (‘000s)

Economically significant?

Washing

machines

Extended product life:

low price +38 +48 0 No

Extended product life: high price

-19 +57 0 No

Leasing -18 +16 0 No

Clothing Take-back +1,008 +341 +4

Yes: slightly positive

Take-back with

improved durability +1,014 +399 +3

Yes: slightly

positive

TVs Take-back +772 +107 +4

Yes: slightly positive

Furniture Re-manufacture +10 +7 0 No

Leasing +14 0 0 No

Of course the results are subject to various assumptions, and the scenarios were tested

using sensitivity analysis in order to validate the robustness of the results. In all cases, it is

essential that manufacturers and consumers have an incentive to change, and this

behavioural change is an issue that it might be useful to explore further. There are also

several conditions that are critical to the success or demise of any given model. For example,

the clothing take-back model may depend on strong working partnerships between the

retailers and charities.

In conclusion, uptake of these types of business models in the UK economy would represent

a step in the right direction towards the Circular Economy, by using fewer resources yet still

providing opportunities for business growth and a positive contribution to the UK economy.

Economic Impacts of Resource Efficient Business Models 3

Contents

1.0 Introduction ................................................................................................. 1 1.1 Project scope ............................................................................................. 1 1.2 Methodology .............................................................................................. 1

1.2.1 Identifying and analysing the REBM scenarios to take forward to the modelling phase (Phase One) ...................................................................... 1 1.2.2 Modelling and interpreting the macroeconomic impacts of REBMs (Phase Two) .............................................................................................. 2

2.0 REBMs in the White Goods Sector ................................................................ 8 2.1 Scenario description: Washing machines extended product lifetime ................ 8

2.1.1 Changes in sales profile .................................................................. 11 2.1.2 Changes in revenue ....................................................................... 11 2.1.3 Costs and prices ............................................................................ 13

2.2 Economic modelling assumptions: Washing machines extended product lifetime .............................................................................................................. 14 2.3 Modelling results: Washing machines extended product lifetime ................... 15

2.3.1 Scenario 2: higher retail price of more-durable washing machines ...... 18 2.3.2 Sensitivities ................................................................................... 20 2.3.3 Overall impacts of more-durable washing machines .......................... 20

2.4 Scenario description: Washing machines leasing ......................................... 22 2.4.1 Leasing assumptions ...................................................................... 23 2.4.2 Sales displacement ......................................................................... 24 2.4.3 Imports and exports ....................................................................... 24

2.5 Economic modelling assumptions: Washing machines leasing ...................... 24 2.6 Modelling results: Washing machines leasing .............................................. 26 2.7 Potential barriers to uptake ....................................................................... 28 2.8 Conclusions ............................................................................................. 28

3.0 REBMs in the Clothing Sector ..................................................................... 30 3.1 Scenario description ................................................................................. 30

3.1.1 Increasing Clothing Durability – The ‘Enabler Effect’ .......................... 31 3.1.2 Shifts in spending .......................................................................... 33 3.1.3 Consumer spending shifts ............................................................... 34 3.1.4 Retailer costs and revenues ............................................................ 34 3.1.5 Per-unit costs ................................................................................ 36 3.1.6 Employment requirements .............................................................. 37 3.1.7 Changes in imports ........................................................................ 37 3.1.8 Impacts on profits .......................................................................... 38

3.2 Economic modelling assumptions............................................................... 39 3.3 Modelling results ...................................................................................... 39 3.4 Potential barriers to uptake ....................................................................... 43 3.5 Conclusions ............................................................................................. 43

4.0 REBMs in the Electronics Sector ................................................................. 45 4.1 Scenario description ................................................................................. 45

4.1.1 Sales volume ................................................................................. 49 4.1.2 Changes in imports ........................................................................ 49 4.1.3 Retailer impacts ............................................................................. 50 4.1.4 Refurbisher impacts ....................................................................... 50 4.1.5 Reseller impacts ............................................................................. 50

4.2 Consumer impacts.................................................................................... 51 4.2.1 Labour requirements ...................................................................... 51

4.3 Economic modelling assumptions............................................................... 51 4.4 Modelling results ...................................................................................... 52

4.5 Potential barriers to uptake ....................................................................... 54 4.6 Conclusions ............................................................................................. 54

5.0 REBMs in the Furniture Sector (Business to Business) ............................... 56 5.1 Scenario description: Furniture re-manufacture........................................... 56

5.1.1 Re-manufacturing .......................................................................... 57 5.1.2 Overseas Re-manufacturing Markets ................................................ 57 5.1.3 Description of re-manufacturing REBM ............................................. 58 5.1.4 Sales assumptions .......................................................................... 59 5.1.5 Cost assumptions ........................................................................... 60

5.2 Economic modelling assumptions: Furniture re-manufacture ........................ 60 5.3 Modelling results: Furniture re-manufacture ............................................... 61 5.4 Potential barriers to uptake: Furniture re-manufacture ................................ 62 5.5 Scenario description: Furniture leasing ....................................................... 63

5.5.1 Leasing assumptions ...................................................................... 64 5.5.2 Costs ............................................................................................ 65

5.6 Economic modelling assumptions: Furniture leasing .................................... 65 5.7 Modelling results: Furniture leasing ............................................................ 66 5.8 Potential barriers to uptake: Furniture leasing............................................. 68 5.9 Conclusions ............................................................................................. 68

6.0 Project Conclusions .................................................................................... 69 6.1 Approach ................................................................................................. 69 6.2 Results .................................................................................................... 69

7.0 Appendix A - The MDM-E3 Model ................................................................ 71

Glossary

REBM Resource Efficient Business Models MDM-E3 Model Multisectoral Dynamic Model, Energy-Environment-Economy GDP Gross Domestic Product VAT Value Added Tax GVA Gross Value Added

Acknowledgements

The authors would like to acknowledge the support of a large number of individuals at WRAP for their continuous support throughout the development of this report. In particular we would like to thank Valerie Leney, Gerrard Fisher, Leigh Mapledoram, David Moon, Peter Sainsbury and Peter Mitchell. We would also like to thank the organisations and businesses that contributed to the Stakeholder Workshop in February 2013 to test the scenario assumptions, and those that contributed information by email and telephone.

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1.0 Introduction 1.1 Project scope This project investigates the economic impacts of Resource Efficient Business Models (REBMs), and the impacts of their potential wide-scale adoption in the UK economy. Ricardo-AEA led the project, contributing market knowledge of the required sectors, with Cambridge Econometrics (CE) providing the modelling and the macroeconomic assessment.

This report presents the methodology used throughout the project, followed by detailed descriptions of the scenarios that were taken forward to the second (modelling) phase of the analysis. It then presents the results and project conclusions.

1.2 Methodology In broad terms, the project has operated a two-phase methodology, with Ricardo-AEA leading the first phase, and Cambridge Econometrics leading the second phase. The first phase involved the identification and analysis of relevant REBMs to take forward to the second phase of the project. The second phase of the project involved running the detailed scenarios (developed during phase one) in the MDM-E3 model to investigate the potential macroeconomic consequences to the UK economy of greater prevalence of resource efficient business models. The Cambridge Multisectoral Dynamic Model, Energy-Environment-Economy (MDM-E3) is a computer model of the UK economy maintained by CE as a tool for forecasting and scenario analysis.

These two phases are discussed in more detail in the following sections.

1.2.1 Identifying and analysing the REBM scenarios to take forward to the modelling phase (Phase One)

In order to make best use of the MDM-E3 macroeconomic model, the first phase of the project involved a data and evidence gathering exercise of REBMs in each of the four priority sectors selected for this study: clothing, electrical goods, white goods and furniture (business sector).

The REBMs of interest were extended product lifetimes, take-back schemes, rental/leasing systems, servicing models and refurbishment services. Ricardo-AEA investigated current REBMs in the UK economy, matching the priority sectors to the specific business models outlined above, in order to develop credible scenarios to analyse, and subsequently model.

The purpose of the initial data/evidence gathering analysis was threefold:

1) To fully characterise the REBM scenarios to be analysed, and gather as much information as possible on the workings of the business models, particularly on the financial side;

2) To generate data inputs for the MDM-E3 model through data review and stakeholder interaction;

3) To provide a qualitative view of the potential micro and macroeconomic impacts of the REBMs, in order to identify the more meaningful scenarios to model in a macroeconomic context. For clarity, microeconomics is the study of decisions relating to resource allocation at the level of individual households and businesses, whereas macroeconomic impacts are those that occur at the level of the whole economy.

The following six scenarios were taken forward to the second stage of the project:

Clothing – incentivised take-back with enhanced product durability (which enables more take-back as products last longer and are therefore more likely to be re-used) (one scenario in total)

White goods (specifically washing machines) – longer lifetimes, and leasing (two scenarios in total)

Electronic goods (specifically TVs) – incentivised take-back (one scenario in total)

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Furniture (business sector) – leasing, and re-manufacture (two scenarios in total)

The outputs of the first phase of the project were six detailed scenarios, with data templates outlining the key parameters of interest for the modelling phase. The parameters were prioritised in order of their importance for the macroeconomic modelling. Inputs considered high in priority were those that would have the most bearing on the final results and for which it was most important to establish suitable values. Medium and low-priority inputs were deemed to be those that helped to develop a more nuanced and accurate picture of the REBM, but were likely to have relatively less impact on the final macroeconomic outcomes. The parameters included:

Changes/shifts in spending – high priority Changes in imports/exports (where applicable) – medium priority Changes in prices/costs/margins – medium priority Labour requirements (including wages) – low priority

These data inputs were sense-checked with stakeholder experts at an Interactive Workshop in February 2013. Cambridge Econometrics then took the data for each of these scenarios and led the second phase, as outlined below.

1.2.2 Modelling and interpreting the macroeconomic impacts of REBMs (Phase Two) This section explains the approach in this project to assess the macroeconomic impacts of the REBMs. This section provides:

An explanation of the key indicators used to assess the macroeconomic impacts. An overview of the approach, which involving a comparison of scenarios in which

REBMs are implemented against a ‘business-as-usual’ baseline with no REBMs.

CE’s MDM-E3 model of the UK economy is a suitable tool for this project because it explicitly distinguishes sectors such as electrical goods from furniture, reflecting the wide range of goods and services produced in the UK economy. Moreover, each sector has its own supply-chain requirements, which are identified in the model and which may in turn lead to differing economy-wide impacts depending on the sector affected. The model is thus suitable to analyse economic questions that are sector-specific in nature and where the identification of macroeconomic effects is required.

This section presents the key elements of the modelling approach only and how to interpret the key indicators. Further detail on the model and its application can be found in Appendix A.

Assessing the macroeconomic impacts of REBMs The macroeconomic modelling approach consists of a scenario versus baseline analysis. By this approach, the starting point is a baseline that constitutes a view of the world under business-as-usual conditions. A series of scenarios are then run, which represent the introduction of individual REBMs. A comparison of the outcomes from these scenarios to the original baseline yields an assessment of the macroeconomic impact.

The baseline projection to 2020 is consistent with CE’s latest forecast for the UK economy.

Figure 1 shows a scenario versus baseline comparison for UK Gross Domestic Product (GDP) in a notional REBM scenario. GDP measures the production of goods and services in an economy to meet final demands1. It is a common economic measure of living standards

1 GDP excludes purchases of inputs to the production process, because the value of these is already included in the final product.

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because it represents the supply of goods and services to residents of a country. As such, increases in GDP can broadly be interpreted as increases in living standards2.

The blue line represents the baseline projection while the red line shows the alternative trajectory for GDP. The baseline and scenario diverge in the year that the REBM is introduced (2013).

Figure 1: Assessing the macroeconomic impacts of an REBM

The macroeconomic impact of the REBM is the difference between these two lines at a particular point in time. The way the REBMs have been specified in this analysis is such that the year of comparison, with few exceptions, has little bearing on the scale of the impacts. Only in cases where an REBM has a specific time path (e.g. through growing uptake) is there any analysis of the time profile. Even so, all REBMs are assumed to be fully in place by 2020 so that year remains suitable as a representative year for comparison.

The interactions within an economy (and within MDM-E3) are complex and REBMs will have wider impacts on other sectors through supply chains and income effects. Rather than trace through every single flow graphically, a summary of the key drivers of the overall macroeconomic effect is provided for each REBM in a diagram such as that in Figure 2. These are provided later in the report per sector.

The diagram shows, in the centre, the three broad classes of REBM identified in this project:

Longer-life products e.g. washing machines. Take-back/refurbishment schemes e.g. clothing, TVs and furniture Leasing schemes e.g. washing machines and furniture

Of these, the example REBM (purely hypothetical at this point to simply explain the method) introduces longer-life products into the UK economy and that box is coloured green, to indicate that the overall GDP impact is positive. Had the impact been negative, the box would have been coloured red. Because the example REBM is for longer-life products, the other two boxes (take-back/refurbishment schemes and leasing schemes) are left white. Were the diagram for another class of REBM, one of the other boxes would have been in colour and the other two would have been white.

2 It is, however, important to bear in mind that GDP may be too narrow an indicator in some contexts because it ignores, for example, resource consumption or environmental emissions.

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Around that, the diagram identifies the key macroeconomic impacts of the example REBM. Some of the impacts may be positive for GDP (and coloured in green) while others may be negative (coloured in red) and, in the example, the net impact is positive for GDP (hence the green box for longer-life products):

The good is more durable, reducing expenditure on new goods through lower replacement demand. This lowers expenditure and thus has a negative economic impact in expenditure terms, hence the box being coloured red.

Because the goods are, at least in part, imported, there is a reduction in imports which, in accounting terms, raises GDP (a green box).

Finally, the income saved by the lower replacement demand frees income and consumption on other products rises in its place. Higher expenditure raises GDP (another green box).

Later on in the report, this method is applied to each REBM. This discussion was provided only as an example. Figure 2: Key drivers of macroeconomic impact in an example REBM

The analysis focuses on the following key macroeconomic impact indicators:

GDP Inflation Household income and expenditure Net trade (exports minus imports)

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Employment Tax revenues

GDP is the headline indicator and a standard measure of macroeconomic performance, based on the amount of production that takes place in the economy3. It is a common way to gauge economic activity, as measured by the goods and services it produces to meet demand. REBMs that increase economic activity and raise GDP relative to the baseline can be considered to have positive macroeconomic effects. This is not to say that the UK performs well by all measures, because GDP does not capture, for example, the nature of the income distribution, nor does it acknowledge the (non-quantified) potential resource savings brought about by REBMs.

Some indication is also given in the results as to the size of the GDP impact relative

to the sector affected. While it is not the case that all that impact (whether positive

or negative) accrues to the sector in which the REBM was implemented, such a

comparison does help to give some sense as to the size of the overall

macroeconomic impact compared to the size of that sector.

Inflation, a measure of the UK price level, might be higher with a particular REBM if that business model entails increases in costs. If businesses pass some or all of this cost onto the consumer, this may, in turn, lead to higher prices and higher inflation in the scenario compared to the baseline. Conversely, if an REBM introduces cost savings, then inflation would likely be lower in the scenario compared to the baseline. In general, higher inflation in a scenario compared to the baseline represents a negative impact on the economy (and may also depress GDP growth).

Household income and household expenditure are reported in the analysis because the majority of the REBMs in this project target consumer spending, such as shifts to longer-life or second-hand products. When economic growth is higher and there is higher employment, there is typically higher income to households (through higher total wages and profits). Higher income, in turn, tends to drive higher household expenditure.

Household income and expenditure however are not necessarily directly related: £1 of additional (disposable) income does not necessarily lead to a £1 increase in household expenditure. Households may choose to save a portion of that income (thus living within their means). Conversely, expenditure may outpace income, at least for a time, if households fund additional expenditure by either running down their savings or incurring more debt (living beyond their means).

Net trade (the difference between UK exports and imports) is of interest because a number of products covered by the REBMs are manufactured overseas. Indeed, all washing machines in the UK are imported.

Consequently, some REBMs will not just have macroeconomic impacts through changes in domestic expenditure; they may also have trade effects. Some REBMs also create an export opportunity for UK businesses (e.g. sales of old TVs and washing machines), which also has macroeconomic impacts. The UK now runs an annual trade deficit (its imports exceed its exports) so the net trade figures in the baseline are negative. Improvements in the balance of trade register as less negative net trade figures.

The analysis also presents the impacts on UK employment. The aim of the macroeconomic assessment is to gauge the wider economic impacts of the REBMs. This analysis extends to

3 The analytical framework from which GDP is derived is such that GDP is also equivalent to: a) final expenditure in the economy (domestic demand, exports and less imports) or b) the sum of incomes that arise from the production process. Both of these alternative approaches should in principle, and by virtue of macroeconomic accounting, yield the same value.

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the employment that the REBMs generate both in the sectors covered by the REBMs but also elsewhere in the economy from changes in supply chains and incomes.

The final indicator reported for each REBM is the overall change in UK labour and indirect tax revenue (taxes on labour through income and social security, as well as that levied on products). Changes in expenditure patterns may lead to changes in government tax revenues. Some taxes, like VAT, are related to the price of the goods sold. In cases where REBMs raise prices, other things being equal, they will generate higher tax revenue. Higher employment and incomes lead to greater labour tax revenue accruing to the government.

Taxes are of interest because of concerns about the UK’s fiscal situation and the government’s desire to reduce UK debt. Other things being equal, higher tax revenue would help pay off some of this debt. Conversely, lower tax revenue would contribute less to debt and deficit reduction. There is the potential for high taxes to curb economic growth but this is not an effect identified in the modelling results.

At the level of households and companies, the REBMs are potentially quite significant (as is their aim). The objective of the macroeconomic analysis is to gauge how this impact might look at the level of the UK economy, in order to assess the potential for wider economic benefits. At such a level of detail, the REBMs typically show small macroeconomic impacts (although many are positive in nature). The results are accompanied by an assessment of the economic significance of the outcomes i.e. whether a result is so small as to effectively count as an impact of zero at the macroeconomic level.

The model does allow for the reallocation of funds from, for example, savings on one category of spending being spent elsewhere in the economy (although, equally, some of it might be saved). The results presented are net of all direct and indirect effects. In macroeconomic terms, the initial spending shifts modelled are not, for the most part, particularly large and secondary impacts of this type have little overall bearing on the headline results.

Note that at the macroeconomic level, savings made by households in aggregate also represent lost income to households, because they represent money not spent in the economy and thus a decrease in production. This in turn represents a corresponding reduction in wage income to workers. The income and expenditure results in the modelling reflect this flow of funds.

Modelling REBM scenarios in MDM-E3 The key considerations when translating the individual REBMs into inputs to the macroeconomic model are:

The need to identify the relevant sectors in MDM-E3 and to specify more formally the nature of the transaction such that it is consistent with the accounting principles in macroeconomic statistics.

While sectorally-disaggregated, the level of detail in the model is still coarser than some of the definitions of the REBM sectors. That is to say, the sub-sector of particular interest for the project may sit within a broader sector in the current model, so is assumed to behave as the rest of the sector for modelling purposes.

The requirement for MDM-E3 to deal in volumes (broadly, quantities) separately from price. Sales of TVs in one year may amount to £1m in one year and £1.2m in another but it is not possible to know whether this actually represents 20% more goods and services until price has been accounted for. For modelling purposes, this distinction is important.

