resource efficiency metrics – initial...

1 Resource Efficiency Metrics – initial findings

Resource Efficiency Metrics – initial findings Anne Owen, Jannik Giesekam and John Barrett for the University of Leeds 02.08.18

Executive summary

This study aims to develop a set of indicators to understand the changing resource productivity

in the UK in a way that has more policy relevance. This document sets out initial findings and

suggests how the research can be further developed. In particular, it:

develops and calculates a carbon-based metric of resource efficiency;

looks into decomposition of the metric by sector to enable comparisons across sectors;

shows how well the metric indicates the extent of decoupling of raw material consumption from economic activity.

The document also sets out a work plan for a comparison of carbon intensity of materials and

products over their lifetime.

Analyses undertaken

Calculated the raw material footprint (MF) associated with UK consumption using a multi-regional input output database for the period 1997-2014. This MF includes all material extraction associated with the supply chains servicing UK final demand.

Compared the calculated MF with results from the ONS, Eora and Global Resource Accounting Model (GRAM) databases.

Conducted a decomposition analysis to understand the effect on UK carbon emissions of the changing carbon intensity of materials; material intensity; and final demand.

Conducted a secondary decomposition analysis on selected product groups.

Proposed an approach to identify key sector products and a means of compiling a supplementary Index of Product Resource Efficiency.

Findings

In 2014, the UK’s MF was 1,092 million tonnes of raw material.

81% of the UK’s MF is from materials extracted abroad.

The UK’s MF is made up of construction minerals (45% by weight), biomass (27%), fossil fuels (21%) and metal ores (7%).

Measured as extracted tonnes of material used by the economy per £ of GVA, the material intensity of UK production and imports reduced throughout the analysis period. The same is true of carbon intensity (carbon emissions per £ of GVA). This change in intensity reflects changes in efficiency, the structure of the economy and trade.

Between 1997 and 2012 the carbon intensity of materials (carbon emissions per tonne of extracted materials) produced in the UK was increasing. This is because the carbon intensity of production (tonnes CO2e per £’000 of GVA from UK production) fell but at a slower rate than the material intensity. Post 2012 the carbon intensity of materials produced in the UK reduced.

The carbon intensity of materials imported has reduced slightly over the time period 1997-2014. This is because, for imported materials, the carbon intensity of production fell (improved) at a faster rate than the material intensity of production.

Results from the decomposition analysis reveal final demand to be a positive driver of the emissions increase in non-recession years. The carbon intensity of materials acts as a negative driver of emissions as the proportion of imports increases (because the carbon intensity of imports has fallen).

Results from the product group decomposition analysis show that decreasing material intensity has contributed to emissions reductions for agriculture, forestry & fishing products and finance & business services but is a positive driver for energy & water products (where material intensity has increased). Effects of final demand and carbon intensity of materials on carbon emissions are similar to the total UK results.


There is a strong link between resource use and carbon emissions, with a small number of sectors accounting for the majority of the carbon and material footprints. For instance, the 30 sectors accounting for 80% of the total carbon footprint in 2014 also accounted for 62-85% of each material footprint. This suggests that an index which tracks key sectors and products, could indicate progress in improving resource efficiency and reducing carbon emissions.

Recommendations

We recommend the annual production of a UK material footprint account with refinements to that which is currently available. Key metrics, such as the carbon intensity of materials, could be tracked using this account.

Further project work packages should develop a supplementary indicator of progress across key products, such as the proposed Index of Product Resource Efficiency.

An additional project should adopt alternative econometric modelling approaches to deliver the desired secondary and tertiary outputs, including a deeper understanding of how prices might affect consumption of materials.


Terminology

When describing the results of the analyses in this report a common terminology is adopted.

The following pages are intended to provide a handy reference summarising the model

regions, materials, product groups and commonly used terms.

The model used in this report is a UK multi-region input-output database with a materials

extension. The model contains 106 defined sectors. The output of each of the model’s defined

sectors is referred to as a ‘sector product’. The output of a sector located in a particular region

is referred to as a ‘regionalised product’; and the particular goods or services produced in any

given sector are simply referred to as a ‘product’. The model regions and resources are:

Model resources

Code Resource Code Resource

GHG Greenhouse gases COAL Coal

BANI Biomass animals CNST Construction minerals

BFEE Biomass feed GAS Gas

BFOO Biomass food OIL Oil

BFOR Biomass forestry ORES Metal ores

Model regions

Code Region Code Region

UK United Kingdom KOR South Korea

AUS Australia NAM North America

CHN China CSA Central and South America

IND India EU European Union

IDN Indonesia REU Rest of Europe

JPN Japan MID Middle East

RUS Russia ROW Rest of World

Where results are classified by product groups these sectors are grouped into:

Product group

Manufacturing Agriculture, forestry & fishing

Transport & communication Construction

Energy & water Financial & business services

Public admin, education & health Other services

Wholesale and retail trade

Definitions

The following terms are used throughout the report and the definitions that we use are

explained below with accompanying notes:

Carbon footprint: the full amount of greenhouse gas emissions required to meet a nation’s

final demand for all goods and services.

Note that the carbon footprint for the UK is an official statistic (Defra, 2016). The carbon

footprint in this report was calculated using a slightly different model (one with a greater

disaggregation of trade regions) and therefore the values reported are slightly different. The

carbon footprint includes emissions arising from a product’s production, use and disposal and

so includes emissions arising from the process of recycling, incineration and landfill. If a

product is manufactured using a greater proportion of recycled material as opposed to virgin

material compared to previous years, the model will identify that waste material is part of the

product’s supply chain. If the emissions associated with waste (recycling output) are smaller

than emissions associated with processing the virgin material, we will observe a reduction in

the carbon footprint of the final good compared to the previous year. However, the present


industrial classifications in the model are quite aggregated and there are not separate sectors

for recycling of different types of material. This means that it is not possible to accurately

analyse the effects of recycling.

Carbon intensity: tonnes of greenhouse gas emissions per £’000 of gross value added. The

carbon footprint divided by gross value added.

Carbon intensity of materials: tonnes of greenhouse gas emissions per tonne of material

extraction. The carbon footprint divided by the material footprint.

Consumption-based account: a method for allocating resource use or emissions to the point

of consumption rather than where the materials were extracted or emissions released.

Footprints are calculated using consumption-based approaches.

Domestic extraction: materials extracted within a country’s border.

Domestic material consumption: domestic extraction plus imported materials minus

materials that are exported.

Embodied emissions: emissions released as part of the supply chain of producing a final

demand product

Embodied materials: materials extracted as part of the supply chain of producing a final

demand product.

Note that this includes: materials that make up the final product; materials that were waste

products during the production stages; and the materials that were used to make machinery

and transport the product during its production.

Gross value added: a measure of the value of goods and services produced in the UK. This

value is calculated in the UK national accounts and is used in the calculation of Gross

Domestic Product.

Material footprint: the full amount of raw materials required to meet a nation’s final demand

for all goods and services.

Material intensity: tonnes of materials per £’000 of gross value added. The material footprint

divided by gross value added.

Note that where separate figures are presented for the material intensity of imports and

domestic production, these are based upon a calculation of the embodied value added by

source. Therefore the GVA of domestic production is the portion of UK GVA that remains in

the country associated with UK goods bought by UK consumers. Meanwhile, the imported

GVA is foreign GVA embodied in imports to satisfy UK final demand.

Material intensity of carbon: tonnes of material extraction per tonne of greenhouse gasses

emitted. The material footprint divided by the carbon footprint.


1. Introduction

Desired project outputs

This initial paper is part of a larger project with the following desired outputs:

Primary outputs:

1. A carbon-based metric of resource efficiency based on a full life cycle and full supply chain approach and which includes embodied emissions.

2. A comparison of carbon intensity of materials and products used over their lifetime. 3. An appraisal of feasibility of decomposition of the metric by sector to enable

comparisons across sectors. 4. An analysis of how well the metric robustly indicates the extent of decoupling of raw

material consumption from economic activity.

