A STUDY TO SCOPE AND DEVELOP URBAN NATURAL CAPITAL...

A STUDY TO SCOPE AND DEVELOP URBAN

NATURAL CAPITAL ACCOUNTS FOR THE UK

Final Report

For Defra

June 2017

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Scoping and Developing UK Urban Natural Capital Accounts Final Report

eftec i June 2017

This document has been prepared for the Department for Environment, Food and Rural Affairs

(Defra) by:

Economics for the Environment Consultancy Ltd (eftec)

73-75 Mortimer Street

London

W1W 7SQ

www.eftec.co.uk

in association with the Centre for Ecology and Hydrology (CEH), Collingwood Environmental

Planning (CEP), Peter Neal Consulting, the University of Exeter, Countryscape and the Woodland

Trust.

Study team

Phil Cryle (eftec)

Ian Dickie (eftec)

Erin Gianferrara (eftec)

Laurence Jones (CEH)

Dan Morton (CEH)

Clare Twigger-Ross (CEP)

Peter Phillips (CEP)

Matthew White (University of Exeter)

Peter Neal (Peter Neal Consulting)

Laura Partington (Countryscape)

Peer reviewer / Reviewer

Ece Ozdemiroglu (eftec)

Bill Sheate (CEP)

Kieron Doick (Forest Research)

Acknowledgements

The study team would like to thank would like to thank members of the steering group and others

for the time and effort they have contributed to developing this interim report: Colin Smith

(Defra), Rocky Harris (Defra), Thomas Robertson (Defra), Emily Connors (ONS), Hamish Anderson

(ONS), Richard Haw (Forestry Commission) and Damian Crilly (Environment Agency).

Disclaimer

This publication has been prepared for general guidance on matters of interest only, and does not constitute

professional advice. You should not act upon the information contained in this publication without obtaining

specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or

completeness of the information contained in this publication, and, to the extent permitted by law Economics

for the Environment Consultancy Ltd, their members, employees and agents do not accept or assume any

liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act,

in reliance on the information contained in this publication or for any decision based on it.

eftec offsets its carbon emissions through a biodiversity-friendly voluntary offset purchased from

the World Land Trust (http://www.carbonbalanced.org) and only prints on 100% recycled paper.

http://www.eftec.co.uk/

http://www.carbonbalanced.org/


eftec ii June 2017

CONTENTS

LIST OF ABBREVIATIONS AND ACRONYMS................................................ iv

KEY MESSAGES ................................................................................. 5

SUMMARY ....................................................................................... 6

1. INTRODUCTION ......................................................................... 16

1.1. Background ............................................................................................... 16

1.2. Study objectives ......................................................................................... 16

1.3. Report structure ......................................................................................... 17

2. OVERVIEW ............................................................................... 19

2.1. Scope of natural capital accounts .................................................................... 19

2.2. Principles of natural capital accounting ............................................................ 19

2.3. Scope of natural capital benefits..................................................................... 21

3. METHODOLOGY ......................................................................... 25

3.1. The urban boundary (Step 1) .......................................................................... 25

3.2. Evidence and logic chains (Step 2) ................................................................... 27

3.3. Physical account of natural capital extent (Step 3) .............................................. 27

3.4. Physical account of natural capital condition (Step 4) ........................................... 28

3.5. Physical account of ecosystem service provision and use (Step 5) ............................. 28

3.6. Accounting for the supply and use of ecosystem services (Step 6)............................. 36

3.7. Monetary account of annual provision of ecosystem services (Step 7) ........................ 36

3.8. Monetary account of future provision of ecosystem services (Step 8) ......................... 44

4. RESULTS ................................................................................. 46

4.1. The urban boundary .................................................................................... 46

4.2. Physical account of natural capital extent ......................................................... 48

4.3. Physical account of natural capital condition ...................................................... 49

4.4. Physical account of ecosystem service provision .................................................. 56

4.5. Monetary account of annual provision of ecosystem service .................................... 62

4.6. Monetary account of future provision of ecosystem service .................................... 71

5. DISCUSSION .............................................................................. 72

5.1. Scope and Interpretation .............................................................................. 72

5.2. Overlap with other UK natural capital accounts ................................................... 73

6. CONCLUSIONS AND NEXT STEPS ..................................................... 75

6.1. Summary .................................................................................................. 75

6.2. Maintaining ecosystem accounts ..................................................................... 76

6.3. Review of existing urban accounting approaches ................................................. 76

6.4. Future refinement of urban natural capital account ............................................. 78


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REFERENCES ................................................................................. 85

ANNEX 1. ASSESSMENT OF URBAN NATURAL CAPITAL ............................... 96

ANNEX 2. DEFINING THE URBAN BOUNDARY ..........................................103

ANNEX 3. JUSTIFICATION FOR EXCLUDED BENEFITS ................................105

ANNEX 4. SUPPLEMENTARY NOTES .....................................................108

ANNEX 5. SCOPING ACCOUNT FOR URBAN NATURAL CAPITAL IN GREATER

MANCHESTER................................................................................128


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LIST OF ABBREVIATIONS AND ACRONYMS

ANGSt Accessible Natural Greenspace Standard

BUA Built Up Area

CCG Clinical Commissioning Group

CICES Common International Classification of Ecosystem Services

CNCA Corporate Natural Capital Account

CO2e Carbon Dioxide Equivalent

DBH Diameter at Breast Height

EEA European Environment Agency

GIS Geographic Information Systems

IGCB(N) Interdepartmental Group on Costs and Benefits Noise Subject Group (UK)

ISO International Standards Organisation

LCM Land Cover Map

LCZ Local Climate Zones

LIDAR Light Detection and Ranging

MENE Monitor of Engagement with the Natural Environment

MET Metabolic Equivalence of Task

NACE General classification of economic activities in Europe (Nomenclature Generale des Activites Economiques dans les Communautes Europeennes)

NHS National Health Service

NICE National Institute for Health and Care Excellence

OA Output Area

ONS Office for National Statistics

ORVal Outdoor Recreation Valuation Tool

PAF Population Attributable Fractions

QALY Quality Adjusted Life Year

SEEA-CF System of Environmental-Economic Accounting Central Framework

SEEA-EEA SEEA Experimental Ecosystem Accounting

SNA System of National Accounts

VOLY Value of a life Year


eftec 5 June 2017

KEY MESSAGES

This scoping study tested how urban natural capital accounts can be developed in the UK, building

on the principles of natural capital accounting outlined by Defra/ONS (2017). It shows that an initial

urban natural capital account can be constructed for the UK (Table below). The table also presents

the certainty associated with each estimate to signal where future research effort could focus.

Summary physical and monetary flow accounts for urban natural capital and assessment of

certainty

Ecosystem service Scale Physical flow

account RAG

Monetary flow account

(£/year) RAG

Food UK 80,000,000kg/yr. £114m

Global climate regulation UK 494,000 tCO2e/yr. £31m

Air quality

regulation

Total GB 43,000 tonnes/yr -

PM10

GB

-0.065 ug/m3 -

PM2.5 -0.056 ug/m3 £195m

SO2 -0.023 ug/m3 £0.3m

NH3 -0.018 ug/m3 -

NO2 -0.007 ug/m3 £13m

O3 -0.140 ug/m3 £3m

Noise regulation Manc. 429,000 buildings with dBA mitigation

£59m

Climate regulation – local GB -0.42 oC £70m

Physical health from outdoor recreation

UK

2,076,000 ‘Active’ visitors

£900m (total avoided health)

74,000 QALYs £1,482m

RAG Description

Evidence is partial and significant assumptions are made that require further research

Evidence is based on assumptions grounded in science and using published data but with some

uncertainty regarding the combination of assumptions

Evidence is peer reviewed or based on published guidance

This initial UK urban natural capital accounts shows the significant value provided by the UK’s

urban natural capital assets. It also shows how methods can be applied across both national and

local scales, using Manchester as a case study (not shown here). In addition to the ecosystem

services that have been possible to include here, the study provides discussion of how others could

be included in the accounts in future. An accompanying Excel document provides detail on the data

sources, assumptions, method steps and calculations that underpin this analysis. Key

recommendations for future work include:

Outline principles for the appropriate definition of the baseline and the assumptions used in

asset valuation in natural capital accounting generally;

Fill data gaps for for the proposed indicators in the urban natural capital condition account;

Refine the approach taken to estimate the local climate regulating effects of urban natural

capital, and

Assess the ecosystem services from specific green/blue infrastructure using location specific

datasets such as Bluesky National Tree Map


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SUMMARY

This scoping study tests how urban natural capital accounts might be developed in the UK. It takes

into account the unique characteristics of the urban environment and builds on the principles of

natural capital accounting outlined by Defra/ONS (2017). Initial physical and monetary estimates

are produced for a subset of ecosystem services at a scale that is commensurate with a reasonable

level of robustness given the data and evidence available. The structure of the account follows that

set out by SEEA (UN, 2013) and the Defra/ONS principles paper (Defra/ONS, 2017) as used in

existing UK natural capital accounts. It features five accounts: extent, condition, physical flow,

annual monetary flow and monetary flow over time. A set of recommendations for refining and

further developing the initial UK urban natural capital account are also provided.

What is natural capital accounting?

Natural capital accounts show the value of the stock of natural capital on the basis of the flows of

ecosystem services and their economic value. This initial urban account will form part of the suite

of interconnected accounts that are being developed for the Office for National Statistics and Defra

for incorporation to UK Environmental Accounts by 2020. They are two kinds of account (i) physical

accounts compile information on the extent, condition and annual service flow from assets and (ii)

monetary accounts show the economic value of quantified services on an annual basis and based on

the asset’s ability to generate future flows of services.

This scoping study provides initial estimates for the following ecosystem services: physical health,

local climate regulation, noise regulation, air quality regulation, food provision, and global climate

regulation. The resource spent on quantifying and valuing each service is proportionate to the

originality of the analysis and the expected value of benefits produced (e.g. climate regulation is

not expected to be high value and much analysis of carbon sequestration from trees has already

been undertaken). Ecosystem services have been excluded on the basis of low/no provision or lack

of data/methods for analysis.

A natural capital account consists of a series of accounts: extent, condition, generation and use,

physical flow, annual monetary flow and monetary flow into future (asset value). Each of these

accounts has been scoped within this study for urban natural capital, with quantified figures

produced as far as possible within the resources of this project.

Defining the ‘urban’ boundary

The Defra/ONS (2017) principles paper states that the starting point for any classification of

ecosystem types is the Land Cover Map (LCM). However, because this is based on land cover, the

definition of ‘urban’ includes gardens, roads and buildings but excludes most green and blue spaces

which are captured under other categorisations (i.e. grassland, freshwaters). Therefore, defining

the urban area for the purposes of natural capital accounting requires a departure from the use of

the LCM, with subsequent reconciliation (see Table S1) to avoid double counting across UK natural

capital accounts by identifying the extent of overlaps with other broad habitat accounts that have

been developed using the LCM.

The ONS has a number of datasets that could be used as alternatives to define the urban boundary,

albeit they have not been produced for the purpose of accounting for urban natural capital. As with

the LCM, these identify urban areas by the built environment, defining the boundary according to

how built up an area is or by population density. Large natural capital assets in the centre of

towns/cities are therefore outside of the defined urban boundary because they are not densely

populated or built-up areas and/or do not have a neatly confined boundary within the urban area

(e.g. River Thames which is estuarine flows through London). These ONS datasets are not fit-for-


eftec 7 June 2017

purpose within this study but could be ‘enhanced’ to include the assets that are intuitively thought

of as urban natural capital.

The ONS (2011) Built-Up-Areas dataset was selected on the basis that (i) it captures all built-up-

areas and therefore all areas that will not be included in other broad habitat accounts (this is not

the case for Rural Urban Classification 2011 (RUC2011), Major Towns and Cities); (ii) other urban

classifications (e.g. major towns and cities) can be looked at within this dataset and (iii) it is based

on physical settlement morphology and not statistical units (i.e. Output Areas that RUC2011 uses)

which will extend into rural areas. The basic methodology to ‘enhance’ the urban boundary

involved temporarily applying a variable sized buffer to the existing ONS2011 built up area (BUA)

layer. . The buffer is scaled in proportion to the area of the polygon (using the equation Buffer

width = 0.012 * √Polygon area) 1. This effectively ‘captures’ the majority of urban green and blue

space within each urban area. The buffer is then collapsed back to the original extent, but

including any new ‘captured’ green and blue infrastructure.

Accounting for the extent of urban natural capital

Table S1 reports the total ‘urban area’ as defined within this study is 1,765,700 ha. It also shows

the extent of overlaps with other broad habitat types by comparing to the LCM2007 and green

infrastructure features (e.g. street trees) that could be captured in future iterations of the account

using high resolution datasets.

Table S1. Extent of UK urban natural capital within enhanced ONS BUA (2011) urban extent

Indicator - Extent Scale Amount Unit Source

Total urban area UK 1,765,700 Ha Enhanced ONS BUA (2011)

Area of ‘broad’ UKNEA habitatsc

Coastal margins UK 4,000 Ha LCM2007; Enclosed farmland 403,400

Freshwater 9,100

Marine 4,100

Mountains, moors and heaths 11,200

Semi-natural grassland 34,200

Woodland 87,900

(Urban – LCM2007 definition) 1,212,000

Green

infrastructure

features

Park areaa GB 420,400 Ha OS Mastermap

Trees 99,400 Ha

Allotments UK 163,000 Number National Society of Allotment and Leisure Gardeners

Blue infrastructure features

Lakes/Ponds/Riversb GB 22,700

Ha OS Mastermap

a Park area includes enclosed grasslands, arable and horticulture b Rivers includes canals c There is some

discrepancy between the area of broad habitats stated here and the area used for the estimation of air quality

regulating benefits of urban green space because it uses OS Mastermap (GB only): woodland (99,400ha),

grassland (420,400ha), freshwater/saltwater (22,700ha) and urban (1,223,200ha).

1 We apply a buffer scaled relative to an absolute size rather than relative to the largest polygon (i.e. London)

because if the largest polygon expanded and the buffer was expressed relative to this polygon, the size of the

buffer applied to other areas would change, even for polygons experiencing no change in size. This could

potentially lead to inconsistencies in what is included compared with previous accounts. The scaling results in

a buffer of approximately 500m for a polygon the size of Greater London.


eftec 8 June 2017

Figure S1 shows the proportion of different land cover types captured within the defined urban area

(as reported in Table S1). It shows that the majority of land cover captured is ‘urban’ as defined by

the Land Cover Map and a significant chunk is enclosed farmland. Although the other land covers

represent a small amount of the land covered within this initial urban account, they are important

to identify separately from their individual broad habitat account (e.g. urban woodland, urban

areas on coastal margins, urban rivers) because of the potentially enhanced benefit they provide

due to their location near beneficiaries.

Figure S1. The proportion of land cover types captured within the scoping account for urban

natural capital

Accounting for the condition of urban natural capital

A series of potential condition indicators to consider for future iterations of the urban condition

account are identified. Table S2 is a matrix which shows which of the proposed extent and

condition indicators are relevant to which ecosystem services.

This initial account captures evidence on the condition and spatial configuration of urban natural

capital according to the broad dimensions outlined in the Defra/ONS (2017) principles paper. This

includes information on other forms of capital (e.g. specific types of environmental management or

access points) that are important in the delivery of ecosystem services from urban natural capital

assets. The condition account is important because downward trends in condition indicators over

time indicate a degradation of the natural capital stock. This could undermine the sustainability of

flows into the future, especially where ecological thresholds exist. The focus for this study was on

understanding the dimensions of urban natural capital that are important determinants of provision

for different ecosystem services and selecting key indicators and potential data sources for

inclusion in future iterations of the urban condition account (see Table S2).

(Urban – LCM2007 definition)

Coastal margins

Enclosed farmland

Freshwater

Marine

Mountains, moors and heaths

Semi-natural grassland

Woodland


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Table S2. Matrix linking natural capital extent and condition to ecosystem services

Broad

dimension

Indicator Unit Source Food AQ Reg’n

Noise Reg’n

Local climate

Global climate

Pollintn Cult Hertge

Recrtn Phys Health

Flood

Extent See Table S1 Ha See Table S1

Biodiversity Species abundance Bee hives Number National Bee Unit

Total of a species Number/Index RSPB (2016)

Species diversity TBC TBC

Soil Carbon content (clay content) Ha by soil type BGS

Surface permeability Permeable Ha BGS

Impermeable Ha

Ecological condition

River/Lake water quality WFD status Good/Poor etc. EA

Vegetation age

Vegetation height Ha by cm LIDAR

Vegetation width Ha by cm TBC

Vegetation size DBH LIDAR; NTM

Spatial configuration

Extent of private gardens/hedges Ha OSM; LCM

Vegetation near road/rail Ha by dBA OSM; LCM; NTM

Location of blue infrastructure TBC TBC

Contiguous habitats Ha by habitat TBC

Accessible Natural Green Space Standard Population NE (2010) Access

Paths Km OSM; ORVal

Bridleways Km

Car parks and other amenities Number

Management practices

Green flag status parks Number Green Flag Awards

Extent of SSSIs (% status by habitat type) Ha NE; ORVal

NAO quality survey Good/Poor NAO (2005)

Green roofs/green walls Number TBC

Sustainable Urban Drainage Systems Area TBC

http://www.greenflagaward.org.uk/


eftec 10 June 2017

Accounting for the annual ecosystem service flow – physical flow account

This account (Table S3) captures the annual physical quantity (e.g. tonnes, m3, kilogram, number)

of ecosystem service produced by natural capital within the defined UK urban boundary. Note that

the estimated flow for some services is not for the UK scale but for GB only or for a city region

(Manchester is used to align with the Defra Pioneer area) due to data and resource limitations.

Table S3. Physical account: annual ecosystem service flows from UK urban natural capitala

Benefit Coverage Amount Unit Source(s)

Food UK 80,000,000 kg/yr Cook (2006); Perez-Vazquez (2000); UKNEA (2011) Campbell and Campbell (2013); Pretty, 2000; NSALG; Crouch, 2006

Climate regulation – global (carbon)

UK 494,000 tCO2e/yr Forestry Commission, 2014; ONS, 2016

Air quality regulation

Total

GB

43,000 tonnes/yr EMEP4UK

PM10 -0.065 ug/m3

PM2.5 -0.056 ug/m3

SO2 -0.023 ug/m3

NH3 -0.018 ug/m3

NO2 -0.007 ug/m3

O3 -0.140 ug/m3

Noise regulation Manchester 429,000 No. of buildings with dBA reduction

OS MasterMap; Defra, 2014

Climate regulation – local

GB

-0.42 oC Bowler et al. (2010); Larondelle and Haase (2013)


UK 74,000 QALYs/yr Beale et al. (2007); White et al (2016)

UK 2,076,000 ‘Active’ visitor numbers/yr

NE (2015); NICE (2013); DoH, 2004; White et al (2016)

a The analysis of each ecosystem service requires the combination of a range of evidence. Whilst effort has

been made to use the most up-to-date information, it has been necessary to use data from a number of

different years. This means that it is not possible to attribute the estimates to a specific year. This is deemed

suitable to demonstrate proof-of-concept under this scoping study.

The following is a summary of the methods used to estimate the annual quantity of ecosystem

services produced by urban natural capital in the UK and the main caveats associated with them:

– Food: The total number of allotment plots in the UK is estimated to range between 280,000

and 330,000 (Pretty, 2000; the National Allotment Society (NSALG); Crouch, 2006). The higher

figure of 330,000 from the NSALG (the National Allotment Society) and restated by Tomkins

(2006) is used as this is provided by a national association and likely to include allotments

provided by local authorities, parish and town councils, and other providers. Of these 55% are

in urban areas (UKNEA, 2011) and it is calculated that 10% are unoccupied (Crouch, 2006,

proportion of vacant plots in England).

This provides an estimate of 163,350 allotment plots being occupied and in productive use in

urban areas in the UK. Productivity yields per plot from the literature range from 487 kg/year

to 259 kg/year (Cook, 2006; Perez-Vazquez, 2000). The 487 kg/year figure quoted by Cook

(2006) is taken from an RHS study in 1975 which has been considered to be ‘the only known

statistical record of vegetable crop produce harvested from allotment plots’. This provides an

estimated total production of 80,000,000 kg/year.


eftec 11 June 2017

– Global climate regulation (carbon): The estimated amount of carbon sequestered from UK

woodland in 2014 is 15.6MtCO2e (ONS, 2016). The area of woodland in the UK at 31 March 2014

is estimated to be 3.14 million hectares (Forestry Commission, 2014). This suggests an average

rate of sequestration of 5tCO2e/ha/year across the UK. Applying this to the estimated area of

urban woodland within the UK of 99,397ha, results in an estimated carbon sequestration in

2014 of 494,000 tCO2e/year.

– Air quality regulation: Calculation of the physical amount of air quality regulation uses the

EMEP4UK atmospheric chemistry and transport model which generates pollutant concentrations

directly from emissions, and dynamically calculates pollutant transport and deposition, taking

into account meteorology and pollutant interactions. The role of vegetation in removing air

pollutants is assessed using a comparison of two scenarios (i) ‘current urban green and blue

space’ where all non-urban habitats from CEH Landcover 2007 within the defined urban extent

were classified into three broad categories of green/blue space, based on OS MasterMap (urban

woodland, urban grassland and urban fresh/saltwater) (ii) ‘no green/blue space’ derived from

CEH Landcover 2007 represented by replacing all UK vegetation with a neutral ‘bare soil’ cover.

The effect of vegetation is calculated by subtracting the ‘no vegetation’ scenario from the

‘current vegetation’. The tonnes of pollutant removed by urban vegetation are also reported in

this study (although these are not used to calculate the health outcomes or monetary value of

air quality regulation).

– Noise: Estimates of noise regulating benefits provided by UK urban natural capital have been

generated for Greater Manchester as a case study. The method identifies patches of tree cover

greater than a threshold area of 200 m2 (using Bluesky National Tree Map), therefore likely to

be providing a noise mitigation service. It then calculates which urban areas are potentially

mitigated by those trees using spatial noise maps for road noise. The location of beneficiaries in

those zones is then identified using buildings from OS Mastermap. The analysis estimates that

429,000 buildings receive some noise mitigation by urban trees in Greater Manchester.

– Local climate regulation: A GIS based approach has been used to model the temperature

reduction effects of vegetated land cover in the urban area based on assumed temperature

differentials for different classes of vegetated land cover (as informed by the literature) and an

application of those differentials on the basis of the percentage of urban extent comprised of

each category of urban vegetation. For the UK overall, the proportional cooling effect of parks

is -0.23oC and -0.20oC for woodland, the associated combined effect is -0.42oC.

– Physical health associated with active outdoor recreation: This is based on evidence from the

Monitor of Engagement with the Natural Environment (MENE) survey of the number and

frequency of users/visitors to the urban natural environment and the activities undertaken by

these users (i.e. physical activity of different intensities/durations) to estimate ‘active visits’ in

England that meet physical activity guidelines (150 minutes (or more) at adequate intensity.

CMO, 2011) The analysis estimates there to be 2.1million active visitors in in 2015, providing a

Quality Adjusted Life Year estimate of 74,000 QALY’s.

Accounting for the value of annual ecosystem service flows – monetary account

The monetary account (Table S4) captures the annual economic value of the ecosystem services

that have been quantified in the physical flow account. Where possible, ‘exchange values’ that are

observed in markets have been used or ‘imputed exchange values’ (i.e. indirectly measured or

estimated) where markets do not exist. Alternative welfare-based measures that capture consumer

surplus have also been included to provide a range of values for Defra/ONS.


eftec 12 June 2017

The following is a summary of the methods used to estimate the value of annual ecosystem service

flows produced by urban natural capital in the UK (note the different scales at which the estimates

are relevant):

– Food: The value of productivity of allotments from the literature varies from around £350/year

to £1,870/year for a standard plot. This analysis uses a value from Cook (2006) of £560 per

allotment plot (£695 when uprated to £2016). Based on an estimated 163,350 plots the

estimated value of urban allotment productivity is £114million/year;

– Global climate regulation (carbon): The Department of Energy and Climate Change (DECC,

2009) central non-traded price of carbon has been used to value carbon sequestration from

natural capital. This price reflects carbon mitigation and meeting the UK’s short and long-term

greenhouse gas emissions. The estimated value for climate regulating benefits provided by UK

natural capital is £31m/year. This is likely to be an underestimate because it does not include

street trees.

Table S4. Annual value of ecosystem service flows from UK urban natural capitala

Benefit Coverage Amount Unit Type of value Source(s)

Food UK £114m £m/yr Market value Cook (2006); Pretty (2001)


UK £31m £m/yr Cost of carbon mitigation

DECC (2014)


PM2.5 GB £195m £m/yr Welfare value and avoided market costs

Defra (2014)

SO2 £0.3m £m/yr

NO2 £13m £m/yr

O3 £3m £m/yr

Noise regulation Manchester £59m £m/yr Welfare value of dBA reduction

Defra (2014)


GB £70m £m/yr Market values - avoided loss in GVA and avoided air-conditioning cost

Costa et al (2016); ONS (2016)


UK £1,482m £m/yr Welfare value (QALY)

Beale et al. (2007); White et al (2016)

UK £900m £m/yr

Avoided total cost

Public Health England (2015); Bird (2004); DoH (2004)





– Air quality: The health benefits of air quality regulation were calculated from the change in

pollutant exposure from the EMEP4UK scenario comparisons (i.e. the change in pollutant

concentration to which people are exposed). Damage costs per unit exposure were then applied

to the benefitting population at the local authority level for a range of avoided health

outcomes (i) respiratory hospital admissions (ii) cardiovascular hospital admissions (iii) loss of

life years (long-term exposure effects from PM2.5 and NO2) (iv) deaths (short-term exposure

effects from O3).

– Noise: The value of noise regulating benefits provided by urban natural capital in Greater

Manchester is in the order of £59million/year for road noise alone. The approach uses the UK

government economic valuation guidance on noise exposure (decibel reduction) from road (also

includes rail and aviation) to estimate monetary values (Defra, 2014; Nellthorp et al, 2005).

This indicative figure is based on highly conservative estimates of decibel reduction, and a


eftec 13 June 2017

conservative approach to application of the valuation guidance, but other assumptions may

lead to an over-estimate of the figures. Overall, we expect this to be a reasonably robust

order-of-magnitude estimate of the service, but further work could refine the methodology,

and reduce uncertainty.

– Local climate regulation: The net losses (productivity losses and energy costs) avoided due to

the cooling effect of urban vegetation is estimated for the UK at £70,000,000/year, calculated

as (i) £26m/year avoided reduction in productivity (measured as Gross Value Added) over and

above that which can be avoided using air conditioning/behavioural change and (ii) ~£45m/year

avoided energy costs associated with air conditioning to mitigate most (~85%) of the (potential)

GVA losses due to high temperatures.

– Physical health associated with active outdoor recreation: The annual avoided direct and

indirect costs are estimated at approximately £900m/yr (£760m/yr in England; £74m/yr in

Scotland; £26m/yr in Northern Ireland and £43m/yr in Wales). The estimated gain associated

with active visits to urban green spaces based on QALYs is over £1.4bn/yr (£1.2bn in England;

£120 million per year in Scotland; £42 million per year in NI; and £71 million per year in Wales).

The health related value that greenspaces support through physical activity of visitors in terms

of avoided direct medical costs to Clinical Commissioning Groups (CCGs) (for 5 conditions) are

estimated at approximately £34 million per year for England. This value is expected to be a

significant underestimate of costs because they only consider costs associated with five of the

over 20 conditions preventable and manageable by physical activity. While it is not possible to

gauge the proportion of these costs relative to all conditions, they comprise some of the more

serious and costly conditions. It is not reported in the headline results because it will be

captured within the direct and indirect cost estimate above (£900m).

Accounting for monetary value of future ecosystem service flows

This account (Table S5) captures the asset value of urban natural capital as measured by the

present value of the stream of (annual) ecosystem services that the asset(s) will provide over the

100year period selected for the analysis (in line with Defra/ONS principles paper).

A constant flow assumption is assumed for all ecosystem services (i.e. the amount of ecosystem

services produced remains the same over the 100years) except for local climate regulation for

which a projection of the number of days at elevated temperatures in 2080 is estimated using

UKCP09 projections. Prices/monetary values are assumed to remain constant over the 100 year

period (in present value terms for 2016) for all ecosystem services except for global climate

regulation which follows the profile of DECC carbon values which increase over the 100 year period.

It shows the largest values are from physical health benefits of active outdoor recreation with the

avoided healthcare costs valued at £26.8bn and welfare values of £44bn (PV, 100yrs). The value of

local climate regulation becomes the next highest valued service because of the impact of climate

change increasing the avoided losses in productivity (and air conditioning costs).


eftec 14 June 2017

Table S5. Asset value of ecosystem service flows from UK urban natural capitala (PV, 100years)


Food UK £3,386m £m Market value Cook (2006); Pretty (2001)


UK £2,399m £m Cost of carbon mitigation

DECC (2014)


PM2.5 GB £7,168m £m/yr Welfare value and avoided market costs with income uplift

Defra (2014)

SO2 £13m £m/yr

NO2 £304m £m/yr

O3 £234m £m/yr

Noise regulation Manchester £1,741m £m Value of dBA reduction

Defra (2014)


GB

£4,974m £m Market values - avoided loss in GVA



UK £44,169m £m Welfare value (QALY)


UK £26,835m £m

Avoided total cost






Future work to refine and expand the UK natural capital account

This initial UK urban natural capital account shows the significant value provided by assets that are

located within urban areas most significantly for impacts on physical health and air quality

regulating impacts (the full extent of which is still being analysed). The study has shown that

methods can be applied across both national and local scales, with Manchester used as a case study

example. This study has provided proof-of-concept for a range of ecosystem services and the work

undertaken provides a basis for future research to understand and account for UK urban natural

capital. Next steps for the expansion and refinement of the UK urban natural capital account

include consideration of:

The treatment of transboundary effects: natural capital assets that are situation on the edge

of urban areas, outside the urban boundary used in this study, influence on urban residents’

wellbeing and property prices but are not included within this analysis. Consideration should be

given to how such edge effects are treated in natural capital accounts;

Individual green/blue infrastructure features: Assessing the ecosystem services from specific

features requires high resolution data on the assets, the wider environment (e.g. the range of

annual ambient pollution levels and temperatures) and beneficiaries. Such analysis is best

produced at the local level as we have produced for Manchester in this study. Further work

should be undertaken to develop estimates for the majority of UK city areas;

Condition account: Further work is needed on how to report on the condition of urban natural

capital stocks over time (i.e. including the impact of climate change, human impacts etc.),

using Table S1.

Refining urban cooling: because this ecosystem service is so spatially specific, significant

assumptions are needed to achieve an aggregated (Great Britain) estimate and there are

limitations associated with this. It also raises an important issue of magnitude versus

significance, where in this case urban cooling is likely to be significant at a more local rather


eftec 15 June 2017

than national level, i.e. where it happens and where it is felt. An average magnitude in that

context is not very meaningful.

Table S6 shows the physical and monetary value estimated for each ecosystem service and the

certainty associated with each so as to provide a guide as to where future work could be focused to

improve the robustness of estimates. The ratings for the monetary aspects do not relate to the

total value shown (which depend on the physical estimates) but the unit values that have been

applied. Note that these values are not commensurate in scale with some being relevant to the UK,

Great Britain (GB) or Manchester (Manc.).

Table S6. Overview of certainty associated with physical and monetary value estimates


account RAG


(£/year) RAG

Food UK 80,000,000kg/yr. £114m


Air quality

regulation


PM10

GB

-0.065 ug/m3 -

PM2.5 -0.056 ug/m3 £195m

SO2 -0.023 ug/m3 £0.3m

NH3 -0.018 ug/m3 -

NO2 -0.007 ug/m3 £13m

O3 -0.140 ug/m3 £3m


£59m



UK



74,000 QALYs £1,482m

RAG Description






eftec 16 June 2017

1. INTRODUCTION

1.1. Background

This report presents a scoping account for natural capital assets and ecosystems services in the

urban environment in the UK and in Manchester (Annex 5) as much as currently available data and

methods allow. The outputs will contribute to the development of UK natural capital accounts for

the eight UK National Ecosystem Assessment (UKNEA) broad habitat types. It follows from the

Office for National Statistics (ONS, 2012) 2020 Natural Capital Accounting Roadmap, which sets out

proposals to produce experimental national natural capital accounts. These accounts follow the

framework of the ONS UK Environmental Accounts, which are satellite accounts to the main

National Accounts (ONS, 2014). The aim is to capture the benefits of nature in the nation’s balance

sheet in a way that is consistent with the Defra/ONS natural capital accounting principles

(Defra/ONS, 2017) and 2013 System of Environmental-Economic Accounting framework for

Experimental Ecosystem Accounting (SEEA-EEA).

The framework features two main types of account: (i) stock (assets) accounts which capture

information on the natural capital assets (e.g. freshwaters, grasslands) and (ii) flow (ecosystem

services) accounts which report information on the annual benefits produced by the natural capital

assets (e.g. recreation, climate regulation). Both stock and flow accounts are made up of several

accounting schedules that record monetary or physical (non-monetary) benefits, as shown in Figure

1.1. The accounts are developed by collating and analysing financial, economic, social and

environmental data on natural capital across the UK, including via the use of Geographical

Information Systems (GIS).

Figure 1.1. The framework of national natural capital accounting schedules (Defra/ONS, 2017)

Changes in the extent and condition of natural capital assets over time and associated changes in

the level of provision of ecosystem services can be tracked through repeating these accounts

periodically. This can inform strategic priorities and policy objectives for natural capital

management at UK level.

1.2. Study objectives

The purpose of the study was to scope and produce (as far as possible) an outline natural capital

account for the urban “broad habitat” across the UK. This work will enable Defra and ONS to make

further progress in developing a full set of natural capital accounts for the UK in line with the 2020

Natural Capital Accounting Roadmap. The methodological development, outputs and practical


eftec 17 June 2017

experience from this study will also help inform the UK’s continuing contribution to the

development of international standards in natural capital accounting, such as the SEEA-EEA.

