DEVELOPING UK NATURAL CAPITAL ACCOUNTS: …sciencesearch.defra.gov.uk/Document.aspx?Document=...Developing UK Natural Capital Accounts: Woodland Final Report Where spatially sensitive

DEVELOPING UK NATURAL CAPITAL ACCOUNTS: WOODLAND ECOSYSTEM ACCOUNTS

For the Department for Environment, Food and Rural Affairs (Defra) March 2015

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Developing UK Natural Capital Accounts: Woodland Final Report

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 Cascade Consulting Study team: Phil Cryle (eftec) Sarah Krisht (eftec) Rob Tinch (eftec) Allan Provins (eftec) Ian Dickie (eftec) Anne Fairhead (Cascade Consulting) Acknowledgements We are grateful for advice and analysis on the recreation trip-generating function by Anthony De-Gol and Antara Sen of the University of East Anglia. This study has benefitted from inputs from a steering group led by Rocky Harris and Colin Smith of Defra. 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.

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CONTENTS

SUMMARY ............................................................................................... iii

1. INTRODUCTION ................................................................................... 1

BACKGROUND ............................................................................................... 1 1.1 STUDY OBJECTIVES .......................................................................................... 1 1.2 REPORT STRUCTURE ......................................................................................... 2 1.3

2. OVERVIEW ......................................................................................... 3

SCOPE OF ECOSYSTEM ACCOUNTS ............................................................................ 3 2.1 KEY PRINCIPLES FOR ECOSYSTEM ACCOUNTING ............................................................... 3 2.2 SPATIALLY DISAGGREGATED ECOSYSTEM ACCOUNTS .......................................................... 5 2.3

3. METHOD ..........................................................................................16

PRACTICAL METHODOLOGY ................................................................................. 16 3.1 DEVELOPING AN ACCOUNT – STEP-BY-STEP ................................................................. 17 3.2

4. RESULTS – INITIAL WOODLAND ECOSYSTEM ACCOUNT ...................................24

PHYSICAL STOCK ACCOUNT ................................................................................. 24 4.1 PHYSICAL FLOW ACCOUNT .................................................................................. 31 4.2 MONETARY ACCOUNT OF ECOSYSTEM STOCK AND FLOW ..................................................... 40 4.3 GENERATION AND USE OF ECOSYSTEM SERVICES FOR AN ACCOUNTING AREA ................................. 44 4.4

5. CROSS-CUTTING ISSUES FOR ECOSYSTEM ACCOUNTS ....................................47

SCOPE AND INTERPRETATION ............................................................................... 47 5.1 VALUATION OF ECOSYSTEM SERVICE FLOWS ................................................................. 48 5.2 ISOLATING RESOURCE RENT FROM ECOSYSTEMS ............................................................. 50 5.3 ACCOUNTING FOR BIODIVERSITY ............................................................................ 51 5.4 LAND COVER VS LAND USE .................................................................................. 52 5.5

6. CONCLUSIONS ...................................................................................54

SUMMARY .................................................................................................. 54 6.1 APPLICATION OF ECOSYSTEM ACCOUNTS .................................................................... 54 6.2 FEASIBILITY OF SPATIALLY DISAGGREGATED ECOSYSTEM ACCOUNTS ......................................... 58 6.3 MAINTAINING ECOSYSTEM ACCOUNTS ....................................................................... 60 6.4 FUTURE REFINEMENT OF ECOSYSTEM ACCOUNTS ............................................................ 61 6.5 RECOMMENDATIONS ........................................................................................ 73 6.6

REFERENCES ...........................................................................................75

GLOSSARY ..............................................................................................82

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SUMMARY

Introduction This study is an exploration into the development of spatially disaggregated ecosystem accounts for woodland. ‘Ecosystem accounting’ is a coherent and integrated approach to the assessment of the environment through the measurement of: (i) ecosystems; and (ii) flows of services from ecosystems into economic and other human activity. Ecosystem accounts are a set of related accounts that provide an overview of the state of an ecosystem asset. This includes a physical account reporting on the condition and extent of the ecosystem stock, an account reporting the provision of ecosystem service flows (the ‘return’ to the stock), as well as monetary accounts setting out values for these stocks and flows. To enable the future development of the initial woodland accounts and that of other ecosystem assets, this report presents: • The conceptual framework that has been applied to link ecological condition and other

characteristics to economic inputs (ecosystem services), which can be applied consistently across different ecosystem accounts;

• The reporting structure for the accounts, including the underlying rationale to explain how the accounts can - in principle given sufficient data - capture: (i) changes in the capacity of the ecosystem to provide key ecosystem services; (ii) changes in both stocks of assets and flows of services; and (iii) limits or thresholds relevant to the delivery of ecosystem services from the underpinning ecosystem assets;

• Practical steps for compiling spatially explicit accounts, with reference to the initial woodland accounts that have been prepared as part of the project; and

• Discussion on cross-cutting issues for producing a set of ecosystem accounts. Recommendations for further work to address gaps and improve and update woodland ecosystem accounts in the future are also provided.