Secondary outputs:

1. A critical appraisal of supplementary metrics based on monetised carbon. 2. A supplementary value-based metric that would take into account the price of

materials.

Tertiary outputs:

1. Identification of material flow interrelationships which facilitate economic modelling. 2. An appraisal of the implications of any new metrics for modelling, e.g. for forward

looking models for energy demand, resource efficiency and growth, including policy impacts.

This initial report provides calculations for primary outputs 1, 3 and 4, sets out a work plan for

output 2, and next steps more generally.

Approaches to accounting for resource use

The study aims to develop metrics that can be used to measure resource efficiency in the UK.

It is important that these metrics can be tracked over time and that they can be used as policy

relevant evidence. Before developing a metric of resource efficiency, we first examine many

ways in which a country’s resource use can be accounted for. This is widely known as the

study of material flow accounting (MFA).

Domestic extraction (DE) takes a fully territorial perspective on materials use and accounts

only for those materials extracted within the country’s border (Hirshnitz-Garbers et al., 2014).

This indicator does not account for materials that are imported to or exported from a nation.

Domestic material consumption (DMC) is calculated by taking the DE and adding imported

materials and subtracting those that are exported (Fischer-Kowalski et al., 2011). The DMC is

described as “the most prominent indicator in MFA and accepted as a headline indicator for

resource use and resource efficiency” (Eisenmenger et al., 2016, p178). The DMC Trade flows

are measured according to the mass that crosses the country’s border. However, as

Eisenmenger et al. (2016) point out, because traded commodities can be at differing stages

of processing, their mass on crossing the border differs from the initial mass of extracted

material required to produce them meaning that the full resource use is not captured.

Raw material equivalents (RME) account for the full upstream material requirements needed

to produce traded goods (Wiedmann et al., 2015). If the RME of imports is added to the DE

and the RME of exports removed, we arrive at a measure of full material consumption. This

measure will account for the full mass of material required in the production of traded goods.

The terms raw material consumption (RMC) and material footprint (MF) are seemingly

used interchangeably (Wiedmann et al., 2015) with both meaning the full resource use

associated with a country’s consumption. However, in order to account for the full supply chain

resource use in traded goods we would need an understanding of the production processes


involved in making the product. Eisenmenger et al. (2016) describes two approaches to

quantifying RME.

1) Using material coefficients from life cycle inventories (LCI). 2) Using environmentally extended input-output analysis (EE-IOA).

The main issues with the LCI approach is that there is a possibility of double-counting if

products pass multiple borders; truncation errors are introduced when the full material supply

chain is not accounted for; and conversion factors are sourced from multiple years making it

difficult to assess improvements over time. An IO approach to calculating raw materials in

imports and exports avoids these problems but brings its own unique issues. IO tables tend to

use monetary data to describe the production processes required in making a finished good.

Using money as a proxy for material use can lead to some misallocation of impact. In addition,

the sectors involved in IO tables tend to group many products together which can cause issues

when price to weight ratios differ across the aggregated products. For further discussion of

the advantages and disadvantages of LCI and EE-IOA approaches see Hirshnitz-Garbers et

al. (2014).

Since this work on Resource Efficiency Metrics aims to be policy relevant and used alongside

the existing greenhouse gas (GHG) consumption-based account (CBA) for the UK1, we will

be using a similar IOA approach to calculating the resource use of the UK. There is some

discussion in the literature as to whether the full material use calculations should follow the

description of:

𝑅𝑀𝐶 = 𝐷𝐸 + 𝑅𝑀𝐸𝑖𝑚𝑝𝑜𝑟𝑡𝑠 − 𝑅𝑀𝐸𝑒𝑥𝑝𝑜𝑟𝑡𝑠

(the ONS approach) or whether MRIO (multiregional input-output) analysis should be used to

calculate the full materials used to satisfy a nation’s final demand. While these approaches

seem similar they are subtly different. The first, a Trade Adjusted Inventory (TAI) (Kanemoto

et al., 2012; Peters & Solli, 2010) removes exported materials by using IO tables to consider

the full material requirements needed to produce that product in the exporting country. Import

impacts are calculated by determining the full material requirements needed to produce that

product in the import country. This method does not allow for re-imports to be accurately

accounted for; where a semi-finished product is exported from the source country, processed

abroad and then re-imported. The MRIO approach takes the DE figures and reallocates them

to measures of final demand. In essence calculating the materials extracted to satisfy a

country’s final demand. The MRIO approach can quantify flows that are re-imported, which is

a key advantage.

The UK GHG CBA, uses the MRIO approach. So an additional advantage with using the MRIO

approach in this study is that it will ensure that the two metrics (UK GHG CBA and the metrics

recommended for adoption later in this paper) are consistent and can be used in combination

to further understand resource efficiency. In addition, the most recent and extensive work in

this field, the ‘material footprint of nations study’ (Wiedmann et al., 2015) uses an MRIO

approach. In this paper, the term MF is used rather than RMC. Since we are following the

same method, we will refer to the RMC as MF. In the supporting information accompanying

the study, Wiedmann et al. (2015) also provide a method for calculating 𝑅𝑀𝐸𝑖𝑚𝑝𝑜𝑟𝑡𝑠 and

𝑅𝑀𝐸𝑒𝑥𝑝𝑜𝑟𝑡𝑠 using an MRIO approach.

Resource use calculations for the UK

Domestic Material Consumption basis

The UK reports a Material Flows Account (ONS, 2016a) which includes the domestic

extraction (DE) of biomass, metal ores, non-metallic minerals and fossil energy materials. This

1 https://www.gov.uk/government/statistics/uks-carbon-footprint


account is compiled using a methodology developed by Eurostat and the estimates are

classed as experimental. In addition to DE, this account also includes a measure of the total

DMC and DMC by material type. DE and DMC accounts for the UK are also constructed by

Vienna University (WU, 2016). Table 1, below, shows that the figures for both DE and DMC

are slightly higher in the WU account compared to the ONS figures. DE is also described in

the Eora database (which was used in Wiedmann et al's. 2015 ‘material footprint of nations

study’) as a territorial material account. Eora’s DE figures are lower than both the ONS and

WU and the DE has not been updated past 2008.

Material Footprint basis

Turning now to calculations on a raw material basis, in 2016, the ONS also reported a measure

of RMC (ONS, 2016b). ONS uses the 𝑅𝑀𝐶 = 𝐷𝐸 + 𝑅𝑀𝐸𝑖𝑚𝑝𝑜𝑟𝑡𝑠 − 𝑅𝑀𝐸𝑒𝑥𝑝𝑜𝑟𝑡𝑠 approach and

so will not take account of re-exports and re-imports. In addition, the Eurostat model used to

calculate the materials intensity of traded goods assumes that UK imports have the same

profile as the European average, when in reality the UK’s trade partners will be different. This

is important because production practices vary worldwide and knowing exactly where the UK

imports come from will give a more accurate estimate of the material footprint. The final three

columns in Table 1, below, compare the ONS results (on a raw material basis) with both the

Eora MF figures and the 2005 estimate from Bruckner et al.'s 2012 study, which uses the WU

DE figures with the Global Resource Accounting Model (GRAM) which is an MRIO database.

As the final three columns show, the ONS MF calculations are significantly lower than those

calculated using Eora and GRAM. However, Eora calculations post 2008 are likely to be

inaccurate since they use 2008 DE figures in the calculations.

Table 1: A comparison of DE, DMC and MF from the ONS, WU and Eora and GRAM MRIO databases. All figures in million tonnes.