The specific objectives for the study are summarised as:

1. Produce a scoping study for urban natural capital accounts in the UK: review existing efforts in

urban areas, define urban extent, identify key services, establish condition characteristics,

assess data and valuation options, conclude on account structure and provide proof-of-concept;

2. Develop a fit for purpose methodology and initial valuation for a minimum of one ecosystem

service (associated with urban areas) for which a methodology has not been adequately or

separately developed in existing work to date under the Roadmap; and

3. Draw conclusions and make recommendations regarding methodological issues, data gaps,

reconciliation with other accounts, potential applications and applicability/replicability of

method to individual urban areas.

Achieving these ambitious objectives requires addressing both conceptual and practical challenges

in the process of estimating and reporting the extent and condition of urban natural capital assets

and the associated ecosystem service flows. Inevitably the present work is subject to gaps in both

scientific understanding of urban ecosystems and the availability of data. Overall the key

contribution of the study is to demonstrate and test the feasibility of developing urban natural

capital accounts, and assess how they could be refined in the future. Because this is a scoping

study, the account presented measures the current value of the urban stock and not changes over

time.

1.3. Report structure

The remainder of this report is structured as follows:

Section 2

Overview: the background to the project and the review of existing approaches and

guidance which informed the urban natural capital account.

Section 3

Method: the steps undertaken to develop the initial urban natural capital account,

including establishing the urban boundary and the approach to quantifying and

valuing natural capital stocks and flows.

Section 4

Results: proof-of-concept physical and the monetary accounts for UK urban areas.

Section 5

Discussion: how to deal with key issues such as baseline definition and

reconciliation with other natural capital accounts.

Section 6 Conclusions and recommendations: the application (purpose) and feasibility of

developing urban natural capital accounts along with acknowledging the current

limitations of data and future refinement of the accounts.

The report is accompanied by supporting annexes:

Annex 1 presents a review of existing assessments of urban natural capital.

Annex 2 reports additional details on how the urban boundary has been defined in this study.

Annex 3 provides justification for the services (benefits) excluded from the initial UK urban

natural capital account.


eftec 18 June 2017

Annex 4 collates supplementary notes on developing physical and monetary accounts for food

and local climate regulation. It also presents logic chains for all the ecosystem services of

interest which may be useful for future iterations of the account.

Annex 5 provides results for Greater Manchester to show how the national accounts approach

can be applied at the scale of a city.

In addition to this document, an accompanying Excel file provides detail on the data sources,

assumptions, method steps and calculations that underpin this initial urban natural capital account.


eftec 19 June 2017

2. OVERVIEW

This section provides an overview of the scope of natural capital accounts as set out in the System

of Environmental-Economic Accounting, the principles of accounting as defined in the Defra/ONS

(2017) principles paper, the scope of benefits included in the initial urban natural capital account

developed in this study and the justification for their selection.

2.1. Scope of natural capital accounts

Natural capital accounting is a systematic approach to reporting on the physical and monetary

value of natural capital assets. It incorporates the elements of environmental accounting covered

by the System of Environmental-Economic Accounting (SEEA). ‘Environmental assets’ are defined as

“the naturally occurring living and non-living components of the Earth, together constituting the

biophysical environment, which may provide benefits to humanity” (SEEA-CF; UN, 2012).

In the SEEA, environmental assets are considered from two perspectives:

1. The Central Framework (SEEA-CF) is an international standard for environmental-economic

accounting that is within the framework of the System of National Accounts (SNA). It applies a

standard asset accounting model (for produced assets) to the measurement of ‘individual

environmental assets’ and expected flow of the benefits as reported in basic resource accounts

(e.g. for timber and fossil fuels);

2. Experimental Ecosystem Accounting (SEEA-EEA) which encompasses the same environmental

assets but “focuses on the interactions between individual environmental assets within

ecosystems, and on the broad set of material and non-material benefits that accrue to the

economy and other human activity from flows of ecosystem services” (SEEA-EEA, UN, 2013).

This includes ecosystems (e.g. woodlands, grassland, and freshwaters) and the ‘ecosystem

service’ benefits produced from these such as timber, fish and flood protection, as well as

abiotic/non-living resources such as fossil fuels and aggregates. For the purposes of this study

the term ‘ecosystem services’ is used as the focus is on this subset of services provided by

natural capital in urban areas.

Defra and ONS (2017) outline the key principles to be followed when developing natural capital

accounts in the UK as part of the ONS Environmental Accounts. Whilst the scope of natural capital

accounts is consistent with the SEEA-CF (i.e. biotic and abiotic resources), the accounting principles

are consistent with the System of Environmental-Economic Accounting Experimental Ecosystem

Accounting (UN, 2012), which takes an ‘ecosystem approach’2. This considers how different

ecosystem characteristics interact through/within an ecological system (stock) to provide a range

of ecosystem services (flows). Overall this perspective is concerned with reporting the state of the

natural environment in terms of the capacity of ecosystems to produce flows of services over time

and ‘ecological dynamics’ including thresholds.

2.2. Principles of natural capital accounting

The links between natural capital assets and their services that are captured in the natural capital

accounts are made through ‘logic chains’. These show the conceptual ‘pathways’ by which natural

capital assets generate benefits for society and in doing so contribute to individual and societal

well-being. The starting point is to identify the ‘natural capital asset characteristics’ that are key

2 SEEA-EEA takes an “ecosystem accounting” approach which is a slightly narrower concept than the SEEA-CF’s

coverage of environmental assets which is consistent with a “natural capital accounting” approach because the

latter also includes abiotic assets (such as minerals and sub-soil assets).


eftec 20 June 2017

determinants of the status and productive capacity of an asset. These characteristics relate to (i)

size (extent) of the assets; (ii) their quality (ecological functioning) and (iii) where the assets are

(spatial configuration), all of which are determinants of the service value.

As an example, Figure 2.1 illustrates the logic chain for the air quality regulation service provided

by urban woodland, from which society benefits in terms of improved health outcomes. As the

figure shows, the value of a natural capital asset (urban vegetation) in terms of its capacity to

absorb pollutants and reduce exposure of people is determined by its extent, type (i.e. species)

and location as well as pollutant concentrations and the location, density of people and their

current levels of morbidity and mortality. Asset values can be appreciated (or depreciated) by

management actions, for example one way to increase the asset value would be to plant trees that

absorb lots of pollution in locations with high pollution concentrations and large beneficiary

populations.

Figure 2.1: Illustrative logic chain for the ecosystem service of air pollution absorption

The logic chains also help identify which indicators and data should be used to populate each link in

the chain. The pragmatic approach to quantification is not one of determining functions and models

but to make the best use of available evidence. The indicators that are considered to be relevant

will depend on the resolution (aggregated vs disaggregated) and scale (national vs local) of

accounting and the potential use of the account for decision-making. Following the example of air

quality regulation services from woodland, the selection of relevant indicators will vary depending

on if the accounts are:

To input to policy/investment decisions about where to optimise the benefits of natural capital

nationally and/or where spatially explicit (disaggregated) accounts are being developed. Here

it is relevant to note the distribution of factors like wind speed, soil type, solar exposure,

precipitation because these influence the distribution of air quality regulating capacity from

existing or potential woodland (for example) across the UK;

Where aggregated accounts are produced at the national level, reporting such factors (e.g.

average wind speed across all UK woodland locations) is essentially meaningless;

For local level accounts/decision-making (i.e. site level), these ecosystem characteristics are

fixed (i.e. they cannot be influenced through management) and so are less relevant to account


eftec 21 June 2017

for, instead the account should focus on reporting indicators such as tree species, kilometres of

paths for recreational value, which can be influenced through local level management.

The Defra/ONS (2017) principles paper sets out five types of accounts which are compatible with

the logic chain relationship:

1. Physical account of natural capital extent (stock account): reporting data on the extent (size)

of natural capital assets within the defined boundary;

2. Physical account of natural capital condition (stock account): reporting bio-physical data on

the key characteristics of the condition of natural capital assets;

3. Physical account of ecosystem service provision and use (flow account): reporting data on

the physical flow of ecosystem services linking natural capital assets to economic and other

human activity. Where possible the (spatial) area within which the ecosystem services are

generated and the areas in which ecosystem services are used should be distinguished.

4. Monetary account of annual provision of ecosystem service (flow account): reporting the

economic values of annual ecosystem services produced; and

5. Monetary account of future provision of ecosystem service (stock account): deriving

economic values for the assets by aggregating forecasted future flows (in this case 100 years).

Tracking these accounts over time enables assessments of changes in the extent and condition of

ecosystem assets, along with associated changes in the level of provision of ecosystem services. An

account typically reports the opening and closing value of a stock of natural capital assets as well

as the reconciliation of these stocks by recording intervening (net) changes to assets over the

accounting period. Because this is a scoping study, the initial urban account is concerned with

measuring the current value of the urban stock and not changes over time.

2.3. Scope of natural capital benefits

The benefits in the scope of natural capital accounts are those ecosystem services: (i) with a higher

level/value of provision in the UK urban area and (ii) for which data and evidence for quantification

and valuation exists, as outlined in Table 2.1. The services that are not provided (or provided at

very low levels) in the UK or for which there is no evidence (e.g. disease regulation) are excluded.

The justification for the benefits included in this study, and the priority placed on analysing them

for the account, is explained below for the excluded benefits in Annex 3.

Lower priority benefits

These benefits are included in the initial urban natural capital accounts but their expected value is

relatively low. This means that a more approximate ‘order-of-magnitude’ approach to estimating

the quantity and value of these benefits is taken (i.e. a proportionate approach).

Food

This includes the production of food from urban locations that is currently not recorded in UK

accounts, potentially including allotments, gardens, community gardens, orchards and parks.


eftec 22 June 2017


Carbon sequestration by urban natural capital is expected to be of low value given the spatial scale

and volume of natural capital (i.e. the amount of biomass and soil) in urban areas relative to other

areas.

Table 2.1. Scope of UK Urban Natural Capital Accounta

a Note the ∆ have either been included in other accounts or new studies will come to provide new data so for

now they are excluded from the initial urban account.

Higher priority benefits


Poor air quality is estimated to result in 40,000 (+/-25%) equivalent attributable deaths in the UK

every year (Royal College of Physicians, 2016) and is a major cause of morbidity. It also impacts

negatively on the status of habitats and species. Although some atmospheric pollutants have

reduced in concentration (such as sulphur dioxide and nitrogen dioxide) over recent years,

particulate matter (PM10 and PM2.5) remains a considerable concern, and ammonia (NH3)

concentrations are projected to rise. Furthermore, climate change is expected to exacerbate other

atmospheric pollutants such as ozone (UKNEA 2011), which is typically greater outside of major

metropolitan areas (Freer-Smith et al. 2007) because high levels of pollutants such as nitric oxide

(NO) in cities actually reduce ozone levels due to photo-chemical oxidation.

The total value of the PM10 absorbed by vegetation in 2012 in the UK was estimated to be around

£4.5 billion, while the value for SO2 was £5.2 million based on Defra (2015a) avoided damage cost

estimates which are dominated by chronic mortality health impacts (Defra 2015a; AECOM 2015).

The methodology for calculating this service is currently under revision by another study for Defra

and ONS, with updated estimates expected in summer 2017. The value of this service may increase

or decrease in the future. The value (benefit) of the service will increase with increased vegetation

as more pollution is captured, increases in population and hence number of people exposed to

pollution; and with higher temperatures which are likely to exacerbate emissions of some

pollutants such as NH3. The value will decrease if emissions controls reduce the pollution load that

is then absorbed by vegetation.


eftec 23 June 2017

Noise regulation

According to the World Health Organisation, environmental noise is the second largest

environmental health risk in Western Europe (WHO, 2011). Evidence suggests that noise is

associated with hypertension (Barregard et al. 2009; Jarup et al. 2008), impaired cognitive

development in children (Stansfield and Matheson 2003) and psychological stress (Evans et al. 1995,

2001). The annual cost of road traffic noise in England is estimated at £7 billion to £10 billion

(Defra, 2014). This places it at a similar magnitude of value to road accidents (£9 billion). Noise is

complex, and there are aspects of perception (i.e. it can be determined to some extent by an

individual’s subjective feeling), and masking of unpleasant noise with other sounds. But these are

not possible to account for and the main effects are well-proven.

The scope of the urban natural capital accounts is determined by noise dissipation which depends

in part on the presence and height of vegetation in relation to the height of the noise source3. For

this reason, aircraft noise is scoped out because natural capital has limited capacity to reduce this.

Construction noise is not (usually) consistently located in the same place over time and therefore

there is limited scope to manage vegetation in order to provide a service to reduce it. Therefore,

the scoping accounts include the benefits of vegetation reducing urban road and rail noise

exposure. We developed a method that is applicable to both road and rail, but we only

demonstrate the approach for road noise in Greater Manchester.


This service focuses on the role urban ecosystems play in modifying temperature and providing an

urban cooling effect through evapo-transpiration, shading and lower radiative temperatures as

defined by CICES4. The evidence in the second UK Climate Change Risk Assessment (CCC, 2016)

included several heat related risks that urban natural capital (via micro climate regulation services)

can help mitigate, including:

Temperature mortality - the number of heat-related deaths in the UK are projected to

increase by around 250% by the 2050s (median estimate), due to climate change, population

growth and ageing, from a current annual baseline of around 2,000 heat-related deaths per

year.

Loss of staff hours - past events suggest extreme outdoor temperatures can have significant

effects on productivity. The 2003 European heatwave is estimated to have resulted in a

reduction in manufacturing output in the UK of £400 to £500 million. Another analysis covering

all economic sectors in London alone predicted productivity losses for the 2080s of €1.9bn

(2003 prices) (Costa et al., 2016);


The ONS recently commissioned a study to develop an approach for valuing cultural ecosystem

services, focusing on recreational trips to the outdoor environment, for inclusion within the UK’s

ecosystem accounts (Ricardo, 2016). The scoping account for urban natural capital does not

reproduce this work but extends the scope by focusing on the physical health benefits (in terms of

avoided medical costs) arising from active outdoor recreation. Indirect (or passive) engagement

3 For example, while some studies suggest that hedges have little effect, hedges higher than 1.5 meters can

act as a noise barrier if they come between the noise and the receiving human population (Fang & Ling 2003). 4 CICES (Common International Classification of Ecosystem Services) is a classification of ecosystem services

developed from the work on environmental accounting undertaken by the European Environment Agency (EEA)

that is based on the well-established split into provisioning, regulating and cultural services.


eftec 24 June 2017

with the natural environment (e.g. nice views of a park) is scoped out because of overlaps with

the aesthetic valuation that the ongoing ONS hedonic-pricing work aims to capture.

There is evidence linking the following of recommended physical activity guidelines5 to various

health benefits and the benefit of urban green spaces in enabling and encouraging physical

activity. Access to local, safe and natural green space can help motivate individuals to exercise,

evidenced by studies showing that people living in close proximity to green space have a higher

propensity to exercise (eftec and CRESR, 2013; Jones et al. 2009; Nielsen and Hansen, 2007;

Pretty et al. 2003). There is also evidence that individuals exercising in the natural environment

are more likely to sustain physical activity for longer and at a higher intensity (Bird, 2004).

It is important to be specific about what contributes to human health as several ecosystem services

from urban natural capital can provide benefits: provisioning services (e.g. allotment grown food),

regulatory services (e.g. improved air quality), and cultural services (e.g. relaxation felt while

walking with a friend along a river bank).

5 As outlined by the Chief Medical Office, at least 150 mins per week of moderate intensity activity in bouts of

10 minutes or more. For example, one way to do is to exercise 30 minutes at least 5 days a week (CMO, 2011).

Scoping and Developing UK Urban Natural Capital Accounts Final report

eftec 25 June 2017

3. METHODOLOGY

The urban natural capital accounts are scoped following an eight step methodology:

Step 1: The urban boundary

Step 2: Evidence and logic chains

Step 3: Physical account of natural capital extent

Step 4: Physical account of natural capital condition

Step 5: Physical account of ecosystem service provision and use

Step 6: Accounting for the supply and use of ecosystem services

Step 7: Monetary account of annual provision of ecosystem services

Step 8: Monetary account of future provision of ecosystem services

This chapter describes this method used to quantify and value the ecosystem services from urban

environments in the UK (including both national and local applications. See Annex 5 for a scoping

account for Manchester). The description in this section is more detailed than it is usual to include

in the main body of a report but it is provided so as to ensure greater transparency in the methods

adopted. An accompanying Excel document outlines the data sources, assumptions, method steps

and calculations that underpin this analysis. Supplementary notes on the selected benefits are

provided in Annex 4.

3.1. The urban boundary (Step 1)

Review of boundary options

The Defra/ONS (2017) principles paper states that the starting point for any classification of

ecosystem types is the Land Cover Map (LCM). However, the LCM definition of ‘urban’ includes

gardens, roads and buildings, but excludes most green and blue spaces which are captured under

other broad habitat categorisations (i.e. grassland in public parks, freshwater rivers). Parks,

gardens, trees, rivers and canals are a key natural capital assets and a significant part of the urban

fabric with importance for public and private organisations. Such assets should therefore be

considered ‘urban’ to develop urban natural capital accounts.

Therefore, defining the urban area for the purposes of natural capital accounting requires a

departure from the use of the LCM, with subsequent reconciliation to avoid double counting across

UK natural capital accounts by identifying the extent of overlaps with other broad habitat accounts

that have been developed using the LCM. As a result a new dataset is needed, and has been

developed under this study, that aligns with what we would consider to be the ‘urban fabric’ and

has the following properties:

Intuitive: the boundary of the area must fit with what is typically considered to be an ‘urban’

location and align with the spatial remit of decision makers at local and national levels

interested in urban vegetation and blue infrastructure;

Flexible: the definition must cover the entire ‘urban fabric’ so that other ecosystem types

(such as freshwater, grasslands or woodlands in urban areas) can be identified and included/

excluded as required in the analysis;

Reflective of changing land use: the area of urban natural capital must be defined by a rule

that consistently reflects ‘urban land use’ over time as the ‘urban’ area changes (e.g. through

built development). Administrative or political boundaries are not determined by what is

considered to be ‘urban land use’ and therefore should not be used;


eftec 26 June 2017

Evidenced: there must be spatial data on the UK urban boundary for use in Geographical

Information Systems (GIS) so that analysis can be undertaken; and

UK-wide: the accounts are to be developed across the UK and so a boundary definition that

could be applied across the UK is needed.

The ONS has a number of datasets that could be used as alternatives to define the urban boundary

based on the above requirements (pers. comm. Bill South, ONS, 2017), albeit they have not been

produced for the purpose of accounting for urban natural capital. They are Rural-Urban

Classification (RUC2011), Major Towns and Cities (2015) and the Built-up-Areas (2011) datasets; for

an explanation of each see Annex 2. As with the LCM, these datasets identify urban areas by the

built environment, defining the boundary according to how built up an area is or by population

density (i.e. Output Area6). Large natural capital assets in the centre of towns/cities (e.g. urban

parks) are therefore outside of the defined urban boundary because they are not densely populated

or built-up areas and/or do not have a neatly confined boundary within the urban area (e.g. the

River Thames which is estuarine where it flows through London). These ONS datasets were

considered as being potentially suitable for this study, but would need to be ‘enhanced’ by

applying mapping rules in GIS to include the assets that are intuitively thought of as urban natural

capital.

Based on the three options (RUC2011, Major Towns and Cities and the Built-up-Areas dataset) the

Built-up-Areas (BUA) was deemed (subject to ‘fixing’) to be the most appropriate to use as an

urban boundary because:

It captures all built-up-areas and therefore all areas that will not be included in other broad

habitat accounts (this is not the case for RUC2011, Major Towns and Cities);

Other urban classifications (e.g. major towns and cities) can be looked at within this dataset;

and

It is based on physical settlement morphology and not statistical units (i.e. Output Areas that

RUC2011 uses) which will extend into rural areas.

The BUA dataset covers England and Wales. For Scotland and Northern Ireland, it is possible to use

‘settlement’ layers, which are broadly equivalent to the ONS BUA layer.

‘Fixing’ the urban boundary

The basic methodology to ‘enhance’ the urban boundary involves applying a variable sized buffer to

each polygon in the existing ONS2011 built up area (BUA) layer (and equivalent Scottish and

Northern Ireland layers), re-drawing each polygon to account for overlaps, then shrinking back the

new boundary by the same buffer width. The buffer draws in any areas enclosed by the buffer (i.e.

if the outer edges meet then it captures the entire area). This includes patches of land (e.g. large

parks in central London) that are mostly surrounded by urban built-up-areas.

Initially, two sizes of buffer were trialled: one capped at a maximum of 250m, the other to 500m

around each polygon area in the BUA. The final approach taken used a variable buffer which was a

function of the size of the polygon (using the equation Buffer width = 0.012 * √Polygon area). The

calculation is scaled to give a buffer of approximately 500m for a polygon the size of Greater

6 Output areas (OA) were created for Census data by ONS, specifically for the output of census estimates. OAs

are required to have a specified minimum size to ensure the confidentiality of data and come in varying sizes

based on minimum numbers of resident households and resident people.


eftec 27 June 2017

London (173,785 ha)7. This proportional approach has been applied so that small built-up-areas do

not have a large buffer applied to them. Such a calculation and scaling rule is efficient in

automating the calculation, and can be applied consistently in future iterations of the account.

Table 3.1 shows example polygon areas and the corresponding buffer widths that have been used to

calculate the urban extent in this project, ranging from Eardington (a small (20ha) village and civil

parish in Shropshire, England) having a 5 metre buffer to London (173,785 ha) having a 500metre

buffer.

Table 3.1. Example polygons from ONS BUA layer, and corresponding buffer widths used to

calculate the urban extent for natural capital accounting

ONS Built-Up-Area Area (ha) Buffer width (metres)

Eardington 20 5

Leicester 10,937 125

Bournemouth/Poole 13,099 137

Bristol 14,443 144

Sheffield 16,748 155

Nottingham 17,636 159

Tyneside 18,052 161

South Hampshire 19,203 166

Liverpool 19,961 170

West Yorkshire 48,779 265

West Midlands 59,888 294

Greater Manchester 63,025 301

Greater London 173,785 500

3.2. Evidence and logic chains (Step 2)

A full UK urban account would attempt to compile evidence on the extent and condition of all

relevant ecosystem services using logic chains as a basis (see Annex 4 for logic chains for the

services assessed in this study). Whilst indicators are proposed for the full range of urban ecosystem

services (based on the concept of a logic chain), the scoping account only populates indicators in

the extent and condition account where this is required for the quantification and monetisation of

the ecosystem services included in the account. Further work is therefore needed on how to report

on the condition of urban natural capital stocks.

3.3. Physical account of natural capital extent (Step 3)

‘Natural capital extent’ refers to the quantity of natural capital assets within the urban

environment. The typical indicators are hectares of land cover (e.g. park, pond), km for linear

features (e.g. rivers) and number of small features (e.g. street trees). The following are the

proposed indicators to use in the physical account of the extent of UK urban natural capital:

Total urban area: the quantity of natural capital assets within the urban environment, as per

the urban boundary definition;

Area of broad UKNEA habitats: the quantity of area that falls within each habitat type to

distinguish characteristics of land that link to all environmental goods and services;

7 We calculate the buffer as a proportion of the polygon size to allow for changes in urban extent over time.

For example, where an urban polygon expands with time so will the buffer and the urban boundary might then

include additional patches of natural capital not previously adjacent to that urban extent.


eftec 28 June 2017

Area of green infrastructure features: high resolution information on assets (e.g. street trees)

that cannot be distinguished in LCM2007. Data for this scoping account came from OS

Mastermap and Bluesky’s National Tree Map (for the Manchester area), provided by the

Woodland Trust.

3.4. Physical account of natural capital condition (Step 4)

The condition account plays a critical role in representing the role of natural capital in providing

benefits, not all of which could be included in the physical or monetary account. This is particularly

significant for biodiversity, which is, at best, only partially captured in monetary terms. The broad

dimensions of natural capital condition from the Defra/ONS (2017) principles paper align to the

following split:

The state of the natural capital asset: as measured through relevant volume estimates (e.g.

timber biomass), biodiversity indicators (e.g. abundance), soil indicators (e.g. carbon content),

ecological condition indicators (e.g. water quality) and spatial configuration (e.g. connectivity).

The status of these broad indicators (and the trends over time) has implications for the urban

environment’s capacity to sustain the provision of ecosystem services into the future, as well

its capacity to be restored or to deliver a different suite of ecosystem services under different

future land use/management choices;

Other forms of capital: as measured through access (e.g. proximity of open access areas to

population) and management practices (e.g. conservation designations). In many cases the

delivery of environmental goods and services from natural capital relies upon contributions

from other forms of capital (e.g. km of bicycle routes for outdoor recreation services).

Therefore, the status of these other capital assets (which can be measured through appropriate

indicators) has implications for the urban environment’s capacity to sustain the provision of

ecosystem services into the future.

The ability of an account to record such information is both data dependent and subject to

scientific understanding of the links between condition (of natural and other capital) and service

provision. This also affects the selection of natural capital asset characteristics that should be

included in logic chains, and hence in a stock account.

For this proof-of-concept study the project team have focused on populating indicators in the

condition account only where this is required for the quantification and monetisation of ecosystem

services that are within the scope. The findings from the scoping of an urban condition account are

reported in section 4.3 and should form the basis for a future development of this account.

3.5. Physical account of ecosystem service provision and use (Step 5)

The physical flow account captures the physical quantity of ecosystem service produced by natural

capital within the defined UK urban boundary. Below is an overview of services included in the

scope in this scoping phase, as listed in Section 2 with further detail for food and local climate

regulation provided in Annex 4.

Food (kg per year from urban allotments)

Yields from urban allotments (kg/year) are estimated based on a review of literature on the

number and size of occupied allotment plots in urban areas in the UK, and their productivity per

m2. Key sources include:

Cook (2006) Study of allotments and small land plots: benchmarking for vegetable food crop

production. PhD Thesis;


eftec 29 June 2017

Perez-Vazquez (2000) The future role of allotments in food production as a component of urban

agriculture in England;

UKNEA (2011) Urban Chapter: Allotments, Community Gardens, and Urban Farms;

Campbell and Campbell (2013) Allotment waiting lists in England 2013;

The National Society of Allotment and Leisure Gardeners8; and

Crouch (2006) Allotments in England. Report of survey 2006.

Climate regulation – global (carbon) (tonnes of carbon dioxide sequestered by urban woodland)

In the context of urban natural capital accounts, urban ecosystems can be considered as a managed

landscape, thus the carbon stock and the change in this stock can be reported as the flow of net

carbon sequestration or emissions. Although data exists on the carbon stocks within urban areas,

there is no time series data for above and below ground biomass. This means that we cannot

estimate the carbon sequestration from urban natural capital based on changes in stocks over time.

Instead, the proposed approach is to use the ONS (2016) estimates of carbon sequestration across

the UK woodland area and assume a proportional approach based on the estimated area of

woodland within UK urban areas. Therefore, this is a crude approach based on average tree size

and carbon sequestration factors. Differences between urban and other woodlands could render the

approach inaccurate. For example, if risks to buildings from tall trees mean that the average size of

urban trees is smaller than the UK average this may lead to an over-estimate. Differences in the

species composition of urban trees compared with the national picture may also lead to uncertainty

in the estimates. The benefits are understated also because non-woodland trees in urban areas and

street trees are not picked up in the Forestry Commission approach and hence are not included in

the urban natural capital account.

Air quality (tonnes of SO2, NO2, PM2.5, O3 pollutant captured by urban vegetation)

Calculation of the physical flow account uses the EMEP4UK atmospheric chemistry and transport

model which generates pollutant concentrations directly from emissions, and dynamically

calculates pollutant transport and deposition, taking into account meteorology and pollutant

interactions. This differs from the previous approaches used to estimate air quality regulating

benefits of vegetation in the UK which used a static methodology where pollutants were considered

in isolation, incorporating only limited effects of meteorology, and where effects of pollutant

transport in the atmosphere as well as the feedback of the deposition on air concentration were

not considered.

The role of vegetation in removing air pollutants is assessed using a comparison of two scenarios (i)

‘current urban green and blue space’ where all non-urban habitats from CEH Landcover 2007 within

the defined urban extent were classified into three broad categories of green/blue space, based on

OS MasterMap (urban woodland, urban grassland and urban fresh/saltwater) (ii) ‘no green/blue

space’ derived from CEH Landcover 2007 represented by replacing all UK vegetation with a neutral

‘bare soil’ cover. The effect of vegetation is calculated by subtracting the ‘no vegetation’ scenario

from the ‘current vegetation’. The tonnes of pollutant removed by urban vegetation are also

reported in this study (although these are not used to calculate the health outcomes or monetary

value of air quality regulation). Although not used to calculate the health outcomes, the tonnes of

pollutant removed by vegetation are also reported in this study.

8 http://www.nsalg.org.uk/

http://www.nsalg.org.uk/


eftec 30 June 2017

Noise (decibel reduction over geographic area)

Noise regulation by vegetation is a function of (i) the width of the vegetation (greatest benefit is

delivered by vegetation greater than (15-) 25 m wide9 (HOSANNA “Holistic and Sustainable

Abatement of Noise by optimized combinations of Natural and Artificial means” report; EC, 2013)),

(ii) the height and structure of the vegetation and its location in relation to sources of noise and

benefitting population. Wide belts of trees can reduce noise levels by 7 dB(A); hedges and street

trees can deliver a relatively small noise reduction of 1-2 dB(A) (HOSANNA report).

The main challenge is in making the most of datasets that have national coverage to identify and

characterise vegetation in sufficient detail to quantify its potential to mitigate noise. For this

scoping study, the approach developed is tested on Manchester as a case study.

Noise data, at 10m x 10m resolution, has been produced as a one-off study for Defra for major

roads and rail links and for major urban conurbations in England (Defra 2014). Similar data should

be available for Wales, Scotland and Northern Ireland but this has not been sourced for this project

given the focus on Manchester. For this scoping study we used Bluesky’s National Tree Map

(Bluesky, 2017), working in collaboration with The Woodland Trust. The National Tree Map shows

all tree canopies greater than 3 metres in height, in England and Wales, but does not show other

vegetation features such as hedges. We applied this analysis within our demonstration area of

Greater Manchester. The approach could be applied at UK level.

The relevant physical indicator to quantify noise is the excess attenuation of noise by vegetation

(which accounts for natural noise attenuation over open ground due to friction in air and spherical

divergence (Gomez-Baggathun et al. 2013)) across the frequency range of human hearing (ca 0.02 -

20 kHz) and the unit is A-weighted decibels (dBA) (which adjusts decibels for human perceptions of

different frequencies). Table 3.2 shows the data sources that have been identified as being

potentially useful in quantifying noise regulating service of vegetation within the UK urban

environment.

Table 3.2: UK urban noise regulation

Indicator Unit Source Year Coverage Use?

Vegetation location

- OS Mastermap; CEH Landcover; Bluesky tree canopy.

- UK/GB All these datasets are available, but only Bluesky National Tree Map is sufficiently fine resolution and consistent enough to identify candidate urban vegetation providing a service, at the moment

Vegetation height

m LIDAR - UK/GB Vertical and spatial resolutions are sufficient for an urban application in some areas, but not widely available for the UK yet.

Vegetation width

m OS Mastermap; CEH Landcover; Bluesky tree canopy.

- UK Could be derived from some datasets, but not applied in this case study.

(Spatial) population data

TBC ONS; OS Mastermap - UK/GB Required to identify the location and density of the beneficiary population. Or, use OS Mastermap to identify buildings as a proxy for spatial location of population at finer resolution.

Spatial noise data

dB(A) Defra (2014) Noise exposure data

2011 England, (Wales, Scotland, NI also available)

Yes, obtained from Noise mapping by Defra for major Road and Rail routes affecting urban agglomerations

9 15 m wide vegetation strip can reduce noise by 5-6 dB(A), if designed specifically to optimise this service.


eftec 31 June 2017

The following steps were taken to estimate the noise regulating benefit of vegetation in Greater

Manchester as a test for other UK urban areas using the data from Table 3.2:

Vegetation providing a noise regulating service was identified from the Bluesky National Tree

Map by merging adjacent tree canopies, and filtering to only include canopy patches greater

than 200 m2. This was done to establish patches of a size deemed large enough to be providing

a service.

The resulting tree layer was overlaid on the noise data in GIS to identify the areas where noise

levels were potentially mitigated by vegetation (Figure 3.1).

The location and extent of beneficiaries focuses on buildings (identified from OS Master Map)

within the areas mitigated by vegetation, making the assumption that buildings identified from

OS Master Map are primarily dwellings or places of work, and that all are occupied.

Buildings lying within these mitigated zones were identified, allocated to the noise levels to

which they were exposed, in bands of 5 dB(A).

A highly conservative assumption of a 2 dB(A) reduction in noise levels was applied to all

buildings occurring in these mitigated areas This simple approach was taken due to uncertainty

in the methodology, and a lack of knowledge of the precise characteristics of the vegetation at

any location. This effect may attenuate further away from the source. However, the literature

does not provide information on whether attenuation of the benefit occurs, or any

relationships that would be required to model this sort of linear decay.

Figure 3.1 below is a map showing the noise levels from roads (red=high to yellow=low), patches of

tree canopy greater than 200m2 providing a service, areas where noise levels are calculated to be

partially mitigated by those trees (grey shading). Figure 3.2 shows aerial imagery of the same

extent.

Figure 3.1. Heat map showing road noise (yellow – low; red - high) regulating effect of trees

greater than 200 m2 canopy (green) and areas where noise level is reduced due to tree

presence (grey)


eftec 32 June 2017

Figure 3.2. Aerial imagery of the location analysed in Figure 3.1

Climate regulation – local (°C cooling effect due to urban vegetation)

The temperature experienced in urban areas is (in part) a function of the urban natural capital

present (e.g. in terms of extent, type, quality, spatial configuration etc.). The key determinant of

the so called “urban heat island effect”, and therefore of vegetation capacity to offer ‘cooling’, is

the different thermal (specific) heat capacity of the built environment compared to green and blue

space. Green and blue spaces have lower thermal heat capacities, which means that (i) much more

energy is needed to heat these spaces (and raise temperature by 1°C) compared to buildings and

(ii) green and blue spaces cool more quickly whereas buildings act as thermal stores holding heat

well through the night.

The urban heat island phenomenon is relevant to daily temperatures, long-term trends in

temperatures (climate) and the temperatures experienced in extreme events (heatwaves). While

there may be an ‘average’ cooling effect associated with vegetation across the whole urban area

this may not be nearly as significant as the much larger cooling effect at the local level, within and

adjacent to a park/green space for people immediately local to that area, and/or as a cool place

where people can go during heatwaves / heat events.