The first of the above simply requires that transactions in an REBM are modelled in the same way that they would be recorded in actual economic statistics (because MDM-E3 requires

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accounting consistency). For example, it is important to know whether the owner/purchaser of the furniture is also the user (some REBMs involve a leasing arrangement), as these would be treated differently to the case where the owner is also the user.

The second point is important to bear in mind when interpreting the results because MDM-E3 is not able to identify goods and services at a level of detail that corresponds exactly to those of the REBMs.

In the case of washing machines and TVs, the products belong to broader categories of product: domestic appliances and (audio-visual) electrical goods, respectively. Clearly, a TV differs from a computer but this is not a distinction made in MDM-E3. The macroeconomic modelling in these cases is thus not able to capture as many of the subtleties of each product because the electrical goods category, as a whole, represents a wider range of more heterogeneous products.

The same holds true for office furniture which forms part of the entire UK furniture sector and high-end clothing (part of the entire UK clothing sector). In these cases, the products arguably resemble the ‘representative’ good somewhat more closely than washing machines or TVs, though.

The final point is important because, without knowing the price level, it is not possible to say whether £1.2m of TV sales in one year actually represents 20% more TVs than £1m of sales in another year. Macroeconomic models require this distinction in order to meaningfully analyse whether production of goods and services in an economy has increased or if there has simply been an increase in price. In economic terms, this is known as “nominal” changes (i.e. changes to prices) versus “real” changes (changes in quantities).

Throughout this report the results from the macroeconomic analysis are presented in 2012 prices and so changes are presented in ‘real terms’. This is such that £1m of TVs in 2012 prices, represents, broadly, the same number of TVs, whether the year is 2012 or 2020. Real-terms values allow for comparisons of like with like through time by stripping out the effects of inflation. More details on the need for ‘real-terms’ values can be found in Appendix A.

In all cases, the scenarios model the introduction of the REBMs in 2013 and assume that they continue to operate until at least 2020 (in order to carry out the main comparison against the baseline in 2020).

For each sector and REBM considered in the chapters that follow, there is an explanation of the core assumptions (translated from the earlier business model analysis) before the macroeconomic impacts are presented.

The inputs to the macroeconomic model depend on a series of assumptions. While these assumptions might be considered a best estimate of the likely REBMs (and have been validated by industry), they remain subject to uncertainty. As part of the macroeconomic assessment, there is also some sensitivity analysis on key inputs, in order to shed some light on assumptions that have relatively more or less impact on the final macroeconomic outcome. This is included in each of the individual REBM analyses.

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2.0 REBMs in the White Goods Sector Within the white goods sector, washing machines were chosen as the white good of interest. Household expenditure data for 2011 shows that “Household Appliances” (which include washing machines) represented £6.63bn in 2011. Recent work by WRAP has found that a noticeable amount of washing machine fail prematurely, within less than 3 years. In addition a large proportion of the market is represented by low value products with limited lifetimes.

For washing machines, two possible REBMs are considered:

Extended product lifetime, in which a shift towards higher-durability appliances is observed.

A leasing model, where businesses lease out washing machines to housing associations.

2.1 Scenario description: Washing machines extended product lifetime Electrical household appliances represented 2.1%4 of the UK’s total annual retail market in 2011. In 2011, 2.8 million washing machines were sold, representing a value of £879 million. This compares to 2.7 million washing machines sold for a total value of £811million in 20075. Within the UK the market is saturated, with around 84%5 of all households owning a washing machine and a further 13% owning a washer-dryer, thus sales represent mainly a replacement market. Assuming that 97% of all households are equipped with a washing machine or a washer drier (based on adding the 84% to the 13%), then the current stock of installed product is in excess of 26 million units.

In this first scenario, an REBM is modelled in which consumers replace washing machines less frequently owing to longer product lifetimes. This extended product lifetime is achieved by a change in the composition of new-product sales (in favour of more durable products).

In the UK, the washing machine market can currently be broken down into three categories:

Low cost products, with the cheapest washing machine costing just £1596, up to £300

Mid-range products from £300 to £750 High end products priced £750 upwards (the most expensive washing machine

found aimed at domestic consumers is a Miele at £2,8997)

The Whitegoods Trade Association estimates that over 80% of all washing machines sold in the UK cost under £500 and that over 40% cost under £300. It can be noted that most manufacturers sell their products with a 1 or 2 year warranty, but brands like Miele and ISE offer 5 to 10 year warranty. The REBM focuses on the low end of the market (i.e. products retailing for less than £300), which accounts for 40% of all sales, and is mainly products that last just over a thousand wash cycles, and typically are estimated to last no more than five years. The business model aims to extend the life of the washing machines concerned from five years to seven years (an implied increase in lifespan from a minimum of 1,300 cycles to 1,820 cycles8). This could 4 Office of National Statistics http://www.ons.gov.uk/ons/dcp171778_250868.pdf

5 Mintel report Washer dyer report June 2012

6 Essential washing machine as sold by Currys http://www.currys.co.uk/gbuk/washing-machines/332_3119_30206_0_xx/1_12/price-asc/xx-300-criteria.html, or ProAction from Argos http://www.argos.co.uk/static/Product/partNumber/4788737.htm

7 http://www.johnlewis.com/miele-w5877-edition-111-washing-machine-8kg-load-a-energy-rating-1600rpm-spin-white/p231726450

8 Based on an average 5 wash cycles per week

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be achieved by retailers and manufacturers raising consumer awareness of product durability and lifetime costs at point of sale (including websites) through an education campaign, better labelling and product guarantees. In this scenario, we model a shift in sales towards extended product-life appliances such that the share of this market accounted for by five-year products falls to 25% (a 15 percentage point shift). The shift in sales is underpinned by an active awareness campaign driven by retailers at the point of sale (including websites) to engage with consumer and influence their purchasing pattern. The retailers are expecting to invest the equivalent of 1% of the revenue generated by the sales of these products with extended lifetime every year. It is anticipated that without a campaign and no incentives, there would be few reasons for consumers to change their behaviour. The main incentives for consumer would concern the capital cost of purchasing a washing machine and how often: comparing the capital investment for household over a 20 years period show that low cost washing machine would cost an estimated £840, compares with £690 for washing machine with an extended product lifetime. Two variants of the extended product-lifetime scenario are considered, which differ only in the retail price of the longer-lifetime washing machines. In both cases, the retail price of the low-end appliances that are replaced is estimated at an average £2109 excl. VAT. From that baseline, the two scenarios are as follows:

Scenario 1: extended product-lifetime appliances sell for an estimated £23010 excluding VAT; this scenario assumes that manufacturers pass on additional costs directly to consumers

Scenario 2: extended product-lifetime appliances sell for an estimated £28011 excluding VAT. This higher sales price is based on WRAP research showing that consumers would be willing to pay up to 34%12 more for products with guaranteed product lifetime

The calculation of these prices is discussed in more detail later on.

9 Based on calculated average of washing machines available at a number of retailers and priced at less than £300.

10 Based on conversation with industry stakeholders

11 Based on conversation with industry stakeholder and finding from WRAP’s survey on consumer spending

12 WRAP survey focused on consumer expectation on product lifetime. To be published.

10

Figure 3: Schematic of washing machines extended product lifetime scenario

Implications of extended product lifetime business model on resource efficiency This business model contributes toward achieving a circular economy by influencing both manufacturers and consumers. By influencing manufacturers to put products on the market that are affordable and yet last a minimum number of washing cycles and by promoting and raising awareness at the point of sale of products with extended lifetime, consumers are influenced to choose a product that will last longer thus ultimately costing less over the lifetime of the product. The model helps to reduce the waste of resources on short-lived products and focuses on promoting the use of resources for an extended period of time, thus by reducing the frequency of products breaking down and being replaced. This result in less manufacturing (less energy and less resources use) and less waste arising on a yearly basis. It is often the case that it will be a better higher quality product using less energy and water as well. The model is based on the assumption that the lower-end of the market is targeted where household budgets are very tight and so the decision making of purchasing a large electrical appliance is driven by the need (i.e. capacity) and primarily by the price. By raising awareness at the point of sales, these consumers can be influenced to buy the longer lifetime products.

11

2.1.1 Changes in sales profile Owing to longer product lifetimes, annual sales of new washing machines will be lower, compared to the baseline case without this REBM.

In Year 1 of the scheme (2013), total sales are the same as if the REBM had not been implemented. In this year, 420,000 of the new washing machines sold are of extended product life. The assumed profiles of sales volumes over time, accounting for increases in household numbers and the changes in replacement rates, are shown in Table 1 below.

The number of products sold is reduced over time as the length of product replacement is extended over time.13 The change in the numbers of longer lifetime products bought is driven by an awareness campaign at the point of sale, encouraging consumer to replace broken down products with new washing machines with extended lifetimes.

Consumers are only buying a new product as a replacement of a broken one, though a small proportion of the market is for new products bought for a new home or due to house moving.

Table 1: Sales profiles of market affected by REBM

Total number of washing machines sold in each scenario per year

Baseline

Scenario 5 years’

product

lifetime

Scenario 7 years’

product lifetime

Scenario

Total

Difference:

scenario vs. baseline sales

2013 1,119,604 699,752 419,851 1,119,604 0

2014 1,136,740 710,462 426,288 1,136,751 11

2015 1,153,551 720,969 432,556 1,153,526 -25

2016 1,167,711 729,820 433,630 1,163,449 -4,262

2017 1,176,620 735,387 393,658 1,129,046 -47,574

2018 1,183,690 739,806 271,459 1,011,265 -172,425

2019 1,192,383 745,240 186,929 932,169 -260,215

2020 1,207,241 754,526 241,199 995,724 -211,517

2.1.2 Changes in revenue Compared to the base case (no shift towards extended product lifetimes), revenue to retailers is higher in Year 1 because the sales volume is unchanged but the average price per unit has increased. We show this in Table 2 below.

12

Table 2: Year 1 change in revenues compared to baseline (differences between scenarios in bold)

Scenario 1

(The retail price is £230)14

Scenario 2

(The retail price is inflated to £280)15

Change in total sales compared to baseline 0 0

Change in sales of extended lifetime products +420,000 +420,000

Change in sales of low-end products -420,000 -420,000

Price of extended lifetime product, excl. VAT £230 £280

Price of low-end product, excl. VAT £210 £210

Change in revenue from extended lifetime

products

+£96.1m +£117.7m

Change in revenue from low-end products -£87.8m -£87.8m

Net change +£8.4m +£29.9m

Overall, sales revenue in each scenario follows the path set out in Table 3 below. The average retail prices for the two scenarios exclude VAT and, in line with the initial assumptions about the sales shift, are based on a 15:25 weighting between longer-lifetime and low-end products.

Table 3: Sales revenue through time

Sales Revenue (£M)

Baseline Scenarios16 Baseline

Scenario 1 (The retail price for extended

product lifetime is £230)

Scenario 2 (The retail price for extended product lifetime is inflated to

£280)

Total 5 years 7 years Total 5 years 7 years Total

2013 1,119,604 1,119,604 £235.1 £146.9 £96.6 £243.5 £146.9 £117.6 £264.5

2014 1,136,740 1,136,751 £238.7 £149.2 £98.0 £247.2 £149.2 £119.4 £268.6

2015 1,153,551 1,153,526 £242.2 £151.4 £99.5 £250.9 £151.4 £121.1 £272.5

2016 1,167,711 1,163,449 £245.2 £153.3 £99.7 £253.0 £153.3 £121.4 £274.7

2017 1,176,620 1,129,046 £247.1 £154.4 £90.5 £245.0 £154.4 £110.2 £264.7

2018 1,183,690 1,011,265 £248.6 £155.4 £62.4 £217.8 £155.4 £76.0 £231.4

2019 1,192,383 932,169 £250.4 £156.5 £43.0 £199.5 £156.5 £52.3 £208.8

2020 1,207,241 995,724 £253.5 £158.5 £55.5 £213.9 £158.5 £67.5 £226.0

The values presented in Table 3 above are used in Table 4 below to calculate the changes in revenues relative to baseline (Total for Scenario 1 minus Baseline and Total for Scenario 2 minus Baseline).

14

Scenario 1: extended product-lifetime appliances sell for an estimated £230 excluding VAT; this scenario assumes that manufacturers pass on additional cost directly to consumers

15 Scenario 2: extended product-lifetime appliances sell for an estimated £280 excluding VAT. This higher sales price is based

on WRAP research showing that consumers would be willing to pay up to 34% more for products with guaranteed product lifetimes

16 Sales volumes are the same for scenario 1 and 2.

13

Table 4: Changes in sales revenue relative to baseline

Scenario 1

(The retail price is £230)

Scenario 2

(The retail price is inflated to £280)

2013 +£8.4m +£29.9m

2014 +£10.5m +£32.6m

2015 +£13.0m +£35.5m

2016 +£16.0m +£39.1m

2017 +£11.4m +£34.2m

2018 -£12.9m +£7.9m

2019 -£35.9m -£16.9m

2020 -£36.4m -£17.2m

2.1.3 Costs and prices An additional cost borne by the retailer covers an awareness campaign to promote a change in consumer behaviour toward purchasing longer life products. This annual cost is assumed to be equivalent to 1% of revenue generated from sales of the longer-lived products in 2013 (the first year of the scheme); this would be equivalent to an estimated £1m each year. The campaign would focus on sales staff training so that they can influence consumers, as well as posters at points of sale. The retailers’ websites would also be uploaded with some materials to inform consumer and demonstrate the cost saving that can be realised. The campaign would run every year for at least 7 years to cover the first full cycle of longer life products.

All washing machines are manufactured abroad, so any changes in UK sales volumes are reflected in changes in import volumes.

The increase in manufacturing cost (overseas) associated with production of a more durable washing machine is assumed to be around +20%/unit17 compared to low-end washing machines. We assume that retailers pass this cost on in its entirety, giving an initial retail price increase of just 5% at £220, compared with the low-end unit, which has a price of £210.

The additional mark-up (which also includes additional costs such as a longer guarantee commensurate with longer product life) is what distinguishes the two scenarios modelled.

Table 5: Breakdown of costs and prices (annual)

Scenario 1

(The retail price is £230)

Scenario 2

(The retail price is inflated to £280)

Price of low-end product excl. VAT £210/unit £210/unit

Price of extended-lifetime product excl. VAT

(assuming 100% cost pass through)

£220/unit £220/unit

Additional mark-up +5% +28.5%

Final retail price or extended-lifetime product excl. VAT £230/unit £280/unit

Final retail price or extended-lifetime product incl. VAT £276/unit £336/unit

17 Figure estimated from discussion with the industry, 20% of manufacturing cost before any margins.

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2.2 Economic modelling assumptions: Washing machines extended product lifetime The key inputs to the macroeconomic modelling are:

Changes in sales, to reflect the alternative sales profile, as set out in the previous section.

Changes in the corresponding imports, to reflect the changes in the sales profile (on the basis that all washing machines are imported).

The annual cost to the retailers of running the consumer-awareness campaign. Changes in import prices to reflect the shift towards more expensive, longer-life

appliances shipped into the UK. Changes in retail prices to reflect the shift towards more expensive, longer-life

appliances bought by consumers.

As defined in the previous section, the two scenarios differ only in the last point: the price that retailers are able to charge consumers. In real terms, the sales volume of washing machines does not change between the two scenarios.

In order to model the impact of the changes in sales over time it is necessary to identify, in real terms, the proportion of consumer expenditure on all household appliances accounted for by washing machines and adjust this element of the expenditure downward to reflect the alternative sales profile generated by this REBM:

Sales in the early part of the period (2013-15) are barely different to the profile in the baseline, as consumers gradually replace their older appliances with more durable new products. This change is brought about by the retailers’ consumer-awareness campaign.

Over time, the sales profile in the scenarios falls compared to the baseline, reflecting lower annual sales because consumers do not need to replace their washing machines as often. There is lower replacement demand each year.

The same reductions on imports of electrical equipment are imposed on the basis that all washing machines sold in the UK are imported.

The shifts in sales are not large, relative to all consumer expenditure on household appliances, amounting to a less than 1% reduction in any year, from lower replacement demand.

As part of the business model, the scenario also incorporates the cost to retailers of the awareness campaign to promote the scheme. This cost is around £1m per year and, relative to the retailing sector’s gross output (approximately, turnover) of more than £120bn, is not a substantial cost to this sector, let alone to the UK economy as a whole. It is unlikely that there would be substantial macroeconomic impacts from this business model.

As mentioned previously, most macroeconomic models deal in real quantities. The consequence of this is that while nominal revenues (value) for firms clearly changes across price variants, their “real” (volume) equivalents do not. Instead, these differences in price affect sectors’ profitability and the spending power of those who receive income from wages and profits. Note that only the retail price changes between these scenarios: the price paid by retailers to import the more-durable washing machines is the same across the two scenarios. Because washing machines only represent a fraction of imports of appliances, the overall increase in import prices in MDM-E3 is small (fractions of a percent each year).

The effect of different prices (but with no other differences in the direct economic impacts modelled) is simply to raise costs within the economy. For given inputs to production, per unit of output, higher prices generate more profit for the washing machines sector. Because the assumption is that (import) costs do not change in the two scenarios, the profit in the second, higher-price, scenario accrues entirely to retailers in the UK, rather than flowing to

15

overseas manufacturers. The higher price and corresponding margin represents £140m extra profit to UK retailers (none of which is captured by the manufacturer). This of course increases UK retailers’ share of the value added in the product by 7%, but only by virtue of the higher margin accruing entirely to these retailers.

However, at the macroeconomic level, this additional profit in the sector does not necessarily lead to a net benefit. This is because, while the washing machines sector itself may well benefit from the higher profits ( and within the scenarios modelled the higher mark-up is assumed to be captured in whole by the UK retailers), these additional revenues will typically come at the expense of consumers and other sectors of the economy. The difference in prices in the two scenarios may potentially alter the distribution of income within the economy, with the washing machines sector capturing relatively more of the value added in the economy in the higher-price variant (because consumers must divert income to washing machines, and away from goods and services produced by other sectors). This again will benefit UK retailers only and not the manufacturers; the ratio of value added in the higher price (scenario 2) would be on average 7% higher than in scenario 1.

Another impact of higher prices is to raise revenues from taxes that relate to the price of goods such as VAT. If prices are higher, then, other things being equal, VAT receipts will also be higher.

At the macroeconomic level, changes in domestic prices also depend on the relative size of the washing machines market. As with imports, washing machines represent a relatively small volume within the wider domestic appliances sector and the increase in costs or retail prices is small; again, in the fractions of percent each year in both scenarios.

It is important to make clear that a comparison between the two scenarios does not quite represent a ‘complete’ assessment of the macroeconomic outcomes because the scenario ignores any demand response brought about by differences in price (the sales profile ought to change in response to alternative prices). Instead, the scenarios represent, in some sense, the impact of consumer willingness to pay higher prices for their washing machines (especially where a guarantee of longer lifetime is offered) and how those effects spread through the economy.

2.3 Modelling results: Washing machines extended product lifetime This section presents the results from the two extended product lifetime scenarios for washing machines. As mentioned previously, the scenarios only differ in the retail price of the more-durable appliances and thus also constitute the key sensitivity analysis for this REBM.

Scenario 1: lower retail price of more-durable washing machinesTable 6 below presents the results in 2020 of the first extended product lifetime scenario for washing machines, with a price of £276/unit (incl. VAT) for the more durable appliances.