Domestic extraction (DE) Domestic material consumption (DMC) Material footprint (MF)

ONS Eora WU ONS WU ONS Eora GRAM

1995 735 518 783 799 967

1996 726 525 761 777 1,103

1997 727 522 763 782 1,169

1998 723 516 752 776 1,276

1999 733 527 766 777 1,318

2000 718 507 767 739 780 896 1,332

2001 698 481 748 743 788 955 1,271

2002 683 477 729 722 763 895 1,252

2003 664 459 706 723 759 867 1,332

2004 655 437 705 752 787 876 1,406

2005 622 410 673 733 774 871 1,442 1,166

2006 601 367 655 724 764 842 1,512

2007 590 388 649 719 760 859 1,562

2008 558 371 600 675 703 796 1,470

2009 490 371 523 593 625 683 1,294

2010 474 371 504 577 604 698 1,311

2011 460 371 500 582 693 1,276

2012 421 371 459 563 696 1,272

2013 419 371 454 570 712 1,257

2014 435

This study aims to calculate the UK’s MF using the best available data. For this, we require a

database built using the most accurate model of the UK economy; an understanding of how

the UK trades with other countries and how these countries trade with each other; and a

dataset of DE data for the UK and all other countries. Section 2 explains the source data and

methods used to calculate the UK’s MF.


2. Data and methods

Choice of domestic extraction data

As Figures 1 and 2 show, the pattern of DE is similar across all three datasets, with WU

reporting slightly higher than the ONS and significantly higher than Eora.

Figure 1: Comparison of databases of domestic extraction


Figure 2: Comparison of databases of domestic extraction - construction minerals and fossil fuels

For subsequent analysis in this paper we have decided to construct the UKMRIO using the

ONS DE for the UK and the WU data for every other country’s DE. We made this decision

because the Eora data has not been updated since 2008 and the UKMRIO model philosophy

is to use UK Government data wherever possible.

Construction of the input-output database

The University of Leeds (UoL) calculates the UK’s officially reported CBA (consumption based

account) for CO2 and all other GHG emissions (Defra, 2016). To calculate the CBA, UoL has

constructed the UKMRIO database. Since the CBA is a National Statistic2, the MRIO database

is built using IO data produced by the UK’s Office of National Statistics (ONS). This data is

supplemented with additional data on UK trade with other nations and how these other nations

trade between themselves from the EXIOBASE MRIO database (Tukker et al., 2013; Wood et

al., 2015). The ONS produces Supply and Use tables (SUT) on an annual basis at a 106

sector disaggregation (ONS, 2016). These SUTs are the data that underpins the UKMRIO

database. The use tables are combined use tables, meaning that the inter-industry transaction

table is the sum of both domestic transactions and intermediate imports, and the final demand

table shows the sum of both domestic and imported final products. On a 5-yearly basis, the

2 https://www.gov.uk/government/statistics/uks-carbon-footprint


ONS produces a set of analytical tables where the use table is of domestic use only. Final

demand is also split to show domestic purchases separately. Taking proportions of domestic

versus imports from the analytical tables, we are able to extract domestic and import data from

the annual SUT tables. Imports to intermediate industry is now a single row of data and exports

to intermediate and final demand forms a single column of data.

Data from the EXIOBASE MRIO database (Tukker et al., 2013; Wood et al., 2015) is used to

further disaggregate the import and export data to sectors from other world regions. Data from

EXIOBASE is also used to show how foreign sectors trade with each other, but first the data

must be converted to Great Britain Pounds (GBP). The EXIOBASE MRIO database is mapped

onto the UK’s 106 sector aggregation. Once this step has been performed, the data can be

further aggregated by region. Since EXIOBASE contains data from 49 regions, we are able to

select the most appropriate regional grouping for the trade data.

2.2.1. Choice of regions

For this MRIO study, we construct 14 regions (see Table 2). These regions were chosen

because they represent a combination of the UK’s most important trade partners and show

the regions which extract large volumes of particular materials. For example, fossil fuels from

the Middle East and metal ores from Australia.

Table 2: Regional groupings for the materials extended UK MRIO database

Region code Region Region code Region

UK United Kingdom KOR South Korea

AUS Australia NAM North America

CHN China CSA Central and South America

IND India EU European Union

IDN Indonesia REU Rest of Europe

JPN Japan MID Middle East

RUS Russia ROW Rest of World

2.2.2. Allocation of domestic extraction data to UKMRIO sectors

The ONS and WU materials databases report 10 types of material extraction. Each of these

must be mapped to one or more of the 106 sectors in the UKMRIO database. Table 3 shows

how the sectors correspond to the material extension.

Table 3: Mapping extracted materials to extraction sectors

Products of agriculture, hunting and related services

Products of forestry, logging and related services

Fish and other fishing products; aquaculture products; support services to fishing

Coal and lignite

Extraction of Crude Petroleum And Natural Gas & Mining of Metal Ores

Other mining and quarrying products

Biomass animals 1 0 1 0 0 0

Biomass feed 1 0 0 0 0 0

Biomass food 1 0 0 0 0 0

Biomass forestry 0 1 0 0 0 0

Coal 0 0 0 1 0 0

Construction 0 0 0 0 0 1

Gas 0 0 0 0 1 0

Industrial materials 0 0 0 0 1 1

Oil 0 0 0 0 1 0

Ores 0 0 0 0 1 0


Input-output analysis

Consider a transactions matrix Z , showing sales by each sector (rows) and the purchases by

each sector (columns). Reading across a row reveals which other sectors a single industry

sells to and reading down a column reveals who a single sector buys from in order to make its

product output. A single element, 𝐳𝐢𝐣, within 𝐙 represents the contributions from the ith supplying

sector to the jth producing sector in an economy. The 𝐙 matrix is in monetary units.

Reading across the table, the total output (𝑥𝑖) of a particular sector can be expressed as:

𝑥𝑖 = 𝑧𝑖1 + 𝑧𝑖2 + ⋯ + 𝑧𝑖𝑛 + 𝑦𝑖 (1)

where 𝑦𝑖 is the final demand for that product produced by the particular sector. The IO

framework shows that the total output of a sector is the sum of its intermediate and final

demand. Similarly if a column of the IO table is considered, the total input of a sector is the

sum of its intermediate demand and value added in profits and wages (𝐡).

If each element, 𝑧𝑖𝑗 , along row 𝑖 is divided by the output 𝑥𝑗, associated with the corresponding

column 𝑗 it is found in, then each element in 𝐙 can be replaced with:

𝑎𝑖𝑗 =𝑧𝑖𝑗

𝑥𝑗 (2)

forming a new matrix 𝐀, known as the direct requirements matrix. Element 𝑎𝑖𝑗 is therefore the

input as a proportion of all the inputs in the production recipe of that product.

(2) can be re-written as:

𝑧𝑖𝑗 = 𝑎𝑖𝑗𝑥𝑗 (3)

Substituting for (3) in (1) forms:

𝑥𝑖 = 𝑎𝑖1𝑥1 + 𝑎𝑖2𝑥2 + ⋯ + 𝑎𝑖𝑛𝑥𝑛 + 𝑦𝑖 (4)

Which, in matrix notation is + 𝐲 . Solving for 𝐱 gives:

𝐱 = (𝐈 − 𝐀)−𝟏𝐲 (5)

(5) is known as the Leontief equation and describes output 𝐱 as a function of final demand 𝐲.

𝐈 is the identity matrix, and 𝐀 shows the inter-industry requirements. (𝐈 − 𝐀)−𝟏 is known as

the Leontief inverse (denoted hereafter as 𝐋). (5) can be re-written as:

𝐱 = 𝐋𝐲 (6)

Consider a row vector 𝐟 of annual material extraction from each production sector. It is possible

to calculate material intensity (𝐞) by dividing the total emissions of each sector by total sector

output (𝐱). 𝐞 can be any of the raw materials that we have data for, or the sum of all extracted

materials.

𝐞 = 𝐟�̂�−𝟏 (7)

In other words, 𝐞 is the coefficient vector representing extraction per unit of output.

Multiplying both sides of (6) by 𝐞 gives:

𝐞𝐱 = 𝐞𝐋𝐲 (8)

and from (7) we simplify (8) to:


𝐟 = 𝐞𝐋𝐲 (9)

However, we need the result (𝐟) as a flow matrix (𝐐), showing the source sector and region of

extraction embodied in all products, and so we use the diagonalised �̂� and �̂�:

𝐐 = �̂�𝐋�̂� (10)

𝐐 is the consumption based material extraction account, or Material Footprint calculated by

pre-multiplying 𝐋 by extraction per unit of output and post-multiplying by final demand.