Street trees may well operate differently in providing shading benefits to local properties (and

therefore less direct heat), rather than mitigating the heat capacity of the local built environment.

Again the effects may be quite significant at a local level – a double sided street lined with mature

street trees is likely to benefit from considerable shade during hot weather, allowing for greater

comfort during the day and at night since the buildings won’t have heated as much as they would

otherwise.

The purpose of this scoping study is to estimate aggregate average cooling effects across all urban

areas for a UK national account. However this service is extremely spatially dependent and so even

if the aggregate effect of cooling by parks across all UK urban areas (or even at a city level) is not

estimated to be particularly large, its benefits will be felt disproportionately at very local levels.


eftec 33 June 2017

Therefore, the aggregate estimates in the scoping account are necessarily a simplification of what

is actually spatially varied.

The empirical concepts that underpin the approach used in this scoping account are outlined below

with further information in Annex 4:

Studies show that urban vegetation (e.g. parks) has a cooling effect (Turban – Tgreen in oC)

whereby vegetated sites are cooler than the surrounding area or other non-vegetated locations

in the same town / city as measured on the same day (Bowler et al., 2010);

Ambient temperatures (surface and air) at the city-wide level will be influenced by urban

vegetation (see Annex 4);

There is no conclusive evidence that climate (e.g. ambient temperature) influences the cooling

effect provided by urban vegetation (Bowler et al., 2010); and

The area of vegetated land cover (patch size) may influence the cooling effect provided by

urban vegetation (Barradas, 1991; Chang et al., 2007; Bowler et al., 2010; Doick and Hutchings,

2013) though the evidence is inconclusive.

Based on these concepts, the following method steps were used with data for England, Wales and

Scotland to estimate the urban cooling effects of vegetation10:

i) Define the extent of urban vegetation where there is evidence (empirical and / or

theoretical) of cooling effect of parks and urban woodlands11;

ii) Apply a buffer of 100 meters around “parks” and “urban woodland” larger than (>) 3ha12.

Evidence suggests that parks exert a cooling effect on the surrounding area of -0.52oC

(Bowler et al., 2010; Larondelle and Haase, 2013). Although it was not possible to identify

any explicit evidence of this effect for urban woodlands, we have assumed that it applies

(indeed we might expect this edge effect to be greater for forests due to their physical

characteristics – e.g. more consistent shading);

iii) Apply a patch size threshold for sensitivity analysis. While inconclusive, there is some

evidence suggesting a positive correlation between vegetated patch size (>3ha) and cooling

effect. This correlation is not linear (Vaz Monteiro et al, 2016) and it is not possible to

define a clear threshold (Chang et al., 2007; Bowler et al., 2010). Therefore, the following

thresholds (either side of which there are differential cooling effects) are tested (a) All

patches; (b) Patches >3ha only; (c) Patches >0.5ha; and (d) Patches <0.5ha;

iv) Sum the patch size for each category of urban natural capital assessed, with and without

patch size threshold(s). Sum the values for the area of 100m buffer around patches >3ha13;

10 Data is only available for “urban park” and “urban wood” in England, Scotland and Wales and no data for

Northern Ireland. 11 Although there is empirical evidence of the cooling effect of street trees it was not possible to assess this

category of urban vegetation within this scoping study because: (1) there were issues sourcing data on street

trees; and (2) the processing required to convert a raw street tree dataset to a polygon (e.g. based on canopy

extent) for assessment purposes in the physical account would have been too onerous for this scoping project. 12 In our physical account analysis undertaken in this pilot project, the cooling effect of the buffer was only

calculated for patches >3ha. Further work could consider the cooling effect of the buffer for different patch

sizes, for sensitivity analysis. 13 This indicator relates to just the area encompassed by the buffer (i.e. not patch + buffer).


eftec 34 June 2017

v) Calculate the percentage of urban extent comprised of each category of urban vegetation

assessed, with and without patch size threshold(s) and for total area of 100m buffer around

patches >3ha14;

vi) Calculate the proportional impact on city-level temperatures from the urban cooling effect

of each category of urban natural capital assessed, with and without patch size threshold(s)

using the temperature differentials (Turban – Tgreen in oC) below:

Parks: -0.95oC (Bowler et al., 2010: Larondelle and Haase, 2013)

Parks buffer: -0.52oC (ibid)

Urban woodlands: -3.5oC (ibid)

Urban woodlands buffer: -0.52 oC15

Worked example: for an urban area comprised of 25% “parks”, the cooling effect of these parks is

assumed to be 25% of the full cooling effect value for parks; i.e. 25% of -0.95oC or -0.24oC.

vii) Sum the city-level proportional urban cooling effects, per category of urban natural capital

assessed, from the step above, with and without patch size threshold(s). The summed

value is the city-level aggregation of cooling effects for all urban natural capital assessed.

We assume, therefore, that temperatures (e.g. seasonal averages, extreme events) would

be this much warmer without the cooling effects provided by the extant urban natural

capital.

Worked example: for an urban area comprised of 25% parks, 2% 100m parks buffer and 6% urban

woodlands, cooling effect of each category would be: 1) urban parks -0.24oC (i.e. 25% of 0.95 oC);

2) parks buffer -0.01oC; 3) urban woodlands -0.21oC. Therefore, the total cooling effect of all

urban natural capital in this urban area is assumed to be: -0.46oC.

Physical health from outdoor recreation (QALY improvement due to active physical recreation

in urban natural environment)

There are various pathways through which the cultural ecosystem services associated with urban

natural capital may influence human health and well-being. The three most researched are: a)

stress reduction and mental health promotion (e.g. eftec and CRESR, 2013; UK NEA, 2014; Gascon,

2015); b) provision of opportunity to engage in health enhancing physical activity (e.g. Hunter et

al., 2015; Lachowycz and Jones, 2011); and c) encouragement of positive social interactions and

enhancement of community cohesion (e.g. Holtan et al., 2014; Weinstein et al. 2015)

There have, however, been few attempts to estimate the value of these benefits in monetary

terms. A summary is provided in Defra’s Evidence Statement on the Links Between Natural

Environments and Human Health (2016). Estimates include: a) individual level benefits of i) green

space views from home (£135-£452 per person per year), and ii) gardens (£171-575 per person per

year; Mourato et al. 2010 cited in Bateman et al. 2011); and b) societal level benefits in terms of i)

annual health care costs avoided from having access to good quality green space (e.g. £2.1 billion,

Natural England 2009), and ii) the social value of improved quality of life from physical activity in

natural environments (e.g. £2.03-£2.33 billion per year, White et al. 2016).

As the Defra report states, “It is important to note that this is a developing area and reliable values

are limited. Further valuation evidence is needed, including work to understand health values

14 Ibid. 15 It was not possible to find any explicit evidence on an edge related cooling effect of urban woodlands.

However, given the characteristics of urban woodlands (e.g. more consistent shading) we expect this edge

effect to be at least that of urban parks, if not greater.


eftec 35 June 2017

associated with the natural environment and the benefits and cost effectiveness of different policy

and intervention options”(p.3). This analysis aims to support such development.

This scoping account follows the method developed in recent work by White et al. (2016) which

explored the use of the natural environment for undertaking physical activity in England (not the

UK). Because physical activity needs to be both regular and sustained to benefit health (Haskell et

al., 2007), the assessment uses only those visitors who undertake physical activity in urban natural

environments and meet the recommended activity levels either fully, or partly (i.e. the sites are

integral in supporting people achieving guidelines each week).

Therefore, to estimate the number of ‘active’ visitors to urban natural capital in the UK (who also

achieve physical activity guideline thresholds partly or fully through urban green space), the

following steps were undertaken:

i) Use visitor data from the Monitor of Engagement with the Natural Environment (MENE)

survey (2009/2010 – 2014/2015, n= 280,790) survey (Natural England, 2015) to estimate the

total visits to urban green spaces in the UK (by extrapolating from the visits from England

based on population). Respondents are asked the location of their activity and these can be

isolated in GIS using the urban boundary within this study.

ii) Identify ‘active visitors’ to urban green space based on MENE responses using the approach

of White et al (2016) which identifies visitors that meet:

(a) Duration of activity threshold (30 minutes or more) based on Beale et al. (2007) which

used Health Survey for England data to estimate that 30min a week of moderate-

intense physical activity, if undertaken 52 weeks a year, would be associated with

0.010677 Quality Adjusted Life Year (QALYs)16 per individual, per year;

(b) Intensity of activity thresholds (>3 METs - Metabolic Equivalence of Task17) which uses

the Compendium of Physical Activities (Ainsworth et al., 2011) MET rates which had

been applied to each MENE activity (Elliott et al., 2015));

(c) Physical activity guidelines, through either:

Aggregate weekly physical activity requirements (30 minutes of exercise 5

times a week; NICE, 2008; DoH, 2004) fully, or in part, through these visits to

the urban environment (i.e. they might meet the requirement through a mix of

outdoor and other activity); or

Visits by individuals that report they did not meet guidelines but have made ≥5

active (≥30 minutes and ≥3 METs) nature visits in the last week (as this implies

they are meeting guidelines, although reporting otherwise);

16 A measure of the state of health of a person or group in which the benefits, in terms of length of life, are

adjusted to reflect the quality of life. One QALY is equal to 1 year of life in perfect health.

QALYs are calculated by estimating the years of life remaining for a patient following a particular treatment or

intervention and weighting each year with a quality-of-life score (on a 0 to 1 scale). It is used in economic

evaluation to assess the value for money of medical interventions. For more information see: National Institute

for Health and Care Excellence. 17 METs are a ratio of the metabolic rate of oxygen consumption associated with an activity compared to the

resting rate. Low intensity activities (<3 METs), moderate intensity activities (3–5.99 METs), vigorous intensity

activities (≥6 METs). For more information see Ainsworth et al. (2011).

https://www.nice.org.uk/Glossary?letter=Q

https://www.nice.org.uk/Glossary?letter=Q


eftec 36 June 2017

iii) Assign a QALY score using Beale et al. (2007)18 to each group of active individuals taking

into account their differing visit frequencies (i.e. from 1 time per week to 5). Multiply the

number of individuals who made 1–5 active visits by the relevant QALY scores and sum to

estimate gross QALYs based on active visits to urban green space.

3.6. Accounting for the supply and use of ecosystem services (Step 6)

Accounting for the economic sectors that supply and use ecosystem services (defined in the

Defra/ONS (2017) principles paper as enterprises, households, governments, rest of the world) is

outside the scope of this study. However, an overview of the users (beneficiaries) of each

ecosystem service is provided in Table 3.3. This assumes that benefits to:

Enterprises - includes improved worker productivity which can be boosted through improved

wellbeing.

Households – includes general wellbeing as well as cost savings (e.g. associated with allotment

grown food);

Government - includes improvement in societal wellbeing and therefore reduction in

government expenditures to achieve these outcomes as well as meeting legal obligations (e.g.

air quality targets);

Rest-of-the-world – this includes impacts on the wellbeing of populations in other countries

(i.e. excluding tourists) and includes issues such as climate change and air pollution.

Table 3.3. Beneficiaries of each ecosystem service included in the scoping account

Benefit Enterprises Households Governments Rest of the world

Food (allotments)



Noise regulation



3.7. Monetary account of annual provision of ecosystem services (Step 7)

The monetary account captures the economic value (£) of the ecosystem services that have been

quantified in the physical flow account. For commensurability with other national accounting data,

this should be the ‘exchange value’ observed in markets or ‘imputed exchange value’ (i.e.

indirectly measured or estimated) where markets do not exist. In practice, alternative welfare-

based measures including consumer surplus can be provisionally included as if they were proxy

exchange values (Day, 2013; Defra/ONS, 2017) with an indication given of the likely overestimation

of value. This section provides a summary of the approach taken to valuing (£) the physical flow of

the selected ecosystem services.

18 Based on analysis of Health Survey for England data, (Beale et al. (2007)) estimated that 30 min a week of

moderate-intense physical activity, if undertaken 52 weeks a year, would be associated with 0.010677 QALYs

per individual, per year. Beale et al. (2007) also assumed that the relationship between physical activity and

QALYs is both cumulative and linear.


eftec 37 June 2017

Food (£ of food production per allotment plot)

The value of food yields from urban allotments (£/kg/year) are estimated based on a review of

literature on the average value of produce. It is assumed that these are gross values (i.e. the

equivalent market price of produce as opposed to being net of resource costs associated with

allotments). To get to a resource rent, the value should deduct the cost of the plot, tools and

labour (which does represent a resource cost to society) but the benefit estimate should also

include the additional benefits to people from using allotments such as mental health, nutritional

value and the benefit to society of avoided transport costs. For the purposes of this analysis, the

gross estimate of the value of food production from allotments is deemed to be appropriate. Key

sources include:

Cook (2006) Study of allotments and small land plots: benchmarking for vegetable food crop

production. PhD Thesis;

Pretty (2001) The Real Cost of Modern Farming; and

UKNEA (2011) Urban Chapter: Allotments, Community Gardens, and Urban Farms.

Air quality regulation (£ of improved health outcomes)

The health benefits were calculated from the change in pollutant exposure from the EMEP4UK

scenario comparisons (i.e. the change in pollutant concentration to which people are exposed).

Damage costs per unit exposure Defra (2015a) were then applied to the benefitting population at

the local authority level for a range of avoided health outcomes:

Respiratory hospital admissions

Cardiovascular hospital admissions

Loss of life years (long-term exposure effects from PM2.5 and NO2)

Deaths (short-term exposure effects from O3)

This differs from previous approaches to estimating the value of air quality regulation for national

natural capital accounts which applied a simple urban or rural damage cost per tonne of pollutant

removed.

Noise regulation (£ of reduced sleep disturbance, annoyance and improved health outcomes)

The UK government (Defra, 2014) economic valuation guidance provides marginal values for

changes in noise (decibels) associated with road and rail (and aviation, but this is not relevant for

this study) to monetary values (£ per household) from baseline values for the following impacts

based on these changes to decibel levels:

a) Amenity values: There are two parts to the amenity values from noise:

i) Sleep disturbance: The recommended approach for valuing sleep disturbance is (Defra, 2014):

population

exposed

x proportion

sleep disturbed

x disability

weight

x health

value

(1) (2) (3) (4)

The population exposed is a site specific value (e.g. the population of a community); the disability

weight recommended by the WHO Night Noise Guidance for Europe is approximately 0.07 (WHO,

2009); and the proportion that are sleep disturbed can be estimated using a dose response function

which is based on the noise levels at night, following IGCB(N) (2007). These components are


eftec 38 June 2017

multiplied by the associated QALY (Defra, 2014). The economic value of a QALY is estimated using a

willingness to pay estimate and is therefore a welfare-based measure19.

ii) Anoyance: The recommended approach for valuing annoyance is (Defra, 2014):

population

exposed

x proportion

highly annoyed

x disability

weight

x health

value

(1) (2) (3) (4)

(1), (2) and (3) are estimated in methods that are very similar to those for sleep disturbance and

the estimated health value (4) is identical i.e. welfare based. The ‘proportion highly annoyed’ is

estimated using dose response functions from Interdepartmental Group on Costs and Benefits Noise

Subject Group (Defra, 2014). One key difference between the values from sleep disturbance and

annoyance is that there are benefits from changes in noise levels at lower levels for annoyance

than sleep disturbance.

b) Health values: There are three parts to the health costs associated with exposure to noise:

strokes, dementia and heart attacks (although in practice, the estimated values of strokes and

dementia are combined to estimate the impact on hypertension from noise). They all use some

kind of exposure and dose-response measures to calculate QALY impact which is estimated

using a willingness to pay estimate and is therefore a welfare-based measure. The

recommended approach for valuing strokes and dementia follow the same approach:

Change in risk x health impact specific QALYS x value of QALY

(1) (2) (3)

The guidance for estimating the impacts on heart attacks (AMI) is based on the IGCB(N) report

(IGCB(N), 2010). This is dependent on the additional risk of AMI, based on a dose response

function outline in Babisch (2006). This is combined with the probabiliy of AMI occuring within a

population at a specific location, the population in that location and the health value (i.e. the

value of the QALY). Therefore, as with the other estimates, this value is also welfare based.

c) Productivity values: The productivity costs are not a part of the formal recommendations of

Defra (2014) and are not included in this study. However, the report presents a prospective

method to estimate productivity loss (e.g. from lack of sleep/disturbed sleep) based on the

financial cost of labour. As with other wage-based estimates, these are closer to an exchange

value. However, the estimates in Defra (2014) were only considered partial, based on a mix of

national-level estimates from around the world, and does not take into account workers who

are ill but still active in the workplace, and the scale of loss of productivity (e.g. accounting for

the type of work) (Morgan et al., 2011).

We apply the marginal values which combine these impacts for each unit decibel change (Defra,

2014). We make the assumption that the mitigated buildings lying within each 5dBA noise band will

experience a 2dBA reduction in noise levels due to the presence of trees providing a noise-

reduction service. This is a conservative approach given the maximum potential attenuation is

7dBA, the extent of actual attenuation will depend on structure (i.e. trees are the main attenuator

of noise but there is the possibility of noise travelling beneath the canopy). As Table 3.4 shows, our

application of these values takes a conservative approach that the lowest 2dbA reduction will be

experienced for each banding i.e. for the banding 65dBA to 69.9dBA we assume a reduction in noise

19 This is closely related to the estimation of the value of a life year (VOLY). A QALY is a VOLY weighted by

their quality of life.


eftec 39 June 2017

of 67dBA to 65dBA, whereas actually the reduction will be experienced by individuals exposed to

noise levels over the entire band range.

The value per change in decibel, by noise band is shown in Table 3.4. Overall, we expect this to be

a reasonably robust order-of-magnitude estimate of the service, but further work could refine the

methodology, and reduce uncertainty.

Table 3.4 Marginal values for road noise reduction from Defra guidance (2014)

Noise banda dB reduction applied for valuation Discounted marginal value £

>=80 82 to 80 390

75.0-79.9 77 to 75 358

70.0-74.9 72 to 70 282

65.0-69.9 67 to 65 214

60.0-64.9 62 to 60 155

55.0-59.9 57 to 55 106

50.0-54.9 52 to 50 38

45.0-49.9 47 to 45 23

a The noise bands are showing the current range of decibels that are experienced (within Manchester)

Climate regulation – local (£ of avoided air conditioning costs and residual productivity losses)

The monetary account assessment is focussed on avoided energy cost for air conditioning and the

residual impacts (i.e. once air conditioning is taken into account) on workplace productivity

measured as Gross Value Added (GVA) (£). This assumes that high temperatures beyond a certain

threshold lead to productivity losses. With urban vegetation, the need for air conditioning is

decreased in those industrial sectors that use air conditioning. Therefore, the benefit of urban

vegetation is estimated by using the proxy of avoided energy use due to avoided air conditioning.

For those industrial sectors where air conditioning cannot be used, the benefit of urban vegetation

is estimated by using the proxy of productivity loss. This is a very conservative assumption in that it

assumes all affected work places that can install air conditioning do so, and that the avoided costs

are only the running cost (not the installation costs). This section outlines the approach taken with

further information is in Annex 4.

The first step is to estimate the avoided air conditioning costs associated with urban green space in

urban areas in Great Britain. This has been estimated based on the London i-Tree project, assuming

that avoided costs increase in proportion to the total urban extent (ha) in Great Britain and in

London (because the proportion of green space in these areas is broadly similar). This assumption is

deemed to be appropriate given the relative proportions of green space in London and Great Britain

urban areas are similar.

The second step is to estimate the avoided productivity losses for those sectors where air

conditioning cannot be used. The International Standards Organisation (ISO) standard (ISO 7243)

reports estimates of productivity loss at different outdoor temperatures. The guidance is based, in

part, on research undertaken by the US Army many decades ago to estimate time limitations of

work at different levels of heat exposure (Kjellstrom et al., 2009). Heat exposure is measured using

the wet bulb globe temperature (WBGT) index which combines various local climate measurements

to produce an overall measure of heat exposure that is particularly useful for occupational health

and safety (ibid).


eftec 40 June 2017

Using WBGT, ISO7243 shows the percentage of a working hour that can safely be undertaken for

different levels of work intensity (from 180W20 to 415W, where W is a measure of “work intensity”

depending on the energy levels needed by workers to perform different activities) at different

levels of heat exposure (WBGT), see Figure 3.2. Figure 3.2 suggests that for economic sectors

where work intensity is “moderate/ high” (415W includes industries such as construction)

productivity losses begin to occur beyond temperatures of 26.8oC (i.e. where health and safety

standards would require workers to reduce the amount of work undertaken in a working hour). For

sectors where work intensity is lighter (e.g. 180W includes “light work” sectors like financial

services) productivity losses do not occur until higher temperatures over 31oC.

These functions (in Figure 3.3) have been used to estimate productivity losses below an optimum

100% / full work capacity based on the ambient outdoor air temperature. The work intensities from

the ISO standard have been mapped across to different sectors of the economy by Costa et al.

(2016), allowing for analyses of city-scale productivity losses based on the structure of the urban

economy. An important limitation of this approach is that productivity losses are estimated on the

basis of outdoor air temperatures only. As this is only one component of WBGT, it is not directly

comparable with the productivity loss functions shown on Figure 3.3. These limitations are

discussed further in Section 6 and Annex 4.

Figure 3.3. Hourly worker productivity loss functions using ISO standard 7243 (Costa et al.,

2016)

Note: Y-axis is worker productivity. X-axis is wet bulb globe temperature (WBGT) which is an aggregate measure of temperature based on different measurement techniques. W is a measure of “work intensity” depending on the energy levels needed by workers to perform different activities.

The following method steps have been taken to estimate the value of the cooling effects of urban

vegetation in terms of avoided productivity (GVA) losses:

1. Calculate the city-level / aggregated cooling effect of urban natural capital as per physical

account method;

2. Use productivity loss functions from Costa et al. (2016) to identify productivity loss estimates (%

less than “full work”) for each work intensity (economic sector) affected under the different

“hot day” values being used for the city being assessed, see Figure 3.3 above. (Note: depending

on the magnitude of the “hot day” temperatures for a given city, some, all or none of the work

intensities may be affected – the higher the temperature, the more work intensities / sectors

affected);

20 Occupational heat exposure guidelines (such as ISO7243) state maximum heat exposures in jobs / sectors of

economic activity at different levels of work intensity measured in Watts (Kjellstrom et al., 2009).


eftec 41 June 2017

3. Sum the city-level cooling effect of urban natural capital (as a positive value) from step (1)

with the “hot day” temperature(s) being used. This provides an estimate of what the “hot day”

temperature might be without existing urban natural capital;

4. Use productivity loss functions from Costa et al. (2016) to identify productivity loss estimates

(% less than “full work”) for each work intensity (economic sector) affected under the

combined “hot day” + urban natural capital cooling effect temperature(s) obtained in step (3)

above (note: the combined temperature may result in additional sectors being affected). This

provides an estimate of what productivity losses might be without existing urban natural

capital;

5. Calculate reduction in daily GVA per sector21 based on annual GVA22 of the urban economy

(assume 260 working days per year) affected (£) using productivity loss values (%) obtained via

steps (2) and (4) above: (a) GVA loss under the “hot day” temperature(s) used (£); and (b) GVA

loss under the combined “hot day” + urban natural capital cooling effect temperature(s) used;

6. For each sector affected and for each “hot day” temperature(s) value used, subtract daily GVA

loss (5a) from (5b) to obtain the monetary value (in GVA terms) of the cooling effect of urban

natural capital (in terms of annual productivity losses avoided);

7. For each “hot day” temperature(s) value used, multiply the daily value for productivity losses

avoided (GVA/£) obtained in step (6) by the number of days in a year where the “hot day”

temperature(s) was reached.

This approach is applied in Great Britain for the scoping account, using the following data:

GVA at the city economy scale23 is taken from ONS (2015) ‘City Regions Article’;

Structure (sectoral make-up) of the city economy (%) – Costa et al. used the “NACE

statistical classification of economic activities used in the EU”;

Average July temperatures for the city / city-region being assessed taken from Met Office

UK regional climates data24; climate extremes by district – highest daily maximum

temperature records taken from Met Office UK climate extremes data25;

Productivity loss functions for different work intensities are taken from Costa et al. (2016)

as derived from ISO 7243. Hot days will only become an issue in productivity terms where

they exceed 26.8oC so this analysis may not be applicable for all urban centres in the UK

based on current climate data (or only in very extreme events); and

Data on frequency and magnitude of “hot days26” in urban centres – a hot day value for

London from the UK CCRA 2017 (Kovats et al., 2016) has been used as an example value for

21 Table A.5 in the Costa et al. (2016) online appendix maps work intensity categories from ISO 7243 to different sectors of

the economy. We suggest that this categorisation is used to identify work intensity and productivity loss functions for UK

urban economy sectors as part of this aspect of the urban heat account. 22GVA data compiled by ONS for e.g. the city-regions (see:

https://www.ons.gov.uk/economy/economicoutputandproductivity/output/articles/cityregionsarticle/2015-07-24 23 In this scoping account, total economic output (GVA) and GVA by sector is taken from ONS (2016) regional

gross value added analysis. 24 http://www.metoffice.gov.uk/climate/uk/regional-climates 25 http://www.metoffice.gov.uk/public/weather/climate-extremes/#?tab=climateExtremes 26 Hot days can be defined variously, but in the ASC’s climate change adaptation indicators (HR Wallingford,

2014) they are defined as days where the maximum temperature exceeds the 93rd percentile of the 1993 –

https://www.ons.gov.uk/economy/economicoutputandproductivity/output/articles/cityregionsarticle/2015-07-24

http://www.metoffice.gov.uk/climate/uk/regional-climates

http://www.metoffice.gov.uk/public/weather/climate-extremes/#?tab=climateExtremes


eftec 42 June 2017

analysis (28oC). Met Office data (Met Office, 2017) on the number of days that reached or

exceeded the following temperatures in for Central England in 2013 was sourced as follows:

Temp (oC ) Number of days

28 ≥ ≤ 29 4

29 ≥ ≤ 30 1

30 ≥ ≤ 31 0

31 ≥ ≤ 32 0

32 ≥ ≤ 33 0

33 ≥ ≤ 34 0

Estimated avoided losses associated with temperatures reaching 30oC have also been used for the

purpose of sensitivity analysis and is shown in Annex 4.

Climate regulation – global (carbon) (£ per tonne of CO2)

Common practice in UK policy analysis is to value carbon using values related to the cost of

mitigating emissions as captured in DECC (2014). This is split into the value of ‘traded’ carbon

credits in the EU Emission Trading Scheme values and the ‘non-traded’ value for other mitigation

measures.

For natural capital accounting the preference is for exchange values (Defra/ONS, 2017). The traded

carbon value is an exchange value. However, this value does not include carbon sequestration in

ecosystems or market transactions (such as exist for woodland carbon, varying between £3.50/

tCO2e and £6/tCO2e (eftec et al, 2015)). The prices from these market transactions reflect the

institutional setup of carbon markets rather than the true value of carbon sequestration if it were

to be exchanged. If an observed price relating to an ecosystem service is more a reflection of the

regulatory framework and institutional factors than the ‘actual’ value of the ecosystem service in

exchanges, such a price is not considered an exchange value.

Non-traded value (DECC, 2009 central estimate), on the other hand, reflects carbon mitigation and

meeting the UK’s short and long-term greenhouse gas emissions targets. Whilst non-traded carbon

used here is indeed not traded within a market, the value is calculated based on market principles

related to marginal abatement cost curves. In the context of the UK’s carbon mitigation policy

targets, aligning the value of woodland carbon sequestration to this non-traded value therefore

provides a fairer reflection of the cost of achieving the political targets set by the UK (and the

associated benefits) than traded carbon value which is misleading due to institutional factors. The

traded value of carbon may become more representative of the value of carbon sequestration in

the future as the markets become more established. The non-traded price of carbon is used in this

study but the decision of which value to use will should be re-evaluated in the future.

Physical health from outdoor recreation (£ of QALYs and avoided costs of ill health)

The monetary account provides an estimate of the value of physical activity undertaken within

urban green spaces following existing methods developed by Bird (2004) and White et al. (2016).

Both approaches assume a counterfactual of no physical activity occurring in these environments,

2006 daily maximum temperature for individual weather stations. Clearly this will vary with geography – e.g. a

hot day in south-east England is currently ~28oC whereas for Shetland it is 16oC (Kovats et al., 2016).


eftec 43 June 2017

meaning that if the green space did not exist then the exercise (and the associated QALY/avoided

cost) would not take place27:

(i) Quality Adjusted Life Years (QALYs): using the implicit social value of a QALY in England

(not UK) based on the NICE cost-effectiveness threshold28 (at the time of the study) of

£20,000 (White et al, 2016; NICE, 2013). Specifically, NICE states that: “generally we

consider that interventions costing the NHS less than £20,000 per QALY gained are cost-

effective” (NICE, 2013) implying that enhancing health by a single QALY is saving up to

£20,000 in health care costs (White et al, 2016)29;

(ii) Avoided medical costs of ill health: the cost of inactivity in England (not UK) is estimated

using inactivity rates from Public Health England (2015) and data on the number of cases

of treating five conditions that are directly attributable to inactivity30 and the direct costs

of treating these to the relevant NHS Clinical Commissioning Groups31 (CCGs) (Public

Health England, 2016)). The conditions for which costs are provided are:

• Ischaemic heart disease,

• Ischaemic stroke,

• Breast cancer,

• Colon/rectum cancer, and

• Diabetes mellitus.

The cost estimates are recognised as a significant underestimate: they only consider costs

associated with five of the over 20 conditions preventable and manageable by physical

activity and also only the direct costs to CCGs for the five conditions (i.e. not costs to

other parts of the NHS and the wider health and social care system) (Public Health

England, 2016a). They are therefore only a starting point in understanding the economic

costs of physical inactivity for the health care system. While it is not possible to gauge the

proportion of these costs relative to all conditions, they comprise some of the more

serious and costly conditions. One notable omission from this list is obesity (for which PAFs

do not currently exist), the costs for which are very large, and which is preventable and

manageable through physical activity. Although an underestimate, in this way the analysis

uses the best available information and methods as a starting point of exploring this

complex subject.

27 This is consistent with national accounting generally i.e. if gyms didn’t exist there would be no service

recorded for gym exercise. It may be that if green space fell radically more people would go to the gym, but

this “substitutability” is an argument for the integration of ecosystem services with the national accounts. 28 This threshold is assumed to represent welfare value in that medical interventions are seen as cost effective

if the costs are less than or equal to the welfare gains they support. 29 This is analogous to the DECC carbon values which are based on marginal abatement costs i.e. carbon

reduction interventions that cost less than ~£62/t (2015) would be deemed to be cost-effective. 30 These five conditions are those for which Population Attributable Fractions (PAFs) are available for physical

inactivity to estimate costs from these diseases that can be attributed directly to physical inactivity. PAF is

the proportional reduction in disease or mortality that would occur if exposure to a risk factor (such as

physical inactivity) is reduced to an alternative ideal exposure scenario (e.g. activity that meets guidelines). 31 Clinical Commissioning Groups (CCGs) were created following the Health and Social Care Act in 2012, and

replaced Primary Care Trusts on 1 April 2013. CCGs are clinically-led statutory NHS bodies responsible for the

planning and commissioning of health care services for their local area. There are now 209 CCGs in England

(www.nhscc.org)

http://www.nhscc.org/


eftec 44 June 2017

Table 3.5 presents a summary of the data used and resulting estimate of costs per inactive

person. As shown, average direct costs to CCGs are estimated at an additional £2932 per

inactive person per year across England. As a comparison, Bird (2004) estimated that

direct costs to the NHS per inactive person in England were £61 (around £76 in £2016,

adjusting for inflation). This may indicate that the cost data used here, while based on

costs from only five conditions and only costs to CCGs (not the wider NHS), represents

under half (~40%) of the direct costs of inactivity.

In order to capture a larger proportion of the total costs of inactivity, an estimate for the

annual direct and indirect costs (including total costs across the NHS as well as absences

from work) in England, over £10 billion per year (Department of Health 2004) is also used.

The result is an estimate of just under £65033 of additional costs per inactive person per

year. This estimate is inclusive of the direct costs to CCGs detailed above. This figure is

also used in further calculations as the cost per inactive person in Scotland, NI, and Wales,

under the assumption that the distribution of NHS costs between diseases in England will

be broadly representative of the situation in the UK.

Table 3.5: Inactivity levels and medical costs (£) per inactive person in England

% Inactive

(<30 mins/week)

Number

inactive

Cost of inactivity

(£m 2016)

Additional costs per

inactive person (£)

England – direct costs of inactivity for five conditions to CCGs

28.7% 15.7m

£459m £29

England – direct and indirect costs of inactivity to society

£10.2m £648

Notes: The number of inactive people is 54.786 million based on the population of England in 2016

as estimated by ONS (2016). Costs have been converted to £2016 using HM Treasury’s GDP Deflator

(HM Treasury, 2017). The five conditions included are: Ischaemic heart disease, Ischaemic stroke,

Breast cancer, Colon/rectum cancer, and Diabetes mellitus. Direct and indirect costs are inclusive

of direct costs to CCGs and therefore should not be added.

To ensure that only the visits to urban green space are measured from people who meet

the physical activity guidelines (using any location), a relative proportion of the avoided

cost is applied to the proportion of active weekly visits taken to urban green space. For

example, using direct costs to CCGs, for visitors who make 1 of their 5 active visits per

week to urban green spaces, 1/5th of the total cost (of £29), or £5.84, is multiplied by the

number of people making one active visit per week (NE 2015).

3.8. Monetary account of future provision of ecosystem services (Step 8)

This account values the urban natural capital asset(s) based on the present value of the stream of

(annual) ecosystem services that the asset(s) will provide over a future period of time.

The Defra/ONS principles paper states that a 100-year asset life should be used to reflect the

longevity of renewable natural assets. In principle, the asset value estimate should account for

expected variations in both the physical and monetary flow of ecosystem services over the 100-year

period. This could be significant for urban areas, as UK population is estimated to grow by 9.7

32 Average direct costs to CCGs are calculated as the total costs divided by the number of inactive people in

England. The number of inactive people is calculated by taking the inactivity rate (Public Health England,

2015) multiplied by the population of England (ONS, 2016). 33 Direct and indirect costs per person are calculated as the total costs divided by the number of inactive

people in England.


eftec 45 June 2017

million over the next 25 years (ONS, 2014) and most of this growth will be in cities. The impact of

urban cooling is potentially increased by climate change too.