Reading from the left, the first column of numbers in the table shows the results for 2020 from the baseline while the next column along presents the corresponding results from the scenario. The third column then shows the difference between the baseline and the scenario in 2020: GDP is £38m higher in the scenario in 2020 compared to the baseline, with household income £7m lower and an improvement in net trade of £48m, in 2012 prices. In the case of net trade, there is an improvement in the balance of trade as the trade deficit is reduced. The UK is a net importer of goods and services and the MDM-E3 baseline, consistent with CE’s most recent forecasts, projects this to continue into the future. Finally, the rightmost column in the table shows the percentage differences between the scenario and baseline figures in 2020. This final column gives some sense of the small scale of the impacts of this REBM and is not a surprising result given the small values taken by the input assumptions (see the previous section).

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Table 6: Results in 2020 for Washing Machines Extended Product Lifetime Scenario 1

Year Baseline Scenario Difference

(-) Difference

(%)

GDP (£2012m) 1,795,904 1,795,942 38 0.00

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,361 -7 0.00

Household expenditure (£2012m) 1,168,387 1,168,375 -12 0.00

Net trade (£2012m) -36,409 -36,361 48 -0.13

Employment ('000s) 32,979 32,979 0 0.00

Labour and indirect taxes (£2012m) 466,799 466,824 24 0.00

It is clear from the table that the REBM has a negligible impact on UK economic performance, with the GDP impact effectively zero, with similarly small impacts on household income and expenditure. While income (-£7m) and expenditure (-£12m) are slightly lower in the scenario in 2020 compared to the baseline, the difference is too small for it to be reasonably considered a “negative” impact (it does not represent any meaningful deviation from trend economic growth). The slight reduction in these indicators compared to the baseline reflects the lower circulation of funds through the economy as a result of the lower sales profile18.

The overall GDP impact of some £40m in 2020 (which is driven largely by reductions

in imports of new washing machines, keeping more money in the UK economy) does

not represent a large part of the electrical equipment sector in MDM-E3: less than

0.7% of GVA. Note that the GDP impact is not confined to the sector- this share

merely gives some sense of the effect relative to the sector.

In the model, this sector is quite broad in nature, encompassing other appliances

such as refrigerators. Compared to the specific washing machines market in the UK

(£879m in 2011), the impact is larger, approaching 5% once converted back from

real to current prices.

However, it is important to point out that the overall reduction is smaller than the reduction in washing machine sales. The savings in later years from not having to replace washing machines so frequently does permit greater consumption of other goods and services: consumer expenditure on other products is marginally higher in some cases. However, it is difficult to consider any of these impacts as meaningful in a macroeconomic sense: these impacts are effectively zero.

Net trade (exports minus imports) is slightly up in the scenario compared to the baseline owing to lower import demand as purchases of new washing machines from overseas slows over time. The improvement in the balance of trade is the only impact to show up as non-zero (to 2 decimal places) in percentage terms and is the key driver of the slightly positive GDP impact from the scenario.

In this first REBM scenario, the small size of the impacts reflects the small size of the direct changes in expenditure: as mentioned previously, the savings do not amount to much in

18 At the macroeconomic level expenditure generates income.

17

terms of overall household expenditure on appliances, let alone at the level of total consumer expenditure.

MDM-E3 does not show any substantive employment impact from the REBM while the tax take is very slightly higher relative to the baseline. This effect arises from the slightly higher prices of the more-durable washing machines although, at £24m in 2020, does not contribute greatly to the overall UK fiscal situation.

In terms of the dynamics in this scenario, the impacts of this REBM differ through time, although it is important to note that the effects, in macroeconomic terms remain small throughout the period.

In the business model, the scenario sales profile is such that purchases of washing machines in the early part of the period, over 2013-15, are unchanged. In this period, the macroeconomic impacts are slightly negative because households must replace just as many washing machines as in the baseline but do so at higher cost (because they now buy more expensive, but also more durable, appliances). It is only when consumers do not have to replace their washing machines as quickly (the next round of appliances) that the macroeconomic impacts become positive. This arises from lower replacement demand lowering imports of washing machines, and contributing to (very slightly) higher GDP as a consequence. Household expenditure and income also levels off in this later period.

Figure 4 shows how the REBM depresses GDP initially, until 2018, when the reduction in sales becomes more pronounced, lower imports and raising GDP. The peak in 2019 is where sales are lowest in the scenario compared to the baseline, leading to the largest reduction in imports and the largest increase in GDP over the baseline. The assumed sales profile is such that the difference between the baseline and scenario does start to narrow once more but the impacts at the level of the macroeconomy are essentially zero.

Figure 4: Profile of scenario relative to baseline – extended product lifetime for washing machines (Scenario 1)

18

Because prices are higher (and inflation remains positive over the projection period), VAT receipts grow steadily over time.

2.3.1 Scenario 2: higher retail price of more-durable washing machines Table 7 shows the results from the second scenario, in which a higher retail price is modelled for the more-durable washing machines, at £336/unit including VAT. No other inputs differ between this second scenario and the first and the comparison that follows amounts to a sensitivity analysis of price, for fixed sales of more-durable washing machines.

Table 7 shows that the price effect is not large enough to alter inflation substantially although, in contrast to Scenario 1, GDP is marginally lower in 2020 in the scenario compared to the baseline, by £19m. However, the table does suggest a slightly higher price level (higher inflation), although it is small, contributing to a slight negative impact on GDP compared to the baseline. However, as with the positive impacts from Scenario 1, these differences are too small for us to consider the difference against baseline discernible from zero.

The slight negative impact at the macroeconomic level is equivalent to around 2% of

the washing machines market in 2011 (in current prices) although it is important to

note that the washing machines sector only incurs a fraction of that macroeconomic

cost.

The higher retail price of washing machines does represent a higher level of nominal profit to washing-machine retailers from the higher mark-up. In macroeconomic terms, this change in the price of washing machines relative to other products in the economy leads to the sector capturing (some) additional value from elsewhere in the economy. Input costs per unit are not much different but the higher price leads to higher profit in this particular sector at the expense of profits in other sectors: value is diverted from elsewhere in the economy, relatively speaking.

Overall, unless the beneficiaries of the additional retail profit then spend that income, as consumers, on further purchases of washing machines, they are better off in real terms even after accounting for the amount spent on the awareness raising campaign.

Across the UK economy, incomes fall by more in this scenario than in the lower-price variant, with the GDP impact compensated somewhat by the lower import demand (because of lower annual imports of washing machines).

The impact of higher prices is thus a distributional one in macroeconomic terms: higher prices lead to more profit accruing to UK retailers of washing machines. Between the two scenarios, the overseas manufacturers see no change in revenues or profits.

The employment impacts are to all intents and purposes zero in both this high-price variant and the previous low-price variant.

The final impact assessed in this scenario is on tax revenue. Compared to both the baseline and the previous low-price variant, tax revenue is higher. This result follows from the higher retail price, which leads to higher VAT revenue per washing machine19, amounting to £56m in 2020, in 2012 prices. This is up from £24m in Scenario 1, where prices were lower.

19 Remembering that VAT is levied on the price of goods and services, higher prices generate higher nominal VAT receipts. To the extent that the general price level increases by less than the price of washing machines, this leads to higher real VAT receipts.

19

Table 7: Results in 2020 for Washing Machines Extended Product Lifetime Scenario 2

Year Baseline Scenario Difference

(-) Difference

(%)

GDP (£2012m) 1,795,904 1,795,884 -19 0.00

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,299 -69 -0.01

Household expenditure (£2012m) 1,168,387 1,168,316 -71 -0.01

Net trade (£2012m) -36,409 -36,352 57 -0.16

Employment ('000s) 32,979 32,979 0 0.00

Labour and indirect taxes (£2012m) 466,799 466,855 56 0.01

The dynamics of this second scenario are similar to those in the first, lower-price variant: consumers must continue to purchase replacement washing machines in the short term, but net trade improves in the longer term as more durable products lower replacement demand.

The key difference in this second scenario is that the higher prices lead to lower (real) household income and expenditure to an extent that the improvement in net trade later in the period is not sufficient to raise GDP above that in the baseline projection. This is shown in Figure 5.

Figure 5: Profile of scenario relative to baseline – extended product lifetime for washing machines (Scenario 2)

As in Scenario 1, real tax revenues rise steadily over time, reflecting both a higher retail price (tax revenue impacts are positive) and steady price inflation (tax revenues rise steadily over the baseline).

20

However, the overall impact is small and it can be concluded that the resource savings from this REBM are associated with small macroeconomic impacts, even in the high-price case.

2.3.2 Sensitivities A comparison of the low and high-price scenarios suggests that, at least in the vicinity of zero, small changes in price can mean the difference between mild positive and mild negative macroeconomic impacts20.

The other elements of the scenario have much less of a bearing on the macroeconomic outcomes:

Alternative sales profiles would have to differ quite markedly from the baseline (implying much longer/shorter-lived appliances than is reasonable) for there to be more substantive macroeconomic impacts21.

The macroeconomic impacts are less sensitive to increases in import prices than increases in domestic retail prices (where the mark-up and profit implications are larger).

For the impact of the consumer awareness to affect the results in any meaningful way, the campaign would have to be implausibly large in scale: there is no reasonable value for this cost to have a macroeconomic impact.

To the extent that expert consultation suggests the alternative retail price may well be realistic and that smaller impacts arise from changes to the above, on scales that may not be realistic, it seems reasonable to conclude that price effects, and the resulting changes in real incomes and profits, are the key factors in this set of scenarios, for a given sales profile.

2.3.3 Overall impacts of more-durable washing machines Overall, the macroeconomic impact of the extended product lifetime REBM is negligible, with the higher price variant leading to higher profits for washing machine retailers and depressing household income and expenditure slightly at the macroeconomic level. This has the effect of raising tax revenues from VAT (through higher prices) but also leading to overall lower GDP, albeit by an amount that is barely distinguishable from zero.

While higher prices may have (slightly) negative impacts at the macroeconomic level, the REBM would still be appealing to consumers as long as it led to lifetime cost savings (i.e. benefits). To the extent that the resource savings come at little macroeconomic impact, and provided that consumers benefit overall, there is no strong evidence from the analysis above to suggest that the REBM should be dismissed as unattractive.

There are no substantive employment impacts under this REBM.

There are some dynamic effects that arise from the growing penetration of more-durable appliances. Early in the period, replacement demand differs little in the scenarios (which have the REBM) from the baseline (without the REBM). However, because the more-durable products are more expensive, households suffer initially as more of their expenditure must go towards replacement washing machines, squeezing their real incomes.

The benefits from more-durable products are only realised later in the period, at the time when lower-end appliances would ordinarily need to be replaced. Imports of new washing machines are lower under the REBM, helping to increase GDP through lower net trade and leading to real household expenditure levelling off from its initial fall (relative to baseline).

20 “In the vicinity of zero” is the key qualifier; the impacts remain negligible.

21 This particular sensitivity was tested by reducing the level of replacement demand over the period by a further ten percentage periods. This is rather crude as it pays no heed to the dynamics of replacement demand (which would require a tool such as the Defra Market Transformation Programme Model). This sensitivity showed that such a change in sales amounted to an improvement in net trade of less than £10m in real terms; smaller than the effect of higher retail prices.

21

In both of these scenarios, the impacts (both positive and negative) are too small to meaningfully distinguish from zero. To that extent, one could interpret the resource savings achieved under this REBM as being associated with little macroeconomic impact.

The outcomes appear most sensitive to variations in the prices charged by UK retailers to consumers for the more-durable washing machines.

Figure 6 presents the summary impacts from the extended product lifetimes for washing machines. The diagram shows that the impact is, overall, positive in GDP terms, as evidence by the green box around the Longer-life products REBM.

The more-durable goods do lead to lower sales in later periods (including 2020, the main year of comparison) but this is offset in the scenario by an improvement in net trade through lower imports dependence and some element of freed household income being spent elsewhere in the economy, on other goods and services.

No similar figure is presented for the second scenario as the effects are similar with the exception of freed income: higher prices squeeze households’ real expenditure, leaving them worse off owing to a reduction in their purchasing power (and no means to adjust away from the more expensive washing machines, owing to the scenario definition). Overall, the GDP impact is negative albeit slight (the final impact box would be red in colour).

Figure 6: Summary impacts of extended product lifetimes for washing machines (Scenario 1)

22

2.4 Scenario description: Washing machines leasing Under the leasing model, a scenario is considered in which housing associations enter into leasing contracts for washing machines (this model would also work for universities or other organisations/associations handling a large number of tenants). It is assumed the leasing contracts are long-term and include maintenance and repairs of the appliances over the life of the contract.

We assume that this REBM targets the housing association market in England, which concerns 2.5 million houses22. Based on an existing implementation in the Netherlands23 (in collaboration with Bosch), it is assumed that the leased appliances provided under the leasing contract are more durable and more energy- and water-efficient compared to the washing machines that tenants would otherwise have bought individually.

The leasing model assumes contracts which last for five years and at the end of this period, 80% of the products are sold abroad (e.g. to developing countries). The other 20% are not fit for resale and are thus recycled.

Figure 7: Logic map for white goods’ leasing

In terms of take-up, the lease operator24 is assumed to capture 25% of the housing-association market by the fifth year of the scheme, in 5% increments (125,000 additional leases pa).

22 http://www.housing.org.uk/membership/about_our_members/about_housing_associations.aspx

23 http://turntoo.com/en/project/household-applicances/

24 Lease operator can be specialist electrical supplying non domestic market, or they can be a specialised leasing agency working on behalf of manufacturers or independently.

23

2.4.1 Leasing assumptions A monthly charge of £10 (exc. VAT)/month per washing machine is assumed (£120/unit pa) leased directly from the manufacturers. This compares favourably with an estimated monthly average fee of £17/month charged by high street retailers. The leasing operator purchases washing machines at around £250 (exc. VAT)/unit25. For repair costs, it is assumed that 5% of the units each year require one repair, at an estimated £11026 per repair. To note, the 5% is applied in the previous scenario for the low end products only. The same rate is applied here as the lack of ownership can lead to some carelessness on how appliances are used.

At the end of the lease, 80% of the products can be viably sold overseas, at an estimated 20% of the value of the leasing contract: £600 / 5 = £12027. The operators incur a cost of around £75 per unit (30% of the initial sale price) to cover costs such as transport, refurbishment and other labour. Revenues from resale begin in Year 5.

These flows are summarised in the table below.

Table 8: Leasing operator assumptions

Year

Total

units leased

Leasing revenue

@

£120/unit pa

Operator purchases

of washing machines

@

£250/unit

Operator repair

costs

@

£107.69/repair

Operator sales at

end of lease

80% of units @

£120/unit

Operator

costs of resale

@ £75/unit

1 125,000 +£15m -£31.25m -£0.673m - -

2 250,000 +£30m -£31.25m -£1.346m - -

3 375,000 +£45m -£31.25m -£2.019m - -

4 500,000 +£60m -£31.25m -£2.692m - -

5+ 625,000 +£75m -£31.25m -£3.365m +£12m -£7.5m

25 Estimated amount based on research

26 Estimated amount based on discussion with the industry

27 Based on discussion with the industry

Implications of product leasing business model on resource efficiency This business model contributes toward achieving a circular economy by forming partnerships between housing associations (and other organisations with a large number of tenants) and manufacturers to furnish houses with more efficient and longer life products for a small monthly fee. The rental fee covers repair but not electricity or water costs. Tenants are supplied with appliances that are more efficient in energy and water than what they would have been able to afford (if at all able to buy one). This means that the tenants don’t have the high capital investment upfront and benefit from lower energy and water bills, as well as contributing to minimising the use of resources. Once the product has been in place for 5 years, tenants are upgraded to the latest model, and the old models are mainly exported to third world countries overseas markets for a second life. Washing machines that no longer work are dismantled and recycled.

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2.4.2 Sales displacement The leasing business displaces consumer purchases of washing machines as follows:

Table 9: Displaced sales from leasing

Year Total units

leased

Change in sales

to consumers

Change in sales by

retailers

@ £210/unit excl VAT

Change in sales by

manufacturers

@ £170/unit excl VAT

1 125,000 -125,000 -£26.125m -£20.9m

2 250,000 -125,000 -£26.125m -£20.9m

3 375,000 -125,000 -£26.125m -£20.9m

4 500,000 -125,000 -£26.125m -£20.9m

5+ 625,000 -125,000 -£26.125m -£20.9m

Without leasing contracts, tenants are assumed to have purchased low-end washing machines at £210/unit. These would have been supplied by manufacturers to retailers at a price of around £170 (under the assumption that a 25% mark-up would be applied by retailers). This £170 is the price at which manufacturers sell to the retailers (including the manufacturer margin).

2.4.3 Imports and exports All washing machines for sale in the UK are manufactured abroad. The table below summarises the changes in trade arising from:

Lower imports of low-end washing machines (because tenants switch to higher-quality leased appliances).

Higher imports of mid-range washing machines (purchased by the lease operators).

Export sales of old washing machines at the end of the lease period (beginning in Year 5).

Table 10: Imports and exports assumptions

Year Units

affected

each year

Imports of low-end

washing machines

@ £170/unit excl VAT

Imports of mid-range

washing machines

@ £250/unit excl VAT

Exports of old washing

machines

80% of units @

£120/unit

1 125,000 -£20.9m +£31.25m -

2 125,000 -£20.9m +£31.25m -

3 125,000 -£20.9m +£31.25m -

4 125,000 -£20.9m +£31.25m -

5+ 125,000 -£20.9m +£31.25m +£12m

No additional direct labour requirements to run such a leasing scheme are assumed.

2.5 Economic modelling assumptions: Washing machines leasing Under this business model, consumers (as represented by housing associations) shift their spending away from purchases of new washing machines (at the low end of the market) in favour of leasing schemes in which the lease operator installs and maintains the appliance on the households’ behalf.

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This REBM alters the distribution of value added and income across sectors of the economy (but not the overall number of washing machines imported to the UK each year) because household and firms now bear different costs each year.

In particular, households pay higher lifetime costs28 for the leased appliances and firms incur higher import costs from purchasing higher-quality and more expensive appliances.

In the early part of the period, households are better off because they spend less each on leased washing machines compared to the baseline case without the leasing REBM, where they incur higher upfront costs instead.

This leads to an initial reduction in the price of consumer spending on ‘washing machine goods/services’ through the leasing arrangement (as the scheme spreads the overall higher cost out over a number of years).

The scenario also captures the increase in the price of imported washing machines, reflecting the higher-quality products bought by lease operators compared to what tenants would otherwise have purchased. The cost of buying higher-quality washing machines is thus shifted away from households and towards lease operators.

Note that, in these scenarios, no distinction is made between housing associations that lease directly from the manufacturer and those that deal with an intermediary. In principle, there is scope for an additional margin if there is a specialist leasing dealer but, overall, this REBM is too small for this to be significant. This is evident in the final results for this business model.

After five years, the scenario also captures the export of old washing machines overseas, once they reach the end of the leasing period. Thus, from 2017 onwards (Year 5 onwards), there is an additional source of income to the UK economy.

In summary, the scenario models:

A shift in who bears the cost of new (and more expensive) washing machines, towards lease operators and away from households, through changes in price: more expensive imports of higher-quality washing machines. In the early years, UK households pay less for access to washing machines but, because they incur the leasing cost every year, this eventually outstrips the original saving (higher lifetime cost).