Materials are reallocated from production sectors to final products. We can use 𝐐 in a number

of ways. If we sum the columns, it tells us the material footprint of products, with products

classified as the belonging to the ‘country of final purchase’ i.e. where we imported the finished

product from. If the rows are summed we calculate the material footprint by source sector and

nation.

2.3.1. Adjusting the economic data to constant prices

In order to make calculations of the material and carbon intensity of the UK’s resource use

and greenhouse gas consumption-based account we divide the environmental impact (either

in mass of material use or emissions generated) by the global value added (GVA) associated

with UK consumption. Before we can do this, we must convert all economic data in the UK

MRIO to constant prices to allow for year-on-year comparisons to be made. We chose a base

year of 2010 and adjust the tables for the years 1997-2009 and 2011-2014 to 2010 prices.

The adjustments follow the double deflation method explained in (Lan et al., 2016) using

Producer Price Inflation data from the ONS.

3. Results

In this section, we first compare the MF for the UK, as calculated by UoL’s MRIO database,

with the results from the ONS study, Eora and the GRAM databases. We then use the UoL

MRIO database to present the UK’s MF in greater detail, showing the impact by material type

and by the region of extraction. We move on to show the material intensity (kg material per £

GDP) and carbon intensity (kg CO2e per £ GDP) of UK consumption, again using UoL MRIO

data for calculating MF, before introducing metrics of material intensity of carbon and carbon

intensity of materials. The section concludes with an investigation into the drivers of change

in carbon emissions using a decomposition analysis to understand the role of changes in

carbon intensity of materials, material intensity and changes in final demand.

Throughout this section a common terminology is adopted, whereby the output of each of the

model’s 106 defined sectors is referred to as a ‘sector product’. The output of a sector located

in a particular region is referred to as a ‘regionalised product’; and the particular goods or

services produced in any given sector are simply referred to as a ‘product’. Results are

presented for the resources shown in Table 4.

Table 4: Model resources

Resource code Resource Resource code Resource

GHG Greenhouse gases COAL Coal

BANI Biomass animals CNST Construction minerals

BFEE Biomass feed GAS Gas

BFOO Biomass food OIL Oil

BFOR Biomass forestry ORES Metal ores


UK raw material footprint – comparison of results

Figures 3 and 4 compare results obtained from the UoL’s MRIO database with those from the

ONS study, Eora and GRAM databases.

Figure 3: Comparison of material footprint between databases


Figure 4: Comparison of material footprint between databases - construction minerals and fossil fuels

Calculating the MF of a nation is cutting edge science and as such, there is considerable

variation in estimates for the UK. We believe that the results calculated using the UKMRIO

database provide the most accurate estimate of the UK’s MF. We take DE for the UK from the

ONS (unlike Eora) but the MRIO approach to calculating the MF allows for a more

sophisticated treatment of the materials embodied in imports. We use the MF as calculated

by the UKMRIO database for all further analyses in this report.


UK’s raw material footprint by region of extraction

Figure 5 shows the contribution of extracted material to the UK material footprint between

1997 and 2014, broken down by the region of extraction of all materials (see ‘Terminology’ on

page 2 for regions key).

Figure 5: Raw material footprint for the UK between 1997 and 2014, broken down by region of extracted material

UK’s raw material footprint by material

Figure 6 shows the contribution of extracted material to the UK material footprint between

1997 and 2014, broken down by material.

Figure 6: Raw material footprint for the UK between 1997 and 2014, broken down by material


UK’s raw material footprint by material and region of extraction

Figures 7 and 8 show the contribution of extracted material to the UK material footprint

between 1997-2014, broken down by region of extraction for each of the materials.

Figure 7: Raw material footprint for the UK between 1997 and 2014, broken down by region of extracted material


Figure 8: Raw material footprint for the UK between 1997 and 2014, broken down by region of extracted fossil fuels


UK’s raw material footprint by source region and material

Figures 9-13 show the contribution of extracted material to the UK material footprint broken

down by source region.

Figure 9: Raw material footprint for the UK between 1997 and 2014 by source region



Trends in material and emissions intensity

In this section we consider the changing material and carbon intensities of UK consumption

over the time period 1997-2014 with a view to developing an indicator linking carbon and

materials.

3.6.1. Materials intensity

Dividing the material footprint by the GVA of the UK gives a measure of material intensity (in

tonnes of material per £’000 GVA). This can be split to show the intensity of domestic and

imported material (see Figure 14a overleaf). The material intensity of imports (in tonnes of

imported material per £’000 GVA) increased until 2007, after which it decreased. The material

intensity of domestic consumption has gradually decreased over time.

Similarly, dividing the GVA by the material footprint of the UK gives a measure in GVA per

tonne of material (see Figure 14b). The GVA per tonne of material of imports is quite stable,

and the GVA per tonne of material for domestic increases until 2012 before decreasing slightly.


Figure 14a: Material intensity of UK consumption (MF/GVA) broken down by domestic and imported sources

Figure 14b: Inverse material intensity of UK consumption (GVA/MF) broken down by domestic and imported sources

Table 5: Reduction in material intensity between 1997 and 2014

Region Reduction in intensity (MF/GVA)

All 22%

UK 61%

Imports 9%

Figure 15: Material intensity (MF/GVA) of UK consumption broken down by import sources


3.6.2. Carbon intensity

Dividing the carbon footprint by the GVA of the UK gives a measure of carbon intensity (in

tonnes of CO2e per £’000 GVA). This can be split to show the intensity of domestic and

imported emissions (in tonnes of imported emissions per £’000 GVA). Both the carbon

intensity of imports and domestic consumption has decreased.

Figure 16: Carbon intensity of UK consumption broken down by domestic and imported sources

Table 6: Reduction in carbon intensity between 1997 and 2014

Region Reduction in intensity (CF/GVA)

All 50%

UK 45%

Imports 38%

Figure 17: Carbon intensity of UK consumption broken down by import sources


3.6.3. Carbon intensity of materials

Dividing the carbon footprint by the material footprint gives a measure of the carbon intensity

of material measured in tonnes of CO2e per tonne material used. This can be done for each

of domestic production, exports and imports, by using the materials (tonnes) and carbon

emissions (tonnes CO2e) attributed to each of the three elements.

In UK production, the level of carbon emissions associated with each tonne of material used

increased until 2012 and has decreased in the last two years. Imports to the UK are less

carbon intensive in material terms. Over time, for every tonne of imported materials, there are

less emissions involved.

Figure 18: Carbon intensity of materials associated with UK consumption broken down by domestic and imported sources

For UK production over the period until 2012, material intensity (tonnes per £’000 GVA) fell

(improved) faster than carbon intensity, so the carbon intensity of materials (carbon emissions

per tonne of materials) increased. In the last two years, material intensity has increased,

reversing the trend in the carbon intensity of materials.

In our imports, carbon intensity is improving at a faster rate compared to material intensity so

the carbon intensity of materials decreased.

Figure 19: Carbon intensity of materials associated with UK consumption broken down by import sources


3.6.4. Material intensity of carbon

Dividing the material footprint by the carbon footprint gives a measure of the material intensity

of carbon measured in tonnes of material per tonne CO2e. This can be done for each of

domestic production, exports and imports, by using the materials (tonnes) and carbon

emissions (tonnes CO2e) attributed to each of the three elements.

In the UK, in terms of what we domestically produce, less and less material use is associated

with each tonne of CO2e emitted but our imports are more material intensive in carbon terms.

Over time, for every tonne of imported CO2e, there are more materials involved.

Figure 20: Material intensity of carbon associated with UK consumption broken down by domestic and imported sources

With UK production, material intensity is improving faster than carbon intensity, so the material

intensity of carbon (materials used per tonne of carbon emissions) decreases (dark blue line).

In our imports, material intensity is improving at a slower rate compared to carbon intensity so

the material intensity of carbon increases.