However, in practice, forecasting future flows of benefits and market prices/values is subject to

significant uncertainty. For the purpose of this scoping study, the following assumptions are used:

1. A constant flow assumption is made for all ecosystem services (i.e. the amount of ecosystem

services produced remains the same over the 100 years) except for local climate regulation for

which a projection of the number of days at elevated temperatures in 2080 is estimated using

UKCP09 projections. This is presented in Annex 4 but in summary the productivity losses

associated with elevated temperatures in 2080 are estimated and a linear progression between

the current value of avoided productivity losses in 2016 (estimated to be £24m) and the value

in the future in 2080 (estimated to be £244m/year, see asset valuation in Annex 4) is assumed.

The estimated avoided energy cost associated with air conditioning is assumed to remain the

same into the future for the purposes of this assessment. However in reality this could be

expected to increase with rising temperatures as the number of hot days requiring air

conditioning increase.

2. Prices/monetary values are assumed to remain constant over the 100 year period (in present

value terms for 2016) for all ecosystem services except for global climate regulation which

follows the profile of DECC (2014) carbon values which increase over the 100 year period.

It is recommended that Defra/ONS review the time related assumptions used across all broad

habitat accounts to make sure there is consistency in approach to asset valuation.

For discounting the future flows of ecosystem service values Green Book guidance for project

appraisal (HM Treasury, 2011) will be applied in line with Defra/ONS (2014). This starts with a 3.5%

discount rate for the first 30 years, 3.0% for years 31 – 75 and 2.5% for years 76 – 100.

Whether accounts for previous years can be compiled depends on the availability of data which

varies across ecosystem services. For example:

Previous versions of the MENE could be used to estimate physical activity benefits of

recreation, but would require the underlying model developed by White et al (2016) to be re-

run;

Estimating food provision in previous years is challenging as the data on allotment numbers and

yields are taken from a range of sources and years to provide an indicative estimate that is

representative of the order-of-magnitude of food production from allotments in the UK (i.e. not

attributable to any one year in particular); and

It is understood that there is no time series data on noise so previous estimates cannot be

provided for this service.


eftec 46 June 2017

4. RESULTS

This section describes the outputs from this study including illustrations of the urban boundary for

natural capital accounting, the physical accounts of natural capital extent, condition and

ecosystem service provision and the monetary accounts estimating annual values and asset values.

An accompanying Excel document provides detail on the data sources, assumptions, method steps

and calculations that underpin this analysis. Furthermore, there is a scoping account for

Manchester in Annex 5.

4.1. The urban boundary

The urban boundary for the purpose of this account is defined using a variable sized buffer rule

being applied to the existing ONS (2011) Built-Up-Area boundary (see Section 3.1). The rule results

in significant natural capital assets (such as the River Thames in London) being incorporated within

the urban boundary (they are excluded from the Built-Up-Area dataset). Figures 4.1 to 4.3 illustrate

the results using the 250m and 500m buffers for London, showing that both rules result in these

assets being incorporated into the urban boundary. For London as a whole, Figure 4.1 shows that

the 250m buffer captures much of the Thames and associated natural areas such as Richmond Park

which might be considered urban natural capital. The 500m buffer starts to include a little more

agricultural land. There are certain anomalies associated with both buffers, such as neither buffer

capturing Wanstead Park (North East London), or the two woodland corridors running northwards

from it.

Figure 4.1. The mapped extent of London showing (i) extent of BUA (yellow) and additional

extent captured by (ii) 250 m buffer (blue) and (iii) 500 m buffer (lilac)

Figure 4.2 shows in more detail what each buffer captures for a small area of North London

between Edgware and East Barnet (i) grid squares show the boundary of the BUA dataset; (ii) the

250m buffer is shown by the large grey areas (iii) the 500m buffer is the additional semi-

transparent areas which mostly capture agricultural land.


eftec 47 June 2017

Figure 4.3 shows a similar sized area that is part of the Thames Estuary in East London. In this case,

the additional area included under the 500m buffer is primarily industrial (and therefore urban),

with parts of the Thames that might be considered blue infrastructure and Southmere Park and

Crossness Nature Reserve which would be considered green infrastructure.

Figure 4.2. Mapped extent of an area of North London between Edgware and East Barnet

Figure 4.3. Mapped extent for part of the Thames estuary

It is clear from Figures 4.2 and 4.3 that there is no perfect solution to enhancing the BUA dataset to

include natural capital assets that we might intuitively consider to be part of the urban fabric. The

500m buffer captures more of all types of land cover and in some cases this will be predominantly

agricultural land (Figure 4.2) and in others this will be land cover that we would intuitively consider

as ‘urban’ (Figure 4.3).


eftec 48 June 2017

Given that the 500m buffer is shown to capture some areas that are intuitively considered ‘urban’,

constraining the boundary to the 250m buffer would mean that these areas are missed from the

broad habitat accounts altogether. There is a balance to be had here between increasing the buffer

(e.g. 750m/1km) to include a greater area around the BUA dataset and what can reasonably/

intuitively be considered ‘urban’. A greater buffer size would potentially capture a greater

proportion of the LCM2007, but also include more agricultural land. For this reason, 500m was

deemed to be a suitable compromise as the maximum size of the buffer and has been used in

this study.

Therefore, we designed a variable buffer, proportional to the size of the urban polygon, with a

maximum size of 500m buffer for a polygon the size of Greater London. This method is therefore

used to ‘enhance’ the urban boundary, so that typical urban blue and green space is adequately

captured within the ‘urban’ account. This includes some agricultural land that we might not

intuitively consider to be urban - but that we report the extent to which other land cover types are

captured within this ‘urban boundary’ so that overlaps with other broad habitat accounts can be

acknowledged.

4.2. Physical account of natural capital extent

Table 4.1 reports the total ‘urban area’ of 1,765,700 ha as defined within this study. It also shows

the extent of overlaps with other broad habitat types by comparing to the LCM2007 and green

infrastructure features (e.g. street trees) that could be captured in future iterations of the account

using high resolution datasets.

Table 4.1. Extent of UK urban natural capital within enhanced ONS BUA (2011) urban extent.

(Only GB data available for some features)

Dimension Indicator Scale Amount Unit Source

Extent

Total urban area UK 1,765,700 Ha Enhanced ONS BUA (2011)

Area of ‘broad’ UKNEA habitats

Coastal margins UK 4,000 Ha LCM2007;

Enclosed farmland 403,400

Freshwater 9,100

Marine 4,100


11,200

Semi-natural grassland 34,200

Woodland 87,900


1,212,000

Green

infrastructure

features

Park areaa GB 420,400 Ha OS Mastermap

Trees 99,400 Ha

Allotments UK 163,000 Number National Society of Allotment & Leisure Gardeners

Blue infrastructure features

Lakes/Ponds/Riversb GB 22,700

Ha OS Mastermap

a Park area includes enclosed grasslands, arable and horticulture b Rivers includes canals.


eftec 49 June 2017

Figure 4.4 shows the proportion of different land cover types captured within the defined urban

area (as reported in Table 4.1). It shows that the majority of land cover is ‘urban’ as defined by the

Land Cover Map and a significant chunk is enclosed farmland. Although the other land covers

represent a small proportion of the land in this scoping account, they are important to separate

from their individual broad habitat account (e.g. urban woodland, urban areas on coastal margins,

urban rivers) because of the potentially enhanced benefits that these specific areas provide due to

their location near beneficiaries.

Figure 4.4 The proportion of land cover types captured within the scoping account for urban

natural capital

4.3. Physical account of natural capital condition

This section explains the potential indicators for the natural capital condition identified by this

study for use in future urban natural capital accounts. It then provides, for each ecosystem service,

an explanation of which dimensions of urban natural capital assets are important determinants of

ecosystem services provision and how/why (through the use of logic chains)34. A selection of key

indicators and potential data sources for inclusion in future iterations of the urban condition

account are then outlined. Table 4.2 is a matrix which shows which of the proposed extent and

condition indicators are relevant to which ecosystem services.

4.3.1. Dimensions of urban natural capital important for provision

Food

Demand for allotments is high, with the National Society of Allotment and Leisure Gardens

recording a waiting list of 83,000 in the UK in 2011 (Bird, 2014). There are many demands on land

34 Note this includes pollination, cultural heritage, recreation and flooding which are not included in the

scoping accounts.


Coastal margins

Enclosed farmland

Freshwater

Marine



Woodland


eftec 50 June 2017

use within urban areas and the area of land (e.g. allotments, gardens) available for use for food

production may be a concern for local communities.


Vegetation provides an air quality regulating service (UKNEA, 2011) by capturing airborne pollutants

and removing them from the atmosphere through: (a) the internal absorption of pollutants via

stomatal uptake; and (b) the deposition of pollutants on external surfaces such as leaves and bark

(Bignal et al., 2004).

A number of local councils in the UK are considering the active use of vegetation for reducing

pollutant levels, though evidence on the effectiveness of such actions is currently limited. The

location of vegetation is an important determinant of the amount and value of air quality

regulation or ‘purification’ it provides because the amount of service provided is dependent on:

Ambient air quality: urban areas tend to have higher levels of pollution, meaning a given

amount and type of vegetation could remove more pollutants in an urban area than in a rural

area – with the exception of ammonia;

The amount and type of vegetation: urban areas tend to have less vegetation per hectare than

rural areas. This scarcity, combined with considerable local pollution sources, contributes to

lower ambient air quality, and a higher relative value of pollution mitigation that takes place.

Deposition velocities vary across vegetation/land cover types; and

Population densities: the total benefit being delivered by vegetation removing one tonne of

pollution is higher in areas of high population density. This is because more people benefit from

improvements in air quality.

Noise regulation

The impact of UK urban vegetation on attenuating noise occurs primarily by scattering sound and

different components of the environment have different capacities to reduce noise. For example,

soft or permeable soils/lawns dissipate more noise than concrete (Bolund and Hunhammar 1999),

whilst greater tree trunk density and width/height/length of vegetation strips generally provide

increasing noise attenuation (Fang and Ling, 2003; Peng et al. 2014). The benefit provided by

vegetation depends upon its location relative to the noise source and population.


The mechanisms by which the urban cooling effect is achieved are dependent upon the extent and

type of vegetated land cover (parks/woodland) and specific green infrastructure including street

trees (for which planting density is also important), green roofs and walls. Extent and type

(species/land cover) are the main determinants of the scale of urban cooling resulting from:

Evapo-transpiration: all vegetated surfaces / land covers contribute to evapotranspiration as

part of the hydrological cycle. This process helps to dissipate high heat loads in urban areas as

vegetation “consumes” heat to drive the evaporation process (Salmond et al., 2016; Davies et

al., 2011);

Shading: the shade afforded by vegetation (trees in particular) blocks solar radiation from

reaching pedestrians, people in vehicles, people in buildings etc. This shading effect also limits

the solar heating of surfaces with high heat capacity (e.g. concrete), reducing heat storage and

urban heat island effect issues (Picot, 2004; Salmond et al., 2016); and


eftec 51 June 2017

Lower radiative temperatures: vegetated surfaces (Salmond et al., 2016) and water (Davies et

al., 2011) have lower radiative temperatures than impervious surfaces.

Engineered green infrastructure, such as green roofs and walls, are designed to utilise the lower

radiative temperatures functions (Davies et al., 2011; Emmanuel and Loconsole, 2015) and whilst

these are features of the built environment, there is an ‘ecosystem function’ from the vegetation

which cools the atmosphere and so these features is included in the condition account.

Climate regulation – global (carbon dioxide)

The growth in vegetation (biomass) results in carbon sequestration and so vegetation type (species)

and age are key determinants of the level of global climate regulating service produced each year.

Eighty percent of urban carbon is stored in soils (Jo and McPherson 1995; Lorenz and Kandeler 2005;

Edmondson et al. 2012), which are the result of past ecosystem services (i.e. past carbon

sequestration) and can potentially be released as emissions. Detailed inventories suggest that once

urban soils including capped soils are taken into account, urban areas hold considerable carbon

stocks. For example, the total organic carbon in the city of Leicester, across 73 km2 was estimated

to be 1.2 million tonnes (Edmondson et al. 2012). Urban soils have a higher carbon density than

arable soils (Edmondson et al. 2012). Again in the example of Leicester, a detailed inventory

showed that the proportion of organic carbon stored under different land classes was: 69% in

greenspace soil, 18% in vegetation and 13% in capped soil (Edmondson et al. 2012). This suggests

that capped soils (soil sealed by impervious surfaces such as roads, driveways, patios, etc.) also

hold considerable carbon. For non-peat soils, clay content is the primary determinant of the

potential carbon storage.

Ninety seven percent of the above-ground carbon in urban areas is contained in tree biomass, and

particularly large trees, rather than herbaceous and woody vegetation (Davies et al. 2011). Carbon

densities of wood vary across different tree species (Milne and Brown, 1997). However, the most

significant source of spatial variation is likely to be the size of the tree (Davies et al. 2011).

Diameter at Breast Height (DBH) is therefore considered to be a reasonable proxy for carbon stock

where available. However, this requires ground-based measurements and survey data. Surveys of

urban trees have been a focus for much of the carbon inventories of urban areas so far (e.g. Nowak

and Crane 2002). Alternatives might include height derived from LIDAR (Light Detection and

Ranging) data (e.g. from the Environment Agency, 2015), or from existing products such as

Bluesky’s National Tree Map (Bluesky, 2017).

Pollination

The extent of contiguous habitats and number of bee colonies in urban areas are indicators of the

health of the pollinator populations which provide an important supporting service to other

ecosystem services (within and outside of urban areas).

Cultural heritage

Further research should be done to understand which features of the urban environment are

considered as important in contributing to the UK’s cultural heritage.

Recreation and tourism

Recreational visits in the urban environment depend on access to green space and there is some

evidence to suggest that the quality of green space (e.g. Green Flag status) increases the number

of recreational visits.


eftec 52 June 2017

Physical health

The physical health benefits from urban natural capital depend on the number of

beneficiaries/users of natural capital (which is captured in physical account or forms part of the

monetary calculations) which is influenced by the proximity of population to green space and, to

some extent, by the quality of the green space. However our understanding of how visit numbers

are impacted by changes in quality of the natural capital, and therefore which quality indicators

need to be captured in the condition account, is poor.

A study by CABE (2010) found that “individual perception of local green space quality is a predictor

of satisfaction with the local neighbourhood as an area to live, and that the level of satisfaction

with green space is a predictor of its use (p38)”. It also found that “people’s perception on the

quality of green space has a significant influence on whether some people use it (p39)”. It is

intuitive that increased quality would lead to benefits from increased use, however, what

constitutes differing grades of quality remains unclear. There is currently no comprehensive and

consistent grading system used for measuring the quality and condition of urban natural capital in

the UK.

Classifications such as the National Audit Office (NAO, 2005) rating of urban Local Authority green

space in terms of a ‘good’, ‘fair’, or ‘poor’ framework, awards such as the Green and Blue Flag

Awards, and designations such as Site of Special Scientific Interest (SSSI) are informative but they

tend to focus on the ecological and visual amenity value of these sites, rather than their use. For

example, a ‘poor’ quality site may still provide many health benefits if used by many people for

relaxation, exercise or social contact. For this reason the relationship between benefit provision

and quality should be explored further35.

4.3.2. Indicative indicators for urban condition account

Table 4.2 outlines the proposed indicators and units to include in the UK urban natural capital

account. Indicators have been selected based on the project team’s understanding and review of

evidence and data for the selected ecosystem services. The suggested sources are the most recent

published information, in many cases the data will need to be obtained from the relevant

organisations (such as Natural England (NE), the Environment Agency (EA)) before figures can be

estimated for the UK. Where data is not currently available, further work needs to be done to

scope out potential future indicators and how data may be collected.

Indicators of natural capital asset condition

'Condition' indicators should relate to the capacity of urban natural capital to produce ecosystem

services. Therefore, the indicators outlined here relate to the ecosystem services considered in

Section 3.3.

The broad dimensions of asset condition outlined by Defra/ONS (2017) are biodiversity, soil and

ecological condition. Different 'levels' of a specific condition indicator reflect whether it is 'good' or

'bad' e.g. the indicator might be the length (km) of different rivers in terms of their water quality

and the ‘levels’ are ‘excellent’, ‘good’, ‘poor’. The following are the proposed indicators for the

condition of UK urban natural capital (see Table 4.2):

35 At least two projects (IWUN and GHIA) funded by the Valuing Nature Network are working on the

relationship between people and urban green infrastructure and should be monitored for their potential

contribution to future accounting. http://valuing-nature.net

http://valuing-nature.net/


eftec 53 June 2017

Species abundance: a key measure of biodiversity is the number of a particular species within

an area. Data on the number of bee hives, which are important for pollination, is collected

from the UK's National Bee Unit (number of hives in London has more than doubled since 2008

to a total of 3,745). Consideration of the ecological capacity of urban areas to support bee

colonies should be borne in mind (i.e. more hives isn’t always a good thing where colonies do

not thrive as a result). Further work should be done to consider the data on species abundance

in UK urban areas (e.g. urban habitats such as bat roosts protected under schedule 1 of EU

Habitats Directive; Buglife urban projects in relation to pollinators and brownfield landscaping).

Species diversity: another key measure of biodiversity is the number of different species within

an area. Species diversity is generally related to habitat diversity, but broad habitats might be

a very crude way to measure this. Further work is required to consider the data on species

diversity in UK urban areas.

Soil carbon content: this captures the amount of carbon locked up in soils within the urban

environment. The soil properties for non-peat soils which determine potential carbon storage

are primarily clay content.

Surface permeability: permeability is important for the mediation of water flows, noise

regulation and soil carbon content. Therefore reporting the changing extent of permeable and

impermeable surfaces will indicate change in ecosystem service provision but also potentially

improvements in urban vegetation technology (i.e. sustainable urban drainage systems (SuDS)).

River water quality (including canals): this is important for the bundle of cultural services

(recreation, aesthetics, cultural heritage) and the associated physical and mental health

impacts.

Ponds/lake water quality: as above (see river water quality).

Vegetation height and width: both are important for the regulation of noise by vegetation.

Diameter at breast height (DBH) of trees – is a reasonable proxy for carbon stock where

available because the most significant source of spatial variation in carbon in above ground

biomass is likely to be the size of the vegetation.

Indicators of natural capital spatial configuration

The LCM2007, OS Mastermap, Bluesky National Tree Map provide GIS maps of urban green space,

the forthcoming BEIS green space map is also a potential source for the future. These maps are a

useful output but can also potentially be used to develop useful indicators of the proximity of

assets to people (e.g. private gardens/hedges) and to one another (e.g. contiguous habitats). The

following are the proposed indicators for the spatial configuration of UK urban natural capital (see

Table 4.2):

Extent of private gardens/hedges: These assets are important because they are located close

to population (by definition) and provide a network of habitats across the urban landscape in

line with Lawton principles.

Vegetation near road/rail: This indicates the capacity of vegetation to reduce noise by virtue

of its location (near road/rail). This can potentially be produced as an output from modelling of

noise regulating service provided by urban vegetation.


eftec 54 June 2017

Location of blue infrastructure: blue infrastructure provides a range of ecosystem services,

the quantity of which is determined to a large extent by their location relative to beneficiary

populations.

Contiguous habitats: connectivity of habitats is important for a healthy natural environment

that will allow our plants and animals to thrive (Lawton, 2010).

Accessible Natural Green Space Standard (ANGSt): a tool to assess current levels of accessible

natural greenspace according to recommendation that everyone, wherever they live, should

have accessible natural greenspace ranging from 2ha within 300 metres (5 minutes’ walk) from

home, to one accessible 500 ha site within 10km of home.

Indicators of physical access

The following are the proposed indicators for the physical access to UK urban natural capital (see

Table 4.2):

Accessible Natural Green Space Standard (ANGSt): see above.

Paths and bridleways: People who use paths and bridleways that are partially or entirely

within or along features of the natural environment are benefitting from exposure to the

natural environment. Their choice in using those particular routes is also likely to be influenced

by the presence of natural surroundings.

Car parks and other amenities: Whether a site has a car park can influence the visitor profile

(how far people might travel to get there) and frequency (how often they visit). This can

potentially be sourced from ORVal, even though the potential to manipulate this data is yet to

be explored.

Indicators of management practices

The following are the proposed indicators for management practices related to UK urban natural

capital (see Table 4.2):

Green flag status parks: the award recognises the very best green spaces according to eight

key criteria including a mix of social (i.e. healthy, safe and secure; clean and well maintained)

and environmental (i.e. conservation and heritage) issues that are important in determining

people’s use of the parks (and therefore their value);

Extent of Sites of Special Scientific Interest (% of habitat type by status): These conservation

designations are provided for sites with high ecological or geological value. The England

Biodiversity Strategy aspiration is to bring at least 50% of SSSIs in favourable condition and

maintaining at least 95% in favourable or recovering condition;

National Audit Office (NAO) quality survey: This rates urban Local Authority green spaces,

according to ‘good’, ‘fair’, or ‘poor’ as reported by each local authority.

Green roofs and green walls: these consist of vegetation to cool the atmosphere and therefore

they are a form of natural capital that delivers climate regulation.


eftec 55 June 2017

Table 4.2. Matrix linking natural capital extent and condition to ecosystem services

Broad

dimension

Indicator Unit Source Food AQ Reg’n

Noise Reg’n

Local climate

Global climate

Pollintn Cult Hertge

Recrtn Phys Health

Flood

Extent See Table 4.1 Ha See Table 4.1

Biodiversity Species abundance Bee hives Number National Bee Unit

Total of a species Number/Index RSPB (2016)

Species diversity TBC TBC

Soil Carbon content (clay content) Ha by soil type BGS

Surface permeability Permeable Ha BGS

Impermeable Ha

Ecological condition

River/Lake water quality WFD status Good/Poor etc. EA

Vegetation age

Vegetation height Ha by cm LIDAR

Vegetation width Ha by cm TBC

Vegetation size DBH LIDAR; NTM

Spatial configuration

Extent of private gardens/hedges Ha OSM; LCM

Vegetation near road/rail Ha by dBA OSM; LCM; NTM

Location of blue infrastructure TBC TBC

Contiguous habitats Ha by habitat TBC

Accessible Natural Green Space Standard Population NE (2010) Access

Paths Km OSM; ORVal

Bridleways Km

Car parks and other amenities Number

Management practices

Green flag status parks Number Green Flag Awards

Extent of SSSIs (% status by habitat type) Ha NE; ORVal

NAO quality survey Good/Poor NAO (2005)

Green roofs/green walls Number TBC

Sustainable Urban Drainage Systems Area TBC

http://www.greenflagaward.org.uk/


eftec 56 June 2017

4.4. Physical account of ecosystem service provision

This scoping account (Table 4.3) captures the annual physical quantity (e.g. tonnes, m3, kilogram,

number) of ecosystem service produced by natural capital within the defined UK urban boundary.

Note that the estimated flow for some services is not for the UK scale but for England only (because

underlying data was collected at this level e.g. MENE) or for a city region (Greater Manchester is

used to align with the Defra Pioneer area) and this is due to data and resource limitations.

Table 4.3. Annual ecosystem service flows from UK urban natural capitala

Benefit Coverage Amount Unit Source(s)

Food UK 80,000,000 kg/yr Cook (2006); Perez-Vazquez (2000); UKNEA (2011) Campbell and Campbell (2013); Pretty, 2000; NSALG; Crouch, 2006


UK 494,000 tCO2e/yr Forestry Commission, 2014; ONS, 2016


Total

GB

43,000 tonnes/yr EMEP4UK

PM10 -0.065 ug/m3

PM2.5 -0.056 ug/m3

SO2 -0.023 ug/m3

NH3 -0.018 ug/m3

NO2 -0.007 ug/m3

O3 -0.140 ug/m3

Noise regulation Manchester 429,000 No. of buildings with dBA reduction

OS MasterMap; Defra, 2014


GB

-0.42 oC Bowler et al. (2010); Larondelle and Haase (2013)


UK 74,000 QALYs/yr Beale et al. (2007); White et al (2016)

UK 2,076,000 ‘Active’ visitor numbers/yr

NE (2015); NICE (2013); DoH, 2004; White et al (2016)





The following section explains how the estimate of the physical provision of each benefit in Table

4.3 has been derived.

Food

Table 4.4 shows the findings from a review of evidence into the total number and size of urban

allotments in the UK, it shows:

The total number of allotments in England is estimated to be between 7,000 and 8,000 (NSALG,

1997; Crouch, 2006);

The Urban chapter of the UKNEA Technical Report provides a section on Allotments, Community

Gardens, and Urban Farms (Section 10.2.7). This references CABE Space research36 that

recorded a total of 997 allotment sites with an area of 1,357ha. Whist significantly lower than

36 CABE Space, Urban Green Nation, 2010, p9


eftec 57 June 2017

those recorded in the Allotments in England survey (Crouch, 2006) the data sources were

limited to those from NAO’s list of 154 Urban Authorities37;

The total number of allotment plots in the UK is estimated to range between 280,000 and

330,000 (Pretty, 2000; NSALG; Tomkins, 2006));

The number of plots in England that are vacant has been recorded by Crouch (2006) as 25,131

out of a total of 245,000 in 2005 which is 10%;

55% of allotments are in urban areas and are generally smaller in size than rural allotments

(UKNEA, 201138).

The higher figure of 330,000 allotment from the NSALG and restated by Tomkins (2006) is used as

this is provided by a national association and likely to include allotments provided by local

authorities, parish and town councils, and other providers. Of these 55% are in urban areas (UKNEA,

2011) and it is calculated that 10% are unoccupied (Crouch, 2006, proportion of vacant plots in

England). Based on these figures it is estimated that 163,350 allotment plots are occupied and in

productive use in urban areas in the UK.

Table 4.4. Evidence on the number and size of UK Allotments

Data Country Source / Date Date Figure Unit

Allotments

England NSALG (1997)39 1997 7,796 Number

Crouch (2006) 2005 7,000

England – Urban LAs40 UKNEA (2011); CABE Space (2010) 2004/05 997

Wales Stokes (2002) 2002 500

England - Urban LAs CABE Space (2010)41 2004/05 1,356.8 Ha

UKNEA, 2011 2004/05 1,356.8

England NSALG (1997) 1996 10,276

Crouch (2006)42 2005 4,785

England and Wales NSALG (1997) 1978 19,872

Plots

England NSALG (1997) 1997 296,923 Number

Crouch (2006) 2005 245,000

UK

Campbell and Campbell (2013) 2012 280,000

Pretty (2000)43 2000 300,000

NASLG44 n/a 330,000

Number of

vacant plots England

NSALG (1997) 1997 43,000 Number

Crouch (2006) 2005 25,131

Size of plots

UK NSALG n/a

250

m2

England and Wales UKNEA, 2011 (p385) 2011

Scotland SAGS (2013) 2013

Urban vs Rural UK UKNEA (2011) 2011 55 %

Data on the productivity of allotments is limited, see Table 4.5 which shows:

Cook (2006) has analysed the inputs, outputs productivity, efficiency and return of individual

plots for four Welsh allotments in Pontypridd. The study calculated from an Royal Horticultural

37 NAO, Enhancing Urban Green Space, 2006, p73 38 UK National Ecosystem Assessment: Technical Report, 2011, Urban Chapter, p376 39 NSALG (1997) data referenced in House of Commons (1998) Select Committee 40 CABE Space (2010) collected data for 154 ‘Urban Authorities’ in England listed by NAE (2006), p73 41 CABE Space (2010) data for allotments taken from CLG Allotment Sites 2004-05 (GIS Based Data) 42 Crouch (2006, p13) notes area of allotments figure is unreliable when compared with disposals 43 As stated in Cook (2006), p93 44 NSALG website figures http://www.nsalg.org.uk/allotment-info/brief-history-of-allotments/

http://www.nsalg.org.uk/allotment-info/brief-history-of-allotments/


eftec 58 June 2017

Society trial on allotment productivity at Harlow Carr (Tomkins, 2006)45 undertaken in 1975 and

given by Stokes (2002) and understood to be the only known specific statistical record of

vegetable food crop produce harvested on allotment plots, estimated yields of 487Kg/plot/year

for a standard 250 m2 plot;

Perez-Vazquez (2000) considered ‘The future role of allotments in Food Production as a

component of Urban Agriculture in England’ based on measurements of yields from allotment

holders in Kent. This study showed allotment crop output of 259kg/plot/year.

Based on the number of occupied plots in urban areas of 163,350 and the annual yield of 487

kg/plot the UK productivity for allotments is estimated to be 80,000,000 kg/year.

Table 4.5 Evidence on the productivity of UK Allotments

Data Country Source / Date Date Figure (kg/plot/yr)

Produce yield from a plot England Cook (2006)46 1975 487

Perez-Vazquez (2000) 2000 259

Note: Based on a standard plot size of 250m2.

Climate regulation – global (carbon dioxide)

The estimated amount of carbon sequestered from UK woodland in 2014 is 15.6MtCO2e (ONS, 2016).

The area of woodland in the UK at 31 March 2014 is estimated to be 3.14 million hectares (Forestry

Commission, 2014). This suggests an average rate of sequestration of 5tCO2e/ha/year across the

UK. Applying this to the estimated area of urban woodland in the UK of 99,397 ha using OS

Mastermap (see Table 4.1), results in an estimated carbon sequestration in 2014 of 494,000 tonnes

CO2e per year.


Tables 4.6 and 4.7 report the physical flow account for 2015 and 2030.In 2015, urban green and

blue space is estimated to remove 43,000tonnes of PM2.5, SO2, NO2 and O3. Overall, there is a

projected decline in the amount of service provided, as measured by the change in pollutant

concentration (Table 4.6) and the quantity of pollutant removed (Table 4.7).

Table 4.6. Estimated change in pollutant concentration in 2015 and 2030 due to urban

vegetation

Pollutant 2015 2030 Unit

PM10 -0.065 -0.041 ug/m3

PM2.5 -0.056 -0.034 ug/m3

SO2 -0.023 -0.012 ug/m3

NH3 -0.018 -0.016 ug/m3

NO2 -0.007 0.000 ug/m3

O3 -0.140 -0.160 ug/m3

45 The trial produced 1.95kg/m2 of vegetable food crops to a total value of £745.00 (at 2004 organic produce

values) harvested from a standard 333m2 plot. 46 This figure is based on the 1.95 Kg/m2 of vegetable food yield calculated by the 1975 RHS study from a 333

m2 plot and recalculated as an equivalent for a standard 250 m2 plot (1.95 x 250 = 487.5 Kg)


eftec 59 June 2017

Table 4.7. Estimated quantity of pollutant removed in 2015 and 2030 due to urban vegetation

Pollutant 2015 2030 Unit

PM10 4.45 3.08 ktonnes

PM2.5 2.78 1.61 ktonnes

SO2 1.82 0.98 ktonnes

NH3 2.41 1.98 ktonnes

NO2 0.56 0.29 ktonnes

O3 31.15 32.16 ktonnes

All pollutants 43.16 40.09 ktonnes

Both urban land cover and meteorology are held constant from 2015 to 2030 so these changes

reflect only the projected difference in pollutant concentrations due to changes in emissions

between these years. The change in concentration due to pollution removal by urban natural

capital is much smaller than in the national account (CEH et al, forthcoming), up to a maximum of

2.6% reduction for SO2, and only 1% reduction in concentration for PM2.5. Nevertheless, there are

still health and monetary benefits.

Noise regulation

Table 4.8 shows the decibel reduction by natural capital within the defined urban boundary –

illustrated by application to Greater Manchester alone. The method identifies patches of tree cover

greater than a threshold area of 200 m2 (using Bluesky National Tree Map), therefore likely to be

providing a noise mitigation service. It then calculates noise in which urban areas are potentially

mitigated by those trees using maps for road noise. The locations of beneficiaries in those zones are

then identified using buildings from OS Mastermap. The analysis estimates that 428,980 buildings

receive some noise mitigation by urban trees in Greater Manchester. The numbers of buildings

receiving a noise reduction service are shown in Table 4.8, by noise band.

Table 4.8. Number of buildings where road noise levels are mitigated by 2 dB(A) by natural

capital in Greater Manchester

Noise band Number of buildings mitigated by 2 dB(A)

>=80 *

75.0-79.9 1,390

70.0-74.9 11,164

65.0-69.9 45,433

60.0-64.9 107,414

55.0-59.9 263,579

50.0-54.9 *

45.0-49.9 *

* Not calculated

There are a number of assumptions to be borne in mind when interpreting this estimate:

The noise levels are at fine resolution, generated by noise consultants for Defra. Their

calculations take into account location of buildings and topography, but not vegetation. Our

calculations work out the potential additional effect of vegetation in further mitigating noise

levels;

The science underpinning noise reduction is summarised in the final report of the HOSANNA

project (EC, 2013). We take a conservative estimate of the amount of noise reduction by

patches of vegetation. We have taken the simplest approach using data that is readily

available, by screening out vegetation that provides little or no service, and applying a single


eftec 60 June 2017

conservative estimate of noise reduction to the remaining vegetation. Differentiating between

levels of noise removal among vegetation types and patch sizes would require more

comprehensive and more complex analysis;

Calculating the number of buildings where noise is mitigated by vegetation is dependent on

their spatial locations;

In order to identify the locations of beneficiaries at the fine spatial scale of the noise data, we

have made the assumption that people are uniformly distributed among buildings, whereas in

reality, some buildings are residential while others are for work. Similarly the numbers of

people exposed to noise per building will vary depending on the building size and its use.

Given these assumptions, we feel that this indicative calculation provides an order-of-magnitude

estimate of the numbers of people experiencing a reduction in noise levels due to vegetation, and

in Greater Manchester alone.

Local climate regulation

Table 4.9 shows the selected evidence used to estimate temperature differential afforded by

different types of urban green space based on the review of literature, further information is

provided in Annex 4.

Table 4.9 Selected evidence on temperature differential from urban green space

Description of

temperature differential

Issues / limitations / comments Temperature

reduction47 (oC)

Source

Urban parks vs city

average in Leipzig.

Definition of urban park unclear in this

context (would need to review primary

reference further).

-0.95

Bowler et al.

(2010)

Larondelle

and Haase

(2013)

100m buffer around urban

parks vs city average in

Leipzig.