New exports of old washing machines at the end of the lease life, from 2017 (Year 5) onwards.

The overall small negative impacts arise from consumers bearing higher costs for the leased goods, as well as reducing their income to spend on other goods and services in the economy. Note that these higher costs are over the lifetime and that consumers may prefer to pay less each year through leasing rather than incur a larger upfront purchase cost, even if the lifetime leasing cost is higher. This is because the cost is spread over a longer period of time, potentially easing cash-flow constraints. Also, the leased products would be of better quality (more energy and water efficient) than the one the tenants would have been able to afford, generating saving on water and energy bills.

The net macroeconomic impact of leasing in Figure 8 is negative but small.

28 It is assumed that the rental fee will be £600 over a five year period, compared with the purchase of a low end washing machine at an estimated cost of £210. Even if a purchased washing machine breaks down before 5 years, the overall cost of purchasing 2 washing machines is estimated to be £420.

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Figure 8: Summary impacts of leasing for washing machines

2.6 Modelling results: Washing machines leasing As with the two extended product lifetime scenarios, the overall macroeconomic impact of the leasing scenario is small and, to all intents and purposes, zero, owing to the small sums of money involved at the macroeconomic level.

As with those extended-lifetime scenarios, the small size of the impacts is a result of the small size of the initial impacts in macroeconomic terms.

GDP is down slightly in 2020 in the scenario that incorporates the REBM compared to the baseline. This arises from household incomes being squeezed somewhat (in aggregate) over time, as they bear the higher lifetime cost of the leased washing machine compared to the overall cheaper one-off payment.

The size of the economy-wide impact in 2020 is equivalent to about 2% of the value of the washing machines market in 2011, although, of course, the washing machines sector does not bear this macroeconomic cost in its entirety; other sectors bear some of that impact.

The increase in exports does offset some of the GDP impact from 2017 onwards, once formerly-leased products are available for resale overseas.

The only indicators to register a percentage impact that is not zero are:

Net trade, which improves marginally, by around £16m (in 2012 prices) in 2020 (following the opening of export markets in 2017).

Tax revenue, owing mainly to the higher prices driving higher VAT receipts.

Any employment impacts are too small to register in MDM-E3.

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Table 11: Results in 2020 for Washing Machines Leasing Scenario

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,795,886 -18 0.00

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,337 -31 0.00

Household expenditure (£2012m) 1,168,387 1,168,355 -32 0.00

Net trade (£2012m) -36,409 -36,394 16 -0.04

Employment ('000s) 32,979 32,979 0 0.00

Labour and indirect taxes (£2012m) 466,799 466,832 33 0.01

From Figure 9, which shows how the difference between the scenario and baseline projections evolves through time, it can be seen that households are initially better off in real-expenditure terms as they save money in the early years by not having to spend so much on washing machines; they lease them out instead. GDP improves as a consequence while the lower initial price level leads to marginally lower tax receipts.

However, as the business model continues to run, more and more consumers lease and the higher lifetime cost sets in (reflected in higher prices for the same quantity of washing machines in use). This leads to falling real household expenditure and GDP as prices squeeze consumers’ spending power. Tax revenue rises as a consequence of higher prices.

The dynamics continue like this until 2017, when the business model stabilises as the leasing scheme reaches saturation and the old leased machines are sold overseas.

While the impacts on GDP appear to be negative in this REBM, it is important to note that the overall scale of the impacts is still little different from zero.

Figure 9: Profile of scenario relative to baseline – leasing of washing machines

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2.7 Potential barriers to uptake There are potential barriers to the uptake of the scenarios, outlined above:

Longer lifetime products: One issue is how to influence manufacturers to incentivise them to increasingly

manufacture products that last a minimum number of washing cycles. Manufacturers that supply the UK market also supply most of the EU market. It would seem appropriate to have an EU driver to incentivise manufacturers to supply longer life products. This could be in the form of voluntary code of conduct or through legislation such as Eco-design. The recent Eco-design proposal for vacuum cleaners included two specific requirements on product lifetime.

There is an additional insurance cost that manufacturers might have to bear if they have to provide a minimum of 7 years’ (or estimated 1,820 cycles) guarantee with the products. These can be expensive and thus result in much higher retail prices over and above those modelled here. To note, if the product comes with a 7 year warranty, the manufacturers or retailers (depending on who is providing the warranty) will have additional costs to cover such warranty. This was mentioned by stakeholder groups at the workshop. Stakeholders raised the issue that consumers would want a warranty that the product will last a minimum amount of time in order to be incentivised to spend more on the product upfront.

A major barrier to the extended product lifetime scenario is how to influence consumer purchasing behaviour. Consumers only purchase a washing machine because they need one (i.e. their current one has broken down, they are moving home, setting up home etc.). They will have a budget and will choose a product that is affordable within their budget. Brands and retailers would need to clearly communicate the benefit in trading up to a longer-life product with a longer guarantee.

Suppliers would need to influence consumers on the benefits of repairing and servicing appliances, if an appliance expected to last a minimum of cycles fails before that minimum.

Leasing: Manufacturers or the lease financing company bear the upfront capital investment

and its return is over a 5-year period. There are risks associated with damages to the appliances, which are partly

covered by the monthly leasing fee, but if there are a large number of misuses/malicious damage claims to appliances, it can become uneconomical for the manufacturers. It would be expected that the housing association would have contractual responsibility for this.

Unpaid monthly leasing fees are the other main barrier to the success of such a programme. It would become expensive to chase every tenant that is not paying their fees. This can be overcome if the leasing cost is included within the tenants’ monthly rent.

Tenants to report faulty / broken down product promptly to arrange for repair or exchange, in order to ensure product longevity. There is a risk that products might fail if errors are left unchecked. This will cause additional costs to the leasing company.

2.8 Conclusions In both models, the overall macroeconomic impact is small, owing to the small volume of trade in washing machines in the UK compared to other forms of economic activity. To the extent that the business models both appear, at the macroeconomic level, neutral in their impact, an assessment of their appeal or viability is perhaps not a macroeconomic issue.

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In the case of the extended product lifetime scenarios, the results show that higher mark-ups can have negative impacts on the economy through (slightly) higher inflation, leading to lower GDP compared to the baseline. The retail sector ends up capturing additional value in the economy through higher prices and thus higher profits and household real incomes are squeezed. When comparing the two extended product-lifetime scenarios, the comparison is between two scenarios that embody similar increases in product durability, but at differing cost (prices). For a given sales volume, Scenario 2 models a higher cost, hence the more negative macroeconomic outcome. These impacts are small both on the sector and on the UK economy as a whole.

The two extended product lifetime scenarios serve to illustrate the potential sensitivity of the macroeconomic impacts to changes in retail prices (the price premium, rather than the cost premium) that industry experts would consider entirely plausible. In contrast, the other assumptions that form these scenarios have comparatively little effect on the outcomes. It is thus the retail-price effect (which depends on the decisions of retailers) that has the largest bearing on the overall macroeconomic outcomes.

In macroeconomic terms, an improvement in lifetime cost would not necessarily increase GDP substantially (because of wider interactions across the macroeconomy) but, at the consumer level, there would of course be clear benefits.

The impacts of a leasing model are also small in macroeconomic terms. There is some additional benefit from the export opportunities as the leased models reach the end of the lease period, but relative to the UK economy as a whole, the impacts remain small and are outweighed by consumers bearing an overall higher lifetime cost for the leased appliances over a period of 5 years29.

29 It is assumed that the rental fee will be £600 over a five year period, compared with the purchase of a low end washing machine at an estimated cost of £210. Even if a purchased washing machine breaks down before 5 years, the overall cost of purchasing 2 washing machines is estimated to be £420.

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3.0 REBMs in the Clothing Sector 3.1 Scenario description For clothing, one REBM is considered, a clothing take-back scheme, with one sensitivity to simulate the possible further (“enabling”) impact of higher-durability clothing on the business model.

The basic take-back scenario involves retailers providing a voucher incentive to encourage consumers to return “high-end” clothing to retailers’ stores. For the purposes of the analysis that follows, the incentive is set at 50%30 off ‘re-used clothing’ purchased by consumers.

A key aspect of this business model is developing strong partnerships between retailers and clothing charities. This partnership will enable a greater quantity of suitable clothing to be accessed for re-use by both the retailer and charity partner. The retailer targets the high end, high fashion clothing fraction while the charity obtains greater tonnage of good quality clothing which would otherwise remain unused in wardrobes across the UK. Both retailers and charities reap the benefits of the partnership by encouraging consumers to donate unwanted clothing in their wardrobes.

In the UK at present, around 50% of clothes are re-used, and over two-thirds of these go overseas. Two-thirds of consumers in the UK buy or receive pre-owned clothes, indicating significant willingness to do so. According to WRAP research, some 30% of the clothes in household wardrobes (on average) have not been worn during the past year. Assuming 25% of this clothing may be accessed by incentivising consumers to donate good quality clothing, there is potential to increase clothing for reuse by almost 188,000 tonnes31. Retailers would sell the ‘higher end’ proportion of the clothing donated, which equates to approximately 28,000 tonnes per year of high end clothing32.

The clothing is collected and transported to a centralised distribution warehouse for sorting. Here, trained textile sorters identify high-end clothing that is suitable for resale. In addition to the 28,000 tonnes of high end clothing suitable for direct resale, a small proportion (around 3%, or 6,000 tonnes) of this clothing may require slight repair or redesign prior to resale. Clothing not considered suitable for retail sale may be delivered to partner charities while the remainder is reused abroad, recycled, or disposed of.

The aim of the business model is thus to shift consumer spending through incentives and charity/retailer partnerships away from the default purchase of new clothing and towards purchases of high quality second-hand clothing. By increasing the active life of clothing through take-back schemes, the UK can enable a further step in the transition towards the circular economy.

A conceptual diagram of the model is provided in Figure 10 below. As can be seen from this figure, by keeping the clothing within the circular economy loop, clothing may have more than one active life.

30 Discussion with industry indicated that a relatively high incentive would be required in order to encourage consumers to trade-in clothing.

31 For clarity, this figure includes the tonnage which may be recycled or disposed of (circa 56k tonnes). Further to this, approximately 131k tonnes is available for reuse (in the retailers, UK charity shop and abroad).

32 Based on the ‘high end’ clothing market representing 6.5% of total spend on adult clothing (Mintel Research) and assuming 30% is not worn for over one year (WRAP research) plus an additional 3% for restyling (circa 6,000 tonnes)

31

Figure 10: Conceptual diagram for clothing take-back33

3.1.1 Increasing Clothing Durability – The ‘Enabler Effect’ A variant of the main take-back scenario is also analysed, to assess the potential impacts of more durable clothing, which will allow for more clothing take-back and reuse (as clothes last longer and can be re-sold more readily).

In order to increase the active life of clothing, it is not practical at this stage to assume such a scenario may occur across the whole clothing sector, and sufficient data was not available to model the impacts of sub-sectors (e.g. school wear). In some cases, clothing of a higher durability is likely to act as an enhancer or ‘enabler’ of other resource efficient business models in the clothing sector. If a proportion of new clothing on the market is manufactured using fabrics of greater quality/durability and making use of improved garment construction techniques and longer-lasting designs, this is likely to boost the supply of clothing suitable for re-use and re-sale within the UK. This is mostly a consequence of the clothing remaining in a better condition at the end of successive ownerships.

Within the model, the enabling effect increases the amount of unwanted clothing extracted from the wardrobe from 25% for the baseline take-back scheme to 27% for the increased durability scenario. This increase results in over 200,000 tonnes of additional clothing on the market with a potential for re-use or recycling. A conservative estimate of the enabling effect of durability was applied as the reasons why consumers donate are not solely linked to the appearance or condition of the clothing. The enabling effect of durability is likely to increase other REBMs for clothing such as peer to peer exchange however these models were not dealt with in this model.

For conceptual purposes, the enabler effect of the increased durability model is illustrated in Figure 11. As illustrated in the diagram, the durability enabler effect may increase the active life of clothing (the increase indicated in the diagram is for illustrative purpose only). The extent to which the active life may be increased will differ depending on, for example, the purpose of the clothing, types and blends of fabric used, consumer care (excessive washing and tumble drying), etc. Additionally, the durability enabler effect may increase the number

33 Note: Active/Inactive life of clothing are indicative only

32

of active lives a particular garment may have due to the clothing being in better condition throughout its life and more suitable for trading in at the end of each active life.

Figure 11: Conceptual diagram for the enabling effect of increased durability on the clothing take-back business model34

The enhanced durability scenario may deliver the following benefits:

Making available a larger number of garments that are suitable for resale (longer-lasting garments boost the supply of suitable clothing). Thus, consumers return more garments to retailers, of which, in turn, more are suitable for resale. This enables higher sales of second-hand clothing and, in turn, displaces more household spending on new clothing. The higher volume of clothing may also raise some costs (e.g. from there being more clothing to be sorted).

Reducing the amount of returned clothing that requires repair/restyling, because the garments last longer/are harder wearing. This may lower some costs.

Leading, on average, to higher-quality garments for resale. In some cases (but not all), this allows retailers to charge higher prices for second-hand clothing. A modest increase in the average retail price for second hand clothing is assumed, from £35/kg to £37/kg35.

For the purposes of this analysis, no additional impacts of durability are assumed on, for example, increased garment hire or peer-to-peer exchange. To that extent, the following analysis may understate the potential enabling effects. The following table lists the key impacts of the enhanced durability scenario on the clothing take-back model.

34 Note: Active/Inactive life of clothing are indicative only

35 Discussions with industry suggest the price premium associated with more durable clothing is at most 10%

33

Table 12: Impacts of the enhanced durability on the clothing take-back business model

Impact Quantitative

Impact Reasoning

Greater

quality/tonnages of clothing

suitable for UK

reuse

Increased capture of

unwanted clothing from 25% to 27%

Due to increased durability, clothing is likely to be in a

better condition. Therefore, higher quality clothing will be donated. Consequently, less clothing is sent for

recycling and more clothing is used within the UK. As

a result, there is potentially more tonnage available for charities and retailers to resell.

More tonnage of

clothing suitable for reuse

Increase of clothing

available for reuse (UK or abroad) from 70%

to 85%

In the fashion sector, clothing durability is not a

priority. If clothing is more durable, this does not mean it will be used for a longer period of time. Owing

to the enabler effect of durability, at the end of a fashion cycle, the clothing is likely to be more suitable

for re-use.

The boosting effect is an estimate only.

Retailer sale price

impact

Increased price from

£35/kg to £37/kg

More durable clothing is likely to be in a better

condition when donated. The retailer may charge a

greater price for clothing of better quality (although the real value may be in greater volumes of higher

quality clothing available for reuse and increased volumes of this clothing).

3.1.2 Shifts in spending The high-level implications of the main REBM and the high-durability variant are summarised in the tables below, separately distinguishing household effects (changes in spending and income) from business revenues and costs, as well as the implications for production/supply of new clothing.

Implications of clothing take-back business models on resource efficiency The impact of the ‘clothing take-back model’ on resource efficiency results in a positive step towards achieving a circular economy. The basis of the model is to drive a shift in consumer attitudes to clothing that they may no longer want or see a value in. Through an incentivised business model, consumers are encouraged to trade in unwanted clothing for re-used clothing which may better suit their preferences or needs rather than the default option of purchasing brand new clothing every time such a need arises. On average, 30% of the clothes in householder wardrobes are unwanted, yet every year around 1.14 million tonnes of new clothing are purchased, having an adverse impact on the environment. This business model aims to extract the value of unwanted clothing in the wardrobe in order to displace new clothing bought and reduce the UK’s global clothing consumption footprint. The durability scenario aims to extend the life of clothing by improving the condition of the clothing at the end of its ‘first’ life, thereby making it more suitable for a second life as clothing. According to WRAP’s research, extending the life of clothing by an extra nine months of active use would reduce carbon, waste and water footprints by around 20-30% each, and cut resource costs by around 20% (£5 billion).

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3.1.3 Consumer spending shifts For consumers, the following are assumed:

An increase in purchases of high-end second-hand clothing from retailers. A reduction in purchases of new clothing from retailers (owing to a shift towards

second-hand clothing). A displacement rate of 60%36is assumed where high-quality pre-owned clothing is re-sold.

Receipt of vouchers to encourage return of old clothing and stimulate demand for reused clothing.

Table 13: Consumer assumptions (annual)

Scenario

Durability

sensitivity

Consumer spending on second-hand clothing +£984m @ £35/kg +£1,120m @ £37/kg

Displaced consumer spending on new clothing -£691m @ £41/kg -£745m @ £41/kg

Consumer receipt (and use) of vouchers on re-

used clothing

+£328m +£374m

3.1.4 Retailer costs and revenues Accordingly, retailers see changes in revenue from:

Increases in sales of second-hand clothing. Decreases in sales of new clothing (displacement from higher demand for second-

hand clothing).

Retailers also incur costs under this REBM, to cover:

The voucher incentive to consumers, to stimulate both supply and demand for second-hand clothing.

Running costs, such as logistics, laundry, restyling and sorting.

Table 14: Retailer assumptions (annual)

Scenario

Durability

sensitivity

Retail sales of second-hand clothing

(matching increase in household spending)

+£984m @ £35/kg +£1,120m @ £37/kg

Reduced retail sales of new clothing

(matching reduction in household spending)

-£691m @ £41/kg -£745m @ £41/kg

Incentive to households for re-used clothing

(counterpart to household receipt of vouchers)

-£328m -£374m

Cost: logistics -£9m -£9.3m

Cost: laundry -£24m -£26m

Cost: restyling -£28m -£10m

Cost: staff wages -£84m -£50m

As can be seen from Table 14, the costs associated with the durability sensitivity scenario are higher for logistics and laundry (because a higher volume is resold) but lower for

36 http://www.wrap.org.uk/sites/files/wrap/Clothing%20reuse_final.pdf

35

restyling and wages (because the clothes are in better condition). The increased costs are associated with a larger tonnage of re-used clothing requiring a laundry service. The lower staff wages and restyling costs are due to less re-used clothing requiring repair or alterations due improved durability and condition for direct re-use.

The following is a list of points specific to sensitive variables and assumptions:

All clothing is sourced from non-active clothing within wardrobes (30%37 - equating to 750,000 tonnes):

25% of this tonnage is assumed to be accessed through incentives (188,000 tonnes) This assumption is increased to 27% for the increased durability scenario

It is difficult to accurately predict consumer behaviour; however, research has shown there is willingness to buy and receive pre-owned clothing.

Given the uncertainties, the 25% quoted above is assumed as a cautious estimate but may be varied in light of further research. Similarly, it is difficult to predict if increasing durability is likely to encourage more consumers to trade in clothing. However, if they perceive their clothing to be in a better condition, this may encourage them to participate – therefore, a conservative increase to 27% was assumed for the increased durability scenario.

Market research suggests 6.5% of all adult clothing sold is high-end or designer38 (74,000 tonnes):

Assumed 30% becomes no longer in use every year39 (22,000 tonnes) 22,000 tonnes of high end clothing is assumed to be captured in the take-back

scheme plus an additional 6,000 tonnes40 of high end clothing which may be suitable for restyling or repair (Note: this leaves a potential 103,000 tonnes for reuse within charities).

The composition of household wardrobes (taking account of socio-economic groups/spending patterns) is unknown and will vary significantly. Therefore, it is difficult to verify this number, but it is assumed a reasonable estimation of the likely tonnage.