Figure 21: Material intensity of carbon associated with UK consumption broken down by import sources


3.6.5. Material intensity of carbon versus carbon intensity of material

The material intensity of carbon is the reciprocal (inverse) of the carbon intensity of material.

Therefore, the rate of increase in one is equal and opposite to the rate of decrease in the

other. It does not make sense to prioritise improvement in one metric over the other since they

essentially measure the same thing. We recommend the latter indicator (carbon intensity of

materials) be annually reported as talking about emissions associated with making materials

used in products makes more sense from a supply chain perspective and it allows for

calculations to consider the role of materials in reducing emissions.

Results by product group

In this section we argue that the carbon emissions associated with UK consumption will be

high if one or more of the following is true:

1. The carbon intensity of the material used to make the product is high; 2. The product is materially intensive; 3. The UK consumes large quantities of the product.

We consider the three factors above across a set of broad product groups (grouping results

for the 106 products from the UK’s supply and use table into 9 overarching groups shown in

Table 7).

Table 7: Product groups

Product group

Manufacturing Agriculture, forestry & fishing

Transport & communication Construction

Energy & water Financial & business services

Public admin, education & health Other services

Wholesale and retail trade

We compare results across the product groups for 2014 (the latest year of data) and also

present trends in material intensity, carbon intensity and carbon intensity of materials over

1997-2014.

3.7.1. Material intensity

Figure 22 overleaf reveals that the group of agriculture, forestry & fishing products is the most

materially intensive, containing over 6 tonnes of material per £’000 GVA. Figure 23 shows how

the material intensity of each product group has changed over time. There have been

substantial reductions in material intensity across all product groups over the analysis period,

but this trend has reversed in recent years for some product groups, such as construction.


Figure 22: Material intensity of product groups consumed by the UK (2014)

Figure 23: Material intensity of product groups consumed by the UK (1997-2014)


3.7.2. Carbon intensity

Figure 24 shows the relative carbon intensity of each product group. The most carbon

intensive product groups are agriculture, forestry & fishing and energy & water. Figure 25

shows how the carbon intensity of each product group has changed over time. There have

been substantial reductions in carbon intensity across all product groups over the analysis

period.

Figure 24: Carbon intensity of product groups consumed by the UK (2014)

Figure 25: Carbon intensity of product groups consumed by the UK (1997-2014)


3.7.3. Carbon intensity of materials

Figure 26 shows that transport & communication, followed by energy & water, are product

groups with the most carbon intensive materials involved in their production. Agriculture,

forestry & fishing and construction products have the least carbon intensive materials involved

in their production.

Figure 26: Carbon intensity of materials for product groups consumed by the UK (2014)

Figure 27 shows a mixed picture, in terms of the changing carbon intensity of materials over

the analysis period. Some product groups, such as public admin, education & health, have

significantly declined, whereas others, such as transport & communication have increased in

recent years.

Figure 27: Carbon intensity of materials for product groups consumed by the UK (1997-2014)


3.7.4. Final demand

When comparing product groups, it is important to consider not just the carbon intensity and

material intensity, but also total spending on each group. Figure 28 indicates that the most

money is spent on financial & business services, followed by public administration, education

& health services and manufactured products.

Figure 28: Final demand spend on product groups consumed by the UK (2014)

3.7.5. Material footprint

The total material footprint of the product groups is compared in Figure 29 for 2014.

Manufacturing has the largest total material footprint, whilst wholesale and retail trade has the

smallest.

Figure 29: Total material footprint of product groups consumed by the UK (2014)


The total UK material footprint and the relative contribution of each product group has changed

substantially over the analysis period (Figure 30). We see an increase in the footprint of all

product groups in the decade until 2007. This is followed by a general reduction through to

2009. Since 2009 the picture has been mixed with some product groups, such as construction,

substantially increasing, whilst others, such as agriculture, forestry & fishing have been

declining.

Figure 30: Changing UK material footprint by product group (1997-2014)

3.7.6. Carbon footprint

The carbon intensity of materials, material intensity and volume of spend (final demand) can

be multiplied together to form the carbon footprint of the product groups (Figure 31). The

product groups with the highest carbon footprint are manufactured goods, transport &

communication and agriculture, forestry & fishing.

Figure 31: Total carbon footprint of product groups consumed by the UK (2014)


The total UK carbon footprint and the share associated with each of these product groups has

changed over time (Figure 32). We see a general increase in emissions between 1997 and

2007, followed by a dip to 2009, followed by a period of minimal change from 2009 to 2014.

But how much has the carbon intensity of materials, material intensity and volume of spend

respectively contributed to these changes? Section 3.8 introduces a technique called

decomposition analysis to begin to explore this.

Figure 32: Changing UK carbon footprint by product group (1997-2014)

Decomposition analysis

In this section we use decomposition analysis to determine the role that each of the three

factors identified in Section 3.7 has on the UK’s changing carbon footprint.

𝐶𝑝 is the carbon footprint of product 𝑝

𝑀𝑝 is the material footprint of product 𝑝

𝑌𝑝 is the final UK expenditure (in 2010 prices) of product 𝑝

Then

𝐶 = 𝐶

𝑀.𝑀

𝑌. 𝑌

For simplicity, let

𝐶𝐼𝑀 =𝐶

𝑀 carbon intensity of material

𝑀𝐼 =𝑀

𝑌 material intensity of spend

Now the difference in the carbon emissions in time 𝑡 and time 0 is

𝐶𝑡 − 𝐶0 = 𝐶𝐼𝑀𝑡𝑀𝐼𝑡𝑌𝑡 − 𝐶𝐼𝑀0𝑀𝐼0𝑌0

Using a mathematical technique called decomposition analysis (Dietzenbacher & Los, 1998),

we can calculate the difference in carbon emissions as the product of three effects:

𝐶𝑡 − 𝐶0 = 𝐶𝐼𝑀𝑒𝑓𝑓𝑒𝑐𝑡𝑀𝐼𝑒𝑓𝑓𝑒𝑐𝑡𝑌𝑒𝑓𝑓𝑒𝑐𝑡


These are: the effect that the carbon intensity of material has on the changing UK carbon

footprint; the effect that the materials per £ of final demand spend has on the changing UK

carbon footprint and finally the effect that changes in demand has on the changing UK carbon

footprint. This can also be written as:

𝐶𝑡 − 𝐶0 = ∆𝐶𝐼𝑀 (𝑀𝐼0. 𝑌0 +1

2(𝑀𝐼0. ∆𝑌 + ∆𝑀𝐼. 𝑌0) +

1

3(∆𝑀𝐼. ∆𝑌))

+ ∆𝑀𝐼 (𝐶𝐼𝑀0. 𝑌0 +1

2(𝐶𝐼𝑀0. ∆𝑌 + ∆𝐶𝐼𝑀. 𝑌0) +

1

3(∆𝐶𝐼𝑀. ∆𝑌))

+ ∆𝑌 (𝐶𝐼𝑀0. 𝑀𝐼0 +1

2(𝐶𝐼𝑀0. ∆𝑀𝐼 + ∆𝐶𝐼𝑀. 𝑀𝐼0) +

1

3(∆𝐶𝐼𝑀. ∆𝑀𝐼))

We use the equation above to calculate the effect of each factor. Figure 33 overleaf shows

the drivers of change in the UK’s Carbon Footprint. Changes in final demand spend is

generally a positive driver of change. The recession (2007-2009) was a period of reduced

spend and this had the effect of reducing GHG emissions. The material intensity of products

has a smaller positive effect between 1997 and 2007. During the recession there was a

change in the material intensity of products bought by the UK which contributed to reduced

emissions. During the recession, fewer goods were purchased from abroad and, as shown in

Figure 14, the UK’s material intensity is lower than our import partners’ and is reducing. The

falling carbon intensity of the materials used in the products purchased by the UK acted to

reduce the UK’s GHG emissions from consumption between 1997 and 2007. As Figure 18

shows, the carbon intensity of domestic production increased during this period because

material intensity in UK production was reducing at a faster rate than carbon intensity. The

trend of reducing carbon intensity of materials is due to the portion of imports increasing in

this time period, over which imports showed a declining carbon intensity of materials (Figure

18).