Definition of urban park unclear in this

context (would need to review primary

reference further).

-0.52

Tree cover / forests vs

city average in Leipzig.

Definition of tree cover / forests unclear in

this context (would need to review primary

reference further).

-3.5

Table 4.10 shows the estimated average cooling effect (oC) for all park and woodland patches

(different patch sizes are estimated because there is some evidence suggesting a positive

correlation between vegetated patch size (>3ha) and cooling effect, see section 3.5). No sensitivity

analysis is undertaken on this within this study but the possibilities are provided in order that this

could be undertaken if desired). The estimate is based on the percentage of total urban area (as

per the urban boundary defined in this study) that is covered by these green spaces across Great

Britain, is -0.23 oC for parks and -0.20 oC for woodland. Therefore, the combined effect of urban

parks and woodland on reducing average urban temperatures in Great Britain is estimated to be

-0.42oC.

This estimate illustrates the problem of magnitude versus significance which is inevitable when

averaging for something that is spatially related. The magnitude (average cooling effect) may be

small, but its significance may be much greater where it actually occurs, which is locally. And even

greater during heat waves since can help to mitigate the impact of a heat wave (cool places to go

as well as greater temperature differential between built environment and green/blue

environment). And under extremes the effect may be more significant, e.g. help reduce mortality

47 Values are for air temperature unless otherwise stated.


eftec 61 June 2017

from high temperatures. Nevertheless, it provides an indicative city-level estimate for the purposes

of this scoping study.

Table 4.10. Physical account – urban heat flow indicators for urban areas in Great Britain

Element of urban vegetation

providing the cooling effect

Cooling effect (oC) Percentage of urban area

covered (%)

Proportional

cooling effect (oC)

Parks

All patches

-0.95

23.8 -0.23

Patches >3ha 5.6 -0.05

Patches >0.5ha 13.4 -0.13

Patches <0.5ha 10.3 -0.10

100m buffer around patches >3ha -0.52 14.4 -0.07

Woodland

All patches

-3.50

5.6 -0.20

Patches >3ha 0.6 -0.02

Patches >0.5ha 2.9 -0.10

Patches <0.5ha 2.7 -0.09

100m buffer around patches >3ha -0.5248 2.1 -0.01

Combined cooling effect of parks and woodland

All patches -0.42

Patches >3ha -0.07

Patches >0.5ha -0.23

Patches <0.5ha -0.19

100m buffer around patches >3ha -0.08

Patches >3ha AND 100m buffer around patches >3ha -0.16


Table 4.11 provides a breakdown of those ‘active’ visitors (in 2015) who said they met weekly

activity guidelines (whether through engaging in the natural environment or otherwise) and made

from 1 (n = 467,167) through to 5 or more (n = 469,667) active visits to nature last week; alongside

those who apparently did not realise they did in fact meet guidelines because they made ≥5 active

nature visits in the last week (n =369,167). In total this is over 1.7 million people in England.

Table 4.11. ‘Active visits’ to natural environments in urban areas in England by ‘active

individuals’ and associated QALYs

Self-reported

exercise a week

Active visits per

weeka

Number of active

visitors

QALYs per

person/year

QALYs per

population/year

≥5 x 30 mins

1 467,167 0.010677 4,988

2 227,333 0.021354 4,854

3 126,833 0.032303 4,097

4 87,000 0.042707 3,716

5 469,667 0.053384 25,073

<5 x 30 mins 5 369,167 0.053384 19,708

TOTAL - 1,747,167 - 62,435 aActive visits = ≥ 30 minutes and ≥ 3METs.

A QALY score was assigned to each group of active individuals taking into account their differing

visit frequencies (i.e. from 1 time per week to 5). Using Beale et al. (2007), one active visit a

48 From the literature reviewed, it was not possible to obtain a cooling effect value for urban woodland edge (i.e. the 100m

buffer). As a proxy, the value for parks has been used (Bowler et al., 2010; Larondelle and Hassen, 2013), however, the

cooling effect of the woodland edge is likely to be higher than this due to the physical characteristics of woodland vs parks.


eftec 62 June 2017

week for every week in the year (i.e. 52 visits) is associated with an annual QALY gain of 0.010677.

Multiplying the number of individuals who made 1–5 visits by the relevant QALY scores (assuming

that last week was representative of all 52 weeks), and summing the results, gives a total

population annual estimate of 62,435 QALYs per year.

The proportion of urban active visits and visitors in England (Table 4.11) was extrapolated to

Scotland, NI, and Wales based on their relative urban populations, and QALY scores were applied.

This calculation therefore assumes that active visits to the natural environment will be the same

proportion of the population in each country as it is in England. In absence of country-specific data

this assumption is deemed to be reasonable, however, estimates could be refined in future.

It should be noted that this estimate is for the total amount of physical activity within urban areas

only (rather than activity undertaken by urban residents – many of whom visit rural locations for

physical activity), and also does not reflect the different numbers of visitors to different

greenspaces in different regions, which in turn reflects the different characteristics of

greenspaces (e.g. size, location, population density of surrounding area). This analysis is also set

against the counterfactual that none of this physical activity would take place in the absence of

the urban natural capital sites (which is consistent with SNA).

4.5. Monetary account of annual provision of ecosystem service

The scoping account in Table 4.12 captures the annual economic value (£) of the ecosystem

services that have been quantified in the physical flow account. Where possible, ‘exchange values’

that are observed in markets, and, otherwise ‘imputed exchange values’ (i.e. indirectly measured

or estimated) where markets do not exist have been used. Alternative welfare-based measures that

capture consumer surplus have also been included to provide a range of values.

Table 4.12. Annual value of ecosystem service flows from UK urban natural capitala


Food UK £114m £m/yr Market value Cook (2006); Pretty (2001)


UK £31m £m/yr Cost of carbon mitigation

DECC (2014)


PM2.5 GB £195m £m/yr Welfare value and avoided market costs

Defra (2014)

SO2 £0.3m £m/yr

NO2 £13m £m/yr

O3 £3m £m/yr

Noise regulation Manchester £59m £m/yr Welfare value of dBA reduction

Defra (2014)


GB £70m £m/yr Market values - avoided loss in GVA and avoided air-conditioning cost



UK £1,482m £m/yr Welfare value (QALY)


UK £900m £m/yr

Avoided total cost






The results in Table 4.12 are based on the methods described for each physical flow and the

following calculations.


eftec 63 June 2017

Food

Table 4.13 shows that the literature suggests that the financial value of annual allotment produce

from a standard 250m2 plot varies from around £438/year to £1,870/year. Note that these are net

returns (i.e. after deducting the costs of capital).

Table 4.13 Evidence on the productivity of UK Allotments

Data Country Source / Date Date Figure (£/plot)

Value of annual yield

England

Perez-Vazquez (2000)49 2000 £677

Cook (2006)50 2004 £560

Gill (2014)51 2014 £933

Gill (2014)52 2014 £1,078

Wales Cook (2006)53 2004 £438

UK Pretty (2001)54 2000 £1,870

UKNEA (2011) p385 2011 £1,128

n/a NASLG (2011)55 2011 £1,362

n/a Walne (2011) 2011 £1,564

Note: Based on a standard plot size of 250m2.

Based on the number of occupied plots in urban areas of just over 160,000 and the average value of

food produced of £695 (uprated to £2016 from Cook, 2006) the estimated value of urban allotment

productivity is estimated at nearly £114 million/year. Based on the estimated production of

between 80,000,000kg/year in section 4.4, the estimated value per kg is ~£1.40/kg.

Global climate regulation

The estimated carbon sequestration of UK urban woodland in 2015 is 494,000 tonnes CO2e per year.

The unit value per tonne of non-traded carbon in 2016 (DECC, 2014) is £63/tCO2e/year giving a

total estimated value across all UK urban woodland of £30m/year for 2016. This shows that the

value of carbon sequestration from urban woodland is relatively small compared to some of the

other benefits from urban natural capital in the UK.


The health benefits of air quality improvement are estimated using reductions in three key forms of

health care costs, currently incurred due low air quality:

49 Calculation of the average net value of produce per plot of 120 m2 estimated to be £325, or £677 for a

standard 250 m2 plot. 50 Figure quoted by Cook (2006) from 1975 Royal Horticultural Society experiment at Harlow Carr (Stokes

2005). This showed produce of 1.95 kilograms per square metre with a total value of £745.00 (at 2004

organic produce values) harvested from a standard 333 m2 allotment. The figure is recalculated as an

equivalent for a standard 250 m2 plot (x0.75) 51 Figures calculated for the value of crops produced from 300 yard3 (250.8 m2) plot in Devon 52 Figures calculated for the value of crops produced from 200 m2 plot in Bristol 53 Figure from Cook (2006) p193, for four Pontypridd allotment gardeners included in the survey for the thesis

produce an annual average annual crop valued at £438.00. Crop produce weights from these plots are

known but have not been extrapolated forward. 54 Figure quoted by Cook (2006), p93, from Pretty (2001) 55 Figure quotes valuation published by Peerless, V. and produced by NSALG


eftec 64 June 2017

a) Mortality costs: from the lowered life expectancy or deaths brought forward. These range from

£18,000 to £35,000 (Defra, 2014). The primary source used in the IGCB guidance for the

valuation of this evidence is the paper by Chilton et al. (2004), which estimates the value of a

life year (VOLY) in good health (i.e. a willingness-to-pay value);

b) Morbidity costs: from the increased incidence of certain illnesses, such as those affecting the

cardiovascular and respiratory systems. These range from £2,600 to £10,700 (Defra, 2014).

These costs can be broken down into three components (IGCB, 2007):

Resource costs: the medical costs to the National Health Services and private costs of

dealing with the illness, these are exchange values;

Opportunity costs: the lost productivity and opportunity cost of leisure (including unpaid

work) which are valued based on salary costs of absent individual (i.e. exchange value); and

Disutility: disutility of ill health to the individual and their family and friends which is a

willingness-to-pay value.

Damage costs (Defra, 2015a) are provided by pollutant, source and location. Table 4.14 shows the

change in number of hospital admissions/life years lost/deaths attributable to presence of GB

urban green and blue space for 2015 and 2030 and Table 4.15 shows the monetary accounts over

time for 2015 and 2030.

Table 4.14 Change in number of hospital admissions/life years lost/deaths attributable to

presence of GB urban green and blue space (number)

Pollutant Health impact Number

Unit 2015 2030

PM2.5 Respiratory hospital admissions

-123 -81 No./yr

Cardiovascular hospital admissions -108 -71 No./yr

Life years lost -5538 -3043 No./yr

SO2 Respiratory hospital admissions -51 -30 No./yr

NO2 Respiratory hospital admissions

-25 -8 No./yr



O3 Respiratory hospital admissions

-338 -424 No./yr


Deaths -105 -124 No./yr

All pollutants

Respiratory hospital admissions -538 -542 No./yr



Deaths -105 -124 No./yr

The health and monetary accounts show a decline over time, with the exception of O3 effects

where there is no change. Table 4.14 shows the avoided mortality and morbidity due to air

pollution removal by UK urban green and blue space. It is estimated that in 2015 there were 105

avoided deaths, 5,900 avoided life years lost, 538 fewer respiratory hospital admissions and 182

fewer cardiovascular hospital admissions. The economic value arising from these avoided health

costs was substantial, £211m in 2015 (Table 4.15).


eftec 65 June 2017

Table 4.15. Value of avoided health impacts due to air quality regulation in GB urban green and

blue spaces (£/yr)

Pollutant Health impact Value Unit

2015 2030

PM2.5 Respiratory hospital admissions £821,000 £537,000 £/yr

Cardiovascular hospital admissions £700,000 £458,000 £/yr

Life years lost £193,840,000 £106,497,000 £/yr

SO2 Respiratory hospital admissions £340,000 £198,000 £/yr

NO2 Respiratory hospital admissions £167,000 £50,000 £/yr


Life years lost £12,613,000 £3,772,000 £/yr

O3 Respiratory hospital admissions £2,248,000 £2,817,000 £/yr


Deaths £632,000 £743,000 £/yr

Total £211,836,000 £115,534,000 £/yr

As the urban cross-cutting account was calculated based on a bespoke reference land cover, the

model results are not directly comparable with those for the national account, and the urban

accounts are not a simple subset of the total pollutant removal in the national accounts. However,

broad comparisons can still be made between the accounts.

The health benefits from urban green and blue space have an equivalent value of 20% of the

estimate for the UK vegetation as a whole, and are far greater than might be expected from the

urban land area, where the urban extent is only 7% of the UK total land area. This occurs for two

reasons. Firstly urban green and blue space reduces air pollution concentrations in neighbouring

areas outside of the urban extent, thereby providing benefits outside of the urban area. Secondly,

the bulk of the UK population live in urban areas, so the population benefiting from reduced

exposure to air pollutants due to pollution removal by urban green/blue space will be greater.

Noise regulation

Table 4.16 shows the monetary benefits of noise regulation based on the avoided loss of QALYs due

to noise as explained in section 2 and are estimated to be around £59 million/year for Greater

Manchester.

Table 4.16 Annual road noise regulating benefits from urban natural capital – Greater

Manchester

Noise band Annual value - noise regulation by noise band (dBA)

82 to 80 Not calculated

77 to 75 £0.5m

72 to 70 £3m

67 to 65 £10m

62 to 60 £17m

57 to 55 £28m



TOTAL £59m


eftec 66 June 2017


The total benefit associated with the cooling impact of urban green space (i.e. when temperatures

reach ≥28oC) is the avoided cost of additional air-conditioning that would be needed on hot days in

the absence of vegetation, plus any residual productivity loss (primarily for outdoor occupations

such as construction, where air-conditioning is not feasible).

The avoided energy costs associated with urban green space have been estimated for urban areas in

Great Britain based on the London i-Tree project (Rogers et al., 2015) which used the established i-

Tree Eco Tool56. One key service that study considered was the impact of trees and other

categories of urban vegetation on building energy use, including building cooling (air-conditioning)

costs. The London assessment in Rogers et al. (ibid) identifies how the cooling effect of urban

vegetation in terms of air-conditioning costs avoided results in an annual benefit of £4,139,159 per

year (for Greater London).

For the purposes of this scoping study, we suggest that comparable land use data between London

and Great Britain (Table 4.17) can be used to establish a rough proxy of the air-conditioning costs

avoided in Great Britain. The data on the extent of park and woodland in London and Great Britain

in Table 4.18 show that urban areas in Great Britain have substantially more parks than London but

less woodland as a proportion of total area. Therefore it is reasonable to conclude that the cooling

benefits (air conditioning costs avoided) could be similar in urban areas in Great Britain, if not

more positive (due to the higher proportion of park cover in Great Britain). Although this does not

account for the variation in local climate and urban form57 across Great Britain, which is a key

limitation of this aggregated approach to estimating cooling impact of vegetation (see Section 6.4).

Table 4.17: Comparison of park and woodland land use in London and Manchester

City Total urban area that is park (%) Total urban area that is woodland (%)

London 15.5 8

Great Britain 23.8 5.6

Notes: London data sourced from Rogers et al. (2015). For London: park land use figure includes 2.5% golf

courses; the woodland land use figure is reported as ‘agriculture’ – this includes woodland but will include

other land uses also (ibid).

Accordingly, we suggest that the annual benefit of urban vegetation in these terms is calculated

proportionally based on the total urban extent (ha) in Great Britain and in London. This analysis is

shown in Table 4.18 below which indicates that the annual benefit of air-conditioning costs avoided

due to urban vegetation for urban areas in Great Britain might be in the region of ~£45million per

year58. All figures obtained using the Building Effect model within i-Tree need to be approached

with caution as the model has not been adjusted for UK use; it has not been verified (independently

or otherwise) as fit-for- purpose in the UK. A key issue, for example, is the difference in building

56 iTree Eco models energy savings based on survey data on the physical extent and condition of London’s

green infrastructure in terms of the species, age class, biomass, leaf area, physical condition, canopy cover,

tree position, orientation and distance relative to buildings of the city’s trees and shrubs which we have not

quantified in this study. The sample size and methodology required to achieve statistically significant results.

https://www.itreetools.org/eco/ 57 For example, i-Tree uses orientation as a major part of the calculation of energy savings to buildings

provided by vegetation. Trees windward of the prevailing wind shelter buildings and so provide more benefit in

winter (energy saving), but trees to the north offer occasional wind protection but zero shading functionality

in summer. 58 The London assessment in Rogers et al. (2015) shows how urban vegetation actually also results in a cost in

terms of building of heating of £3,832,682 per year. Given this, the annual benefit (AC costs averted) net of

costs (building heating due to vegetation cooling buildings in winter) for London is therefore £315,477.

However, for the purposes of natural capital accounting in this report, this ecosystem “disservice” has been

excluded from the calculations.

https://www.itreetools.org/eco/


eftec 67 June 2017

properties between the UK and the US where i-Tree was developed (e.g. buildings in the US tend to

be of timber construction).

Table 4.18: Comparison of park and woodland land use in London and GB

City Area of urban extent

(ha)

Multiple of London urban

extent

Avoided air-conditioning

costs (£)

London 159,470 1 4,150,000

GB 1,768,616 11 45,650,000

Note: London data sourced from Rogers et al. (2015)

However, air conditioning does not eliminate productivity losses entirely and so we estimate the

residual impact on GVA. Table 4.19 shows the input data used to estimate the monetary value of

avoided productivity losses due to urban green space in Great Britain in the absence of air

conditioning (the impact of air conditioning is adjusted for later in the analysis):

GVA in key urban economies across Great Britain is approximately £1,096 billion/year and is

comprised mainly of public administration and defence (38.8%), wholesale and retail trade

(25.5%), information and communication (12.5%) and financial and insurance activities (12%);

The average July temperature is assumed to be 20.2oC (average temperature for the Woodford

Met Office recording station, 1981-2010 climate period) and the highest daily maximum

temperature (England NW, recorded at Nantwich, Cheshire) is 34.6oC. The cooling effect of

vegetation will have no impact on GVA for the average temperature, because the impacts on

productivity only occur over the range 26.5oC to 33oC (based on Figure 3.2 above, Costa et al,

2016 and notwithstanding the limitations of this approach relating to the use of ambient air

temperatures as a proxy for WBGT – see Section 6);

Table 4.19. Monetary account – input indicators for urban areas in GB

Indicator Value Unit

Total economic output

Gross Value Added (GVA) for key urban economies in GBa 1,096 £bn

Economic output by sectorb

Agriculture, forestry and fishing GVA 0.1 %

(Production &) Manufacturing GVA 8 %

Construction GVA 3.1 %

Wholesale and retail trade GVAc 25.5 %

Information and communication GVA 12.5 %

Financial and insurance activities GVA 12 %

Public administration and defence GVAd 38.8 %

Hot day values

Average July temperature (Woodford, 1981-2010 climate period) 20.2 oC

Highest daily maximum temperature (England NW, recorded at Nantwich, Cheshire) 34.6 oC

London hot day temperature (Kovats et al., 2016) 28.0 oC

Notional hot day temperature for sensitivity analysis 30.0 oC

Hot day frequency

London hot day temperature reached or exceed (Kovats et al., 2016) 28oC 4 Days

Notes:

a. Total economic output (GVA) is for 2012 and has been drawn from various sources: (1) GVA data for

England combines absolute values for GVA (Income Approach) by SIC07 industry for the English economy as

a whole with data on the contribution made by Greater London and England’s eight city regions to

England’s economic output (GVA) by industry. All data is sourced from ONS; and (2) GVA data for city

regions in Scotland and Wales was obtained from the ONS regional GVA (Income Approach) dataset using

data for relevant NUTS3 regions, which is the smallest geography available. However, this approach means

that key Welsh and Scottish urban economies have not been included as they fall within a NUTS3 region

that includes a wider rural area as well (this would skew the analysis given that these rural economies will

include a much GVA contribution from the agriculture, forestry and fishing sector).


eftec 68 June 2017

b. GVA has been calculated based on urban economy structure for Greater London and the eight English city

regions (ONS, 2015). This structure has been used as a model to estimate the structure of urban economies

in Scotland and Wales also.

c. Combines ONS GVA (Income Approach) by SIC07 industry GVA values for ‘wholesale and retail trade; repair

of motor vehicles’, ‘transportation and storage’, ‘accommodation and food service activities’, ‘other

service activities’, ‘activities of households’, ‘electricity, gas, steam and air-conditioning supply’ and

‘water supply; sewerage and waste management’.

d. Combines ONS GVA (Income Approach) by SIC07 industry GVA values for ‘public administration and

defence; compulsory social security’, ‘real estate activities’, ‘education’, ‘human health and social work

activities’ and ‘arts, entertainment and recreation’.

Tables 4.20 and 4.21 show the monetary account results for local climate regulation from for urban

economies (Greater London and England’s eight city regions; ONS, 2016) across Greater Britain.

Table 4.16 illustrates potential productivity losses and associated GVA losses for scenarios with and

without urban vegetation; e.g. the London “hot day” temperature of 28oC would potentially be

0.42oC hotter without the existing urban vegetation, as per the physical account. This higher

temperature would equate to greater productivity losses. In summary, the monetary account

analysis shows that when temperatures reach 28oC (i.e. London “hot day” temperature):

Without existing urban vegetation in GB, a warm day equating to the London “hot day”

temperature would be almost half a degree hotter (28.42oC). This would potentially result in

productivity losses of 13% of the value of GVA from moderate / heavy work industries

(construction industry; agriculture, forestry and fishing) compared to 5% when the effect of

existing vegetation is taken into account; and

Estimated productivity losses: (i) without urban vegetation are estimated to be £17m/day; and

(ii) with urban vegetation are estimated to be £6m/day.

Table 4.20: Monetary account – avoided productivity losses for urban areas in GB

Estimated losses

Work intensity Relevant sectors Productivity

(%)a

Annual GVA

(£M)

Working day GVA

(£M)

Productivity losses WITHOUT existing urban vegetation - London hot day temp. (28.42oC)

Light work Information and communication 0 0 0

Financial and insurance activities 0 0 0

Moderate /

light work

Manufacturing 0 0 0

Wholesale and retail trade 0 0 0

Public administration and defence 0 0 0

Moderate /

heavy work

Agriculture, forestry and fishing 13 140 1

Construction 13 4,420 17

Total losses across urban economies in GB 4,560 17

Productivity losses WITH existing urban vegetation - London hot day temp. (28.0oC)



Moderate /

light work

Manufacturing 0 0 0

Wholesale and retail trade 0 0 0

Public administration and defence 0 0 0

Moderate /

heavy work



Total losses across urban economies in GB 1,750 6 a The productivity loss is 0% where higher temperatures are estimated to have no effect on productivity even

without the use of avertive behaviours such as air conditioning.

The figures in Table 4.20 do not account for the averted losses under adaptation measures such as

through the use of air conditioning or behavioural change (i.e. the impact of changing working

hours in terms of averted losses for labour productivity). Behavioural change is especially relevant

for industries where air conditioning will have little or no impact (i.e. construction). Evidence from


eftec 69 June 2017

Costa et al (2016) suggests that averted losses through (i) air conditioning (in London) can be ≤85%

(ii) behavioural change (in London) can be ≤40%. Unfortunately there is no estimate for the

combined impact of behavioural change and air conditioning. Table 4.21 shows the impact of

avertive actions, assuming that for:

a) Light and moderate/light work the combined impact of these adaptation measures is 90% based

on averted losses of 85% due to air conditioning and an additional 5% through behavioural

change;

b) Moderate/heavy work these industries are predominantly outside and so air conditioning cannot

be used, so a 40% reduction is assumed for this work.

Table 4.21. Monetary account – avoided productivity losses for urban areas in GB adjusted for

avertive actions

Estimated losses

Work intensity Working day

GVA (£m)

Avoided losses due to mitigation Total GVA loss (£m)

(%) (£m)


Light work/ Moderate

/ light work £0m 90% £0m £0m

Moderate / heavy work £17m 40% £7m £10m



/ light work £0m 90% £0m £0m

Moderate / heavy work £6m 40% £2 £4

Table 4.21 shows the reduction in GVA losses due to avertive behaviour. Specifically, it shows a 40%

reduction in estimated GVA losses (due to behavioural change) in moderate/heavy work industries

which are the relevant industries that are assumed to be impacted at 28°C/28.42°C (see Table

4.20). Table 4.22 shows the estimated impact of urban green space in reducing GVA losses for

urban areas in GB, accounting for avertive behaviour, is £24m/year over the duration of “London

hot days” (i.e. 4 days) per year (≥28oC ≤29oC).

Table 4.22. Monetary account – net productivity losses avoided due to cooling effect of urban

vegetation for urban areas in GB

Hot day value Productivity (GVA) losses per working day (£m/day) Number of

days hot

temp. (28oC)

reached/

exceed

Total net

annual GVA

losses

avoided

(£m/year)

Without

existing urban

vegetation

With existing

urban vegetation

(-0.42 oC)

Net losses

avoided

London hot day

(≥28.0oC)

£10m £4m £6m 4 £24m

Therefore the total impact of urban green space on cooling is estimated as £70m/year (Great

Britain), calculated as:

i) ~£45m/year avoided energy costs associated with air conditioning to mitigate most (~85%)

of the (potential) GVA losses due to high temperatures; and

ii) ~£24m/year avoided reduction in GVA over and above that which can be avoided using air

conditioning/behavioural change; and

Note that the greenhouse gas emissions from air conditioning are not included in this analysis. If

included the value of local climate regulation by urban green space would increase.


eftec 70 June 2017

Further analysis is provided in Annex 4 for a notional hot day of 30oC to show the impact of

potential temperature increases in the future given climate change. The avoided reduction in

productivity associated with one day reaching or exceeding 30oC is £36.6m/year (giving a total of

£82m/year if the avoided energy costs associated with air conditioning are included). Furthermore,

the magnitude of hot days (frequency is assumed to stay at 4 days a year) is estimated to increase

from 28oC degrees to 31oC (based on using UKCP09 projections) and this increases the avoided cost

from urban cooling due to vegetation to £210m/year (or ~£250m/year if the avoided energy costs

associated with air conditioning are included). Both of these analyses are shown in Annex 4, the

latter is used in the estimation of asset values.


Table 4.23 shows the estimated gain associated with active visits to urban green spaces in England

based on QALYs is over £1.2 billion per year. White et al. (2016) showed that using the WHO’s

Health Economic Assessment Tool (HEAT) would produce a very similar estimate of welfare gain via

the Value of a Statistical Life Saved approach. Tables 4.24 and 4.25 present the monetary estimates

of the health benefits of physical activity in urban natural capital using both direct costs of five

conditions to CCGs and total direct and indirect costs.

Table 4.23: Value of ‘active visits’ to urban green spaces in England based on QALYs

Active visits per week QALYs (per year) Value per QALY (£) Annual welfare gain (£)

1 4,988

20,000

99,758,770

2 4,854 97,089,520

3 4,097 81,941,943

4 3,716 74,310,180

5 25,073 501,453,707

5 (less than 30min) 19,708 394,151,867

TOTAL 62,435 - 1,248,705,987

Table 4.24: Number of visits by active visitors meeting guidelines and annual direct avoided

medical costs to CCGs for five conditions in England

Self-reported

exercise a

week

Active visits

per week

Number of active

visitors

Avoided medical cost applied

per visitor(£)

Annual avoided

medical costs £

≥5 x 30 mins 1 467,167 6 2,729,151

2 227,333 12 2,656,127

3 126,833 18 2,222,851

4 87,000 23 2,032,989

5 469,667 29 13,718,780

<5 x 30 mins 5 369,167 29 10,783,214

TOTAL - 1,747,167 - 34,143,114

Table 4.25: Number of visits by active visitors and annual avoided direct and indirect costs in

England

Self-reported

exercise a week

Active

visits per

week

Number of active

visitors

Avoided medical cost applied

per visitor (£)

Annual avoided

medical costs £

≥5 x 30 mins 1 467,167 130 60,500,617

2 227,333 259 58,881,798

3 126,833 389 49,276,812

4 87,000 518 45,067,887

5 469,667 648 304,121,905

<5 x 30 mins 5 369,167 648 239,045,428

TOTAL - 1,747,167 756,894,448


eftec 71 June 2017

Tables 4.24 and 4.25 show that the health related value that greenspaces support through physical

activity of visitors is significant. The annual avoided medical costs to CCGs (for 5 conditions) are

estimated at approximately £34 million per year. This is expected to be an underestimate of costs

because they only consider costs associated with five of the over 20 conditions preventable and

manageable by physical activity and also only the direct costs to CCGs for the five conditions (i.e.

not costs to other parts of the NHS and the wider health and social care system) (Public Health

England, 2016). While it is not possible to gauge the proportion of these costs relative to all

conditions, they comprise some of the more serious and costly conditions.

The direct and indirect costs for Scotland, NI, and Wales were estimated by applying the

proportion of active visits and the costs per inactive person in England to the relative populations

in each country. The annual avoided direct and indirect costs are estimated at approximately

£900m/yr (£760m/yr in England; £74m/yr in Scotland; £26m/yr in NI; and £43m/yr in Wales). The

estimated gain associated with active visits to urban green spaces based on QALYs is over

£1.4bn/yr (£1.2bn in England; £120 million per year in Scotland; £42 million per year in NI; and

£71 million per year in Wales). Both of these values are presented within the monetary accounts,

as they represent two ways of measuring the physical health benefits (welfare versus exchange

values), and each can equally provide useful information for the focus of different decision-

makers.

4.6. Monetary account of future provision of ecosystem service

The account in Table 4.26 captures the asset value of urban natural capital as measured by the

present value of the stream of (annual) ecosystem services that the asset(s) will provide over the

100year period selected for the analysis (in line with Defra/ONS principles paper).

Table 4.26 shows the largest values are from physical health benefits of outdoor recreation. The

value of local climate regulation becomes the next highest valued service because of the impact of

climate change increasing the avoided losses in productivity (and air conditioning costs) due to

higher temperatures in future.

Table 4.26 Asset value of ecosystem service flows from UK urban natural capitala (PV,

100years)


Food UK £3,386m £m Market value Cook (2006); Pretty (2001)


UK £2,399m £m Cost of carbon mitigation

DECC (2014)


PM2.5 GB £7,168m £m/yr Welfare value and avoided market costs with income uplift

Defra (2014)

SO2 £13m £m/yr

NO2 £304m £m/yr

O3 £234m £m/yr

Noise regulation Manchester £1,741m £m Value of dBA reduction

Defra (2014)


GB

£4,974m £m Market values - avoided loss in GVA



UK £44,169m £m Welfare value (QALY)


UK £26,835m £m

Avoided total cost







eftec 72 June 2017

5. DISCUSSION

This section provides an overview of issues and challenges that were encountered when developing

the scoping account for urban natural capital. The intention is that these will inform the future

development of all broad habitat accounts.

5.1. Scope and Interpretation

Baseline

The accounts should, in principle, show the total ecosystem service provision by the natural

capital. This requires a definition of the land cover (i.e. a baseline) that would have been there

(without the existing natural capital). There are a few options for defining this baseline land cover:

i) Another type of natural capital. However this would estimate the net ecosystem service

provision and not the total level of provision because some level of ecosystem service

provision would be delivered by the alternative land use;

ii) No natural capital (i.e. a “concrete” baseline). While this is not realistic, it is the only

baseline to give us the total provision of ecosystem services of the current natural capital.

It also makes it easier for estimating some ecosystem services (e.g. carbon sequestration)

as concrete would not provide them. For other services, however, further thought is

needed: for example, a concrete baseline has some absorption capacity for different

pollutants;

The Defra/ONS principles paper does not explicitly define the baseline to use when estimating the

provision of ecosystem services. However, it implies that it is the total amount of benefit produced

- “valuation considers the value of goods and services produced during an accounting period”

(Defra, ONS, 2017). Therefore, this analysis adopted a baseline which assumes no natural capital

(i.e. concrete). Further consideration needs to be given to the appropriate baseline to use for

national accounting purposes by Defra/ONS.

Treatment of built capital

Further guidance could be provided on how to acknowledge the role of built capital in natural

capital accounting, which can be relevant where (i) ecosystem services are “co-produced” relying

both on natural capital and built capital (e.g. timber requires harvesting and a resource rent

approach by using stumpage prices) and (ii) built capital has the capacity to substitute for the

ecological functioning of natural capital. For example sustainable urban drainage systems mimic

the ability of natural ecosystems to deal with precipitation and some built infrastructure is used as

a habitat for nesting birds. For this account, these have been acknowledged in the condition

account but their full treatment could be an interesting conceptual issue to explore in future

iterations of the urban account and more widely in broad habitat accounts;

Treatment of transboundary effects

The urban boundary has to be drawn somewhere and this means that (i) people at the edge of

urban areas benefit from natural capital just on the outside of the defined urban area (e.g. through

house prices and availability of substitute sites for recreation) and (ii) from an ecological

perspective the wider landscape beyond the urban area is important for biodiversity and habitat

connectivity. This is important especially given the recommendation for the 25-year plan to

enhance wildlife corridors in line with the Lawton report (2010). These effects are not reflected in

the analysis but should be borne in mind when interpreting the account.


eftec 73 June 2017

5.2. Overlap with other UK natural capital accounts

One of the key criteria when determining the urban boundary for this study (see section 3.1) was

that it had to define the urban boundary in a way that covers the entire ‘urban fabric’ so that other

ecosystem types (such as freshwater, grasslands or woodlands in urban areas) could be identified

and included/excluded as required in the analysis. This is important for Defra/ONS when developing

a suite of urban natural capital accounts across all broad habitat types, so as to avoid the double

counting of benefits from assets which do not fit neatly into a single account (e.g. urban parks and

canals vs grassland and freshwater broad habitats).

By being able to isolate the value of specific natural capital assets, adjustments can be made

across the set of UK natural capital accounts to ensure that the value of these assets are not

reported in multiple accounts. For the urban definition adopted, Table 5.1 reports the extent of

overlaps with other broad habitat types that have been developed using the LCM2007.