The tonnage captured is assumed reasonable and has been tested with industry, but tonnage may decrease if consumers are less responsive to the scheme.

Based on WRAP research41 on the value of re-used clothing, the model assumes a resale price of £35/kg42 for garments sold in charity shops (as discussed, this price is increased to £37/kg for the increased durability scenario).

Costs and potential revenue will be impacted by the quality of garments received, the tonnage of clothing sent for repair/restyling and the tonnage of clothing sent for recycling/disposal. Based on clothing received by the charity TRAID, it is assumed that 12% of clothing is sold directly (equivalent to the 22,000 tonnes of high end clothing), 3% is restyled (6,000 tonnes), 55% is sent for other re-use (UK charities and abroad – 103,000 tonnes), and 30% is recycled or disposed of (56,000 tonnes). Figures are assumed reasonable based on the experience of TRAID; however, their variance will have an impact on costs43.

37 http://www.wrap.org.uk/sites/files/wrap/VoC%20FINAL%20online%202012%2007%2011.pdf

38 http://www.nesta.org.uk/library/documents/UK%20Designer%20Fashion%20Economy%20-%20CFE%20report.pdf

39 30% assumption applied to high-end market

40 6000 tonnes is equivalent to 3% of all clothing provided by consumers for re-use (

41 http://www.wrap.org.uk/content/uk-textile-product-flow-and-market-development-opportunities

42 UK re-use prices are typically £26/kg, premier grade clothing £40/kg and vintage £90/kg, therefore a price nearer £30-£40/kg would be expected given the section of the market we are targeting.

43 http://www.wrap.org.uk/sites/files/wrap/Textiles_Guide_CS_Bexley.pdf

36

Increased durability will have an impact on the fate of clothing traded-in. It is assumed that, as the clothing is more durable, it is likely that a greater tonnage will be suitable for re-use generally and less clothing is likely to be recycled or disposed of. Therefore, for this scenario, it is assumed 60% is suitable for re-use in the UK or abroad (increased from 55%) while 25% is sent for recycling or disposal (decreasing from 30%). Retailers still capture 15% of the clothing donated, leading to a slight increase in tonnage available for sale (increasing from 28,000 to 30,000 tonnes). However the greater value to the retailer is in the lower tonnage (and hence lower costs) of clothing requiring repair or restyling (decreasing from 3% to 1%).

Further details on the per-unit costs are in the next section.

3.1.5 Per-unit costs The tables below present further details of the cost calculations.

Table 15: Per-unit costs (annual)

Scenario Durability sensitivity

Cost per tonne to retailer for reused clothing £17,000/tonne £15,500/tonne

Voucher to incentivise take back – 50% off

purchase of reused clothing

50% x £35/kg

= £17.5/kg

50% x £37/kg

= £18.5/kg

Laundry £0.25/garment (laundry) £35/tonne (transport)

£0.25/garment (laundry) £35/tonne (transport)

Logistics – cost of fuel to transport clothing

between store and warehouses and back again

£1.35/l £1.35/l

Restyling – minor repairs and alterations £5/kg £5/kg

Table 16: Total costs (annual)

Scenario Durability sensitivity

Cost to retailer for reused clothing £473m (28,000 tonnes)

£469.3m (30,000 tonnes)

Voucher to incentivise take-back – 50% off

purchase of reused clothing

£328m

(18,750 tonnes)

£374m

(20,000 tonnes)

Laundry £24m (laundry) £0.984m (transport)

£25m (laundry) £1m (transport

Logistics – cost of fuel to transport clothing

between store and warehouses and back again

£9m (20m km pa/

6.9m l pa)

£9m (20m km pa/

6.9m l pa)

Restyling – minor repairs and alterations £28m

(5,600,000 tonnes)

£10m

(2,000,000 tonnes)

Key assumptions/sensitivities:

Incentive: 50% off voucher valid for re-used purchases only Discussion with stakeholders reveal the incentive would have to be high in order to

encourage consumers to trade in clothing Resale price is set at £35/kg based on WRAP research44 (increased to £37/kg for

more durable clothing scenario)

44 http://www.wrap.org.uk/content/uk-textile-product-flow-and-market-development-opportunities

37

3.1.6 Employment requirements The expected employment requirements for the business model are as follows:

Table 17: Employment requirements (annual)

Scenario Durability sensitivity

Sorting teams 375 400

Stylists 2,717 978

Additional warehouse staff 941 941

For the increased durability scenario, the required number of sort staff increases due to the increased tonnage of clothing. Sort staff numbers are calculated based on the sort rate (tonnes/hour) that one team member can achieve. The number of stylists decreases between the two scenarios, owing to the reduced tonnage of clothing required repair or alterations. The number of additional warehouse staff remains unchanged, as the total tonnage divided amongst all warehouses across the UK leads to a marginal increase in the need for additional staff.

The cost in wage/salary terms is presented below:

Table 18: Employment costs (annual)

Scenario

Durability

sensitivity

Sorting teams £15m £17m

Stylists £54m £20m

Additional warehouse staff £14m £14m

Baseline assumption for staff salaries remained the same for both scenarios. The changes in costs are related to the tonnage of clothing captured and the fate of the clothing (direct resale, repair/restyling, clothing sent to charities, recycling or disposal).

The number of stylists calculated is based on an assumption of a rate of repair/restyling of 30 items per day and a salary costs of £20,00045.

Estimation of the number of sort staff required is based on two approaches, the results of which were averaged. The first approach is based on a sort rate of 3 tonnes per day and assumed salaries46. The second approach is based on sort team costs of £112 per tonne47 (combined costs of supervisor and sort staff).

The number of additional warehouse staff required is based on an assumed number of centralised warehouses and an additional 3 staff per warehouse (warehouse staff numbers do not increases for the durability scenario as discussed previously).

3.1.7 Changes in imports A reduction in sales of new clothing (demand) entails a reduction in the supply of clothing, of which most is imported.

45 https://nationalcareersservice.direct.gov.uk/advice/planning/LMI/Pages/textiles.aspx

46 Sort staff salary = £12,500; Supervisor Salary = £35,000

47 http://www.wrap.org.uk/system/files/private/Impact%20of%20textile%20feedstock%20source%20on%20value%20summary%20report.pdf

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Table 19: Changes in imports of new clothing (annual)

Scenario

Durability

sensitivity

Lower imports of newly-produced

clothing

-£433m -£483m

The following points provide a breakdown of the key assumptions and calculations relating to clothing imports:

Second hand clothing displaced by purchasing reused clothing in retailers is 9,281 tonnes. This calculation is based on the assumption that 30% of clothing sent for re-use in charities (circa 103 tonnes) is sold within the UK48 with a 60% displacement rate49. Within retailer stores 16,900 tonnes of new clothing is displaced. This figure is calculated assuming the circa 28,000 tonnes of high end re-used clothing is sold within retail stores displacing 60% of new purchases.

This amounts to around 26,000 tonnes of clothing displaced by purchasing re-used clothing in retailers.

It is assumed that the same amount of clothing is displaced from imports. According to research from WRAP, new clothing entering the market each year

amounts to approximately 1.14 million tonnes. Additionally, 90% of clothing in the UK is imported (Sustainable Clothing Action Plan50). Therefore, around 1.03 million tonnes of clothing is imported. In 2011, the reported value of clothing imported to the UK was £17 billion51 and so it can be assumed that the value of imports per tonne was £16,700.

The value of imports displaced is thus £434 million (2.55% of total clothing import value in 2011). The value of imports displaced for the increased-durability scenario is estimated at £483 million.

3.1.8 Impacts on profits The tables below summarise the annual impact of the REBM on business revenues, costs and profits vis-à-vis both second-hand and new clothing sales.

The second-hand clothing business generates net profits of £511m pa to retailers, increasing to £651m as a result of improved durability improving both the volume and quality of clothing.

Table 20: Retailers’ profits from second-hand clothing business

Scenario

Durability

sensitivity

Retail sales of second-hand clothing +£984m @ £35/kg +£1,120m @ £37/kg

Retail costs of selling second-hand clothing

(incentive cost +

logistics/laundry/restyling/wage costs)

-£473m -£469m

Retail profit from selling second-hand clothing

(retail sales – retail costs)

+£511m +£651m

48 Based on current flow of reused clothing http://www.wrap.org.uk/sites/files/wrap/VoC%20FINAL%20online%202012%2007%2011.pdf

49 http://www.wrap.org.uk/sites/files/wrap/Clothing%20reuse_final.pdf

50 http://www.defra.gov.uk/publications/files/pb13206-clothing-action-plan-100216.pdf

51 http://ei.wtin.com/article/zioAVrhCRrU/2012/11/08/a_new_dawn_rebuilding_uk_textile_manufacturing/

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However, retailers also see reductions in sales of new clothing owing to displacement effects, amounting to lost revenue of £691m, increasing to £745m of lost revenue under higher durability. The loss in profit, under the assumption of a 20% profit margin, is £138m, rising to £149m if clothing is more durable. Net, profits to retailers (accounting for changes in both second-hand and new clothing sales) are £373m higher under this business model, with the potential for even higher additional profits, of £502m, under the higher-durability case.

Table 21: Retailers’ displaced profits

Scenario Durability sensitivity

Displaced revenue from lower

sales of new clothing

-£691m @ £41/kg -£745m @ £41/kg

Displaced profits from lower sales of new clothing

-£138m (assumed 20% profit margin)

-£149m (assumed 20% profit margin)

3.2 Economic modelling assumptions In the clothing take-back scenarios, the following are modelled in each year:

The net increase in consumer spending on clothing, differentiating between decreases in new clothing sales (affecting clothing manufacturers) and increases in second-hand clothing (through retail sales)52.

The cost of the incentive (the vouchers) borne by retailers, also representing an increase in consumers’ disposable income. Scenario/durability sensitivity: £328m/£374m.

Reductions in imports of new clothing as a result of the shift to second-hand garments: £433m/£483m.

The costs of running the REBM, in terms of logistics/laundry and restyling. Additional employment requirements for sorting/restyling/warehouse support

(modelled as increases in jobs; see above).

Changes in profits are not explicitly modelled, as these cannot be easily identified in MDM-E3 (and are further complicated by the fact that the REBM only covers a portion of the sector).

3.3 Modelling results GDP in the clothing REBMs is higher than in the baseline, owing to overall greater demand for clothing (see Table 22). GDP is higher in the durability sensitivity compared to the main scenario (see Table 23) but the difference is small in macroeconomic terms. Both suggest increases in GDP by 2020 of around 0.06%. This is in line with higher household income and expenditure of around 0.05%.

These impacts are at a scale that can be considered to be meaningfully positive, with an increase in both GDP and employment.

The results are consistent with the shifts in spending modelled, away from new clothing (some of which is imported; in accounting terms, imports contribute negatively to GDP) and towards second-hand clothing. This leads to an improvement in net trade.

The GDP impact, of more than £1bn in 2020, is larger than the initial changes in expenditure modelled. These additional positive impacts arise from indirect effects transmitted to other sectors of the economy as a result of the interdependencies between sectors. The increase

52 The calculation to derive the net increase in consumer spending is somewhat complicated by the need to deflate the figures and account for different quantities of garments. In order to arrive at these figures, the second-hand expenditure figures are converted into new-clothing equivalents based on the price per kg, before deflating to obtain the values in real terms.

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in GDP, spread across the economy, is equivalent to some 2% of the value of the UK clothing market (estimated to have been worth almost £43bn in 2010).

The employment effects are largely in line with those fed in as assumptions while tax revenue is higher. This is predominantly through VAT levied on the additional clothing sold (the total volume of clothing sold is higher under this REBM, with a substantial proportion from resale) but also VAT receipts from other increases in consumer purchases (owing to higher income).

Table 22: Results in 2020 for Clothing Take-back Scenario

Year Baseline Scenario Difference

(-) Difference

(%)

GDP (£2012m) 1,795,904 1,796,912 1,008 0.06

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,224,077 709 0.06

Household expenditure (£2012m) 1,168,387 1,168,948 561 0.05

Net trade (£2012m) -36,409 -36,069 341 -0.94

Employment ('000s) 32,979 32,983 4 0.01

Labour and indirect taxes (£2012m) 466,799 467,216 417 0.09

The overall positive impacts of the clothing take-back model in Figure 12 can be summarised as:

Switching to reused clothes, lowers sales of new clothing (negative economic impact), but also imports of new clothing (positive economic impact)

Consumers spend more overall (uplift in clothing)

Overall, the macroeconomic impact of the main clothing scenario is positive.

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Figure 12: Summary impacts of take-back for clothing

It is interesting to note that the household and income effects relative to the baseline are actually smaller in the durability sensitivity than in the main scenario. This is because the durability sensitivity generates lower wage income to households, owing to lower employment requirements for restyling.

Higher durability requires fewer restyling jobs than the main scenario, leading to slightly lower income and household expenditure when that scenario is compared against the ‘main’ scenario run. Lower expenditure also generates fewer transactions, hence the slightly lower tax receipts from VAT. Tax revenue is still higher in both scenarios compared to the baseline value in 2020, though.

However, this increase in economic activity in the domestic economy is offset by lower import demand in the durability sensitivity (lower requirements for imported new clothing), leading to higher GDP overall in the durability sensitivity from the larger improvement to the balance of trade.

The overall macroeconomic impact of the durability-enhanced clothing take-back scenario is marginally more positive than the main take-back scenario, as a result of the larger imports reduction.

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Table 23: Results in 2020 for Clothing Take-back (Durability Sensitivity) Scenario

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,796,918 1,014 0.06

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,224,030 663 0.05

Household expenditure (£2012m) 1,168,387 1,168,899 512 0.04

Net trade (£2012m) -36,409 -36,010 399 -1.10

Employment ('000s) 32,979 32,982 3 0.01

Labour and indirect taxes (£2012m) 466,799 467,187 388 0.08

The results above show that lower restyling requirements lead to lower employment (there is no need to employ restylists). This generates lower income and drives the expenditure result in this scenario. The durability scenario has a smaller (but still positive) impact on household expenditure compared to the main scenario. This was further assessed by running a variant of the main take-back scenario, but with no restyling of clothing, which was modelled by simply removing the labour requirement for restyling. The results are shown in Table 24.

Table 24: Results in 2020 for Clothing Take-back (No Restyling) Sensitivity

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,796,773 869 0.05

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,908 540.6 0.04

Household expenditure (£2012m) 1,168,387 1,168,796 408.5 0.03

Net trade (£2012m) -36,409 -36,039 371 -1.02

Employment ('000s) 32,979 32,981 2 0.01

Labour and indirect taxes (£2012m) 466,799 467,121 322 0.07

The results in Table 24 serve to isolate the impact of lower employment requirements (and give some indication as to the sensitivity of the results to this assumption). There is a reduction in household income (and, in turn, expenditure) from the lower employment and wage income. There is also a slightly larger improvement to the balance of trade in the scenario without any restyling. Exports do not differ much between the main and no-restyling scenarios. The difference lies in imports: lower household income leads to somewhat lower demand for imported goods. Overall lower spending leads to lower tax revenues without restyling compared to the scenario with restyling. This is the result of lower (taxable) expenditure in the economy under this REBM.

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3.4 Potential barriers to uptake The following is a list of the key barriers that will need to be considered if the incentivised clothing take back scheme is to be encouraged:

Resources: Resources are required to operate the scheme, along with costs of providing incentives to the customer. Retailers will need to consider the costs and labour input required for sorting, storing and transporting the received items.

Risk of low demand: Following a recent survey, approximately 32% of consumers surveyed reported that they are not very likely to purchase pre-owned clothing. This indicates a large majority (68%) have an interest in owning pre-owned clothing but further efforts would be required to change the behaviour and attitudes of those not currently interested in the second hand market.53.

Costs recurring from accumulated unwanted stock: The retailers will have to consider textile recycling economics and understand the impacts. The textile recycling market may be volatile, so the margins received from selling for recycling may vary significantly.

Brand Perception – ‘’how will this impact my brand?’’. Evidence suggests there is currently a stigma attached to pre-owned items53. The challenge for retailers is how to drive behavioural change and encourage a positive consumer attitude towards purchasing re-used clothing.

The re-use market stock may be unpredictable, due to a possible lack of variety in sizes, fittings, or designs. Retailers will need to consider how to manage and process the clothing as well as directing appropriate clothing to different branches (some retailers offer different ranges depending on the typical consumer in a given area for that branch).

Reinforcing partnership will be critical for this business model. Otherwise, consumers may perceive an ethical dilemma between using similar schemes that are being run by charities.

Extending the lifetime of clothing has been shown to offer increased environmental benefits in relation to water, carbon and waste. One way to extend clothing lifetimes is to provide more durable clothing. However, there are many complexities associated with more durable clothing, including fibre and textile manufacturing (fibre types, yarn blends and structure etc.), manufacturing and sourcing costs, consumer tastes and understanding of durability. For example, polyester is more durable than cotton but consumers have a greater preference for cotton (which is far less durable). In many cases, more durable clothing is more expensive, resulting in retailers being undercut by cheaper (less durable) alternatives. Durability may yield a premium in certain clothing sectors (e.g. outdoor clothing, children’s clothing) but in the retail sector a 10% premium is the most retailers can charge. Additionally, it is unclear whether consumers understand whether a particular fabric is more durable and it may be difficult to portray this message on a label. Therefore, a large number of factors will need to be considered by the retailers and the textile industry if more durable clothing is to be considered as an option to enhance clothing take-back schemes. 3.5 Conclusions The clothing take-back scenario described in this report is based on the establishment of strong working partnerships between retailers and charities. This model assumes, as a result of the collaboration between private and third sector groups, a significant tonnage of unwanted clothing may be accessed from householder wardrobes. This amounts to circa 187,500 tonnes through the basic clothing take-back model, and over 200,000 tonnes through the enhanced durability model.

53 http://www.wrap.org.uk/sites/files/wrap/10.7.12%20VOC-%20FINAL.pdf

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In addition, this model shows that clothing take-back could offer significant profits for retailers. Profits (taken account of logistics, laundry, cost of repairs and staff costs) could exceed £500 million for the basic model and circa £650 million for the enhanced durability scenario. Profits that are not gained through displacing the sale of new clothing range from circa £138 million for the basic clothing take-back scheme and circa £149 million for the enhanced durability scenario. Therefore, the clothing take-scheme should result in greater profits overall for retailers.

Key variables and assumptions were applied to the MDM-E3 model of the UK economy. Macroeconomic impacts were modelled up to 2020, showing an increase of 0.06% compared to the baseline in each year for both scenarios. Much of the impacts are as a result of greater employment and a decrease in imports of new clothing. The enhanced durability scenario generates slightly fewer jobs, leading to lower incomes and household expenditure. However, this is offset by lower import demand, leading to a slightly higher GDP overall in the durability sensitivity scenario.

Compared to the other REBMs, clothing take-back schemes generate slightly higher macroeconomic impacts for the UK economy. Clothing take-back has the potential to generate significant profits for retailers, increased tonnages of good quality clothing for reuse by charities in the UK and help reduce the UK’s clothing footprint by extending the useful life of clothing.