Figure 33: Change in the UK’s carbon footprint decomposed by 3 factors: carbon intensity of materials, material intensity, and total final demand (1997-2014)

Next we decompose each factor by the broad product groups introduced in Section 3.7. Rather

than look at all nine broad groups, we take the groups that are most important in terms of their

material intensity (agriculture, forestry & fishing), carbon intensity of materials (energy & water

and transport & communication), total spend (financial and business services) and carbon

footprint (manufacturing).


3.8.1. A focus on the group of agriculture, forestry and fishing products

Figure 34: Change in emissions associated with the group of agriculture, forestry & fishing products decomposed into three factors

Figure 34 shows that the emissions associated with the group of agriculture, forestry and

fishing products peaked in 2002, driven by an increase in final demand spend on these

products. Between 2002 and 2008, final demand acted as a negative driver and emissions

reduced. The effect of changes in the carbon intensity of materials and the material intensity

were minimal until after 2008. Post-recession we see a reduction in emissions driven by

reduced material intensity of agriculture, forestry and fishing products. Between 2008 and

2014, final demand acted as a positive driver once more but its effect was not large enough

to cause emissions increases.

3.8.2. A focus on the group of energy and water products

Figure 35: Change in emissions associated with the group of energy & water products decomposed into three factors

Figure 35 shows that the emissions associated with the group energy and water products

increased between 1997 and 2008 driven partly by an increase in final demand for these

goods (until 2004) but mainly due to the material intensity increasing. At the same time, the

carbon intensity of energy and water products reduced. Post 2008, emissions reduced, initially


driven by changes in the material intensity and most recently (2012-2014) by a change in the

carbon intensity of materials.

3.8.3. A focus on the group of manufactured products

Figure 36: Change in emissions associated with the group of manufactured products decomposed into three factors

Figure 36 shows that the emissions associated with the group of manufactured products

increased between 1997 and 2004 driven entirely by an increase in final demand for these

goods. Emissions then stabilised until the start of the recession in 2007. The large reduction

in the emissions associated with these products during the recession was driven by both a

reduction in final demand and the change in material intensity. Post-recession, the emissions

stabilised with the positive final demand driver cancelled out by the effect of the change in

carbon intensity of materials and material intensity.

3.8.4. A focus on the group of transport and communication products

Figure 37: Change in emissions associated with the group of transport & communication products decomposed into three factors

Figure 37 shows a steep increase in the emissions associated with the group of transport and

communication products between 1997 and 2007 driven by an increase in final demand for

these goods and the effect of a change in material intensity (until 2004). The large reduction

in the emissions associated with these products during the recession was driven by both a


reduction in final demand and the change in material intensity. Post-recession, the emissions

stabilised with the positive final demand driver cancelled out by the effect of the carbon

intensity of materials and material intensity.

3.8.5. A focus on the group of financial and business services

Figure 38: Change in emissions associated with the group of finance & business services decomposed into three factors

Figure 38 shows that the emissions associated with the group of finance and business

services increased between 1997 and 2001 driven by an increase in final demand for these

goods and the change in material intensity. Between 2001 and 2007 emissions stabilised with

the positive effect of the change in final demand cancelled out by the change in material

intensity. The reduction in the emissions associated with these products during the recession

was driven by both a reduction in final demand and further change in material intensity. Post-

recession, the emissions stabilised with the positive final demand driver cancelled out by the

effect of the carbon intensity of materials and material intensity.

Proposed suite of metrics

We recommend that the carbon intensity of materials be tracked for UK consumption. This

requires the production of an annual UK material footprint calculation.

The results presented in Section 3.8 demonstrate the potential for using decomposition

measures to understand the drivers of change in emissions that are related to the carbon

intensity of materials, the material intensity and the volume of spend. The level of analysis

shown is quite coarse and we recommend further work which considers an analysis as to the

UK’s changing trade partners. Much of the change in material intensity and carbon intensity

of materials may be explained by where we are sourcing goods and services from. We also

recommend analysis at the individual product level rather than considering product groups.

Section 4 develops this idea further.

4. Understanding the materials and emissions footprints of key products

Preceding sections of this report have proposed a suite of high level metrics based upon an

economy wide analysis using a time series formed from a number of static snapshots. Such

metrics indicate aggregate trends in materials and emissions intensity and highlight how each

sector and region is contributing to the overall trend. However, these metrics do not distinguish

the tangible products within each sector that are driving these trends, nor do they indicate the

whole life impacts of any product entering the stock in a given year.


All products incur material and carbon impacts in their production, use and end-of-life

treatment. Therefore a key objective of product resource efficiency strategies is to reduce

carbon emissions or material use on a whole life basis. In some instances this is best achieved

by expending additional carbon in the production phase to either reduce emissions in the

product’s use phase or to extend the expected product lifetime - thus minimising the need for

further production and delaying end-of-life treatment. In any given year an IO based metric

gathers the production impacts of new products entering the stock, the use phase impacts of

products in the current stock and the end-of-life impacts from products exiting the stock that

year. A metric based on such an approach does not indicate if new products entering the stock

are expected to yield reductions on a whole life basis.

For these reasons we would advise accompanying the proposed economy wide metrics with

a metric that tracks improvements in the whole life carbon and resource efficiency of key

sectors and products. The following sections propose a means of distinguishing ‘key sector

products’ and suggest one approach to developing such a metric. The scope of this report is

limited to a methodological proposal based upon preliminary analysis. The full development

of such a metric would be undertaken as part of a future work package.

Identification of key sector products

There are a number of factors to consider when identifying key products. Principal amongst

these are:

The emissions and materials intensity of the product;

The aggregate production impacts of the sector which produces the product;

The opportunity to mitigate product impacts through the adoption of resource efficiency

strategies.

Some products incur a high impact in the production of each unit, are produced in large

volume, and have impacts that may be effectively reduced using one or more resource

efficiency strategies. By contrast, other products are produced with very low impacts, in low

volume, and these impacts may be better addressed by other mitigation strategies. In reality,

the products of very few sectors fall in either extreme, and capturing the bulk of an economy’s

impacts in a single metric requires the selection of a range of representative sectors and

products.

Figure 39 shows the proportion of the total GHG emissions footprint that is captured by the

inclusion of a given number of regionalised products. The inclusion of a few hundred

regionalised products captures the overwhelming majority of the total emissions footprint.

Figure 40 presents the same results aggregated by sector product, i.e. ignoring the region of

origin. Figure 41 indicates the location of the top 100 regionalised products, which collectively

account for 94% of the total GHG footprint.


Figure 39: Percentage of 2014 GHG footprint captured by inclusion of regionalised products ranked from highest to lowest GHG footprint

Figure 40: Percentage of 2014 GHG footprint captured by inclusion of sector products ranked from highest to lowest GHG footprint

Figure 41: GHG footprint attributable to top 100 regionalised products ranked from highest to lowest GHG footprint in 2014


Figure 42 presents the proportion of each total material footprint captured by the inclusion of

a given number of sector products ranked by GHG footprint. This suggests that sector

products that account for the majority of the GHG footprint also account for the majority of

each material footprint. This relationship holds well for most materials, with the exception of

biomass forestry (BFOR). This is largely due to a small number of sectors with a low GHG

footprint but very high forestry product use e.g. furniture production.

Figure 42: Proportion of total footprint accounted for by material – based on sector products ranked by GHG footprint in 2014

These results suggest that products from a minority of sectors account for the majority of both

material and emissions impacts. This suggests that an agreed cut off value could be used to

determine key sectors. For instance, products of the 30 sectors with GHG footprints greater

than 7 MtCO2e account for 80% of the total GHG footprint and 62-85% of each material

footprint in 2014 (see Figure 43).