Table 5.1. Area (ha) of GB within LCM2007 and urban boundary used in this study

UKNEA habitat type

Land cover class

LCM 2007 Great Britain Urban boundary used (BUA based with 500m buffer)

Woodland Broadleaved woodland 1,318,700 2,758,800

78,900 87,900

Coniferous woodland 1,440,100 9,000

Enclosed farmland Arable and horticulture 6,213,200

11,738,800 128,000

403,400 Improved grassland 5,525,600 275,400


Rough grassland 1,284,100

3,075,700

26,900

34,200 Neutral grassland 129,000 5,700

Calcareous grassland 37,100 100

Acid grassland 1,625,500 1,500

Freshwater Fen marsh swamp 10,000

271,500 200

9,100 Freshwater 261,500 8,900

Mountain, moors and heaths

Heather 732,900

3,658,500

2,500

11,200

Heather grassland 1,306,200 3,000

Bog 1,006,800 200

Montane 491,000 0

Inland rock 121,600 5,500

Marine Salt water 153,900 153,900 4,100 4,100

Coastal margins Supralittoral rock 7,800

335,800

100

4,000

Supralittoral sediment 46,600 1,000

Littoral rock 49,200 400

Littoral sediment 208,000 2,100

Saltmarsh 44,200 400

Urban Urban 312,000

1,400,300 262,500

1,212,000 Suburban 1,088,300 949,500

Total Area 23,413,200 1,765,700


eftec 74 June 2017

Table 5.1 shows:

Total urban area as defined by LCM2007 is 1,400,300 ha (Urban: 312,000 ha and Suburban:

1,088,300ha) whereas it is 1,212,000ha using the urban boundary adopted in this study (Urban:

262,500ha and Suburban: 949,500ha). So the definition of urban areas that is used in this study

for natural capital accounting captures 87% of the LCM definition of urban;

The urban boundary defined within this study captured around 403,000ha of enclosed farmland

including improved grassland; 4,000ha of coastal margin (primarily beaches (littoral sediment)

and vegetated communities on supralittoral sediment (which include sand dunes and other

vegetation found on coastal sand and mud above the high tide line)) and 400 ha of saltmarsh.

The BUA dataset itself captures ~86% of land defined as ‘urban’ or ‘suburban’ by LCM2007. The

remaining ~14% consists primarily of small patches of land cover, scattered across the country.

These are not captured within the BUA layer due to the minimum area of 20 hectares used to

create it, compared with 0.5 ha minimum area that is mapped in LCM2007.

This 14% of LCM 2007 urban area is therefore unaccounted for using the new urban area definition

with variable buffer. This area mostly comprises very small units of urban land cover (<20 ha) which

are not included in any of the ONS definitions of built-up-area because they are so small but needs

to be accounted for in another part of the UK natural capital national accounts. One solution would

be to expand the estimates of urban ecosystem services by 14% (i.e. linear extrapolation) to include

this area but this is not deemed appropriate because these areas are so small that the embedded

green/blue infrastructure is negligible and the ecosystem service provided is likely to be small. In

addition, the quantity and value of the benefits provided by this 14% is likely to be within the

uncertainty range for the quantity and value of benefits estimated in this scoping account. For this

reason, no adjustment has been made for this 14% here, but such an adjustment may be

appropriate for future iterations of the account as the robustness of estimates improves.

A study to scope and develop UK urban natural capital accounts Final report

eftec 75 June 2017

6. CONCLUSIONS AND NEXT STEPS

This section provides a summary of the certainty associated with the scoping account for urban

natural capital and recommendations for maintaining and refining the account based on the

development of the account itself and a review of existing urban accounting approaches.

6.1. Summary

This scoping urban natural capital account shows the significant value provided by natural capital

assets in urban areas. Amongst those benefits captured in the scoping accounts, the most

significant are physical health and air quality regulating impacts (the full extent of which is still

being analysed). The study has shown that methods can be applied across both national and local

(using Greater Manchester as an example – see Annex 5) scales. This study has provided proof-of-

concept for a range of ecosystem services and the work undertaken provides a basis for future

research to understand and account for UK urban natural capital. This concluding section identifies

next steps for the expansion and refinement of the UK urban natural capital account.

Table 6.1 shows the physical and monetary value estimated for each ecosystem service included in

this scoping account.

Table 6.1. Summary physical and monetary flow accounts and assessment of certainty


account RAG


(£/year) RAG

Food UK 80,000,000kg/yr. £114m


Air quality

regulation


PM10

GB

-0.065 ug/m3 -

PM2.5 -0.056 ug/m3 £195m

SO2 -0.023 ug/m3 £0.3m

NH3 -0.018 ug/m3 -

NO2 -0.007 ug/m3 £13m

O3 -0.140 ug/m3 £3m


£59m



UK



74,000 QALYs £1,482m

Note: the following ecosystem services are not included in this scoping account as explained in Annex 3:

recreation and tourism; aesthetic value; cultural heritage; water quality regulation; pollination; freshwater;

natural hazard regulation (incl. flood); property values (bundle of services).

RAG Description






eftec 76 June 2017

The certainty rating is shown as a guide for future work to improve the robustness of estimates.

The ratings for the monetary aspects do not relate to the total value shown (which depend on the

physical estimates) but rather the unit values that have been applied. A red rating does not mean

that the value is not credible. Note that these values are not commensurate in scale with some

being relevant to the UK, Great Britain (GB) or Manchester (Manc.).

6.2. Maintaining ecosystem accounts

With respect to maintaining the initial urban account over time and the frequency with which this

should be done (e.g. every year or every 2 - 5 years etc.), the following main factors should be

considered:

i) Together with the accounts, the datasets used also need to be updated. Section 6.5 makes

recommendations to refine the accounts with forthcoming datasets and new methods;

hence a more informed determination can be made once the use of these data is

established. This determination may vary across different ecosystem services according to

the frequency with which the underlying data updating. For example, Forest Research

recently reflected that analysis involving patterns in tree canopy cover (i.e. woodland land

cover) should be no more than every two years, and a 5 year rolling average would likely

give more convincing results (Kieron Doick, pers. comm)

ii) The purpose of the accounts needs to be discussed in the context of deciding the frequency

with which the accounts are repeated. If the purpose is for a comparison with System of

National Accounts (SNA), then a longer time period for repetition may be justified (e.g. 5

years) as the accounts are more of an administrative tool to attribute economic value to

the environment. However, if the purpose is to assess the sustainability of an ecosystem

stock and the economic value that flows from it then a shorter time period (e.g. 2 years)

might be selected. This would establish a practice of regularly measuring (changes in) the

condition of the stock so that potential impacts on future ecosystem service flows captured

and steps can be taken to avoid declines. For this tracking to be possible, there must be

sufficient confidence in the underlying data (see point (i)) to accurately detect changes in

the condition of the stock over time at the spatial scale of interest. More work is needed to

refine the accounts to achieve this.

iii) The value of the services – services such as food and global climate regulation from urban

environments have been estimated to be low and so it is not a proportionate use of

resources to update these annually.

Given this was only a scoping urban natural capital account, recommendations for future focus on

identifying better data and methods, not on maintaining the estimates provided here. These

recommendations are set out in Section 6.5.

6.3. Review of existing urban accounting approaches

A review of existing assessments of urban natural capital was undertaken to understand the

approaches to urban natural capital assessment, the consistency of principles and concepts and the

quality of data and evidence being used to inform decision making in urban areas, across the UK

and abroad. The full review is in Annex 1. The intention was that the findings from this review

would inform the approach to developing UK urban accounts. In practice, the study followed the

principles outlined by Defra/ONS (2017) and worked within the constraints of the data and

resources available. Therefore, the review was more useful in terms of how the account could be

refined in the future and hence the findings are outlined in this section.


eftec 77 June 2017

The review of existing urban natural capital assessments found a number of differences in approach

had been taken at the local level compared to our national level study based on Defra/ONS

principles. The following recommendations are therefore made based on the review:

The treatment of transboundary effects: an urban study in Wellington (New Zealand) defines

the urban boundary from an ecological perspective and includes the rural hinterland because of

its focus on biodiversity and habitat connectivity. Whereas, a study in Oslo (Norway) is more

socio-economically focused and so includes the peri-urban forest that encircles the city and the

Oslofjord coastline that is in close proximity to the city because these assets influence urban

residents’ wellbeing/property prices and also impact the value of urban green space due to

their existence as substitute sites. These transboundary issues should be considered further in

future iterations of the account in defining the urban boundary and comparing to other broad

habitat accounts (as discussed in Section 5.2). This is important especially given the NCC

recommendation for the 25 year plan to enhance wildlife corridors in line with the Lawton

report (2010).

Indirect and induced impacts: some assessments focus on quantifying and valuing the size of

environmental goods and services sector (EGSS) (in terms of number of organisations and

employees) as opposed to estimating the value of benefits from natural capital assets within an

urban area. These ‘indirect’ and ‘induced’ values associated with environmental goods will

already be captured within the System of National Accounts (i.e. GDP), even though they will

not be reported separately for urban areas. In terms of the structure of natural capital

accounts, this provides an interesting perspective on the location of the ‘users’ of ecosystem

services. For example, timber yards in urban areas rely on woodland in rural areas. The

treatment of induced and indirect effects could be considered in future iterations of the

account (and as a cross-cutting issue for other broad habitat accounts).

Individual green/blue infrastructure features: the literature shows a mix between focusing on

larger broad habitats (parks, lakes, woodland), the more ambiguous “green and blue spaces”

and specific green features such as green roofs and street trees. Assessing the ecosystem

services from specific features requires highly resolute data on the assets, the wider

environment (e.g. the range of annual ambient pollution levels and temperatures) and

beneficiaries. Such analysis is best produced at the local level as we have produced for Greater

Manchester in this study. Further work should be undertaken to develop estimates for the eight

city regions and London as defined by ONS (ONS, 2015).

Capital maintenance associated with disservices/externalities: some studies focus on the

negative externalities/market failures (i.e. air quality, noise, waste/recycling, flooding) and

associated capital expenditures (e.g. Sustainable Urban Drainage Systems (SuDS) or behavioural

changes such as recycling). Accounting for such costs at a national level would be appropriate if

Defra are to implement the NCC’s recommendation for budgetary provisions to be made

annually for natural capital maintenance and enhancements. Given the possibility of

substituting between natural capital and built capital, the focus should be on collating

expenditure information on the ‘ecosystem service’ provided, not expenditure on natural

capital assets alone (i.e. expenditure on flood defence can be through natural capital or built

capital – only by knowing total expenditure can cost efficiencies be identified). The financial

expenditure work undertaken for North Devon as part of the UK Natural Capital Pioneer project

led by eftec for Defra could be used to inform the future development of UK natural capital

accounts (eftec et al, 2017).


eftec 78 June 2017

6.4. Future refinement of urban natural capital account

The datasets used for the scoping accounts (summarised in Table 6.1) have been selected on the

basis of being readily available and of sufficient quality and in appropriate format to allow a

reasonable order-of-magnitude estimate for the physical and monetary value of each ecosystem

service. There are additional datasets and methods that can be explored to refine and/or validate

the values produced in this account. This includes but is not limited to the following sources and

methods:

High resolution data (such as National Tree Map) for the entire UK;

Forestry Commission (2017) publication on tree cover outside woodland in Great Britain;

BEIS green space layer (BESI, forthcoming) which is due to have two versions – an openly

accessible version and a Mastermap version. It is due to be updated over 5/6years and has

8/10 types of greenspace – so can be used to breakdown the area figures in this scoping

account (e.g. 420,000 ha grassland); and

iTree Eco which can be used to estimate the provision of a range of ecosystem services.

The services excluded from the scoping account (see Annex 3) should be considered for inclusion in

future iterations of the account. These services could be assessed using ecosystem service models

(such as iTree) which have shown reasonably high values for services such as flood attenuation at

city level and even though there are elements that need working on in the context of incorporation

to natural capital accounting, these are not insurmountable (Kieron Doick, pers. comm.).

In addition, adjustments need to be made to the relevant broad habitat accounts if these accounts

are to be reported together as part of the set of UK Natural Capital Accounts that ONS and Defra

are developing for 2020 (see section 5.1).

Finally, a number of recommendations are outlined with respect to the development of UK urban

natural capital accounts in line with what could be prepared for the scoping account in Table 6.1.

Condition account

For this scoping study, indicators of natural capital condition have been proposed for the future

quantification based on evidence of their importance to ecosystem service provision. Table 4.2

provides an indication of the potential indicators and sources of data that could be used to develop

the condition account.

Food

The physical and monetary estimates of food productivity are relatively low compared to other

services. However, these estimates could be refined by developing an approach that:

Is based on more robust numbers of urban plots provided by other organisations including Town

and Parish Councils in addition to those provided by Local Authorities;

Accounts for variation in the quality of plots including climate and soil and the associated

impact on productivity;

Includes the production of food from other urban locations, such as gardens, city farms,

community gardens, orchards and parks;

Accounts for potential variation in future food prices into the future when estimating the asset

value, and

More up-to-date figures for yield (kg/year) may come available in due course through the crowd

source study referenced by the NSALG and led by the University of Sheffield

(https://myharvest.org.uk/)

https://myharvest.org.uk/


eftec 79 June 2017

This scoping account has focused exclusively on the comparative financial value of food production

from individual plots. Direct contact with the National Allotment Society could be useful as they

may be able to provide guidance and benchmarks for baseline figures alongside sources of the most

up to date survey information. They may also have a good overview of the differences between the

four UK countries. Additional benefits claimed for allotment production such as: cultural; health;

biodiversity, pollination; waste recycling; and, climate regulation could also be assessed for their

relevance in an accounting context.

Global climate regulation (carbon)

A refined approach to estimating the physical flow of carbon from urban vegetation would be to use

GIS to map the area of vegetation/land cover which stores carbon, classed into functional classes of

carbon stock (e.g. depending on vegetation height etc.) and provide a carbon stock value for

vegetation and soil for each class over time (i.e. a time series). This could be done using OS

Mastermap, in combination with CEH Landcover to identify discrete areas of urban vegetation, but

this will not capture individual trees within urban green space, or street trees. Further resolution

to pick up the larger individual street trees is possible with the BlueSky National Tree Map.

Assumptions will need to be made about the relationship between the height of vegetation and

carbon stock. It will not be possible to identify individual species from satellite data, therefore

generic relationships will need to be derived. Alternatively, tree surveying techniques such as i-

Tree Eco, or maps such as Treezilla, or the London Tree Map and the Bristol Tree Map could be

used. The data sources are developing and give further details not covered by the National Tree

Map. A review of the options should at least be considered.

Ecosystem service models such as iTree Eco, InVEST or statistical modelling techniques such as

Ecomaps can produce UK-wide inventories of carbon (Sharps et al. in press). At present, urban

areas are poorly accounted for in such models, but those such as InVEST which are based on look-up

tables could be modified to account for carbon capture in urban habitats.


In the case of air pollution removal, while the science is fairly robust, different models and

different approaches may produce widely varying estimates. There is a trade-off inherent between

the accuracy of incorporating atmospheric transport and pollutant interactions at national scale,

and the fine detail required to populate information about the type and location of vegetation on

the ground. The approach taken here is robust, but emphasises pollutant transport and chemistry

over fine-scale granularity.

Further work to improve the methodology could:

Conduct separate model runs for each broad habitat type to more accurately estimate the

health and economic benefits of each in isolation;

Add additional habitat classes into the EMEP4UK model to improve modelling of each habitat

type and/or to allow consideration of habitat condition;

Improve the spatial resolution of EMEP4UK by running at 2 km x 2 km;

Add in land cover change, although the effect of this is likely to be small;

Review and update as necessary the damage cost functions.

Noise regulation

Overall, we expect this to be a reasonably robust order-of-magnitude estimate of the service, but

further work could refine the methodology, and reduce uncertainty. Taken together, the

assumptions applied in this analysis may lead to over/under estimate of the benefits because:


eftec 80 June 2017

i) 'sleep disturbance' which accounts for approximately 45% of marginal value for noise

reductions above 54 dBA should only relate to domestic buildings. We apply the combined

damage cost to all buildings, thus over-estimating, because we were not able to

differentiate between residential and work buildings within this study;

ii) the values are per household for the population exposed so could double count individuals

who are exposed in buildings at work and at home. The extent of overestimation will be

mitigated to some extent because office buildings are shared by multiple individuals (i.e.

there is more than one “household” to one building) and this includes people those who are

exposed to noise at work but not at home (i.e. they live outside the urban area) and so

won’t be picked up in the analysis. It also doesn’t include noise impacts on those outdoors,

and of non-permanent noise sources (like construction) suggesting an under-estimate of the

benefit.

As far as we are aware, there have been few attempts to upscale noise reduction by vegetation in

the UK. A study by Bateman et al. (2004) created a method to value changes in noise levels, based

on hedonic pricing. This was reviewed by Nellthorp et al. (2007) and found to be comparable to

other valuation studies in Europe. Noise reduction is included in the EcoServ-GIS toolkit (Durham

Wildlife Trust, 2012), where the capacity of the natural environment is mapped by assigning a noise

regulation score to vegetation types, based on height, density, permeability and year round cover.

It has been applied in a few case studies (e.g. Sussex Wildlife Trust, Horsham case study, Sussex

Wildlife Trust, 2016). Some recognition of noise reduction as a service is also applied in the South

West Peak landscape opportunity mapping case study (Rouquette and Holt 2016), which utilised the

CPRE National Tranquillity Data set 2007 (CPRE, 2007). The CPRE national tranquillity map is based

on 40 positive and negative indicators, the latter includes noise levels from road and rail, but also

includes visual blight from transport networks.

Future refinement could follow two options:

(i) refine the method at a local case study area (e.g. Greater Manchester) addressing the

limitations described above and accounting for changes over time (in the calculation of

asset value) in (a) tree cover (in reality also by changing its structure, but can’t capture

that in our analysis); (b) noise levels (if noise mapping is updated, this could be repeated);

(c) local population as a scalar for effects; (d) change in buildings as a measure of exposure

using a specific areas and (e) technology such as the use of quieter electric vehicles;

(ii) test a roll out of this methodology at a UK scale. This would require a UK dataset that is of

sufficiently high resolution, options include the Bluesky National Tree Map (potential

restrictions on using this will need to be understood), OS Mastermap and potentially the

forthcoming BEIS green space layer which will provide time series data. To provide an

indicative estimate of the UK wide value:

Alternative methods of valuing the benefits provided by vegetation could also be explored,

including:

The use of hedonic pricing to ascertain if the value of noise dissipation by vegetation can be

captured in house price premiums;

Replacement cost methods – focusing on estimating the extent to which technology/built

capital can be used to replace the noise dissipating impacts of vegetation such as through

double/triple glazing, or fencing of dual carriageways and the associated costs of such

technology. This is would align more closely with an exchange value.


eftec 81 June 2017


The purpose of this scoping account is to estimate aggregate average cooling effects across all

urban areas for a UK national account. However, as mentioned in section 3.5, this service is

extremely spatially dependent and so even if the aggregate effect of cooling by parks across all UK

urban areas (or even at a city level) is not estimated to be particularly large, its benefits will be

felt disproportionately at very local levels.

The analysis of local climate regulation services described in this report demonstrates how a simple

modelling approach can be used to estimate the aggregate cooling effect of different categories of

urban vegetation. Whilst the approach is simplistic, a key strength is its use of empirical values

concerning the cooling effect of different categories of urban vegetation. These values have been

taken from the growing literature on urban ecosystem services, green infrastructure and nature

based solutions (e.g. Bowler et al., 2010; Larondelle and Haase, 2013; Salmond et al., 2016).

The scoping account shows that the existing natural capital in the UK in terms of parks and urban

woodlands (all patches regardless of their size) could be providing a cooling effect of -0.42oC.

Despite the limitations of the approach (outlined further below), this may be a conservative

estimate given that it was not possible to incorporate several categories of urban vegetation in the

assessment (e.g. private gardens, allotments, street trees) due to data limitations and the

resources available for the project.

The estimates are indicative only (and subject to the limitations and assumptions set out in section

6 and Annex 4) and further investigation and research is needed at a local scale, especially given

potential increase in future hot days. This service is likely to be particularly important for climatic

regions that experience “hot day” temperatures (e.g. >26oC) more frequently (such as the south-

east) and given anticipated changes in climate (see Annex 4).

Importantly, the cooling effect of urban vegetation is likely to have no productivity benefit in terms

of productivity losses avoided for average summer temperatures in the UK (approximately 20oC

based on Met Office data for climatic period 1981-2010) or for UK extreme hot weather events

(34.6oC for NW England). In the former, all work can continue as normal at these temperatures

(threshold is 26.8oC according to ISO7243). In the latter, the cooling effect (-0.42oC) is not large

enough to reduce temperatures below the point at which all work must cease for health and safety

reasons (although there are limitations with this assessment as explained below).

The following limiting factors and assumptions are likely to result variously in over and

underestimation of physical and monetary account values. As such, it is hard to be precise about

the magnitude or even direction (over / under) of inaccuracies in the assessment. It is therefore

recommended that further analysis be devoted to refining these estimates for inclusion within the

UK’s urban natural capital account given the following limitations:

The cooling effect assessed here only includes certain categories of urban vegetation: those

for which it was possible to obtain empirical cooling effects for (parks, urban woodlands, street

trees) and / or extent values (parks, urban woodlands). This is likely to cause underestimates in

both the physical and monetary accounts (as it excludes street trees and blue infrastructure for

example).

The cooling effect of urban green is likely to be highly context specific: influenced by urban

form and the prevailing general and local climate. The physical account does not include the

impact of local context and it is recognised that generalising effects from individual studies is

problematic (Salmond et al., 2016). For the most part, this is likely to cause overestimates in

both the physical and monetary accounts. The recommendation is to compute a UK figure using


eftec 82 June 2017

a discrete bottom-up method (given variation in climate and accounting for a wider range of

vegetation types), where estimates are made for all the main urban areas.

The monetary account uses recorded and notional values of “hot day” temperatures: to

estimate productivity losses avoided by the cooling effect of urban vegetation. These values are

for ambient outdoor air temperatures for Central England which may not be transferable to all

of the UK. The productivity loss functions (Costa et al., 2016) used in our modelling are based

on reduced productivity at different levels of heat exposure as determined by the wet bulb

globe temperature (WBGT) index (Kjellstrom et al., 2009). As ambient air temperature is only

one component of WBGT, the hot day values used in the monetary account calculations are not

directly comparable to the model of productivity loss functions used in Costa et al. (ibid). This

is likely to cause overestimates in the monetary account.

Impacts on reduced hospital admissions (morbidity) and reduced deaths (mortality): that

may be afforded by urban cooling effects of natural capital during extreme heat are not

considered. Nor does it account for the consequences of the urban heat island effect at night

(e.g. on people’s ability to sleep etc.). Further research needs to be conducted into how these

benefits could be assessed.

GVA data was obtained for two different economies due to data availability: 1) total

economic output was obtained for the Manchester City Region; and 2) economic output by

sector was obtained for Manchester City (a much smaller economy). (2) was used to estimate

productivity losses due to heat exposure at the City Region level on the basis of (1). It is not

clear if this is likely to result in under or over estimates within the monetary account.

However, the Manchester City economy does not contain any agriculture, forestry or fishing

activity whereas the wider City Region economy does (i.e. due to the greater area

encompassed, potential for more diverse land uses etc.). This could potentially result in an

underestimate in the monetary account as this sector is much more sensitive to heat exposure

(due to higher work intensity).

Emissions of CO2 and other greenhouse gases due to energy used for air conditioning are not

included in the scoping account which would increase the cost of air conditioning and indirectly

increase the value of providing cooling through vegetation;

The cooling effect of blue space such as rivers and lakes should be analysed for inclusion in

future iterations of the account. Evidence suggests cooling effects of 2.5°C during the warmest

months in the northern hemisphere (between May and October) can be attributed to urban blue

space (Völker et al, 2013) and that poorly designed bluespace, may exacerbate heat-stress

during high temperatures (Gunawardena et al, 2017) As with the recommendation for refining

the impacts of vegetation, this should be analysed using a bottom-up method taking into

account localised factors.


The scoping account demonstrates that it is possible to estimate the physical health benefit of

physical activity (150 minutes (or more) at adequate intensity) supported by urban green spaces in

England in terms of welfare gains (due to increased QALYs) and avoided medical costs. The results

show that urban green spaces in England support a significant amount of physical activity, helping

some 1.7 million people to achieve recommended guidelines for weekly physical activity. This

corresponding to over £1.2 billion per year in welfare gains and over £34 million - £760 million per

year in avoided medical costs. It is recommended that further analysis be devoted to refining these

estimates for inclusion within the UK’s urban natural capital account. The use of this method and


eftec 83 June 2017

data sources can also be used beyond accounting, for example, to appraise greenspaces, and

communicate their value to the general public.

Overall, the valuation approach adopted remains conservative because:

The analysis only examines visits by adults (16 years and over). Children comprise a large

proportion of visits to urban green spaces but are excluded.

The analysis only examines visits of a specified length (30min or more) involving physical

activity of a specific intensity – exercise in gym cannot be a complete substitute for this

service, and shorter and less intense physical activity is also likely to have positive, significant

impacts on physical (and mental) health.

These calculations assume that the relationship between inactivity and health benefits is linear

(e.g. reaching half of physical activity guidelines is associated with half of the benefits, or half

of the costs). Allowing for a non-linear relationship would likely add value to less frequent users

of urban green space (or those who undertake less vigorous activities) and increase the overall

benefits provided.

The mental health benefits of most visitors to urban green spaces are also not included. This is

still a relatively new field of study, and mental health benefits from exposure to the natural

environment are likely to be at least as significant as the physical health benefits. This is a

particularly significant data gap which should be explored further in future.

The cost estimates for direct costs to CCGs are recognised as a significant underestimate: they

only consider costs associated with five of the over 20 conditions preventable and manageable

by physical activity and also only the direct costs to CCGs for the five conditions (i.e. not costs

to other parts of the NHS and the wider health and social care system) (Public Health England,

2016).

A detailed discussion of caveats to the method used by White et al. (2016) and using MENE data

method is provided in the published paper for that study, additional assumptions that could

increase or decrease the estimates are:

It has also been assumed that the week for which respondents were providing information was a

‘typical’ week. For example, if the respondent reported two trips to the natural environment

for exercise, it is assumed that the respondent takes these two trips each week. However,

MENE is well-structured and representative and so these trips should be considered as a

representative sample of people who take two trips per week. Therefore we don’t consider this

to be a significant caveat.

This study uses national data even though studies have shown that inactivity rates and costs of

inactivity can vary significantly across local areas and regions. For example, the British Heart

Foundation (2013) found that, for individual Primary Care Trusts, the costs from inactivity-

related conditions ranged from around £1 million to £18 million per year. Using local inactivity

rates and costs could refine this estimate, and help to highlight where urban green spaces are

particularly beneficial to local populations, such as in regions that have the highest proportion

of inactive adults. Due to the magnitude of physical health benefits (in avoided costs)

supported by greenspaces it is recommended that this information be the subject of future

work to develop the national natural capital account.

Inactivity cost data for Wales, Scotland, and Northern Ireland has not been organised and

published in the same way as data for England. Specifically, a breakdown of the number of


eftec 84 June 2017

cases and associated costs to the NHS (using Population Attributable Fractions (PAFs)) for these

countries has not been published by their respective Departments of Health. This limits the

ability for providing estimates of direct costs to CCGs as part of this scoping study. It was

possible however to estimate the direct and indirect costs by applying the proportion of active

visits and the costs per inactive person in England to the relative populations in each country.

This assumes that active visits to the natural environment will be the same proportion of the

population in each country as it is in England. This method also assumes that the distribution of

NHS costs from inactivity in England will be broadly representative of the situation in the UK.

The number and frequency of yearly active visits, inactivity rates, values of a QALY, costs of

inactivity are assumed to remain constant over the 100 year time horizon.

Our resulting analysis provides a range of values, using different methods and assumptions about

the proportion of people that are using urban green spaces to meet physical activity guidelines and

its value. The avoided medical costs estimated here can be added to other benefits of greenspaces

including ecosystem services such as improved resilience to climate change.

Finally, whilst there could be a potential risk of double-counting between the benefits of improved

health and the general wellbeing benefit of recreational activity this study has adopted a different

approach to valuing health benefits than is adopted in the estimates of wellbeing as measured by

Ricardo (2016) which means they have negligible overlap. Wellbeing is valued based on

expenditures individuals make to experience recreation (Ricardo 2016 method), whereas health

impacts here is valued through avoided costs to the economy as a whole.


eftec 85 June 2017

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ANNEX 1. ASSESSMENT OF URBAN NATURAL CAPITAL

This annex provides the detailed findings from the project team’s review of existing assessments of

urban natural capital that were summarised in Section 6.3.

A.1. Overview

A review of existing assessments of urban natural capital was undertaken to provide Defra with an

insight into the breadth of approaches being taken to urban natural capital assessment, the

consistency of principles and concepts and the quality of data and evidence being used to inform

decision making in urban areas, across the UK and abroad. Assessments for the following urban

areas were found in the literature:

London, UK

Birmingham, UK

Manchester, UK

Oslo, Norway

Wellington, New Zealand

Lancaster, Pennsylvania, USA

The review focused on identifying the following aspects of urban assessments:

i) Definition of ‘urban area’

Most assessments focus on administrative boundaries and the environment within that

boundary that is used by city and non-city residents – the choice seems to be depend on the

for what purpose the assessment is done and for whom;

Birmingham City Council’s Green Living Spaces Plan defines the city boundary based on

Ordnance Survey mapping (based on administrative boundaries) and excludes areas that are

outside of this boundary which are managed by the Council;

Amongst the assessments that consider transboundary effects, Wellington includes

reference to the rural hinterland because of its focus on biodiversity and connectivity. Oslo

OpenNESS project included peri-urban forest that encircles the city and the Oslofjord

coastline because it focused on residents’ wellbeing/property prices which are influenced

by natural capital in close proximity to the city; and

Other assessments focus on the organisations and the employment in the environmental

goods and services sector, not the natural capital within an urban area.

ii) Definition of ‘urban natural capital’

There is a mix between focusing on larger broad habitats (parks, lakes, woodland), the

more ambiguous “green and blue spaces” and specific green features such as green roofs

and street trees;

In Oslo, both larger habitats and specific features have been recognised according to the

density of the urban area. The analysis of peri-urban areas (with more space) focuses on

the larger habitats, whilst the analysis of built-up areas targets the smaller features; and

Similarly, in Lancaster, Pennsylvania, the city-wide driver for green infrastructure is on the

patchwork of natural areas that provides habitat, flood protection, cleaner air, and cleaner

water. Whereas at neighbourhood and site-level scale the key driver is specifically on

stormwater runoff, other services are quantified albeit is of secondary focus.


eftec 97 June 2017

iii) Ecosystem services considered

Where an ecosystem services framework is used in an assessment, most consider recreation

and physical and mental health associated with access to urban green space;

In some cases, a focus on the status/trend in negative externalities/market failures (i.e. air

quality, noise, waste/recycling, flooding), associated capital expenditures (e.g. SuDS) and

risks/behavioural changes (e.g. recycling) arguably overshadows the more nuanced

narrative on the positives (i.e. ecosystem service benefits) from natural capital assets; and

Many assessments include economic indicators related to growth/output, employment,

productivity, tourism and property values.

iv) Type of value estimated

There is a mix of qualitative, quantitative and monetary evidence used to assess the

benefits of natural capital in urban areas;

Where monetary valuation is estimated a mix of market and non-market values are used.

The EC (2015) MAES study notes how very few stakeholders consider mapping of monetary

valuation to provide policy opportunities; and

Qualitative measures include scoring of properties’ green and blue structures according to

their importance for different ecosystem services. The EC (2015) MAES study notes how the

majority of stakeholders’ concerns address (physical) ecosystem services mapping.

v) Terminology used

Based on the evidence reviewed there is little consistency in the terminology used across or

within assessments of the urban environment. A mix of terms including natural capital;

urban vegetation and blue infrastructure, ecosystem services, goods and services, green

infrastructure benefits, economic, social and ecosystem services are used interchangeably

A.2. London, UK

The following studies are covered in the review of urban natural capital assessments in London:

City of London (2011) City of London Green Roof Case Studies;

eftec (2015) Beam Parklands Natural Capital Account;

eftec (2016) London Borough of Havering Green Infrastructure Strategy;

GLA (2015) Natural Capital Investing in a Green Infrastructure for a Future London;

GLA (2016) The Mayor's street tree initiative;

GLA economics (2016) Economic Evidence Base for London 2016; and

kMatrix (2013) London’s Low Carbon and Environmental Goods and Services.

i) Definition of urban are: analysis within London is based on administrative boundaries (e.g.

GLA, London Boroughs) or UK regions (includes London).

ii) Definition of urban natural capital: the concept of natural capital is gaining increasing

traction within the Greater London Authority (GLA Economics, 2016) and a number of

London Boroughs (including Havering and Barnet (forthcoming)) have produced Natural

Capital Accounts (of different scope). In some cases, analysis focuses on improvements in

specific green features such as green roofs (City of London Corporation, 2011) and street

trees (GLA, 2016);

iii) Ecosystem services considered: Most publications focus on the impacts and dependencies

society has on the environment within London. In some cases, a focus on the status/trend in


eftec 98 June 2017

negative externalities/market failures (i.e. air quality, noise, waste/recycling, water

supply, flooding, climate, energy use) and associated capital expenditures (e.g. SuDS)

/GVA/employment/risks/behavioural changes (e.g. recycling) arguably overshadows the

more nuanced narrative on the positives (i.e. ecosystem service benefits) associated with

natural capital assets.

The current natural capital accounting efforts seek to quantify and value recreation and

amenity and physical and mental health associated with access to green space, with other

services being quantified where data permits (climate regulation, flood risk and the bundle

of services associated with property prices). Other environmental analyses of London focus

solely on output and employment impacts associated with London’s Low Carbon and

Environmental Goods and Services sector (kMatrix, 2013). This is not concerned with the

natural capital within the city but rather the proportion of organisations/employment in

the environmental goods and services sector in London.

iv) Type of value estimated: A mix of evidence is used to justify natural capital benefits with

some using qualitative information only, whilst others provide quantitative and monetary

evidence for a range of ecosystem services (e.g. eftec, 2015; Rogers et al., 2015).

v) Terminology used: The language is mixed with more intuitive terms such as green

infrastructure and urban green space being used alongside natural capital (i.e. the Mayor of

London’s Green Infrastructure Task Force and their report on “Natural Capital: Investing in

a Green Infrastructure for a Future London”, GLA 2015). Similarly, some publications use

the more accessible economic, social and environmental description of benefits as opposed

to an ecosystem services framework.