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4.0 REBMs in the Electronics Sector 4.1 Scenario description Within the electronics sector, TVs have been chosen as the product of interest. From household expenditure data, “Household electrical goods” (which includes TVs) represented £19.86bn in 2011. Under the TV take-back REBM, retailers provide a financial incentive for consumers to return an old TV when buying a new one. There are a number of incentivised take-back schemes for consumer electronics currently operational in the market. These offer incentives as a discount off the newly purchased model, or as a valuation of the traded in model. Valuations vary significantly, depending on the make, age and state of the item. Therefore, for the purposes of modelling consistency, the incentive in this scenario takes the form of a discount on the new TV purchase, with the value dependent on the screen size of the new purchase. In aggregate, we assume a 10% discount (£65 off a TV with a price of £650). Not all sales generate a returned TV, because some TVs are “retired” into secondary or tertiary rooms in the household.

In 2012 in the UK 7.74 million televisions were sold54, at an estimated revenue of £2.6 billion55. Approximately half of the market in 2011 was represented by six leading brand manufacturers, with Samsung and LG accounting for a third of the market between them. The remaining market share is represented by other brands and includes a large proportion of retailer own brand products that are rebranded original equipment manufacturer (OEM) units, sold at a lower price than the top branded products. The range of models sold is very broad, with 160 models in the top six brands selling over 10,000 units annually, and a further 1,000 models with lower sales volumes56. Therefore, it is difficult to identify a typical model, functionality or price, as all these parameters are highly variable.

UK sales are dominated by LED-LCD technology. An estimated 35% of the market (by volume) is represented by large TVs over 32 inches57. The scenario assumes that these large models are likely to be traded in by users rather than retired to secondary rooms because of their large size. As the main household TV, these units are also likely to be upgraded more frequently. Therefore, as an optimistic estimate, this scenario assumes that 35% of the TVs sold can be captured by the trade-in scheme some years after purchase, amounting to approximately 2.7 million units per year. For the purposes of this scenario, although not all large screens will be traded in, the figure will be made up by smaller screens traded in. The scenario is intended as an optimistic estimate to showcase the potential of the business model so represents the upper end of the potential impact.

With an average life expectancy of six years, the value of refurbished TVs will diminish with age (for some electronic products, the diminution is rapid and has been reported to step change as often as every twelve weeks of product life). Therefore, as a starting point, this scenario will assume that most TVs are traded in after four years of use, assuming that their value diminishes rapidly after five, but few users upgrade after only three years of ownership. This will mean that the trade in scenario will reach its peak performance four years into full operation.

54 GfK2012 market assessment data, provided by WRAP (confidential) (Mintel, “Televisions – UK – 2012” 2011 sales reported as 9,880,000 units)

55 GfK 2012 market assessment provided by WRAP (confidential)

56 Mintel, “Televisions – UK – 2012” figures for 2011, no significant change assumed in market segmentation

57 Based on GfK 2012 figures for 32”at sales at 2.7 million

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Implications of TV take-back business models on resource efficiency There is a good economic case and a strong business case for companies to develop business models for TV trade-in and re-sale. However, at first glance the environmental benefits of this scheme may seem less clear. How can boosting TV sales reduce resource consumption? The answer lies in the displacement of new purchases when the older TVs are re-sold. A proportion of consumers will buy a used TV in place of a new one. Provided this reduction in new sales more than offsets the additional sales of new sets to the people who traded in their TVs, then the total sales of new TVs will fall. This trade-off is illustrated below:

Increase in new sales: In the scenario modelled, the ability to trade up to a new TV by trading in an old one gives a 10% sales uplift: 700,000 new TV sales.

Decrease in new sales: The traded-in TVs are refurbished and re-sold. The scenario assumes a 10% yield loss during refurbishment, and that 60% of these TVs displace a new purchase. In total, 1.3 million TV sales are displaced. (In reality, this may be a mix of UK and overseas sales of product.)

The net effect is that 600,000 fewer new TVs are purchased as a result of the trade-in scheme. This will lead to less material consumed in the UK economy and reduced embodied carbon impacts in the supply chain. Displacement rates are difficult to predict. Based on previous WRAP research into displacement effects, 56% of online sales of used electrical and electronic products displaced new product purchase.

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The incentive is intended to encourage the customer to bring back high value used products, which can be refurbished and sold on. Given the low margins in the electronic sector, onward sale of refurbished items is likely to be attractive to retailers where a large margin can be generated on a traded-in product. It also aims to enable the user to realise the value of a used item when they want to upgrade.

In the scenario, it is assumed that consumers return their old TVs to the store, rather than there being a collection service58. The units are then backhauled to the retailer’s warehouse and subsequently collected by a refurbishment partner company (in batches of 50-100 units). The refurbishment company and the retailer have a revenue-sharing agreement.

The refurbishment partner hauls the units to their workshop and conducts PAT testing, minor repairs and follows an appropriate re-use test protocol to confirm re-usability59. Major refurbishment, such as screen replacement, is unfeasible due to the cost of LCD panels, so TVs which do not work must be recycled. Based on stakeholder input, the cost to the refurbishment contractor, including haulage, testing and minor refurbishment averages £40 per unit. Units which cannot be refurbished account for up to 10% of all traded-in items. The value that can be extracted from salvaged components is roughly equal to the cost of disposal of the remainder.

The refurbishment partner sells on the refurbished TVs to smaller resellers or retailer subsidiaries. Stakeholder input suggests that the refurbishment partner receives, on average, £200 per TV (based on a bulk load of 40” units). After the refurbishment costs have been taken into account, the retailer receives 70% of the revenue and the refurbishment partner the remaining 30%.

Resellers are responsible for the onward sale of the refurbished units, some of which are sold in the UK (67% of refurbished units), with the remainder exported (33%). In both cases, we assume a 5% margin and thus a retail price of £210 per refurbished TV.

Figure 13 Fate of UK resale units

The resale market affects the new sales market as some consumers will choose refurbished items instead of new ones. The model assumes that this is particularly potent for customers

58 Under current schemes, retailers have central hubs for trade in and save on transfer through economies of scale, however the modelled scenario assumes a significantly increased volume of units traded in, so it is likely that each store will be able to capture units.

59 An example re-use test protocol can be found here: http://www.wrap.org.uk/sites/files/wrap/Televisions%20-%20product%20protocols.pdf

Refurbished Unit Sales European Export

Bought instead ofsmall new TV

Bought instead ofOEM large new TV

Bought by those whowould not have madea purchase

Displaced new unit sales

Extra sales Export

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who want to purchase a large screen TV, but opt for a small screen because of the price of new items. Instead of buying a new small screen TV, customers opt for 40% of the refurbished stock. Similarly, new large screen units are partially displaced by refurbished items, as buyers look to save money or look for a bigger brand TV than that which they would have purchased new; 20% of refurbished units are purchased by this market. The remaining 40% of refurbished units are purchased by users who were not considering a new TV purchase, and therefore do not affect the new TV market. When calculated by large and small unit values, this scenario presents a market cannibalisation rate of 13% of new unit sales.

However, the scenario also assumes that the incentive generates extra footfall within stores, increasing sales by approximately 10%, mostly for large, high value items as trade-in customers look for an upgrade.

Figure 14: Conceptual diagram for the TV take-back business model

Key assumptions and impacts of the REBM: The equivalent of all large screens bought is traded in after four years, i.e. 2.7

million units annually; this is a simplified market overview and a high assumption designed to explore the maximum potential of the model.

90% of these have a resale value. Retailers and refurbishment contractors share the cost of transfer, testing and

minor repairs at £40 per unit. Discarded units carry a disposal cost, but this is cancelled out by the value of

their salvaged parts i.e. 10% of units are cost neutral. The refurbished units are sold on to a reseller at an average £200 per unit. 70%

of the revenue goes to the retailer, 30% to the refurbishment partner. Smaller players resell two thirds of refurbished units on UK market, and one third

in Europe. Their margin is assumed at 5%.

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60% of units resold in the UK displace sales of new units (including lower value small screens)60, but this market cannibalisation is partially balanced by an assumed uplift in high value new sales.

4.1.1 Sales volume

Table 25: Destination of refurbished TVs

Number of TVs

Traded in at retailers 2,700,000

Successfully refurbished

(10% assumed non-functioning and thus recycled)

2,430,000

Sales: UK 1,630,000

Sales: Exports 800,000

The 1.63 million refurbished TVs sold in the UK displace some new sales. The assumptions are as follows.

Table 26: New sales displacement by refurbished TVs

Number of TVs (approximate)

%

Displaced new TVs (large) 330,000 20

Displaced new TVs (small) 650,000 40

New sales of refurbished TVs 650,000 40

An uplift in new large-TV sales is also assumed as a result of the incentive, of 10% (770,000 new TVs) each year.

4.1.2 Changes in imports Total displacement of new TV sales amounts to 980,000 (330,000 large + 650,000 small units). Imports of new TVs (all are manufactured abroad) fall accordingly. The import cost per TV is assumed to be 80-90% of the sales value. Under the assumption that the reduction in import demand from displaced sales is split equally between large and small TVs, the average value of each imported TV is (£665 + £425)/2 = £545.

Table 27: Changes in imports of new TVs (annual)

Number of TVs Cost/price Value

Lower imports of newly-manufactured

TVs owing to displacement (1:2 large:small)

-980,000 85% x

(£665+£425) / 2

-£528.105m

Higher imports of newly-manufactured

TVs from uplift (all large).

+770,000 85% x £665 +£565.25m

Key assumptions: Naturally, the new product imports displaced are of a higher value than the

exported refurbished units.

60 This rate of replacement is similar to that reported for used electrical and electronic equipment purchased online at 56%,

http://www.wrap.org.uk/content/study-consumer-second-hand-shopping-identify-re-use-behaviour

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£200 per unit is used as an average that the retailer/refurbishment partnership would fetch from resellers for a major brand, large screen used unit. The onward sale prices would vary; a 5% margin for resale operators has been assumed, but this is a sensitive value. In reality, export prices will be higher than for those units resold in the UK, but the £210 value is used as an average.

4.1.3 Retailer impacts From the previous sections, the transactions and changes in revenue for retailers are summarised in the table below.

Table 28: Retailer assumptions (annual)

Number of TVs Cost/price Value

Incentive to households to return old TVs

2,700,000 -£65 -£175.5m

Sales of refurbished TVs to resellers 2,430,000 70% x £200 +£270m

Displaced new sales: large TVs -330,000 £665 -£219.45m

Displaced new sales: small TVs -650,000 £425 -£276.25m

Uplift in new sales: large TVs 770,000 £665 +£515m

Total impact on retailers +£113.8m

4.1.4 Refurbisher impacts Refurbishers incur a refurbishment cost for the old TVs but also receive revenue from the sale of refurbished TVs to resellers.

Table 29: Refurbisher assumptions (annual)

Number of TVs Cost/price Value

Refurbishment cost (shared with retailers)

2,430,000 -£40 -£97.2m

Sales of refurbished TVs to resellers 2.430,000 30% x £200 +£120m

4.1.5 Reseller impacts Resellers pay refurbishers for refurbished TVs (of which retailers see 70% of the revenue) and sell the TVs on to customers in the UK and overseas. Within the exported sector, there are likely to be other stakeholders within the supply chain. However, for the purposes of this model, only the UK based brokers selling units to overseas buyers are considered. In practice, units are exported because they fetch a higher price overseas. However, without conclusive data, a conservative average sales price is applied to both domestic and exported units.

Table 30: Reseller assumptions (annual)

Number of TVs Cost/price Value

Cost of refurbished TVs 2,430,000 -£200 -£486m

UK sales of refurbished TVs (67%) 1,630,000 £210 +£342m

Overseas sales of refurbished TVs

(33%)

800,000 £210 +£168m

Total reseller income +£24.3m

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4.2 Consumer impacts For UK consumers, the following assumptions are applied:

Receipt of the discount on a new TV in exchange for an old one. Purchases of refurbished TVs in lieu of new ones (a combination of large and

small TVs). Additional purchases of new TVs (the uplift) owing to the incentive to trade in old

TVs.

Table 31: Consumer assumptions (annual)

Number of TVs Cost/price Value

Consumer receipt of vouchers on old TVs

+2,430,000 £65 +£175.5m

Purchases of refurbished TVs (UK) +1,630,000 £210 +£340m

Displaced new TVs (large) -330,000 £665 -£220m

Displaced new TVs (small) -650,000 £425 -£275m

Uplift in new sales: large TVs +770,000 £665 +£515m

Total consumer impacts £535.5m

Key assumptions: The displacement factors and sales uplift assumptions have not been confirmed

by industry. It should be noted that, if displacement factors are significantly higher, the basic sales model will become unprofitable to retailers.

Unit prices for small and large units are taken from Defra’s Market Transformation Programme models and present a conservative market average for units under and above 30”. Although the displacement calculations are probably realistic under these prices, the price of new sales would be higher as consumers would likely upgrade to a more expensive model (although the incentive would discount this price). Therefore, the new sale unit price is somewhat sensitive.

4.2.1 Labour requirements There is no cost information to evaluate this:

Retailers should incur no additional labour costs. The £40/unit refurbishment cost includes labour but there is not currently a figure

for the labour input in hours.

4.3 Economic modelling assumptions In this REBM, the annual incentive provided by retailers to consumers at £175m pa is modelled as a transfer from retailers (a cost) to households (the discount is, in effect, an increase in disposable income).

In line with the business model assumptions, there is a simulated overall increase in UK sales of TVs from a combination of sales of refurbished units (which outweigh the volume of new sales cannibalised) and the uplift in sales that arises from the discount from the trade-in. This entails changes to the composition of sales, as a substantial proportion are now refurbished units, drawing on a different, non-manufacturing supply chain to that for new TVs. This refurbishment activity is modelled as higher activity in repair services.

With regard to UK trade, the scenario models both:

Exports of refurbished TVs to mainland Europe.

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Changes in patterns of imports: cannibalised sales of large and small TVs owing to the burgeoning refurbishment market but also the uplift in new TV sales incentivised by this business model.

4.4 Modelling results Table 32 shows the macroeconomic impacts of the TV take-back model by 2020. The results show that the REBM has a small positive effect on UK economy activity, amounting to some 0.04% in the scenario over the baseline (around £770m, in 2012 prices). This amounts, in 2012 terms, to:

An increase of almost £580m in household spending, driven in part by the higher expenditure on TVs.

Higher exports of (refurbished) TVs to Europe of around £270m. An offsetting effect from higher imports of new TVs of around £160m (in real

terms, the inflow of new large TVs slightly outweighs the cannibalised imports of large and small TVs).

The imports offset some of the increase in UK supply, leading to an overall GDP impact that is smaller than the combined change in household income/spending and exports.

The UK-wide GDP impact, relative to the UK TVs market, is around 30%. While this

value is spread across a variety of sectors in the UK economy, it is clear that the

return to the UK macroeconomy is large when compared to the sector in which the

REBM is originally implemented.

The higher output also contributes to higher employment in this business model of around 4,000 jobs.

Table 32: Results in 2020 for TV Take-back Scenario

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,796,676 772 0.04

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,977 609 0.05

Household expenditure (£2012m) 1,168,387 1,168,964 577 0.05

Net trade (£2012m) -36,409 -36,302 107 -0.29

Employment ('000s) 32,979 32,983 4 0.01

Labour and indirect taxes (£2012m) 466,799 466,874 75 0.02

The overall impacts of this REBM are positive, as shown in Figure 15, owing to higher income and expenditure, as well as greater exports, outweighing the slight increase in imports (a result of the uplift in TV sales).

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Figure 15: Summary impacts of take-back for TVs

Sensitivity analysis suggests that the macroeconomic impacts of this REBM are relatively linear in MDM-E3, such that a doubling of the size of the inputs (financial incentive, consumer purchases, imports, exports, refurbishment activity) yields approximately double the macroeconomic impact. The key condition and caveat around this is that it requires a willingness to assume either a larger potential TV market, or that the business model could reasonably be rolled out to other classes of consumer electronics in a similar manner. This presumes, among other things:

That the business model continues to make financial sense at larger scales. That there is a sufficient market both in the UK and overseas to support higher

consumer purchases of such products i.e. the markets are not saturated and could thus support the levels of uplift assumed in this chapter.

That the cost of refurbishment of other consumer electrical goods is similar to that for TVs.

Characteristics for the additional products are similar to TVs, in terms of supply chains, import penetration, production costs and prices etc.

Product turnover (upgrade frequency) is similar to that for TVs.

Clearly the above are strong assumptions but, if one were willing to assume as such, then there is the potential, by scaling the business model up to the entire consumer electronics sector, for a positive GDP impact in the region of 0.33% by 2020.. This figure is based on the entire TV market representing some 13% of total consumer electronics expenditure (as defined in MDM-E3) in 2012. A run of this scenario with the inputs all scaled up by a factor of almost eight (in order to move from 13% of the sector to 100% of the sector) yields the 0.33% increase in GDP by 2020.

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Scaled up in this way, the GDP impact across the economy is equivalent to around

half the GVA in the consumer electronics sector defined in MDM-E3.

4.5 Potential barriers to uptake The scenario assumes the operation of the business model at its optimum, where all purchased televisions have a re-take outlet and most of the traded-in units are successfully refurbished and sold on. However, this is an optimistic and optimal scenario, which is subject to a number of risks and barriers to uptake. In reality, a business model of this type is likely to operate at lower scales, or only for particular retailers. The following is a non-exhaustive list of potential barriers.

Risk aversion in large retailers: large retailers may prefer not to participate as the trade-in incentive is an upfront cost whilst the revenue share of the onward sale is a risky return. However, the £47 margin retailers could make per refurbished unit (£112 revenue - £65 incentive cost) will be attractive, even compared to new unit sale margins.

Small retailer participation: smaller retailers may not participate in the retake scheme if the investment cost of trade-in incentives is too high. However, the impact on the overall operation of the REBM is likely to be minimal as the bulk of TV sales takes place through major retail channels.

Scale of the refurbishment sector: whether the refurbishment sector, in its current flourishing state, will be able to meet the needs of the assumed scale of trade-in is not certain. Although the scenario assumes that take up of the business model will build gradually over a number of years, if the incentive is attractive enough, a surge of traded-in models may overwhelm the refurbishment sector.

Resale operator margin: The margin assumed for resale may not be high enough to stimulate this market. Furthermore, currently resale operators include subsidiaries of the large retailers. Developing a resale operation may be a complex and unattractive prospect for new entrants, making this market dominated by a few major players, pushing up prices.

Consumer willingness: The assumed replacement of new purchases is based on the assumption that consumers will choose refurbished units over new ones for their price to functionality ratio. However, the willingness of consumers to purchase used units may be overestimated.

4.6 Conclusions The TV take-back results show that, based on the scenario depicted, an incentivised take back model for TVs could have a tangible macroeconomic impact. By increasing consumer expenditure, both on new and refurbished TVs, the model has a noticeable positive impact on GDP of 0.04%. Spreading the scenario to encompass other parts of the electronics sector, like computers, laptops and tablets, which share some price and usage characteristics with TVs, can further increase the significance of the business model. At the sector level, retailers may be attracted by the margins that can be made on refurbished units, around £45-£50 per unit, which, based on some anecdotal evidence, may be higher than what they fetch on new sales. The provision of jobs in a growing refurbishment sector is a further benefit of the scenario.