Figure 43: Proportion of total footprint accounted for by 30 sector products with GHG footprint exceeding 7 MtCO2e in 2014


The 30 sector products used for Figure 43 can be categorised into the aforementioned broad

product groups, as in Table 8 overleaf. Figure 44 shows the distribution of the GHG footprint

amongst these groups. Many of these groups, such as construction and manufactured

products, can be effectively targeted with resource efficiency strategies. However, some, such

as energy & water, will predominantly be addressed through alternative mitigation strategies

such as fuel switching. Therefore, when developing a metric that can capture ongoing

improvements in product resource efficiency, it may be desirable to limit the metric to certain

sectors.

Figure 44: Distribution of GHG footprint amongst product groups containing the 30 sector products with GHG footprint exceeding 7 MtCO2e in 2014


Table 8: Categorisation of 30 sector products with GHG footprint exceeding 7MtCO2e in 2014

Product Group Sector products with footprint exceeding 7 MtCO2e in 2014

Manufacturing Computer, electronic and optical products

Motor vehicles, trailers and semi-trailers

Machinery and equipment n.e.c.

Coke and refined petroleum products

Furniture

Textiles

Preserved meat and meat products

Dairy products

Other food products

Wearing apparel

Basic pharmaceutical products and pharmaceutical preparations

Fabricated metal products, excl. machinery and equipment and weapons & ammunition - 25.1-3/25.5-9

Other manufactured goods

Transport & communication

Air transport services

Land transport services and transport services via pipelines, excluding rail transport

Water transport services

Food and beverage serving services

Accommodation services

Public admin, education & health

Human health services

Public administration and defence services; compulsory social security services

Residential Care & Social Work Activities

Education services

Energy & water Electricity, transmission and distribution

Gas; distribution of gaseous fuels through mains; steam and air conditioning supply

Waste collection, treatment and disposal services; materials recovery services

Financial & business services

Owner-Occupiers' Housing Services

Real estate services, excluding on a fee or contract basis and imputed rent

Agriculture, forestry and fishing


Construction Construction

Other services Services of households as employers of domestic personnel


Proposal for additional metric

One means of capturing change over time across sectors with many products is to monitor a

‘basket of representative products’ such as in the calculation of consumer price inflation (CPI)3.

In this approach a number of representative items are selected for each sector, price data is

gathered periodically and outputs of each sector are weighted to produce an aggregate metric.

Table 9, below, shows the current allocation of items for the calculation of consumer price

inflation. An index based upon a similar approach could track improvements in product

resource efficiency.

Table 9: Allocation of items to CPI divisions in 2016 (ONS, 2016c)

CPI weight (per cent)

Observed variation in price changes

Representative items (per cent of total)

1 Food & non-alcoholic beverages

10.3 Medium 24

2 Alcohol & tobacco 4.2 Medium 4

3 Clothing & footwear 7.1 Medium 11

4 Housing & household services 12.0 Medium 4

5 Furniture & household goods 5.9 Medium 10

6 Health 2.8 Low 3

7 Transport 15.3 Medium 6

8 Communication 3.2 High 2

9 Recreation & culture 14.8 High 17

10 Education 2.5 High 1

11 Restaurants & hotels 12.3 Low 7

12 Miscellaneous goods & services

9.6 Medium 11

The development of such an index would proceed in four stages as follows.

Stage 1: Identification of key sectors and index weightings.

The key sectors could be identified by a means similar to that described in the preceding

pages, with the exclusion of sectors that are best addressed through alternative mitigation

strategies e.g. electricity transmission and distribution. The index weightings for each sector

would be apportioned based on each sector’s relative contribution to the total GHG footprint

that year.

Stage 2: Selection of representative products.

A set of representative products would be determined for each key sector in collaboration with

stakeholders from each sector. These products would represent the majority of each sector’s

output, with a greater number of products required for sectors with a highly diverse output.

These representative products would be periodically updated with new models and new

products as consumer preferences change, through a process similar to the annual review of

the CPI basket of goods. This would require the formation of an expert group representing

each key sector, which would convene annually.

Stage 3: Life cycle analysis of representative products.

Life cycle analysis (LCA) data for each representative product would be gathered in a manner

that ensures transparency and future replicability, for example through the use of published

3https://www.ons.gov.uk/economy/inflationandpriceindices/articles/ukconsumerpriceinflationbasketofgoodsandservices/2017 describes the current basket of goods.

https://www.ons.gov.uk/economy/inflationandpriceindices/articles/ukconsumerpriceinflationbasketofgoodsandservices/2017

https://www.ons.gov.uk/economy/inflationandpriceindices/articles/ukconsumerpriceinflationbasketofgoodsandservices/2017


Environmental Product Declarations (EPD) which are prepared in accordance with defined

Product Category Rules. For products where no transparent data or common format for impact

reporting currently exists, initial life cycle analyses would be undertaken to provide a baseline.

These initial analyses would be periodically updated as part of the annual product basket

review and would use a frequently updated source of life cycle inventory data, such as the

ecoinvent or GaBi databases. These initial analyses would be replaced over time with product

data that conforms to sectoral standards as this data becomes available. At first these LCA

could be restricted to one impact category, such as GHG emissions, but could be extended in

future to cover material use or multiple weighted impacts.

Stage 4: Calculation of an Index of Product Resource Efficiency.

The relative changes in the life cycle impacts over time would be multiplied by the sector

weightings and captured in an Index of Product Resource Efficiency. This index would track

the relative change in whole life impacts of new products entering the stock each year.

Simple worked example

To illustrate how this Index could be calculated, consider the following simplified example.

Stage 1: Identification of key sectors and index weightings.

The key sectors included in this example are those previously identified as part of the

manufacturing, construction, agriculture, forestry and fishing product groups in Table 8. The

impact of all of these product groups could be significantly reduced through the implementation

of resource efficiency strategies. The 15 sectors included each had product footprints

exceeding 7 MtCO2e in 2014, and a combined footprint of some 256 MtCO2e. The weightings

attributed to each sector are based on their contribution to the total GHG footprint in 2014 and

are as shown in Table 10.

Table 10: Key sectors and index weightings

Product Group Sector GHG footprint in 2014

(ktCO2e)

Index weight

(%)

Agriculture, forestry and fishing

Products of agriculture, hunting and related services 51932 20.3

Construction Construction 48548 19.0

Manufacturing

Coke and refined petroleum products 22029 8.6

Computer, electronic and optical products 20633 8.1

Motor vehicles, trailers and semi-trailers 17486 6.8

Machinery and equipment n.e.c. 13585 5.3

Preserved meat and meat products 12745 5.0

Furniture 9866 3.9

Other manufactured goods 9552 3.7

Wearing apparel 9087 3.6

Basic pharmaceutical products and pharmaceutical preparations

8533 3.3

Other food products 8397 3.3

Fabricated metal products, excl. machinery and equipment and weapons & ammunition - 25.1-3/25.5-9

8123 3.2

Textiles 7908 3.1

Dairy products 7430 2.9


Stage 2: Selection of representative products.

For this simplified example let us consider the sector ‘Computer, electronic and optical

products’. Within this sector there are a wide variety of products that could be considered as

representative. For instance the CPI basket includes the following:

Table 11: Related products included in the CPI basket

08.2/3 Telephone and Telefax Equipment and Services Telephone Smartphone handset Fixed line telephone charges Mobile phone charges Cost of directory enquiries Mobile phone applications Subscription to the internet Mobile phone accessory Bundled communication services

09.1 Audio-Visual Equipment and Related Products 09.1.1 Reception and Reproduction of Sound and Pictures

Flat panel televisions DVD player Blu-ray disc player Digital television recorder/receiver Digital (DAB) radio Audio systems Personal MP4 player Headphones

09.1.2 Photographic, Cinematographic and Optical Equipment Digital compact camera Interchangeable lens digital camera Digital camcorder

09.1.3 Data Processing Equipment PCs – desktop and laptop PC peripherals Tablet computer Computer software

09.1.4 Recording Media CDs, including CDs purchased over the internet Pre-recorded DVDs, including DVDs purchased over the internet Pre-recorded Blu-ray discs, including discs purchased over the internet Recordable CD Music downloads Portable digital storage device

09.1.5 Repair of Audio-Visual Equipment and Related Products

Rather than assemble a full product list, for the sake of example, let us consider a few common

products, a smartphone handset, tablet computer and laptop PC. The market for each of these

example products is dominated by a small number of producers. For instance, Apple’s mobile

operating system represented nearly 50% of the UK market in May 2017. The models of each

product change frequently and the market share of different brands will change over time. For

each product it may be necessary to take more than one model or brand to represent the

market. Each generation of products may be more or less resource efficient in their production,

operation and end of life treatment. Therefore at each annual review the basket of products

would need to be updated to include products and models that represent a high market share

at that time. To consider a simple historical example let us take one model as representative

of each product in the years 2010-2016 as shown in Table 12 below.