A.3. Birmingham, UK

Birmingham City Council has in many ways been at the forefront of exploring the stock of natural

capital assets and flow of ecosystem services at the city-wide level with its Green Living Spaces

Plan (2013). This evidence base created for the city has led to the ambition of a 25-year Natural

Capital Plan for the city and to the invitation for the city to join the Biophilic Cities Network (SWM,

2014). The following studies are covered in the review of urban natural capital assessments in

Birmingham:

The Green Living Spaces Plan (Birmingham City Council, 2013); and

Planning for Sustainable Land-Use: The Natural Capital Planning Tool (NCPT) (Holzinger, 2016;

RICS, 2015).

i) Definition of urban area: The urban area is defined as that which is within the City

boundaries (sourced from the Ordnance Survey mapping). Although the Plan recognises

Birmingham City Council also manages some parks outside of the City boundary, they are

not included in the analysis. The Natural Capital Planning Tool has been adopted by

Birmingham City Council but is for more site-specific analysis of urban natural capital (e.g.

a major housing development).

ii) Definition of urban natural capital: Birmingham’s Green Living Spaces Plan refers to green

infrastructure, based on the National Planning Policy Framework (DCLG, 2012) definition, as

“a network of multifunctional green space, urban and rural, which is capable of delivering a

wide range of environmental and quality of life benefits for local communities” (p. 6). The

specific broad habitats included are woodland, heathland, wetland and BAP priority

grassland. The NCPT is based on a range of indicators on the status of natural capital (e.g.

land use change, soil drainage/carbon/contamination, agricultural land classification,


eftec 99 June 2017

accessible public space, importance within ecological network), risks (e.g. heat exposure,

flood risk) and environmental designations (e.g. air quality management zone).

iii) Ecosystem services considered: The Green Living Spaces Plan considered the following:

provisioning services (water supply, species diversity), cultural services (recreation,

aesthetic values and sense of place, and education) and regulating services (flood

regulation, storm buffering and water quality regulation). The ecosystem services within

the NCPT are harvested products, biodiversity, aesthetic values, recreation, water quality

regulation, flood risk regulation, air quality regulation, local and global climate regulation

and soil contamination.

iv) Type of value estimated: The Green Living Spaces Plan assessment includes indicative

monetary values in annual terms and asset values (over 100 years) based on value transfer.

The NCPT is qualitative based on ‘expert opinion’ translating indicators into impact scores,

without any quantification or monetisation of physical impacts.

v) Terminology used: Green infrastructure and urban green space are the prominent terms

used in The Green Living Spaces Plan, even though natural capital and ecosystem services

are cited. The NCPT focuses on natural capital and ecosystem services.

A.4. Manchester, UK

The following studies are covered in the review of urban natural capital assessments in Manchester:

The Manchester Green Infrastructure Strategy (BDP et al, 2015); and

The Defra urban natural capital pioneer (Defra, 2016; GMCA, 2016).

i) Definition of urban area: The Manchester Green Infrastructure Strategy (BDP et al, 2015)

was produced for the administrative boundary of Manchester City Council. This boundary

was strictly adhered to and no transboundary flows of services from outside were

considered (except as substitutes). Park users may have included people from across the

boundary. The Natural Capital Pioneer is for Greater Manchester Combined Authority

(GMCA, 2016) which essentially consists of whole conurbation. It’s unclear if this is in any

sense an “official” boundary, and there appears to be some flexibility over it.

ii) Definition of urban natural capital: The analysis to inform the Green Infrastructure

Strategy used GIS to identify urban green spaces over a minimum size of 2 ha and the canal

network. Discussion of analysis micro-features (e.g. green walls, roofs) but the data was

deemed to be insufficient. The analysis quantified the impacts on ecosystem services

associated with (i) an enhancement scenario which was defined as increasing the quality of

all urban green space to high quality (from 80%) and managing the canal network (with

paths, bankside and floating vegetation) to be equivalent to high quality urban green space

and (ii) a decline scenario in which management suspended and services lost over 10 years.

iii) Ecosystem services considered: The Green Infrastructure Strategy developed logic chains

for five benefits including conventional economic indicators and ecosystem services: (i)

economic growth and investment; (ii) land and property values; (iii) labour and land

productivity; (iv) tourism and (v) health and wellbeing.

iv) Type of value estimated: In the Green Infrastructure Strategy, quantitative estimates were

made of the number of residents, businesses and properties with 300m of them and the

number of visitors. Monetary values were produced based on enhanced property

(commercial and residential) values and enhanced health benefits valued through the


eftec 100 June 2017

physical activity supported. Jobs and workforce arguments were also made and potential

visitor spending benefits were discussed but not valued.

v) Terminology used: The focus of the Green Infrastructure Strategy is green infrastructure

and the associated benefits, i.e. “This report presents analysis of the extent of green

infrastructure and evidence of the value of the benefits it provides to the people and

economy of Manchester.” The term natural capital doesn’t feature and ecosystem services

are mentioned sporadically in the Green Infrastructure Strategy, whereas these terms are

central to the Natural Capital Pioneers study.

A.5. Oslo, Norway

The following studies are covered in the review of urban natural capital assessments in Oslo:

The international OpenNESS59 (Operationalisation of natural capital and ecosystem services)

project’s Oslo case study (OSLOpenNESS; Barton et al, 2015); and

The EU MAES (Mapping and Assessment of Ecosystems and their Services) project on urban

ecosystems (EC, 2016) Oslo case study.

i) Definition of urban area: the spatial focus of OSLOpenNESS is determined by the urban

residents’ experiences of nature (as opposed to being confined to the natural assets within

Oslo itself). It therefore includes the natural capital within built areas in the city but also

the sizeable “Marka” peri-urban forest that encircles the city and the Oslofjord coastline

that are also enjoyed by Oslo residents. The rationale is that the proximity of these natural

assets is an important factor in the wellbeing of urban residents and enhances urban

property prices. The EU MAES (Mapping and Assessment of Ecosystems and their Services)

project on urban ecosystems (EC, 2016) covers Oslo and compiles urban indicators on Oslo

at multiple scales (property, municipal, metropolitan region) and resolutions that address

different management levels needs, public and private interests.

ii) Definition of urban natural capital: The OSLOpenNESS project looks at ways of mapping

the value of parks, green spaces, bodies of water, rivers and forest to local residents with

future consideration to be given specifically to street trees. The MAES project defines the

peri-urban blue green infrastructure as island, fjord, coastline, forest, agriculture, lakes,

parks and sports, other open space; whilst the built-up blue green infrastructure is defined

by forest, rivers and streams, communal gardens, private gardens, city trees and green

roofs/facades.

iii) Ecosystem services considered: Ecosystem services considered in the OSLOpenNESS

project are recreation and health, water management, flood control and biodiversity

conservation including a future consideration of pollination. The MAES project defines 17

urban ecosystem services as recreation, aesthetics, education, heritage/sense of place,

tourism, art/toys, storm water management, erosion control, local climate regulation,

soil/water/air regulation, carbon sequestration, noise reduction, pollination, food and

fibre, water provision and habitat for biodiversity.

iv) Type of value estimated: Monetary and qualitative methods are used in the OSLOpenNESS

project to capture the importance of nature in the city. Oslo Municipality is interested in

59 The International OpenNESS (Operationalisation of natural capital and ecosystem services) project which

explores methods to compellingly illustrate the importance of concepts such as ‘natural capital’ and

‘ecosystem services’ in local decision-making processes, specifically (for Oslo) the enhancement of urban

municipal planning.


eftec 101 June 2017

developing existing tools for municipal planning and management including the ‘blue-green

factor’ (BGF). BGF is a proposal for scoring properties’ green and blue structures in terms

of how important they are for managing surface water, pollination and recreation. The BGF

can be used to set minimum targets for property developers in different parts of the city.

The MAES study notes how very few stakeholders consider mapping of monetary valuation

to provide policy opportunities, with the majority of concerns address (physical) ecosystem

services mapping and classification at various scales.

v) Terminology used: Both the OpenNESS and MAES projects uses a range of terms including

‘natural capital’ and ‘ecosystem services’, ‘green’ and ‘blue’ infrastructure.

A.6. Wellington, New Zealand

The following study is covered in the review of urban natural capital assessments in Wellington:

Wellington City Council’s Biodiversity Strategy and Action Plan (Wellington City Council, 2015).

i) Definition of urban area: The City Council’s Biodiversity Strategy and Action Plan does not

give a precise definition of the urban area but makes a reference to the rural hinterland

and the ‘city area’ is stated as being 65% rural. In addition, pressures on biodiversity from

wider resource use patterns are mentioned, suggesting that consideration by the Council is

given to the wider environment in which the city is situated.

ii) Definition of urban natural capital: The Biodiversity Strategy and Action Plan gives sound

definitions of natural capital, ecosystem services and biodiversity’s role within it. The focus

is on biodiversity and the benefits produced from it.

iii) Ecosystem services considered: The focus is on biodiversity and its contribution to (i)

cultural identity; (ii) healthy environments that create healthy people and (iii) economic

sustainability through tourism, by providing a desirable base for businesses, and

contributing to quality of life. The following benefits are defined as ‘services’: freshwater,

local climate and air quality regulation, energy, carbon sequestration and storage,

moderation of extreme events due to climate change, waste-water treatment, pollination

services, recreation and mental and physical health benefits, tourism and economics,

cultural and spiritual wellbeing and sense of identity, and soil formation and stabilisation.

iv) Type of value estimated: costs of actions are laid out in detail. Benefits are not valued in

monetary terms even though the link between biodiversity and economic sustainability

through tourism is made. ‘Value’ is instead discussed as in cultural (i.e. Maori) and intrinsic

value. A qualitative City Biodiversity Index is used.

v) Terminology used: the language is mainly around natural capital and biodiversity “Our

Natural Capital is about Wellington’s indigenous biodiversity. These are the species that

occur or occurred naturally in Wellington.” ‘Our Natural Capital’ is used with double

meaning re: ‘capital’ city. Ecosystem services is mentioned a handful of times. Concept

plans exist around “Community catchments” about conducting integrated whole of

ecosystem approaches to restoration; “Wild safe Wellington” about creating a safe haven

for indigenous fauna and “Blue Belt” where the harbour and coastline interrelate with land.

A.7. Lancaster, Pennsylvania, USA

The following studies are covered in the review of urban natural capital assessments in Lancaster:


eftec 102 June 2017

Environmental Protection Agency (EPA) (2014). The economic benefits of green infrastructure,

a case study of Lancaster, PA; and

City of Lancaster (2011) Green Infrastructure Plan.

i) Definition of urban area: the spatial boundary of the City of Lancaster’s Green

Infrastructure Plan and associated economic assessment (City of Lancaster, 2011; EPA,

2014) is not explicitly stated. However, it is implied that it aligns with the official City

boundaries.

ii) Definition of urban natural capital: at the city-wide level, green infrastructure is defined

as “the patchwork of natural areas that provides habitat, flood protection, cleaner air, and

cleaner water”. At the neighbourhood-level or site-level scale, the primary driver for green

infrastructure (and the US in general) is for the purposes of managing stormwater run-off.

iii) Ecosystem services considered: the analysis included water treatment benefits; energy-

related benefits; air quality-related benefits; and climate related benefits (reduction in

CO2). The analysis assesses the cost-effectiveness of green infrastructure as a solution to

stormwater management compared to traditional ‘grey infrastructure’. For example, total

benefits include avoided (financial) capital costs associated with grey infrastructure.

iv) Type of value estimated: the following benefits were defined and assessed in monetary

terms: (i) reduced pumping and water treatment costs (avoided costs); (ii) energy-related

benefits (reduced electricity cost and natural gas cost due to decreased ‘cooling days’ and

‘heating days’); (iii) air quality-related benefits (reduction in NO2, O3, SO2, and PM10) and

(iv) climate related benefits (reduction in CO2).

v) Terminology used: the terms ‘natural capital’ and ‘ecosystem services’ are not used within

the assessment, instead ‘green infrastructure’ and ‘benefits’ are used.


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ANNEX 2. DEFINING THE URBAN BOUNDARY

Table A2.1 summarises a review of three ONS datasets according to the criteria based on the

literature and agreed with the ONS: intuitive, flexible, reflective of changing land use, evidenced

and UK-wide coverage. These datasets could be used to define the urban boundary based on the

above requirements (pers. comm. Bill South, ONS, 2017), albeit they have not been produced for

the purpose of accounting for urban natural capital.

ONS (2011) Built-Up-Areas (BUA) the built-up area (physical settlement) boundaries are

created using an automated approach (by OS) based on a 50metre grid squares being

transformed into polygons based on land-use percentages. Polygons must have a minimum size

of 20ha and settlements within 200 metres of each other are linked. BUA includes areas defined

as land ‘irreversibly urban in character’ which are villages, towns or cities (e.g. beyond the

London region into the surrounding counties considered Greater London) and likely to capture

smaller towns that might not consider themselves ‘urban’.

ONS Rural-Urban Classification 2011 (RUC2011) define ‘urban’ as any physical settlement (the

polygons from the BUA data) with a population over 10,000 people. If the majority of the

population of a particular Output Area (OA) live in such a settlement, that OA is deemed

'urban'; all other OAs are deemed 'rural'. This includes smaller towns, some of which might not

consider themselves ‘urban’. RUC2011 in itself includes no statistical data, but provides

categorical attributes for the ‘urban’ and ‘rural’ units.

ONS (2015) Major towns and cities: focuses on ‘core’ town/city not the surrounding area and

defines this as any physical settlement (the polygons from the BUA data) with a

resident/workday population of 75,000 people (e.g. central London only). If the majority of the

population of a particular OA live in such a settlement, that OA is deemed 'urban'; all other OAs

are deemed 'rural'. This data is currently ‘experimental’ and so there is uncertainty about its

replicability.


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Table A2.1. Summary of potential data sources and definitions for UK urban area

Data source

ONS (2011) Built Up Areas ONS RUC2011 ONS (2015) Major towns and cities

Definition of urban

Built-up-areas defined as land ‘irreversibly urban in character’ includes villages, towns

or cities (e.g. all of Greater London)

Based on built up areas with a population of >10k

Focuses on ‘core’ town/city not surrounding area – uses built up areas with resident/ workday population >75k (includes 112

towns/cities)

Intuitivea Requires some modification to include

anomalous features (e.g. Thames)

Requires some modification to include


Requires some modification to include


Flexibleb Yes Yes Yes

Reflect land use change

Yes Yes Yes

Evidenced/ GIS available

Yes boundary is on ONS Geoportal In itself includes no statistical data, but provides categorical attributes for the

‘urban’ and ‘rural’ units

Yes it is a statistical geography - on ONS Geoportal

UK coverage England and Wales only England and Wales only England and Wales only

Year 2011 (Census) 2011 (Census) 2011 (Census)

Update frequency

10 years 10 years Currently ‘experimental’

Use to define UK urban area?

Possibly (but could include villages) Possibly (not off-the shelf) Possibly (but experimental)

a Does it fit with what is typically considered to be an ‘urban’ area and with the existing strategic focus of local and national decision makers? b Does the definition cover the entire urban fabric so that other ecosystem types (such as freshwater, grasslands or woodlands in urban areas) can be identified so as to avoid the

double counting of benefits? c Defined as locations where dwelling density exceeds 3.75 dwellings per hectare (dph) at the 1600m scale.


eftec 105 June 2017

ANNEX 3. JUSTIFICATION FOR EXCLUDED BENEFITS

This Annex outlines the justification for the exclusion of certain urban ecosystem services from this

scoping project. Table A3.1 shows the potential benefits from the UK urban environment and which

have been included and excluded from the analysis. The justification for the exclusion of these

benefits is explained below.

Table A3.1. Scope of UK Urban Natural Capital Accounta

a Note the ∆ have either been included in other accounts or new studies will come to provide new data so for

now they are excluded from the initial urban account. Excluded benefits Freshwater

The physical volume of freshwater (i.e. m3) is not something that humans can influence

significantly at a broad scale (i.e. nationally), because it is determined largely by the climate and

the water cycle. At the city level, the ‘provision’ of freshwater (e.g. rivers, rainwater harvesting) is

very context dependent and there is an issue with transboundary effects. Water flows operate at a

catchment level and so the source of a river (for example) could be outside of the urban area

making attribution to urban areas challenging. For these reasons, the provision of freshwater is

considered outside this scoping account, but should be considered for inclusion in future iterations

of the account.

Natural hazard regulation (incl. flood)

The impact of natural capital in attenuating storm-water runoff in urban areas, not just in terms of

trees (such as existing iTree analysis) but the contribution of other green spaces is of interest to


eftec 106 June 2017

Defra, but the provision of the service is considered to be so heterogeneous across locations and

lacking in evidence that it is not considered in this scoping account.

Water quality regulation

The impact of urban natural capital on regulating water quality is expected to be low, given that

most of the water entering the urban environment has little opportunity to be purified by any

natural capital assets. This is because water either flows into the urban area via rivers or falls as

precipitation which will fall mostly on impermeable surfaces and into drains. Therefore, this

ecosystem service is not considered in this scoping account.

Pollination

Pollination is a supporting service and so would not be valued separately, to do so would double

count the value of food production. However, it is an important ecological function and so

consideration will be given to how it can be quantified in the condition account (but not the

physical flow or monetary flow accounts) under this scoping account.

Cultural heritage

No attempt will be made to estimate cultural heritage value in urban areas, partly because

isolating the value of cultural heritage from recreation and aesthetics is challenging and the value

of the bundle of cultural services will be picked up by ongoing ONS work on hedonic pricing in urban

areas and existing urban recreation values captured in the recreation national accounts (Ricardo,

2016).

Aesthetic value

It is hard to separate aesthetic value from that of other cultural benefits such as recreation and

cultural heritage and so there is potential for double counting if these values were included

together. Furthermore, this will be captured in ONS hedonic pricing work, which will analyse house

prices for any association of prices with proximity to natural capital and ecosystem services (i.e.

the bundle of aesthetics, recreation, cultural heritage). For these reasons, aesthetics is not

considered in this scoping account.

Recreation and tourism

Outdoor recreation forms one of the major leisure activities for the UK population. According to

figures published by Natural England (2010), in England alone, there are around 2,858 million visits

made each year involving a direct expenditure of some £20.4 billion. The figures further present

that visits to urban areas make up the majority, as within a 12-month period, 64% of adults had

visited a town/city for recreation. Across England as a whole, 40% had visited a wood/forest in the

past year and 25% of people had visited a stretch of inland water (Antara et al., 2011).

Spending during the recreational trips to the outdoor environment is included in the UK’s ecosystem

accounts through another study (Ricardo, 2016). Therefore, that study (Ricardo, 2016) excludes a

large proportion of ‘free trips’ for which there is no spending such as dog walking. Consideration

was given in this scoping account for urban natural capital as to how / if there could be better

estimates for ‘free trips’ within the national account and to ensure the estimates can be combined

with previous valuations of recreation through spending. The following are the elements of this

methodology:


eftec 107 June 2017

i) The GIS spatial data layer from ORVal (updated version to be developed April 2017 will also

include Wales green spaces) can be combined with the selected ‘urban boundary’ definition,

providing a mapped layer of green spaces within the urban environment for England and Wales;

ii) MENE data can then be used to estimate all trips to these spaces, a proportion of which would

be ‘free trips’. In particular, there is potential to isolate the ‘free trips’ excluded from

previous estimates using the Excel model that accompanies Ricardo’s 2016 study (based on

MENE data);

iii) This approach would involve filtering the underlying data to only include (i) those visits to sites

within the urban definition; and (ii) during which expenditure was ‘zero’;

iv) The resulting estimates for number of ‘free trips’ in England and Wales could then be

extrapolated to estimate visits to Scotland and Northern Ireland. One potential approach is to

apply a similar proportion of ‘free trips’ to the total number of trips for Scotland and Northern

Ireland estimated in Ricardo, 2016 (or in the accompanying model);

v) Once ‘free trips’ have been estimated for each country, a per trip value could be applied;

vi) It should be noted that there may be a degree of double-counting between different measures

of recreation. Any such occurrence, and their potential magnitude, should be explicitly stated.

Property values (Bundle of services)

The UKNEAFO (2014) recognised that well-planned and managed parks, gardens and squares have a

positive impact on the value of nearby properties, attracting people and capital. This higher

willingness to pay a price premium is, in part, an expression of the value of the bundle of

ecosystem services provided by those spaces, such as aesthetics, easier access to recreational

opportunities, cleaner air and so on. In addition, this property price premium reflects the scale of

the competition for this access (e.g. limited greenspace, increasing demand for it).

Hedonic property pricing method has been used to produce extensive evidence of such price

premium for different environmental amenities. While this evidence will be noted in the study, this

is not a priority for further work because of ONS work on hedonic property pricing for valuing urban

natural capital as part of a current European-funded project.


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ANNEX 4. SUPPLEMENTARY NOTES

This annex presents supplementary method notes for developing the physical and monetary

accounts for food (A4.1) and local climate regulation (A4.2) including a sensitivity analysis for

local climate regulation. All other services included within this scoping analysis have adequate

coverage within the main report, but the logic chains developed for each are presented in this

Annex (A4.3 – A4.5).

A4.1. Food

Benefits included in the scoping account: food production from allotments (measured as avoided

cost of purchasing the same)

Benefits excluded from the scoping account: other ecosystem services of allotments including

mental and physical health benefits

Geographical coverage: UK

Metrics used in physical account: kg per year from urban allotments

Metrics used in monetary account: £ of food produced per allotment plot

Background

Food production within the urban environment principally provides a “provisioning service” with

some level of associated “cultural services” capable of enhancing both landscape quality and

physical and mental health of those involved in allotments. Additional and associated “regulating

services” and “supporting services” are provided through the pattern and practice of land use and

land management and include pollination, soil quality, water quality/management and climate

mitigation.

This scoping account covers urban food production that is not picked up in enclosed farmland

natural capital accounts. It principally focuses on identifying and quantifying ecosystem services

from allotments and community gardens. Food produced through city farms and small-holdings is

expected to be relatively modest and sources of data are likely to be minimal, or absent. Further

and more incidental food production may be gained through foraging within urban areas such as

commons, woodlands, hedgerows, greenways, parks and gardens but this is likely to provide limited

benefits and would be very difficult to quantify. Additionally, food production for the benefit to

birds and other wildlife (including berries, seeds, etc.) could potentially be included but is not

considered at this time.

Local authorities have a statutory duty (set out in the Small Holdings and Allotments Act 1908; and,

Allotments Act 1950) to provide allotments and this duty may generate relatively reliable data on

allotment provision for England and elsewhere in the UK. The National Allotment Society60

(Referred to as NSALG - The National Society of Allotment and Leisure Gardeners) is the main

national membership-based organisation that promotes and safeguards the interests and rights of

the allotment community across the UK. The Federation of City Farms and Community Gardens61

60 http://www.nsalg.org.uk/ 61 https://www.farmgarden.org.uk/

http://www.nsalg.org.uk/

https://www.farmgarden.org.uk/


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also represents the interests of some allotment holders. Principle sources of information and data

for this scoping account include:

English Allotments Survey 1977 - Report of the Joint Survey of Allotments in England,

National Society of Allotment and Leisure Gardeners Limited and Anglia Polytechnic62

Environment Select Committee report on allotments in 199863

Allotments, a CABE Space Enabling Briefing Paper published in 200564

Allotments in England, report of survey 200665

Allotments – House of Commons Briefing, March 2012 on the law on allotments and current

government policy66

Figure A4.1 is the logic chain for urban food production from allotments. Note that the focus here is

on food production only and so the benefit is valued in terms of cost savings of food purchased

locally. However, there are a host of other benefits of producing food from allotments including

health benefits associated with recreation/exercise, improved nutritional value etc. (see wider

benefits from allotments section below) which are excluded.

Figure A4.1. Logic chain for urban food production from allotments

Key issues/challenges and resolution

Table A4.1 shows the main issues associated with developing the analysis of urban food production

and the resolution that was used.

62 English Allotments Survey: Report of the Joint Survey of Allotments in England', National Society of

Allotment and Leisure Gardeners Limited and Anglia Polytechnic University, November 1997 63 https://www.publications.parliament.uk/pa/cm199798/cmselect/cmenvtra/560/56002.htm 64 http://www.parksagency.co.uk/wp-content/uploads/2015/03/CABE-briefing-paper-final.pdf 65 http://allotmentresources.org/wp-content/uploads/2013/09/CROUCH_2006.pdf 66 http://researchbriefings.files.parliament.uk/documents/SN00887/SN00887.pdf






eftec 110 June 2017

Table A4.1. Key issues/challenges for estimating urban food production and resolution

Issue/challenge Proposed solution for the scoping account

Food is produced across a variety of habitats

and locations in the urban environment.

Suggestion is to first focus on food production

from allotments in urban areas.

Up to date data on the provision of allotments

is limited, with recent surveys focusing on

England.

The Allotments in England Survey of 2006

provides a good starting point and headline

figures for the provision of allotments.

The project brief focuses on the urban

environment, whilst allotment survey data for

England is combined for all locations.

UKNEA (2011, p376) states that 55% of all

allotments in England and Wales are in urban

areas although this figure is likely to be

outdated.

Scotland’s Greenspace map provides spatial

data for all urban settlements (towns and cities

with a population over 3,000) using PAN65

typologies that include ‘Allotments and

Community Growing Areas’.

See:

http://greenspacescotland.org.uk/1scotlands-

greenspace-map.aspx

Data for allotments in Northern Ireland appears

very limited and restricted to a number of

individual local authorities.

Further research is needed, could contact either

NI Statistics and Research Agency or Ordnance

Survey of Northern Ireland for advice.

Total numbers of allotment sites are generally

calculated through local authority surveys, but

these don’t take account of provision by town

and parish councils.

There are around 9,000 parish and town councils

which could significantly increase total figures

for allotments. The National Association of Local

Councils could be approached for advice and

information - http://www.nalc.gov.uk/

The size of allotments and the number of

individual plots is highly variable.

Average figures on the number of allotment sites

and individual plots can be estimated from the

national surveys.

Productivity of allotments will be driven by a

number of variables including: proportion of

plots under cultivation, quality and intensity of

cultivation and management, soil fertility and

condition, etc.

Assumptions need to be made for the condition

of allotments that will directly influence levels

of productivity.

Wider benefits from allotments

The Environment Select Committee Inquiry on allotments (1998) highlighted that for many of the

250,000 allotment holders in England and Wales, their plot forms an important part of their life.

The practical value which plot-holders place on an allotment stems from the direct benefits

provided by access to affordable, fresh vegetables, physical exercise and social activity. The

Federation of City Farms and Community Gardens has compiled a glossary of research and evidence

on these wider benefits and includes:

Benefits of community growing, green spaces and outdoor education67, and

The true value of community farms and gardens: social, environmental, health and economic68

The Environment Select Committee noted that ‘localised food production also brings ecosystem

services by reducing the use of energy and materials for processing, packaging and distributing

67 https://www.farmgarden.org.uk/sites/farmgarden.org.uk/files/benefits-community-growing-research-and-

evidence.pdf 68 https://www.farmgarden.org.uk/system/files/true_value_report.pdf



http://www.nalc.gov.uk/

https://www.farmgarden.org.uk/sites/farmgarden.org.uk/files/benefits-community-growing-research-and-evidence.pdf

https://www.farmgarden.org.uk/sites/farmgarden.org.uk/files/benefits-community-growing-research-and-evidence.pdf



eftec 111 June 2017

food: around 12 per cent of the nation's fuel consumption is spent on these activities. Many

allotment sites also provide good examples of the principles of sustainable waste management with

extensive re-use, recycling and composting taking place’.

Whilst the full evidence given to the Select Committee is not readily accessible, this 12 per cent

figure for fuel consumption appears to be attributed to the SAFE Alliance (Sustainable Agriculture,

Food and Environment) ‘Food Miles’ report69 published in 1994. This was the first study to

comprehensively assess the environmental and social implications associated with the distance

which food travels from producer to consumer using non-renewable fossil fuel energy. In

emphasising the two major determinants of a country's environmental impact were the nature of its

food economy and its systems of transport, it provided a clear argument for the benefits of more

localised, low energy, forms of food production. Allotments can clearly make a contribution to

more sustainable models of food supply. A further report, published at a similar time was Growing

food in cities (National Food Alliance, 1996) which provided a detailed review of the many benefits

including community and economic development, education, environment, health, leisure and

sustainable neighbourhoods.

Urban soils also play a very important role in the delivery of supporting and regulating ecosystem

services such as carbon, water and nutrient storage. Soils in urban green spaces have recently been

shown to make an important contribution to provision of ecosystem goods and services especially in

holding large stocks of soil organic carbon (SOC) (Edmonson, et al., 2014).

A4.2. Local climate regulation

Benefits included in the scoping account: avoided energy costs associated with air conditioning

and productivity losses

Benefits excluded from the scoping account: avoided emissions of greenhouse gases associated

with air conditioning, avoided excess mortality and morbidity

Geographical coverage: Great Britain

Metrics used in physical account: °C cooling effect due to urban vegetation

Metrics used in monetary account: £ of avoided air conditioning costs and residual productivity

losses

Explanation of how the benefit is provided

CICES 4.3 includes “modification of temperature” as a specific example of micro climate regulation

services. We have interpreted this service as: the role played by urban ecosystems modifying

temperature and providing an urban cooling effect. Figure A4.2 is the logic chain for local climate

regulation from urban natural capital.

69 See: https://www.sustainweb.org/publications/the_food_miles_report/

https://www.sustainweb.org/publications/the_food_miles_report/


eftec 112 June 2017

Figure A4.2. Logic chain for local climate regulation from urban natural capital

The mechanisms by which the climate regulating effect of urban vegetation is achieved are:

i) Evapotranspiration: All vegetated surfaces / land covers contribute to evapotranspiration as

part of the hydrological cycle. This process helps to dissipate high heat loads in urban areas as

vegetation “consumes” heat to drive the evaporation process (Salmond et al., 2016); e.g. one

“large” tree can transpire 450 litres of water a day consuming 1,000 megajoules of heat energy

in the process (Davies et al., 2011);

ii) Shading: the shade afforded by vegetation (trees in particular) blocks solar radiation from

reaching pedestrians, people in vehicles, people in buildings etc. This shading effect also limits

the solar heating of surfaces with high heat capacity (e.g. concrete), reducing heat storage and

urban heat island effect issues (Picot, 2004; Salmond et al., 2016); and

iii) Lower radiative temperatures: vegetated surfaces (Salmond et al., 2016) and water (Davies et

al., 2011) have lower radiative temperatures than impervious surfaces. Engineered green

infrastructure, such as green roofs and walls, can be designed to mimic these functions very

effectively (Davies et al., 2011; Emmanuel and Loconsole, 2015), however these are not

included within the urban scoping account. Demand for urban cooling services is influenced by

various contextual factors including local and regional climate, presence and intensity of locally

generated heat sources (e.g. air-conditioning units), urban form (e.g. openness of streets) and

population density (Davies et al., 2011; Salmond et al., 2016). There are also important

synergies between supply and demand factors to consider; e.g. for urban areas in hot / dry

climates, demand for urban cooling can be high yet key supply-side factors (e.g.

evapotranspiration) are often constrained by climatic factors (e.g. soil moisture).

Description of the importance of the benefit

The urban cooling effects of vegetation cover will be a critical part of the UK’s response to key

anticipated climate change: warmer drier summers and higher incidences of heat related extreme

weather events - heat waves. The evidence in the second UK Climate Change Risk Assessment


eftec 113 June 2017

(CCC, 2016) included several heat related risks that urban natural capital (via micro climate

regulation services) can help mitigate, including:

Temperature mortality - the number of heat-related deaths in the UK are projected to

increase by around 250% by the 2050s (median estimate), due to climate change, population

growth and ageing, from a current annual baseline of around 2,000 heat-related deaths per

year;

Loss of staff hours - past events suggest extreme outdoor temperatures can have significant

effects on production. The 2003 European heatwave is estimated to have resulted in a

reduction in manufacturing output in the UK of £400 to £500 million. The vulnerability of

industries to heat waves varies across sectors; e.g. the construction sector is at greater risk due

to physical activity outdoors, dusty conditions etc. (Surminksi et al., 2016). Another analysis

covering all economic sectors in London alone predicted productivity losses for the 2080s of

€1.9bn (2003 prices) (Costa et al., 2016).

The 2017 CCRA analysis highlights how these risks are likely to become more significant in the

future with climate change; e.g. by the 2040s, half of all summers in Europe are expected to be as

hot or hotter than the 2003 heatwave (ibid). In the health sector analysis, the urban heat island

effect is discussed specifically, highlighting how anticipated climate change will increase this

phenomenon (Kovats and Osborn, 2016). The analysis also highlights gaps in adaptation strategy for

this impact although synergies and win-win measures are identified regarding urban design and the

use of greening interventions to reduce urban heat island.

Condition account

The data on temperature variation across UK towns and cities is recorded as part of meteorological

monitoring but is not sufficiently granular within urban areas to quantify the urban cooling effect of

natural capital (Davies et al., 2011). In this scoping account we have explored ways to get around

this problem (see Section 3.5).

This section provides an overview of how indicators of natural capital condition related to local

climate regulation could be quantified.

Metrics: there are several ecosystem characteristics influencing the provision of micro climate

regulation that could usefully be measured in a condition account. These characteristics relate

to both supply and demand-side issues and include: (1) regional and local level climate,

especially rainfall, soil moisture and temperature; (2) access to alternative irrigation sources /

water resource availability – this may be an issue in dry climates as the evapotranspiration

cooling mechanism can cease to function (Salmond et al., 2016); (3) urban form and its

influence on the typology, number and distribution of local climatic zones (Emmanuel and

Loconsole, 2015) – example characteristics of urban form include housing density, height-to-

width (H:W) ratio of streets relative to surrounding buildings (urban cooling by vegetation is

more effective in wide open streets with low H:W ratios – Shashua-Bar et al., 2010), average

distance to greenspace; (4) population density; (5) extent of urban water; (6) extent and type

of vegetated land cover (the “vegetative fraction”) – i.e. different categories of green land

parcel (as opposed to discrete features within green and grey land parcels – e.g. street trees)

from areas of urban semi-natural habitat (e.g. woodland) to amenity greenspace; (7) number,

extent and type (e.g. intensive vs extensive – Emmanuel and Loconsole, 2015) of green roofs

and walls; (8) planting density of urban trees; (9) extent of canopy coverage for urban trees;

and (10) urban trees species specific issues – e.g. leaf colour and leaf area index (LAI).


eftec 114 June 2017

Methods: the following methods may be useful in determining ecosystem characteristics

(including supply and demand-side aspects): (1) distinct local climate zones (LCZs) defined as

“regions of uniform surface-air temperature distribution at horizontal scales of 102-104m” can

be identified / assessed for an urban area to identify the efficacy of urban vegetation as a

cooling strategy and areas where warming / urban heat island might be an issue (Emmanuel and

Loconsole, 2015). The LCZ methodology integrates various indicators of urban form, including

H:W; (2) mapping different categories of urban green space (green land parcels) using an

appropriate typology such as PAN65 on Planning and Open Space (Scottish Government, 2008).