However, sensitivities around the assumptions, especially the assumption of participation and the yield of high value products through the refurbishment supply chain, bring uncertainty. The scenario presents the business model at its optimum take-up, which may not be a realistic assessment. It also assumes a strong take up of refurbished goods by consumers, which is an optimistic assumption and a significant risk to the model. Low levels of consumption of refurbished products will flood the market with these goods, lowering both their price and the price of competing new units. Even during the short period of the

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preparation of this report, the reported prices for refurbished televisions fluctuated by about 15%.

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5.0 REBMs in the Furniture Sector (Business to Business) For the furniture sector, two different business models were selected: re-manufacture; and leasing. The focus for this sector is on Business to Business (B2B) trade.

5.1 Scenario description: Furniture re-manufacture There are currently an estimated 7,500 furniture manufacturers operating in the UK, of which 13% provide office furniture. The UK consumes approximately 200,000 tonnes of office furniture every year61, with an estimated annual market value of £680m62, or £3,400 per tonne.

Figure 16 provides an overview of the primary interactions in the UK office furniture market, including:

New products are purchased either directly from the manufacturer or through a retailer or specialist furniture dealer.

Consumers can dispose of office furniture in the following ways: Collection for reuse, recycling, disposal by the manufacturer, dealer, retailer or a third

party (typically at a cost similar to landfill charges); Direct recycling or disposal (through commercial recycling centres etc.); Selling or giving unwanted stock to other businesses.

Figure 16: Flow diagram for the office furniture sector

As outlined in Figure 16, the re-use of office furniture predominantly occurs through third-party organisations, with an estimated 9,000 tonnes passing to charities each year. Greenworks, now London Reuse Commercial, collects a significant proportion of this (8,000

61 DEFRA (2010) Revised Government Buying Standards for Furniture: –: Impact Assessment [online]. Available from: sd.defra.gov.uk/documents/20100607furniture-ia.pdf.

62 Centre for Re-manufacture & Reuse. (2009) Reuse of Office Furniture – Incorporation into the ‘Quick Wins’ criteria [online]. Available from: http://www.remanufacturing.org.uk/pdf/furniture_procurement.pdf.

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tonnes), with 10% of that being collected, re-manufactured and sold onto SMEs63 (the equivalent of £2.72m when new).

5.1.1 Re-manufacturing In the furniture re-manufacturing business model, manufacturers receive used office furniture from businesses and re-manufacture it for resale. Furniture not suitable for re-manufacture is either passed to third parties for reuse, recycling or disposal to landfill.

The model enables UK manufacturers to capitalise on their existing facilities and workforce, with minimal investment in supplies and materials.

In addition to other sub-sectors, such as contract and kitchen furniture, office furniture appears to offer the greatest potential for the adoption of re-manufacture, due to pressure from corporate consumers and the opportunity for economies of scale through the movement of large batches of similar products64.

5.1.2 Overseas Re-manufacturing Markets REBMs of a similar nature have been more widely adopted overseas, with Germany and the USA leading the way in economically viable re-manufacturing practices. Currently, over 9% of all commercial furniture sales in the US market involve re-manufactured products65.

A prominent organisation within the industry is Kentwood Office Furniture Inc., which has provided new, reused and re-manufactured office furniture for over 30 years66. The company states that their clients can make savings of between 30-50%, when compared with the purchase of new products, whilst benefitting from the option to choose from a variety of paints, fabrics and laminates.

The opportunity to personalise remanufactured products strengthens their marketability, otherwise these products might rely more heavily on their environmental qualities.

63Centre for Re-manufacture & Reuse (2009) Reuse of Office Furniture – Incorporation into the ‘Quick Wins’ criteria [online]. Available from: http://www.remanufacturing.org.uk/pdf/furniture_procurement.pdf.

64 BFM (2008) ‘Zero Emissions from office, contract and kitchen furniture’ [online]. Available from: http://www.abromhead.co.uk/userfiles/Final%20project%20report%20-%20BFM%20ZEEE8.pdf.

65 Centre for Re-manufacture & Reuse (2009) Reuse of Office Furniture – Incorporation into the ‘Quick Wins’ criteria [online]. Available from: http://www.remanufacturing.org.uk/pdf/furniture_procurement.pdf.

66 Kentwood Office Furniture (www.kentwoodoffice.com).

CASE STUDY: Senator International The market for secondary furniture (reuse) in the UK may be perceived as stagnant, with the majority of recovered items being either sent overseas or donated to educational facilities. Furniture manufacturer Senator International Ltd provides an exception to this rule, having already established the industry’s first dedicated recycling operation. The facility has been extended to enable Senator to provide a remanufacturing/reuse service, where clients have the option to return used furniture stock to be remanufactured, to be donated to third parties or broken down for recycling. Remanufactured products are approximately 30-60% of the price of buying the item new, and have an expected additional life cycle of 5-10 years. The process is thought to be most applicable to clients wishing to remanufacture a large number of the same product. Due to the quality of materials used; Senator products have the potential to achieve several life cycles through remanufacture. The business model has been operating for around 24 months and growth is anticipated, due to the increasing requirement for corporate consumers to demonstrate sustainable procurement practices, coupled with existing economic pressures.

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Furthermore by pitching used office furniture directly to businesses on a tight budget, such as home offices and start-ups, Kentwood is maximising what may otherwise be considered a low value waste stream.

5.1.3 Description of re-manufacturing REBM The UK’s Government Buying Standards for furniture aim to meet the EU targets for Green Public Procurement67. A primary focus of which is the adoption good procurement practice; achieved through procurers reusing at least 5% of their own furniture.

The 5% reuse target applies solely to the public sector, however it is hoped that due to the significant market share held by government departments (10% of office furniture), an example can be set for the remainder of the non-domestic market68.

This level of uptake was used as a basis for the re-manufacturing model in this report, with the 5% expanded to include the total office furniture market.

The following points provide an overview of the re-manufacturing model applied:

The model can be used by manufacturers to target new clients, or offered to their existing client base.

Re-manufacturers offer to collect client’s existing furniture stock, which is reconditioned and sold to consumers at a discounted rate.

Consumers can choose to customise the furniture (selecting fabrics, colours etc.), offering the opportunity to add a personal touch and acting as a unique selling point. Re-manufacturing can be carried out on a manufacturer’s own product range, or on that of a competitor.

Figure 17 below illustrates the increased product longevity provided by the re-manufacturing model. Figure 17: Flow Diagram for the sale of new and re-manufactured office

67 Green Public Procurement – European Commission (ec.europa.eu/environment/gpp/index_en.htm)

68 DEFRA (2010) Revised Government Buying Standards for Furniture – Impact Assessment [online]. Available from: sd.defra.gov.uk/documents/20100607furniture-ia.pdf.

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5.1.4 Sales assumptions The estimated annual value of the UK office furniture market is £680m, of which it is assumed that there is potential for £15.32m of sales of re-manufactured products (equivalent to £34.05m of new furniture).

This is derived as follows:

Annual value of the office furniture market: £680m Of which, one-third represents direct sales between the manufacturer and

consumer (the remaining two-thirds are carried out via dealers/retailers): £226m Of which, 15% will be re-manufactured (5% of annual market value): £34.05m Which is subject to a 55% reduction, due to the lower resale value of re-

manufactured furniture: £15.32m The associated tonnage has been calculated based on the assumption that office

furniture is £3,400 per tonne when new (see Overview). Therefore, £15.32m worth of re-manufactured furniture is equivalent to £34.05m of new furniture, or 10,014 tonnes.

The costs and potential revenue of re-manufactured furniture will be dependent on the quality of the furniture received.

Table 33: Sales assumptions (annual)

Value

Sales of re-manufactured furniture +£15.32m

Displaced sales of equivalent new furniture -£34.05m

Implications of furniture remanufacturing businesses models on resource efficiency The re-manufacturing of office furniture provides an alternative to the recycling or disposal of stock that has reached the end of its life cycle, replacing the requirement for the manufacture of new items and reducing the consumption of virgin materials. The resource efficiency of this model increases with the number of life cycle repetitions achieved, which is largely determined by the quality of materials used. Key assumptions of the re-manufacturing REBM: It has been estimated that approximately one third of the industry’s trade is carried

out directly between the manufacturer and the customer, with the remainder being through a dealer/retailer or reuse organisation.

Consultation with industry stakeholders has indicated that the re-manufacturing model is only applicable to direct purchases because:

o The reduced resale value provides less of an incentive to the dealer/retailer; o Increased logistical costs.

Through consultation with industry stakeholders, the resale value of re-manufactured products has been estimated to be between 30-60% of the original price (averaged to 45%).

It has been assumed that the manufacturer stock levels will not be affected by a 5% uptake of the re-manufacturing model.

The model results in the displacement of new product purchases (either in the UK or overseas). However, at the individual manufacturer level, it has been assumed the sale of re-manufactured goods will be in addition to the sale of new stock.

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5.1.5 Cost assumptions Assuming a 2% margin on sales and a 40:60 split between logistics/materials and labour costs, the overall breakdown of sales by cost is as follows.

Table 34: Cost assumptions (annual)

Share (%) Value

Logistics/materials 39.2 £6m

Labour 58.8 £9m

Margin 2.0 £0.3m

5.2 Economic modelling assumptions: Furniture re-manufacture In order to gauge the macroeconomic impacts, as per the analysis above, the assumption is that the remanufactured furniture displaces just over £34m of new furniture sales each year (based on 2012 data). This represents 15% of the market for direct manufacturer-to-consumer sales or, equivalently, 5% of the entire annual value of the UK office furniture market.

There is not a specific office-furniture sector in MDM-E3, only a broad sector that covers all furniture (including non-office-based businesses as well as household furniture). The £34m reduction in new sales is applied to the broad furniture sector, acknowledging that the detail in the model is not fine enough to distinguish chairs and desks from kitchen or bedroom furniture. Relative to the furniture sector as a whole, the office-furniture remanufacturing business model is small, amounting to less than 1% of UK investment in all furniture in 201269.

Alongside this reduction in new sales, the shift towards remanufactured furniture is modelled as an increase in expenditure on repair services, to proxy furniture remanufacturing activities. The repair sector in MDM-E3 covers more than just furniture repair although, as with the furniture sector, the increase in expenditure remains small at the macroeconomic level, at less than 1% of output in 2012.

In real terms, the increase in remanufactured furniture sales should equal the amount of furniture displaced (to represent the same volume of chairs, desks etc.). The difference in current-price revenue is then achieved by adjusting the price level of repair services. This reflects the difference in price between the new and remanufactured furniture, even though the amount in circulation in the UK should remain unchanged.

Given the small size of the increase in furniture repair expenditure, the modelled change in price amounts to a very small reduction for the sector compared to the business-as-usual case. The change at the sector level (a price reduction of less than 0.25%) barely registers in MDM-E3 and the price assumption has little bearing on the overall macroeconomic result.

The analysis in earlier sections suggested that remanufactured furniture could be sold at 30-60% of the price of brand-new products. In the main scenario, this is modelled as an average value of 45%. In the sensitivities the upper (60%) and lower (30%) bounds are tested to see whether it makes a difference at the macroeconomic level.

In summary, the macroeconomic modelling captures:

a reduction in new sales of office furniture a corresponding increase in sales of remanufactured furniture (represented as

more spending on furniture repair)

69 Once converted into real terms.

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an adjustment to the price of repair services to approximate the difference in current-price revenues: this amounts to a very slight reduction in price which is then varied as a sensitivity

Note that while the initial direct impacts are modelled explicitly, this does not preclude wider economic impacts through, for example, higher economic activity further raising demand for furniture. There is no constraint on the model in this respect although, in practice, this additional impact is likely to be small because of the small scale of the business model.

5.3 Modelling results: Furniture re-manufacture The small scale of the change in expenditure yields small macroeconomic impacts, amounting to a £10m increase in GDP by 2020, in 2012 prices. This does not represent a large share of either the market as identified earlier in this chapter, nor is it large compared to the sector in MDM-E3.

This comes from:

A £7m improvement in the balance of trade (a slight increase in exports and a slight decrease in imports)

A £3m increase in household expenditure, which exceeds the £1m increase in income: households are saving marginally less, although the effects are too small to consider in more detail.

Table 35: Results in 2020 for Furniture Re-manufacture Scenario

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,795,914 10 0.00

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,369 1 0.00

Household expenditure (£2012m) 1,168,387 1,168,390 3 0.00

Net trade (£2012m) -36,409 -36,403 7 -0.02

Employment ('000s) 32,979 32,979 0 0.00

Labour and indirect taxes (£2012m) 466,799 466,782 -18 0.00

The sensitivity analysis for this scenario relates to the retail price. In the main scenario the assumed price of remanufactured furniture is 45% that of the brand new furniture. This is the midpoint of the range 30-60%.

The scenario variant uses alternative price assumptions to reflect the 30% and 60% figures. The impact in macroeconomic terms was small: GDP was about £1m lower in the higher price run (at 60% of the price of new furniture) and about £3m higher in the lower price run. The impacts on the other indicators are broadly in line with the GDP impact.

In macroeconomic terms, this REBM has little impact. However, it does not necessarily follow from this that the business model is not worth pursuing. Such a business model makes cheaper furniture available to individual businesses (who might not otherwise be able to afford it) and may also represent a worthwhile resource saving (compared to newly-manufactured products), in light of the effectively-zero macroeconomic impact.

The small positive effect, as illustrated in Figure 18, arises from lower expenditure on new goods (lowering imports) and shift towards remanufactured furniture. More money stays in the economy, driving slightly higher household income and expenditure.

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Figure 18: Summary impacts of re-manufacturing in furniture

5.4 Potential barriers to uptake: Furniture re-manufacture Logistical costs resulting from the movement and storage of collected

furniture: The associated costs of constructed furniture can be greater than new, flat-packed items, as the value of office furniture is thought to drop by approximately 55% post sale. The high cost of rental space may encourage manufacturers to cherry pick items that require minimal labour inputs, thus cutting down on expenses and capitalising on the limited income from reused products.

A re-manufacturing model is reliant on the use of quality materials: A more widely adopted reuse market may be subject to consumer behaviour change, where the focus moves to furniture that is built to last.

Labour: Manufacturers may increasingly rely on machinery to mass produce furniture, whereas re-manufacture is likely to require skilled labour. The cost of hiring, training and retaining employees may act to discourage the uptake of re-manufacture in organisations with a limited workforce. Therefore, in order to enhance success, business model uptake could usefully focus on larger organisations.

Health and safety: Further costs may arise through the requirement to test items for toxic substances and to confirm appropriate allergy standards. However, it may be possible to avoid these regulations by tracking the history of the product with existing clients.

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5.5 Scenario description: Furniture leasing Under the furniture leasing REBM, rather than purchase the office furniture outright, a business enters a lease contract with a company for new/re-manufactured furniture. The amount of office furniture in use in the UK is assumed unchanged; it is the ownership of the assets that differs.

The quantity of office furniture required by an organisation can fluctuate significantly, as a result of variations in staffing levels and short-term contracts. Furniture leasing schemes can provide a solution to this variance. In these systems, the furniture remains the property of the rental company, who are incentivised to reuse and re-manufacture, in order to maximise profitability. In addition, the customer benefits from the ability to service furniture when required and the flexibility of monthly payments. Furniture leasing models are common for short-term contracts, and a number of organisations provide this service. However, businesses are less inclined to rent furniture over the full life cycle of the product.

The market for commercial furniture leasing is well-established in the UK, being commonly used for hospitality, events and short-term office contracts. However, lease periods often last no longer than 6 months, as long-term rental costs have the potential to significantly outweigh the cost of purchasing a new item.

The industry is more established in the USA70, with an estimated market value in excess of £2bn71. For the purposes of this report, the long-term rental market in the UK is estimated at 3% of the total office furniture market, totalling £20m per annum.

The following considers the potential uptake of long-term furniture leasing/rental, in proportion with the US market.

70 Centre for Remanufacturing and Reuse (2010) The Rental of Office Furniture: Market Survey [online]. Available from

71 Herald Tribune (2006) Furniture Rental and Leasing [online]. Available from: http://www.heraldtribune.com/assets/pdf/advtips/IQ_FurnitureRental.pdf.

Implications of furniture leasing model on resource efficiency The leasing model may reduce the period of time an item of furniture is used by a single consumer, as lease periods typically last up to 5 years. Therefore, if the furniture is disposed of after just one lease period, the model becomes less efficient than traditional furniture consumption. In order for the model to represent increased efficiency, the total number of lease years must exceed the standard life cycle of the furniture. Similar to the remanufacturing model, the efficiency will increase with the number of leasing repetitions. Figure 19 provides an illustration of the long-term leasing model, for office furniture.

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Figure 19: Flow-diagram illustrating the long-term office furniture leasing model

Key assumptions: The US office furniture market has an annual value of approximately $9.7 billion72

(£6.44 billion). Therefore, the office rental/leasing market accounts for approx. 20% of this ($2 billion/£1.33 billion).

Consultation with industry stakeholders has indicated a smaller relative market size in the UK. It has been assumed UK office furniture rental/leasing accounts for approx. 3% of the total market value (£680 million), or £20 million per year. This is equivalent to 5,882 tonnes of office furniture.

Leasing models in the US are typically spread over two, three and five years. A five-year leasing model has been used for the following calculations.

Over the lease period, it has been assumed the products accrue a 25% increase on the retail price. This cost will be spread evenly over this period.

5.5.1 Leasing assumptions Under this business model, an increase in this share is assumed, to 20% (£136 million, an increase of £115.6 million), in line with the US market share. Assuming a premium of 25% and a leasing contract of five years, the increase in revenue to the leasing sector is:

£115.6m x 1.25 = £144.5m over 5 years = £28.9m pa

The furniture that companies lease is assumed to be suitable for two five-year leasing contracts and that this furniture is not then in a fit state for subsequent resale.

72 http://www.bifma.org/ (American Business and Institutional Furniture Manufacturers Association).

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5.5.2 Costs No change in associated costs is assumed. Instead, this model provides a way for cash-flow or credit-constrained firms to gain access to office furniture by spreading the cost over the life of the furniture.

5.6 Economic modelling assumptions: Furniture leasing As stated in the previous section, regardless of whether there is a leasing scheme or not, the amount of office furniture in use in the UK should not change in the scenario. The only difference that must be accounted for directly is who owns the furniture and who uses that furniture. This determines the party to which the value of the asset accrues; the leasing model separates owners from renters.

In MDM-E3 (and according to the principles of national (economic) accounting), acquisition of furniture is classified as investment73. Assuming no change in the rate of replacement (which seems reasonable from the previous section), in real terms, there is no direct change in investment expenditure under this REBM. What differs in this scenario is that businesses and the public sector no longer incur the investment cost themselves. Instead, that cost is borne by the manufacturer. Businesses and public-sector organisations now incur a current expense: the leasing cost. This is in lieu of what would otherwise be upfront investment expenditure on furniture. This shifting of costs over a longer period is the rationale as to why this business model might be attractive.

The key input assumption to this scenario is the cost of renting the furniture: £28.9m pa in 2012 terms. The business model assumes that manufacturers lease directly to customers. In modelling terms, this is represented as payments by business and public services to the furniture sector74.

Table 36 lists the sectors that are assumed to participate in the business model (comprising business services and the public sector) and the costs are shared out according to the size of each sector’s existing payments to furniture manufacturers in 2012. It is assumed that the amount that each sector leases from the furniture sector is proportional to what they paid to that sector in 2012.