Table 12: Example representative products and models

Smartphone Tablet Laptop

2010 iPhone4 iPad (1st gen) MacBook MC207

2011 iPhone4 iPad 2 MacBook MC207

2012 iPhone4S iPad (3rd gen) MacBook MD102

2013 iPhone5S iPad (4th gen) MacBook MD102

2014 iPhone6 iPad (4th gen) MacBook MD102

2015 iPhone6S iPad (4th gen) MacBook MF865

2016 iPhone7 iPad (5th gen) MacBook MLH82

These representative products must be assigned a weighting representing their share of

sector output. For the sake of example let us assign weightings as per Table 13. These

weightings would be reviewed annually by the expert group as purchasing patterns evolve.


Table 13: Product weightings

Product Group

Sector Representative product

Weight (%)

2010 2011 2012 2013 2014 2015 2016

Manufacturing Computer, electronic and optical products

Smartphone 40 45 50 50 50 50 50

Tablet 10 10 15 20 20 25 25

Laptop 50 45 35 30 30 25 25

Stage 3: Life cycle analysis of representative products.

To illustrate how the life cycle impacts of a product may vary over time as new models are

adopted, consider the three example products. Apple have been publicly reporting the

environmental impacts of each of their products to common boundaries since 20084. This

allows for easy comparison over time, with life cycle GHG emissions as shown in Table 14.

Indexing these impacts to a 2010 baseline gives Figure 45. Albeit this example has focussed

on Apple models, other producers of these products - such as Lenovo, Dell, and LG - also

undertake product footprinting.

Table 14: Product life cycle GHG emissions

Product Life cycle GHG emissions (kgCO2e)

Indexed by product 2010 = 100

iPhone4 45 100

iPhone4S 55 122

iPhone5S 65 144

iPhone6 95 211

iPhone6S 61 136

iPhone7 63 140

iPad (1st gen) 130 100

iPad 2 105 81

iPad (3rd gen) 180 138

iPad (4th gen) 170 131

iPad (5th gen) 135 104

MacBook MC207 350 100

MacBook MD102 580 166

MacBook MF865 470 134

MacBook MLH82 430 123

Figure 45: Life cycle GHG emissions of selected products indexed against 2010

4 See https://www.apple.com/uk/environment/reports/ for full range of product environmental reports.

https://www.apple.com/uk/environment/reports/


The underlying calculations reveal an increasing share of life cycle emissions attributable to

the production phase (now 78-86% of total), with changes in body and display materials and

battery size driving increases. Albeit these products are becoming more energy efficient in

operation, the overall life cycle emissions have increased between models.

Stage 4: Calculation of an Index of Product Resource Efficiency.

Following selection of key sectors, representative products and models, and collection of the

associated LCA data, an index of product resource efficiency can be calculated based upon

the designated weightings.

For this simplified example, let us calculate the change in ‘Computer, electronic and optical

products’ and assume all other sectors have remained the same throughout 2010-2016. For

‘Computer, electronic and optical products’ the result would be as in Table 15.

Table 15: Calculation for computer, electronic and optical products

Computer, electronic and optical products

Representative products Total

Smartphone Tablet Laptop

Model Weight (%)

Index Model Weight (%)

Index Model Weight (%)

Index

2010 iPhone4 40 100.0 iPad

(1st gen) 10 100.0 MacBook

MC207 50 100.0 100.0

2011 iPhone4 45 100.0 iPad 2 10 80.8 MacBook MC207

45 100.0 98.1

2012 iPhone4S 50 122.2 iPad

(3rd gen) 15 138.5 MacBook

MD102 35 165.7 139.9


(4th gen) 20 130.8 MacBook

MD102 30 165.7 148.1

2014 iPhone6 50 211.1 iPad


MD102 30 165.7 181.4



MF865 25 134.3 134.0

2016 iPhone7 50 140.0 iPad


MLH82 25 122.9 126.7

Let us assume that the sector weightings (shown in Table 10) remain the same throughout

2010-2016. In reality, these could also be adjusted annually to reflect the relative importance

of each sector. Under this assumption, the overall index of product resource efficiency would

be as in Table 16.

Table 16: Calculation for index of product resource efficiency


Computer, electronic and optical products

Preserved meat and meat products

...[other sectors] Index of product resource efficiency

Weight

(%) Index Weight (%) Index Weight

(%) Index

2010 20.3 100 8.1 100.0 5.0 100 100.0

2011 20.3 100 8.1 98.1 5.0 100 99.8

2012 20.3 100 8.1 139.9 5.0 100 103.2

2013 20.3 100 8.1 148.1 5.0 100 103.9

2014 20.3 100 8.1 181.4 5.0 100 106.6

2015 20.3 100 8.1 134.0 5.0 100 102.8

2016 20.3 100 8.1 126.7 5.0 100 102.2


Translation into practice

In practice, the process would be significantly more complex than indicated by this simple

example and would not be based upon historic data. Central to the process would be the

annual review wherein the expert group representing each sector would determine changes

to the representative products and weightings, and review updates to the product LCA data.

When initially convened, each group would also need to establish the current status of product

footprinting within their sector; identify relevant sector specific standards, product category

rules and so forth; collate compliant product declarations; identify gaps where no declarations

are available, and commission additional baseline assessments where necessary. For some

sectors this will be significantly easier than others. For instance, EPD are well established

within the construction sector, with common product category rules for many products and a

growing body of published declarations. By contrast few LCA have been undertaken for

pharmaceutical products. Ideally these expert groups would also convene as part of a broader

initiative encouraging companies in these sectors to share best practice in the footprinting of

their products and to identify opportunities to implement resource efficiency strategies.

The next steps in development of such an index include: establishment of formal criteria for

selection of key sectors; assessment of the current status quo of product footprinting within

each sector; and a trial attempt to establish a baseline within one sector. These steps would

require a more formal feasibility study that incorporated a broader programme of stakeholder

engagement.

5. Next steps

We have outlined the next steps in delivering a suite of policy relevant resource efficiency

indicators, followed by a brief discussion of future research needs in relation to understanding

resource productivity.

Producing resource efficiency metrics

This report provides a top-down assessment of the UK’s historical resource requirements.

Going forward, we recommend that the material footprint account should be replicated

annually with progress of key metrics, such as the carbon intensity of materials, monitored.

Our analysis also demonstrates the benefit of understanding the resource use and carbon

intensity of key products to the UK economy. This report provided one example of how a

supplementary ‘product level’ resource efficiency indicator could be developed. Such an

indicator is essential to guide the UK’s industrial strategy and highlight the possible resource

productivity gains to the UK economy. A partnership with relevant industries and consultancies

with specific sector experience is required to further develop such a metric, building on the

approaches identified in this initial report. We recommend that this metric is the subject of a

future work package.

Understanding resource productivity

Future research is required to gain a more complex understanding of the role of resource

productivity in the UK. This requires working closely with BEIS and Defra to align the goals of

the industrial strategy with resource productivity as well as understanding future projections

and policy interventions. This initial report has delivered on a number of the desired primary

project outcomes. However, delivering the secondary and tertiary outputs requires a deeper

understanding of the role of prices in shaping the consumption of materials. This will

necessitate other forms of modelling - requiring econometric expertise - that could be

undertaken as part of an additional project.


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