Greenspace Scotland developed a method for mapping PAN65 open space using OS MasterMap

and aerial photo interpretation (AECOM, 2011); and (3) arboriculture audit – i-Tree specifies

sampling requirements and categories of data required (e.g. species, age class, canopy cover,

condition / health etc.) as part of baseline setting prior to modelling (USDA Forest Service,

undated).

Data: the following data may be useful: (1) Met Office local and regional climate data70; (2)

Environment Agency water abstractions map71 and water situation reports for England72; (3) OS

MasterMap73 for 2D urban form data and emerging OS products74 for 3D urban form data; (4)

ONS population density75; (5) urban water features (ponds, lakes etc) from OS MasterMap; (6)

vegetated land cover from existing greenspace maps (e.g. for Scotland76), Urban Atlas77 or CEH

land cover78; and (7) urban tree data from Forest Research i-Tree Eco in the UK project79 (data

may be available for Torbay, Edinburgh, Glasgow, Swansea and London) and / or local authority

arboriculture audits.

Assumptions: there is some uncertainty in the literature regarding the mechanisms by which

street trees contribute to urban cooling; structural issues concerning planting density and

canopy coverage vs species specific issues such as leaf colour and leaf area index (LAI)

(Salmond et al., 2016).

Physical account

Metrics: the final ecosystem service of interest for this account is the temperature reduction

(urban cooling) effect afforded by the shading and evapotranspiration processes provided by

urban trees and other forms of vegetation / vegetated land cover. Various theoretical (model

based) and empirical studies have demonstrated how increasing vegetation cover can

contribute to temperature reduction in urban areas including mean air temperature and

extreme temperature during heatwaves (Gill et al., 2007; Bowler et al., 2010; Davies et al.,

2011; Larondelle and Haase, 2013; Emmanuel and Loconsole, 2015; Salmond et al., 2016).

The following key metrics have been used within the physical account: (1) temperature

differentials between background urban levels (e.g. the Corine Land Cover class “continuous

urban fabric” – Larondelle and Haase, 2013) and different types of urban vegetation /

vegetated surface (oC). These metrics have been taken from existing empirical and theoretical

studies (see Table A4.2); and (2) overall (aggregate) city-wide temperature reduction afforded

70 http://www.metoffice.gov.uk/public/weather/climate/ 71 http://maps.environment-agency.gov.uk/wiyby/wiybyController?ep=maptopics&lang=_e 72 https://www.gov.uk/government/collections/water-situation-reports-for-england 73 https://www.ordnancesurvey.co.uk/business-and-government/products/mastermap-products.html 74 https://www.ordnancesurvey.co.uk/education-research/research/3d-and-height.html 75 https://data.gov.uk/dataset/population_density 76 http://greenspacescotland.org.uk/1scotlands-greenspace-map.aspx 77 http://www.eea.europa.eu/data-and-maps/data/urban-atlas 78 https://www.ceh.ac.uk/services/land-cover-map-2007 79 http://www.forestry.gov.uk/fr/itree

http://www.metoffice.gov.uk/public/weather/climate/

http://maps.environment-agency.gov.uk/wiyby/wiybyController?ep=maptopics&lang=_e

https://www.gov.uk/government/collections/water-situation-reports-for-england

https://www.ordnancesurvey.co.uk/business-and-government/products/mastermap-products.html

https://www.ordnancesurvey.co.uk/education-research/research/3d-and-height.html

https://data.gov.uk/dataset/population_density


http://www.eea.europa.eu/data-and-maps/data/urban-atlas

https://www.ceh.ac.uk/services/land-cover-map-2007

http://www.forestry.gov.uk/fr/itree


eftec 115 June 2017

by urban natural capital (oC) – a methodology has been developed to calculate this based on

temperature differential from temperature differentials for different categories of urban

vegetation. Ideally, temperature reduction would also be calculated at the sub-city level

accounting for the impact on cooling effect of local climate zones (LCZs). However, we suggest

that this level of detail is not appropriate for national level accounting due to the amount of

data processing required.

Methods: most existing theoretical studies of urban heat island use high resolution (local)

climate models to predict the impact of LCZ (type) characteristics (including amount and type

of urban vegetation and various indicators of urban form – see physical account) on air and / or

surface temperatures. Such model based studies tend to assess these cooling effects for

weather conditions during summer months, heatwaves or for predicted future warm

temperature conditions under modelled climate scenarios. For example:

– Emmanuel and Loconsole (2015) used ENVI-met which is a high resolution (0.5m x 0.5m)

computational fluid dynamics (CFD) model to assess the impact of urban green

infrastructure strategies on urban heat island in Glasgow;

– Costa et al. (2016) used the high resolution UrbClim model to assess key meteorological

parameters that could influence heat related productivity losses, although it is not clear if

this model accounts for vegetation cooling effects;

– Rosenzweig et al. (2009) used the lower resolution (1.3km x 1.3km) MM5 regional climate

model to predict the cooling effect of various vegetation and high-albedo urban heat island

mitigation strategies;

– The open source i-Tree streets and eco platforms model the “building energy effects” of

urban trees though it is not clear what metrics are assessed in the model (e.g. reduced

energy demand, temperature reduction).

Our methodology for modelling temperature reduction as part of the physical account takes a

more practicable approach by using empirical data on the cooling effect or temperature

differentials (Turban – Tgreen in oC) afforded by different types of urban vegetation. The

intention is to have an approach that is fit-for-purpose for urban natural capital account at the

national level (i.e. less intensive data and modelling requirements). The temperature

differentials (Turban – Tgreen in oC) data is taken from various empirical studies (see Table 4.8),

especially a meta-analysis by Bowler et al. (2010) and an assessment of multiple urban

ecosystem services (including cooling / urban heat island mitigation) by Larondelle and Haase

(2013). There is scope to refine this as further empirical work is undertaken on the cooling

effect of different types of urban vegetation in different contexts.

The methodology adopted in the physical account calculations is outlined in the main report

(section 3.5). Based on the studies reviewed in Table A4.1, the estimated reduction in cooling

due to urban natural capital is 0.42 degrees centigrade. Crucially this does not account for

sub-city variation in the contextual factors that determine urban heat island and the efficacy

of urban vegetation to mitigate this effect (cooling). These contextual factors are the various

indicators used to assess and define LCZs after Stewart and Oke (2012) (e.g. building height to

width ratio (H:W), sky view factor, building surface fraction etc). An LCZ specific approach is a

key area where the methodology could be improved, though this level of detail (and therefore

processing time) may not be appropriate for national level urban natural capital account.


eftec 116 June 2017

Table A4.2: Temperature differential afforded by different types of urban vegetation

Type of vegetated land cover / description of

intervention

Issues / limitations / comments Temperature reduction

afforded80 (oC)

Source

Street trees in New York. Unclear if the value relates to individual trees, groups

of trees, species specific issues etc.

-0.2 to -0.5 Rosenzweig et al. (2009) 81

Salmond et al. (2016)

Extensive tree coverage on roads in Bangalore,

India.

Empirical study. -5.6 Vailshery et al. (2013)


Shade trees and grass in a courtyard setting in

Israel.

Empirical study. Not clear what is meant by the term

“shade trees” or how many trees were present, what

species etc.

-2.5 Shashua-Bar et al. (2010)


Temp. reduction effect of shade trees on indoor

temperatures.

As above. -9 (wall temp’s)

-1 (indoor temp’s)

Berry et al. (2013)


Increasing tree cover by 25% Scale of intervention (e.g. city-wide), context, species

choice, baseline etc unclear (would need to review

primary reference further).

-5 to -10 (afternoon air

temperatures)

Davies et al. (2011)

Zipperer et al. (1997)

ASLA (2011)

Increasing green cover by 10-20% in Manchester. As above. -4 Gill et al. (2007)

Use of green roofs in Manchester city centre. As above. -6 Gill et al. (2007)

Increasing green cover by 20% in Glasgow. As above. -2 Emmanuel and Loconsole (2015)

Temperature differential between urban parks

and city average in Leipzig.

Definition of urban park unclear in this context (would

need to review primary reference further).

-0.945 Bowler et al. (2010)

Larondelle and Haase (2013)

Temperature differential between tree cover /

forests and city average in Leipzig.

Definition of tree cover / forests unclear in this context

(would need to review primary reference further).

-3.5 Bowler et al. (2010)


Temperature differential between 100m buffer

around urban parks and city average in Leipzig.

Definition of urban park unclear in this context (would

need to review primary reference further).

-0.52 Bowler et al. (2010)


80 Values are for air temperature unless otherwise stated. 81 Rosenzweig et al. (2009) modelled the impact of various urban heat island mitigation strategies on changes in urban air temperature during a summer (heatwave) period in New York. One

mitigation strategy assessed was street trees which they define as “planting trees along streets”. The study assessed the impact of these strategies in detail in six different case study locations

representing different land covers and levels of opportunity / constraint for the implementation of the mitigation strategies addressed. At the New York City level, the street tree strategy would

require the replacement of 7% of impervious land cover with street trees. The street tree cooling effect differential from Rosenzweig et al. identified here (-1.9oC) is an average value for all grid

cells assessed at all times of day. As such, although the street tree scenario assessed in the Rosenzweig et al. model will not be exactly comparable with different street tree configurations found

across the UK urban area, it is considered to provide a reasonable proxy for accounting purposes (in the future). There is scope to improve this figure through empirical studies of street tree cooling

in UK contexts. However, it was not possible to assess the cooling effect of street trees within this pilot study due to data access and resource issues.


eftec 117 June 2017

Limitations and recommendations

Limitation: analysis in both the physical and monetary accounts as reported in the main report

only considered certain categories of urban vegetation where data was available on cooling

effects and extent. Also, evidence concerning the cooling effects is likely to be highly context

specific, being influenced by all the factors listed above. This context specificity means that

generalising effects from individual studies can be problematic (Salmond et al., 2016).

Suggested refinement to address limitation: empirical and / or modelling studies in UK towns

and cities spanning different contexts to obtain cooling effect values for “missing” categories

of urban vegetation. Factoring in all relevant categories of urban vegetation to physical

account calculations will ensure that a truer value for “combined cooling effect” of urban

natural capital can be obtained – this scoping account only considered parks and woodlands.

Securing a street tree dataset (e.g. the Bluesky National Tree Map82) to incorporate the cooling

effect of street trees within the physical account calculations will help ensure a truer value for

“combined cooling effect”.

Limitation: the physical account analysis does not incorporate the impact of urban form and

the prevailing general and local climate on the cooling potential of urban vegetation (i.e. Turban

– Tgreen in oC). It assumes that the cooling effect of the different categories of urban vegetation

(parks, street trees etc.) is the same regardless of location. In a modelling study undertaken for

the Glasgow and Clyde Valley city region for example, Emmanuel and Loconsole (2015) showed

how cooling effect magnitude varies between different classes of Local Climatic Zone (LCZ):

the effect was greater for open low-rise, open mid-rise and extensive low-rise LCZ classes

whereas the effect for city centre locations (compact mid-rise) was less. This is due to the

different properties of LCZs that determine their thermal functioning (e.g. sky view factor,

building height to width ratio, building surface fraction etc. – see Stewart and Oke, 2012); and

influence the effectiveness of cooling from urban vegetation (Emmanuel and Loconsole, 2015).

Although the impact of context / LCZ class has not been studied extensively (e.g. few studies

have explicitly addressed the linkages between LCZ classes and urban vegetation as a cooling

strategy), it has been suggested that greening based cooling strategies for sites with high

building height to width ratios (i.e. compact high rise sites in LCZ class terms) are likely to be

less effective as the role of building shade and thermal mass begins to overwhelm the cooling

effect provided by the urban vegetation (Salmond et al., 2016). This supports Emmanuel and

Loconsole’s (2015) findings and clearly has implications for the approach adopted in this

scoping account which is likely to overestimate the cooling effect as the context of (at least)

some sites where urban vegetation is located will not provide optimal cooling conditions (in LCZ

terms). Conversely, the approach may also underestimate the cooling effect as it does not

consider the combined effect of the different types of urban vegetation assessed (beyond

simply summing the city-level proportionate effects). By way of illustration, Rosenzweig et al.

(2009) modelled the combined cooling effect of trees in open space, street trees and green

roofs and found the combined effect to be consistently higher than the effect of individual

interventions.

Suggested refinement to address limitation: it would be beneficial to produce urban accounts

for this service in a more bottom-up (localised) manner based on differential cooling effects

within different LCZ classes. Stewart and Oke (2012) provide a methodology for assessing and

defining LCZs on the basis of several indicators relating to different aspects of urban form.

Emmanuel and Loconsole’s (2015) modelling study in Glasgow showed how urban vegetation

82 https://www.blueskymapshop.com/products/national-tree-map

https://www.blueskymapshop.com/products/national-tree-map


eftec 118 June 2017

performs differently in terms of its cooling effect dependent on LCZ. Given this, there is

potential to develop a physical account methodology that can account for local context to

produce more robust results overall. However, the modelling and assessment work is likely to

be substantially more onerous at this level of disaggregation so the overall benefit (in terms of

improved accuracy) may not be that significant for a national account.

Limitation: the monetary account assesses productivity losses avoided due to the cooling effect

provided by existing urban natural capital. A key limitation of the monetary account concerns

the use of ambient air temperature for “hot day” values instead of WBGT (wet bulb globe

temperature). This assessment is based on productivity loss functions derived from Costa et al.

(2016) whom in turn derive these functions from ISO Standard 7243 on "hot environments:

estimation of heat stress on working man based on the WBGT (wet bulb globe temperature)

index" (ISO, 1989). The use of WBGT in this regard is a crucial factor affecting the accuracy of

the urban heat account. WGBT is a combination of three local climate measurements: 1)

natural wet bulb temperature, Tnwb; 2) globe temperature, Tg; and 3) air temperature, Ta

(Kjellstrom et al., 2009). Specialist equipment is required for measurements (1) and (2).

Accordingly, these are not routinely measured at weather stations (ibid). The temperature

metrics used in the physical and monetary accounts to assess productivity losses avoided are

ambient air temperatures (Ta). In outdoor WBGT measurements, Ta is weighted at 0.1 whereas

Tnwb and Tg are weighted as 0.7 and 0.2 respectively (ibid). In this sense, the ambient air

temperatures (Ta) used in the urban heat accounting provide only a rough proxy for productivity

losses when based on the ISO 7243 WBGT method. Furthermore, IS0 7243 requires specific

measurement of indoor WBGT excluding Ta.

For simplicity, Costa et al. (2016) assume that work in all sectors other than agriculture and

construction is performed indoors. Accordingly, the monetary account is likely to overestimate

the value of productivity losses avoided because: (1) Ta values used for sectors performing their

work outdoors will provide only a rough proxy of WBGT; and (2) Ta values are not used at all in

indoor calculations of WBGT where air-conditioning and other cooling mechanisms will have a

key impact on indoor WBGT83. For national level accounting purposes however, Ta values may

provide a useful and sufficient proxy for calculating the value of productivity losses avoided as

long as these limitations are articulated in a transparent manner.

Suggested refinement to address limitation: one obvious way of improving the accuracy of the

monetary account calculations would be to obtain empirical or theoretical WBGT values for

urban areas in the UK under different ambient temperatures (Ta). This is particularly important

for various indoor working environments which are most poorly represented in the methodology

used in this study and are where most work takes place in the UK’s urban economies. The use

of WBGT values in the monetary account would mean that the calculations therein are much

more aligned to the model of heat exposure productivity losses used in international standards

(ISO7243) and other recent studies in this area (Kjellstrom et al., 2009; Costa et al., 2016).

Limitation: the approach adopted in this scoping study only seeks to value urban cooling

related benefits in terms of productivity losses avoided (after Costa et al., 2016). This does not

account for the reduced hospital admissions (morbidity) and reduced deaths (mortality) that

may be afforded by urban cooling effects of natural capital during extreme heat. Nor does it

account for the consequences of the urban heat island effect at night (e.g. on people’s ability

to sleep etc.) rather than on daytime productivity (urban temperatures stay high through the

night because of the thermal mass of buildings radiating heat). Therefore, the service is likely

83 However, it is noted that some AC systems are designed for a relatively low maximum temperature (e.g.

29oC) and therefore may have a lesser impact on indoor WBGT above certain temperature thresholds.


eftec 119 June 2017

to be undervalued. This is a particular issue as extreme heat tends to affect the very young and

the very old most acutely (i.e. those that are most vulnerable to the health effects of heat) and

these groups are (generally) not “productive” in economic terms (Twigger-Ross and Orr, 2012).

Suggested refinements to address limitation: further research needs to be conducted on the

value of climate regulation as measured through reduced hospital admissions (morbidity),

reduced deaths (mortality) and loss of sleep that may be afforded by urban cooling effects of

urban vegetation during extreme heat.

Limitation: The approach to the physical account is such that there is potential for a small

amount of double counting whereby patch buffers may intersect. In these instances, the cooling

effect of the patch and the buffer (as determined by the cumulative area of these categories at

the city-level) will both be counted when the city-level proportional cooling effect of each

category is summed at step (vii). It is unclear from the literature reviewed if this type of

additive effect would happen in reality so, for accounting purposes, this can be construed as

double counting. We expect this double counting effect to be small because: 1) depending on

the spatial configuration of patches, the area of overlap between patch and buffer is likely to

be small; and 2) the proportional approach to calculating city-level cooling effects is such that

the magnitude of the cooling effect is small (i.e. a fraction of a degree) so multiplying this by a

small area of overlap in step (vi) is likely to result in only a small increase in cooling effect

overall.

Suggested refinements to address limitation: For future research, the magnitude of (1) could

be calculated in GIS (i.e. intersect of patch and buffer) to quantify the potential double

counting effect;

Limitation: A key limitation of this analysis is the use of hot day data for Central England which

will not be applicable to all urban areas in the UK.

Suggested refinements to address limitation: To obtain more locally specific data (e.g. from a

range of Met Office recording stations and apply to eight city regions and London as

appropriate).

Limitation: the empirical values for the cooling effect of urban green (Turban – Tgreen in oC) used

to model the aggregate cooling effect of urban green across the UK urban area are taken from a

variety of contexts, as per Bowler et al. (2010), including Leipzig in Germany. This may not

accurately reflect the urban context in the UK, including in relation to the cooling effect

afforded by different configurations / management regimes for urban green; e.g. some parks

may actually be warmer than the surrounding urban matrix (Doick, pers comm 15th May 2017).

Suggested refinement to address limitation: further empirical work in UK urban contexts to

obtain empirical values for (Turban – Tgreen in oC) in a variety of LCZs and general climatic zones

and for a variety of categories of urban green.

Sensitivity analysis

Tables A4.3 and A4.4 show the sensitivity analysis for the monetary account results for local

climate regulation from for urban economies (Greater London and England’s eight city regions;

ONS, 2016) across GB. Table A4.3 illustrates potential productivity losses and associated GVA losses

for scenarios with and without urban vegetation; e.g. the notional “hot day” temperature of 30oC

would potentially be 0.42oC hotter without GB’s existing urban vegetation, as per the physical

account. This hotter temperature would equate to greater productivity losses. In summary, the


eftec 120 June 2017

monetary account analysis shows that when temperatures reach 30oC (i.e. notional “hot day”

temperature for sensitivity analysis):

Without existing urban vegetation in Great Britain, a warm day equating to a notional “hot

day” temperature of 30oC would be almost half a degree hotter (30.42oC). This is based on the

combined cooling effect of parks is -0.23oC and -0.20oC for woodland being -0.42oC, see

Section 4.4.

Temperature of 30.42 oC is estimated to result in 17% loss in GVA from moderate / light work

(manufacturing, wholesale and retail trade, and public administration and defence) and 47%

for moderate / heavy work (construction; agriculture, forestry and fisheries)

Temperature of 30 oC (i.e. the temperature that exists as a result of the existence of urban

vegetation) is estimated to result in 5% loss in GVA from moderate / light work

(manufacturing, wholesale and retail trade, and public administration and defence) and 46%

for moderate / heavy work (construction; agriculture, forestry and fisheries);

The evidence from the Met Office suggests that in 2013 there were zero days where the

temperature was above or equal to 30oC. For the purposes of this analysis, and to illustrate

potential impacts of climate change, we provide an indicative estimate for 1 day of elevated

temperatures at 30oC. Note that current temperature evidence for Central England is that

there are no days at this temperature in 2013, but with climate change temperatures could

reach and exceed this level, hence why it is presented in this analysis.

Table A4.3. Monetary account – avoided productivity losses for urban areas in GB

Estimated losses


(%)

Annual GVA

(£M)

Working day GVA

(£M)

Productivity losses WITHOUT existing urban vegetation - Notional hot day temp. (30.42oC)



Moderate /

light work

Manufacturing 17 14,910 60

Wholesale and retail trade 17 47,530 180

Public administration and defence 17 72,330 280

Moderate /

heavy work




Productivity losses WITH existing urban vegetation - Notional hot day temp. (30.0oC)



Moderate /

light work




Moderate /

heavy work




Table A4.3 does not account for the averted losses under adaptation measures such as through the

use of air conditioning or behavioural change (i.e. the impact of changing working hours in terms of

averted losses for labour productivity). Behavioural change is especially relevant for industries

where air conditioning will have little or no impact (i.e. construction). Evidence from Costa et al

(2016) suggests that averted losses from (i) air conditioning (in London) can be ≤85% (ii) behavioural

change (in London) can be ≤40%. Unfortunately there is no estimate for the combined impact of


eftec 121 June 2017

behavioural change and air conditioning. Table A4.4 shows the impact of avertive actions, assuming

that for:

c) Light and moderate/light work the combined impact of these adaptation measures is 90% based


change;

d) Moderate/heavy work these industries are predominantly outside and so air conditioning cannot


Table A4.4. Monetary account – avoided productivity losses for urban areas in GB adjusted for

avertive actions

Estimated losses


GVA (£m)


(%) (£m)



/ light work £520m 90% £468 £52m




/ light work £151m

90% £136 £15


Table A4.4 shows the reduction in GVA losses due to avertive behaviour. Specifically, it shows a 90%

reduction in estimated GVA losses (due to air conditioning) in light and moderate/light work

industries which are the relevant industries that are assumed to be impacted at 30°C/30.42°C.

Table A4.5 shows the estimated impact of urban green space in reducing GVA losses for urban areas

in GB, accounting for avertive behaviour, is £37m/year over the duration of “London hot days” (i.e.

1 days) per year (≥30oC ≤31oC).

Table A4.5. Monetary account – net productivity losses avoided due to cooling effect of urban

vegetation for urban areas in GB adjusted for avertive actions

Hot day value Productivity (GVA) losses per working day (£m/day) Number of

days hot

temp. (30oC)

reached/

exceed

Total net

annual GVA

losses

avoided

(£m/year)

Without

existing urban

vegetation

With existing

urban vegetation

(-0.42 oC)

Net losses

avoided

London hot day

(≥30.0oC)

£89m £52m £37m 1 £37m

Asset valuation

The UKCP09 indicator “projected changes to the warmest day of summer” suggests that for a 50%

probability level (central estimate) under a medium emissions scenario in 2080, warm days for the

southern half of the UK are likely to be 2-4oC hotter and for the northern half of the UK, 4-6oC

(Murphy et al., 2009). Although this does not provide an indication of the increase of the frequency

of “hot days” for future analysis of asset value, it does indicate the increase in magnitude.

As per the analysis of annual provision of local climate regulation benefits (section 3.5), there were

four days in 2013 in Central England that were warmer than 28oC but cooler than 29oC, with

implication for productivity losses in the UK’s urban economies with and without urban green. For

argument’s sake, if it is assumed that the magnitude of warm days in Central England in 2080 would

increase by 3oC (see UKCP09 data presented above), then the magnitude of the temperature of


eftec 122 June 2017

these four “hot days” would increase from 28oC to 31oC. The implication of this rise in magnitude,

with and without urban green, on productivity losses is set out in Tables A4.6 below. This analysis

has not been undertaken for the notional “hot day” value of 30oC considered above; a 3oC increase

in magnitude of this “hot day” value would result in 100% productivity loss across all sectors with

urban vegetation (Costa et al., 2016).

Table A4.6. Monetary account future provision of local climate regulation benefit – avoided

productivity losses for urban areas in GB in 2080

Estimated losses


(%)

Annual GVA

(£M)

Working day GVA

(£M)

Productivity losses WITHOUT existing urban vegetation - London hot day temp. 2080 (31.43oC)

Light work Information and communication 20 27,412 105

Financial and insurance activities 20 26,316 101

Moderate /

light work




Moderate /

heavy work



Total losses across urban economies in GB 476,432 1,832

Productivity losses WITH existing urban vegetation - London hot day temp. 2080 (31oC)



Moderate /

light work




Moderate /

heavy work



Total losses across urban economies in GB 341,320 1,312

Table A4.6 does not account for the averted losses under adaptation measures such as through the

use of air conditioning or behavioural change (i.e. the impact of changing working hours in terms of

averted losses for labour productivity). Behavioural change is especially relevant for industries

where air conditioning will have little or no impact (i.e. construction). Evidence from Costa et al

(2016) suggests that averted losses from (i) air conditioning (in London) can be ≤85% (ii) behavioural

change (in London) can be ≤40%. Unfortunately there is no estimate for the combined impact of

behavioural change and air conditioning. Table A4.7 shows the impact of avertive actions, assuming

that for:

e) Light and moderate/light work the combined impact of these adaptation measures is 90% based


change;

f) Moderate/heavy work these industries are predominantly outside and so air conditioning cannot


Table A4.7 shows the reduction in GVA losses due to avertive behaviour under the with and without

urban green space scenarios.


eftec 123 June 2017

Table A4.7. Monetary account – avoided productivity losses for urban areas in GB adjusted for

avertive actions

Estimated losses


GVA (£m)


(%) (£m)



/ light work £1,729m 90% £1,556 £173m




/ light work £1,218m

90% £1,096 £122


Table A4.8 shows the estimated impact of urban green space in reducing GVA losses for urban areas

in GB, accounting for avertive behaviour, is £244m/year over the duration of “future hot days” per

year in 2080 (≥31oC).

Table A4.8. Monetary account future provision of local climate regulation benefit – net

productivity losses avoided due to cooling effect of urban vegetation for urban areas in GB

adjusted for avertive actions

Hot day value Productivity (GVA) losses per working day (£m/day) Hot day

temp. (31oC)

reached/

exceed

Total net

annual GVA

losses

avoided

(£m/year)

Without

existing urban

vegetation

With existing

urban vegetation

(-0.42 oC)

Net losses

avoided

London hot day

(≥28.0oC + 3oC = 31oC)

£233m £177m £56m 4 £244m

For the purposes of asset valuation, we assume a linear progression between the current value of

avoided productivity losses due to the cooling impacts of vegetation hot days in 2016 (estimated to

be £24m) and the value in the future in 2080 (estimated to be £244m/year). The estimated of

avoided energy cost associated with air conditioning is assumed to remain the same for the

purposes of this assessment (although in reality this could be expected to increase with

temperatures).


eftec 124 June 2017

A4.3. Global climate regulation

Benefits included in the scoping account: carbon sequestration from woodland

Benefits excluded from the scoping account: carbon sequestration from street trees and

vegetation other than forest


Metrics used in physical account: tonne of CO2 equivalent

Metrics used in monetary account: £ of tonne of CO2 equivalent

Figure A4.3 presents the logic chain mapping the steps and necessary data to develop physical and

monetary estimates for global climate regulation.

Figure A4.3. Logic chain for global climate regulation from urban natural capital


eftec 125 June 2017

A4.3. Air quality

Benefits included in the scoping account: improved health outcomes due to air quality regulation

(forthcoming to be reported under Air Quality Natural Capital Account)

Benefits excluded from the scoping account: avoided building soiling and impacts on ecosystem

services associated with air quality regulation


Metrics used in physical account: tonnes of SO2, NO2, PM2.5, O3 pollutant captured by urban

vegetation (forthcoming to be reported under Air Quality Natural Capital Account developed by

CEH, eftec and EMRC for the ONS)

Metrics used in monetary account: £ of health outcomes (forthcoming to be reported under Air

Quality Natural Capital Account developed by CEH, eftec and EMRC for the ONS)


monetary estimates for air quality.

Figure A4.4. Logic chain for air quality benefits from urban natural capital


eftec 126 June 2017

A4.4. Physical health from recreation

Benefits included in the scoping account: physical health benefits associated with outdoor

recreation

Benefits excluded from the scoping account: mental health benefits associated with outdoor

recreation


Metrics used in physical account: QALY improvement due to active physical recreation in urban

natural environment

Metrics used in monetary account: £ of QALYs and avoided costs of ill health


monetary estimates for physical health from recreation

Figure A4.5. Logic chain for physical health benefits from urban natural capital


eftec 127 June 2017

A4.5. Noise

Benefits included in the scoping account: reduced sleep disturbance, annoyance and improved

health outcomes

Benefits excluded from the scoping account: impacts on productivity

Geographical coverage: Manchester

Metrics used in physical account: decibel reduction over geographic area

Metrics used in monetary account: £ of reduced sleep disturbance, annoyance and improved

health outcomes


monetary estimates for noise.

Figure A4.6. Logic chain for noise mitigation benefits from urban natural capital


eftec 128 June 2017

ANNEX 5. SCOPING ACCOUNT FOR URBAN NATURAL CAPITAL

IN GREATER MANCHESTER

This annex presents the physical and monetary flow accounts for Greater Manchester developed as

part of this scoping study. Methods used are those developed as part of the national UK urban

account, and are discussed in detail in the accompanying main report document and annexes. In

addition, detailed steps of each calculation of the analysis are provided within the (automated)

Excel workbook developed for Defra as part of this account. The Excel workbook and accompanying

calculations enable Defra / ONS to update the account in the future.

This exercise illustrates the replicability of the methods developed from national to local-scale.

Greater Manchester was chosen due in part to facilitate collaboration between this work and that

being carried out within the Greater Manchester ‘Urban Pioneer project’ area under the 25 Year

Environment Plan.

Physical and monetary flow accounts have been developed for Greater Manchester for the

following:

Global climate regulation (carbon);

Noise regulation;

Local climate regulation; and

Physical health benefits.

The remainder of this annex presents the results from this exercise.

A5.1. Physical flow account

The physical flow account captures the physical quantity of environmental benefits produced by

natural capital within Greater Manchester’s urban boundary. Table A5.1 presents the physical flow

results of this exercise. As shown, benefits estimated for Greater Manchester include:

Global climate regulation (carbon): Greater Manchester’s urban woodland sequesters nearly

25,000 tonnes of CO2 equivalent per year;

Noise regulation: In total, nearly 430,000 buildings receive noise mitigation benefits due to

Greater Manchester’s urban natural capital;

Local climate regulation: Greater Manchester’s urban parks and woodland (based on the area

within the urban boundary in Greater Manchester) have a combined cooling effect of 0.50

degree Celsius;

Physical health benefits: Around 84,000 people meet their physical activity guidelines through

visits to Greater Manchester’s greenspaces. The activity undertaken by these active visitors is

associated with over 3,000 QALYs per year.


eftec 129 June 2017

Table A5.1: Annual physical benefit flows from Greater Manchester’s urban natural capital

Spatial accounting unit by natural capital benefit

Indicator Amount

(2016/2017) Units


Carbon (equivalent) sequestered

25,000 Tonnes of CO2e per year

Noise regulation

Noise band (dBA):

>=80 -

Number of buildings mitigated by 2dBA

75.0-79.9 1,000

70.0-74.9 11,000

65.0-69.9 45,000

60.0-64.9 107,000

55.0-59.9 264,000

50.0-54.9 -

45.0-49.9 -

Total 428,000


Combined cooling effect of urban parks and woodland (all patches)

0.50 Degrees Celsius

Physical health benefits

QALYs per year 3,000 QALYs per year

Active visitors 84,000 Number per year

Note: Figures have been rounded to avoid false accuracy.

A5.2. Monetary flow account

This account values Greater Manchester’s natural capital asset(s) based on the present value of the

stream of (annual) environmental benefits that the asset(s) will provide over 100 years. Table A5.2

presents the monetary flow results of this exercise. As shown, monetary values for benefits

delivered by Greater Manchester’s natural capital include:

Global climate regulation (carbon): The value of CO2e sequestered by Greater Manchester’s

urban woodland is around £2m per year;

Noise regulation: In total, noise mitigation from natural capital is estimated at £59m per year;

Local climate regulation: Productivity losses avoided due to the cooling effect of Greater

Manchester’s urban parks and woodland is estimated at over £2m per year.

Physical health benefits: Welfare gains associated with active visits to greenspaces are

estimated at nearly £63m per year. This physical activity is also associated with avoided direct

and indirect health costs of inactivity of nearly £40 m per year.


eftec 130 June 2017

Table A5.2: Annual monetary benefit flows from Greater Manchester’s urban natural capital

Spatial accounting unit by natural capital benefit

Indicator Value

(£m 2016) PV100

(£m 2016)


Value of tonnes of CO2e sequestered per year

2 120

Noise regulation

Noise band (dBA):

>=80 - -

75.0-79.9 1 15

70.0-74.9 3 95

65.0-69.9 10 292

60.0-64.9 17 500

55.0-59.9 28 839

50.0-54.9 - -

45.0-49.9 - -

Total 59 1,741


Market values - avoided loss in GVA, avoided air-conditioning cost

2 212

Physical health benefits

Welfare gain (based on increased QALYs)

63 1,875

Avoided direct and indirect costs to society

38 1,136

Notes: Present value has been calculated as the discounted flow of future value over 100 years, using a variable

discount rate as suggested by HM Treasury’s Green Book Guidance (2011): 3.5% for 0 - 30 years, 3.0% for 31-75, and

2.5% for 76 - 100 years.

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