Table 36: List of MDM-E3 sectors leasing furniture

56 Computer programming 63 Head offices, etc. 70 Employment activities*

57 Information services 64 Architect, & related* 71 Travel agencies, etc.

58 Financial services 65 Scientific research 72 Security, etc.

59 Insurance & pensions 66 Advertising, etc. 73 Services to buildings

60 Aux. financial serv 67 Other professional 74 Office admin.*

61 Real estate 68 Veterinary 75 Public admin & def*

62 Legal & accounting* 69 Rental & leasing*

* indicates sectors leasing more than £1m pa

Of the sectors in Table 36, by far the largest intermediate purchaser of furniture is the public sector (Industry 75: Public Administration and Defence). By the allocation rule adopted, this sector accounts for some 60% of the expanded furniture-leasing market. As a test of the sensitivity to this assumption, a version of the leasing model was assessed that excluded the public sector, allocating the expenditure to the other 19 sectors instead. The macroeconomic impacts did not prove sensitive to this adjustment.

73 Strictly, Gross Fixed Capital Formation.

74 This is modelled as a change in intermediate consumption, which represents expenses incurred in the production process that are paid to other firms i.e. payments for inputs to production.

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Because production of furniture, other things being equal, should be unchanged, the additional revenue to the furniture industry from leasing should simply represent additional profit (particularly as there is no assumed change in associated costs from Section 5.5.2). Payments from business and public services to the furniture sector do not entail additional requirements for furniture production and should simply lead to a shift in Gross Value Added (GVA) across sectors.

GVA is an economic indicator of productivity, and of value added in the economy. It is the difference between a sector’s output (in broad terms, its turnover) and its purchases of inputs from other sectors. GVA comprises, principally, payments to workers (wages and salaries) and also includes profit. By modelling a leasing payment, those sectors that lease furniture pay more for inputs (but produce and sell just as much as before), lowering their GVA.

The furniture sector receives more revenue through the leasing scheme, but does not need to increase its own purchases of inputs, so its GVA increases. This effect is represented in the scenario and may be thought of as a shift in the distribution of profit in the economy towards the furniture sector.

The implication of the above is that the direct impact of this scenario should just be a reallocation of GVA from some sectors of the economy (business and public services) to others (the furniture sector).

5.7 Modelling results: Furniture leasing As mentioned in the previous section, in MDM-E3, this REBM amounts to a reallocation of funds, from business and public services (who enter a lease contract) to the furniture sector (who leases out its production). GVA in the furniture sector increases by approximately the amount paid by business and public services: £20m-£25m pa (around 0.3% of the furniture sector’s annual output). This is negligible both at the level of individual sectors of the UK economy, but also at the macroeconomic level. Thus, the overall economic impact (GDP, inflation, employment), is, effectively, zero as shown in Table 37.

There are no discernible impacts on trade (because the same amount of furniture must still be produced/imported) nor is there any clear increase in employment.

The overall small shifts in expenditure do little to affect tax revenue, either.

There are no dynamics of particular interest in this scenario: the impacts grow steadily (but very gradually) to their 2020 levels.

As mentioned in the previous section, in terms of sensitivities, it was noted that the simple allocation rule to spread out the furniture leasing across sectors favoured the public sector quite heavily. An alternative run that excluded the public sector suggested no great sensitivity to this assumption (the impacts remained near zero).

Otherwise, the small size of the impacts arises simply because the overall size of the leasing market remains small in economic terms: a larger market would yield a larger economic impact.

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Table 37: Results in 2020 for Furniture Leasing Scenario

Year Baseline Scenario Difference

(-)

Difference

(%)

GDP (£2012m) 1,795,904 1,795,918 14 0.00

Inflation (2012=1.00) 1.26 1.26 0.00 0.00

Household income (£2012m) 1,223,368 1,223,382 14 0.00

Household expenditure (£2012m) 1,168,387 1,168,399 12 0.00

Net trade (£2012m) -36,409 -36,409 0 0.00

Employment ('000s) 32,979 32,979 0 0.00

Labour and indirect taxes (£2012m) 466,799 466,805 6 0.00

The slight positive economic impact arises from a shift in GVA that would appear to have slight larger wider effects than the flows in the baseline (see Figure 20).

As with the re-manufacturing REBM, the overall impact does not represent a large impact, even at the level of the market (which is more narrowly defined than the relevant sector in MDM-E3).

Figure 20: Summary impacts of leasing in furniture

At the resolution of MDM-E3, this REBM would appear to have little bearing on the performance of the UK economy at the sectoral level, let alone the macroeconomic level. However, this is not to say that the furniture leasing business model is of no value or

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consequence at all. This is because, at the level of individual firms (rather than sectors), enterprises may face cash-flow and/or credit constraints under which it is infeasible to purchase furniture. This may be a particular challenge for fledgling small businesses.

The furniture-leasing REBM may help to ease such constraints and promote business growth. This is not so easily discerned at a macroeconomic level, because it depends, crucially, on the distribution of firms’ profits within a sector and lending conditions. At the level of economy-wide accounts, these data are quite sparse, hence the difficulty in modelling such effects and the impacts of freed funds that may be allocated elsewhere.

It is conceivable that MDM-E3 understates the benefits of this REBM (although, if this is the case, the overall impacts are still likely to be too small to represent a significant macroeconomic gain). Conversely, the higher lifetime cost of leasing may curb business demand for leased furniture (which might lead to MDM-E3 overstating the impacts, small as they are).

5.8 Potential barriers to uptake: Furniture leasing The furniture leasing model appears to offer a flexible and cost effective alternative to the purchasing of new items; however, fundamental barriers impede its uptake in long-term business plans:

Businesses may be more inclined to rent items that require updating more regularly or are more susceptible to damage, such as vehicles and IT.

A leasing scheme may encourage businesses to renew their furniture more regularly (i.e. every 3-5 years) than if the furniture were purchased, bringing into question the resource efficiency of the business model. Fundamentally, if companies require new furniture at each renewal, manufacturing rates will increase along with associated energy consumption. However, this will be influenced by the flexibility of the leasing agreement and what happens to furniture at the end of its life cycle.

5.9 Conclusions Both furniture REBMs are small at the sector level, amounting to less than 0.5% of furniture-sector annual output in real terms. In turn, the furniture sector represents less than 0.25% of UK GVA: small changes to a small UK sector do not have any substantial macroeconomic impact.

While the macroeconomic effect is small, this does not necessarily rule out either business model as viable for adoption because they may lead to improvements in other factors that are not captured by GDP and other associated macroeconomic indicators:

At a firm level, both REBMs provide access to cheaper furniture, whether purchased or leased, that may ease cash-flow and credit constraints of individual firms.

The resource savings embodied in remanufactured furniture are, at least at the macroeconomic level (but not necessarily the firm level), effectively zero cost.

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6.0 Project Conclusions 6.1 Approach In this project, CE’s MDM-E3 model of the UK economy was applied to analyse the macroeconomic impacts of REBMs.

In all cases a scenario versus baseline approach was followed, which compared two (simulated) states of the world:

The Baseline: a ‘business-as-usual’ projection of future UK economic performance consistent with CE’s latest UK forecast and one in which REBMs have not been implemented. The same baseline is used throughout the analysis.

The Scenario, one for each REBM, with the business model introduced from 2013 onwards.

The impact of the REBM is the difference between the scenario and the baseline.

6.2 Results Table summarises the differences between the scenario and baseline for three of the key indicators (replicating the results from the individual sector analyses), alongside an assessment as to whether the impact is large enough to be considered meaningful at the macroeconomic level.

In general, the GDP impacts are positive across the scenarios, driven either by:

Increases in household income and expenditure e.g. from higher wage income through increases/shifts in employment.

Improvements in the balance of trade. None of the REBMs investigated leads to a (further) deterioration in the balance of trade. In most cases, this is because the REBMs reduce imports of new products, keeping more funds in the UK e.g. through longer-life products or by reuse/remanufacture. For TVs, the REBM creates a relatively-large export market (the one created by the washing machines leasing scenario is much smaller).

Table 38: Impacts of REBMs in 2020 compared to baseline

Sector REBM GDP

(£2012m)

Net trade

(£2012m)

Employment

(‘000s)

Economically

significant?

Washing

machines

Extended product life: low price

+38 +48 0 No

Extended product life:

high price -19 +57 0 No

Leasing -18 +16 0 No

Clothing Take-back +1,008 +341 +4

Yes: slightly positive

Take-back with

improved durability +1,014 +399 +3

Yes: slightly

positive

TVs Take-back +772 +107 +4

Yes: slightly

positive

Furniture Re-manufacture +10 +7 0 No

Leasing +14 0 0 No

In this analysis, a macroeconomic impact may be considered ‘significant’, whether positive or negative, if it represents a discernible change in GDP and employment. Of the REBMs

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analysed, this is the case for the take-back models in clothing and TVs. Both have GDP impacts of around 0.05% in 2020 and both show changes in employment (the jobs impact is not zero in Table ). These two sets of business models also appear to yield the largest improvements to net trade, by helping to reduce the trade deficit:

By almost 1% under the clothing take-back REBM. By around 0.3% in the TV take-back REBM.

These REBMs also have the largest labour-market impacts, increasing UK employment in 2020 by 3,000 to 4,000 jobs.

In the case of the TVs take-back scenario, a scaled-up version of the REBM was modelled as a sensitivity, to give an indication as to the possible impacts of the REBM being adopted in the entire consumer electronics sector. Assuming that the take-back model could be more widely applied in other electronic products, indicatively, the positive GDP impact could increase to as much as 0.33% in 2020.

The take-back REBMs appear most promising in terms of improving UK macroeconomic performance.

In order to support the main analysis (the scenarios in Table ), sensitivity analysis was also conducted around some key variables in the REBMs, to better gauge the relative contributions of the various assumptions.

In most cases, the greatest area of sensitivity was found to be around the scale of the spending shifts and/or increases (e.g. from new to reused clothes, or the uplift in TV sales). Other variables have comparatively little impact. The washing machines extended product lifetime business model shows some sensitivity to the retail price although, even in the high-price case, there is no strong evidence of a negative economic impact.

The small size of some of the results in this analysis do not, of course, suggest that the other REBMs have no value or are ineffective in any consumer/company/resource sense. It is simply that the result appears small in macroeconomic terms.

Some REBMs will have benefits by allowing consumers and companies to spread costs out over longer periods of time. While the lifetime cost is typically higher, such REBMs help to ease cash-flow constraints for consumers and companies and may thus be preferred on these grounds (and are not explicitly accounted for in the macroeconomic assessment).

The macroeconomic analysis does not necessarily capture all the potential benefits of the REBMs analysed but does indicate that there are no obvious macroeconomic costs to their adoption and there is the potential for valuable resource savings.

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7.0 Appendix A - The MDM-E3 Model Description of MDM-E3 The Cambridge Multisectoral Dynamic Model, Energy-Environment-Economy (MDM-E3) is a computer model of the UK economy maintained by CE as a tool for forecasting and scenario analysis.

The MDM-E3 model has been used for both economic forecasting and policy analysis, including a recent and relevant study for Defra on ‘The Effect of Resource Efficiency on Employment and Competitiveness’75.

For the purposes of this project, the model has the following key features:

It identifies a wide range of industrial sectors and categories of consumer expenditure, thus recognising that there are a range of economic activities in the UK to meet a wide range of demands for goods and services.

An explicit depiction of the UK economy in supply-chain terms: in order to produce goods and services, industries must buy inputs from others, and the composition of these required inputs differs by sector76.

Behavioural responses (parameters) estimated from UK-specific data: empirical validation.

The model’s sectoral disaggregation (set out in the first two bullets) is critical to this project, because it acknowledges likely differences in economic impact by sector/REBM owing to differences in economic structure. These impacts differ within the sectors of interest because of differences in features such as employment characteristics, including the labour intensities of the sectors (employment requirements per unit of output) as well as average wages. For example, a more labour-intensive sector will generate more jobs for a one-unit increase in output than a less labour-intensive sector. Having accounted for wage costs, differences across sectors can lead to different amounts of wage income to households. This in turn may lead to changes in household expenditure (wider macroeconomic impacts).

The structure of supply chains (interdependencies between sectors) also matters in this project because it leads to differences in impacts across sectors for different REBMs. For example, increases in demand for manufactured goods require manufacturers to buy in more inputs to production from other sectors, generating more economic activity in other parts of the UK economy (as well as in the rest of the world, if some components are imported). Thus it is not just the direct impact on, say, the consumer electronics sector that matters, but also the way in which this transmits indirect impacts to other sectors of the economy, possibly creating jobs and incomes. This leads to further rounds of effects that are induced through changes in household income leading to further changes in expenditure.

Figure 21 illustrates this series of ‘knock-on’ effects’. Assuming a business model that leads to shifts in consumer spending, from new products to second-hand ones, for example, the blue boxes indicate the direct impacts: changes in the pattern of production across sectors, leading to changes in direct employment.

The green boxes show the indirect effects further up the supply chain77 from the direct impacts. These arise from the need for changes in supply to produce goods and services that meet the initial change in demand. These indirect impacts also lead to further changes in employment (indirect employment impacts).

75 http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=16944

76 This representation draws on official UK economic statistics on such linkages, from ‘input-output’ tables for the UK.

77 Sometimes called ‘backward linkages’.

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Finally, the purple boxes show how changes in employment generate changes in income that may drive further changes in household expenditure.

The logic of the above can be summarised as follows:

Demand (from households) generating requirements for production (along the supply chain).

Production leading to further income accruing to households. Household income driving further changes in expenditure (a circular flow).

This is a core feature of macroeconomic models and, at the macroeconomic level, this particular project poses questions that require a sectorally-disaggregated representation, as provided by a model such as MDM-E3.

Figure 21: Direct and indirect effects

The final feature highlighted relates to the behavioural responses in the model. Economic models require assumptions regarding economic behaviour, such as how changes in income or price lead to changes in expenditure. MDM-E3 is an example of an econometric model in which these parameters are estimated on actual historical data for the UK. This yields a set of responses that are consistent with the behaviour of the UK economy, as observed in the history. The approach is conducted in this way with a view to replicating key features and trends specific to the UK through time.

The approach is applied to individual sectors in order to allow for underlying differences in the nature of these sectors. No assumption is imposed beforehand that sectors are alike in, for example, their ability to pass on cost increases to consumers (or, alternatively, increase their mark-ups). Instead, the preference is to allow actual data to inform the model’s behaviour (to ‘let the data speak’) rather than impose any of the modeller’s own pre-conceptions as to how the economy should behave.

MDM-E3 is a dynamic model in the sense that it generates annual projections (time paths) and the econometric approach fits with the model’s aim to simulate the evolution of the UK economy through time. This allows for transitions through time (as observed in the data), rather than instantaneous adjustment.

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Standard outputs from the model are annual projections for:

Gross Domestic Product and its breakdown by component of final expenditure Household expenditure by 51 categories of consumption. Investment by 27 investing sectors. Output, prices, exports, imports and employment for 86 industry sectors.

In this project, to aid comparability across REBMs (which operate by a variety of mechanisms), the focus is on a smaller set of headline indicators and provide additional detail at a more disaggregated level as required to illustrate and explain the impacts.

At the macroeconomic level, some of the REBMs appear relatively small in terms of their impact. This is not to say that the REBMs have no impact or value, only that the impacts are more difficult to discern at the resolution of a macroeconomic model such as MDM-E3, which deals in quantities expressed in the millions of pounds sterling. What is small at the macroeconomic level is clearly not small at the level of individual firms operating in particular markets (and this is the value of the Phase One analysis).

Moreover, MDM-E3 does not capture certain relationships that are either:

Not easily captured in traditional economic statistics, such as resource savings. Difficult to represent at a macroeconomic scale, such as the impacts on small

businesses cash flow of being able to lease furniture and spread the cost over a number of years, rather than face larger upfront costs (and, possibly, credit constraints).

In the main report, the above are highlighted in cases where they may be important (and thus, where a traditional macroeconomic evaluation risks ignoring key facets of the business models’ impact).

Representing REBMs in MDM-E3 In translating the proposed business models into a set of input assumptions to MDM-E3, there are a number of considerations that relate to the representation of the REBMs in a macroeconomic, rather than business-level, context:

The importance of consistency with national accounting principles78. This matters, for example, in the treatment of assets such as furniture and whether the owner is also the renter/user (which affects the distribution of value added in the economy).

The four sectors considered in this project (washing machines, clothing, TVs and furniture) are subsectors of the industries identified in UK economic data: the sectors in MDM-E3 are elaborated at a coarser level of detail than the definition of the REBM sectors. This has implications for the nature of sectors’ behavioural responses, which is discussed in further detail below.

The importance of ‘real’ economic quantities which, broadly, represent volumes (numbers of washing machines) rather than values, by stripping out the price effects. Price effects are of course important (and included in MDM-E3) but macroeconomic models seek to deal with goods and services in such a way that £1m of washing machines in one year, say, should be equivalent to £1m of washing machines in a later year (the changes in the price/cost/value of the washing machines are tracked separately, as price indices).

The first of the above relates to how the information on the REBMs is translated into inputs to the macroeconomic model. This is important in order for various transactions to appear in the correct part of the model (in a way that is comparable to other similar transactions in an economy).

78 The organising framework that unifies statistics about economies in aggregate (the ‘macroeconomy’) and from which concepts such as GDP are derived.

The second point is important to bear in mind when interpreting the results, because not all the sectors considered in this project map well to those identified in MDM-E3. The clothing and furniture sectors are straightforward to identify in the model although the REBMs only affect portions of these sectors, which imposes an implicit assumption that these subsectors (e.g. office furniture) behave similarly to the parent sector (all furniture, including in households). The outputs of the furniture and clothing sectors thus do not separately identify chairs from desks, or specific garments; they instead refer to some notional, and composite, furniture or clothing product.

In the case of washing machines and TVs, the products belong to broader categories of product: domestic appliances and (audio-visual) electrical goods, respectively. Clearly, a TV differs from a computer but this is not a distinction that MDM-E3 can make given the available economic data. The macroeconomic modelling in these cases is thus not able to capture as many of the subtleties of each product because the electrical goods category, as a whole, represents a wider range of more heterogeneous products.

Unless otherwise stated, all results from the macroeconomic modelling are expressed in ‘real’ terms i.e. with the effects of inflation removed from the quantities. This is a key concept in macroeconomics because an increase in a particular set of transactions, sales of TVs, say, can change for two possible reasons:

The change in the number (the volume) of TVs sold. The change in the price of those TVs.

Macroeconomic models seek to make such a distinction because a change in sales may represent some combination of the above and price effects risk masking the actual quantity of goods and services that an economy is able to produce. The productive capacity of an economy is greater if it can produce more TVs, not if it happens to be able to sell the same number (or indeed fewer) at a higher price (although price changes have implications for the distribution of income between firms and households).

At a macroeconomic level, it is not feasible to deal in numbers of TVs or washing machines etc. because there are too many products available in a modern economy and it is not straightforward to sum products with different units together. As such, it is common to still use currency (e.g. pounds sterling) as the unit of measure, but relative to a reference or base year.

For example, if 2,000 TVs in 2012 sell for a total nominal (‘current-price’) value of £1m and, in 2013, 2,000 TVs now cost £1.1m, those 2,000 TVs in 2013 were worth £1m in 2012 prices. Having removed the price effect (a 10% increase in 2013 over 2012) it is possible to express 2,000 TVs in a common unit: the amount of money that would have been required to buy them at the selling price in 2012.

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