Development of spatially disaggregated ecosystem accounts Existing data on woodland condition and ecosystem service flows, including those set out in initial work by the Office for National Statistics (ONS), are a tabulated collation of high level (i.e. UK, national and/or regional) statistical estimates of ecosystem condition and service flows (ONS, 2013a and 2013b). The specific focus of this study is to apply and ‘work through’ conceptual principles to arrive at a practical approach for producing spatially explicit ecosystem accounts via geographical information systems (GIS). Spatially disaggregated accounts are those that set out the geographic distribution of an ecosystem, its condition and service flows at a sub-national level. The methodology presented in this report has been practically developed and tested through the production of initial ecosystem accounts for woodland in Great Britain and the Public Forest Estate (PFE) in England. It is intended that the method developed is relevant for the wider ecosystem accounts (i.e. beyond woodlands). The task is to establish if this approach is currently ‘useful’ in the sense of whether such accounts augment aggregate estimates, and are feasible given the existing published data and available methods; and if not, whether this is likely to be the case in the future.

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The study focuses on four ecosystem services – timber provision, carbon sequestration, recreation and water flow regulation – in order to test the method across provisioning, regulating and cultural services. It explores different ways that ecosystem accounts can be developed in order to assess the use and feasibility of spatially disaggregated accounts. These accounts can be populated by building up data for detailed spatial units (e.g. 1km²) or by disaggregating data from national/regional level to more detailed spatial units. Disaggregated accounts can be considered ‘grid-based’ (e.g. 1km2) or ‘cadastre1-based’ and in principle should enable accounts for different accounting areas (Public Forest Estate, National Parks, River Basin Districts etc.) to be compiled in a flexible way. There may though be issues associated with reconciling data that is ‘grid-based’ with that which is ‘cadastre-based’ because of the different scales at which data is presented. For example, considering trade-offs between ecosystem services is complicated if the estimate of timber provision is on with a km2 basis and carbon sequestration is measured on a cadastre basis.

Main findings For ecosystem services where the level of provision and value of benefits is particularly sensitive to the location of woodland (e.g. recreation, water flow regulation), it is important that ecosystem accounts are constructed in a way that captures this sensitivity. The way that this is achieved is through developing spatially disaggregated accounts at the Basic Spatial Unit (BSU2) (1 km2) or Ecosystem Accounting Unit (EAU3) level (e.g. national park, catchment). This means that an account can identify (through mapped estimates) and report differences in the value of areas of woodland for these services, which can be substantial across locations. For other services where the value is less sensitive to the location of woodland such as timber and carbon, spatially disaggregated accounts (assuming the availability of robust data at BSU level) does not necessarily lead to significantly improved analytical capability or accuracy of accounts. For these services, it is likely to be appropriate to retain coarse aggregate estimates for national ecosystem accounts. However, where the purpose is to analyse trade-offs between ecosystem services due to changes in the extent or condition of woodland or another ecosystem within a specific area, then it is relevant to develop spatially disaggregated accounts for all ecosystem services. The feasibility of producing ecosystem accounts at the spatial scale, required for meaningful analysis, is dependent upon the data and methods available. The present study is limited by data constraints and the availability of spatially sensitive models for estimating ecosystem service provision, but still demonstrates the principles, structure and practical approach needed for spatially disaggregated accounts. It shows that sub-division of national accounts (unless accompanied by an increase in sampling intensity) increases uncertainty around each reported measurement as the underlying data quality is subject to broad assumptions as to its representativeness at a given local area. In this case there is a trade-off between the degree of sub-division and both the capacity to track change and the reliability of evidence to inform decision making. However, caveats to the interpretation of accounts need to be balanced with an understanding of wider ecosystem service provision; for example the relative importance of timber benefits to non-market benefits such as recreation. Hence sizeable margins of error may be acceptable in an account as a greater part of the purpose of interpreting an account is to understand the relative orders of magnitude of benefits.

1 Land parcels delineated by the cadastre, which are variably shaped polygons defined by an official register of land ownership and extent. 2 The Basic Spatial Unit represents a small spatial-scale area (tessellations) formed either by a standard grid (e.g. 1km²) or by cadastre (e.g. variably shaped polygons of land ownership and extent). BSUs need to be small enough so that they can be assumed to be reasonably representative of one particular type of land cover. 3 Ecosystem Accounting Unit, this is a larger scale area over which there is interest in understanding and managing change (e.g. river basins or administrative area).

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Where spatially sensitive models are available and statistically validated they can be used to estimate the distribution of stock condition and service flows across a particular broad habitat type or LCEU4 (e.g. a woodland area within the New Forest). For example, this study demonstrates the use of a trip-generating function to estimate the distribution of recreational visits to GB woodland at 1 km² resolution (BSU) with calibration to data from the Monitor of Engagement with the Natural Environment (MENE) survey (Natural England, 2013). The recommendation is that, for further refinement of the ecosystem accounts, further scoping is needed to explore the use of such spatially sensitive models to populate ecosystem accounts.

Accounting tables Table S1 presents the physical ecosystem stock account (closing stock for 2012) showing the estimated total extent of woodland, the extent of species type (broadleaved and coniferous) and the volume of timber (by species type and age. It also shows the biomass stock (in terms of oven dry biomass), the carbon biomass stock and extent of woodland in Great Britain which is designated a Site of Special Scientific Interest (SSSI), as well as the area of woodland in flood risk zones in England and Wales and estimated soil carbon stocks in south west England. A similar table (Table 4.3) for woodland in the Public Forest Estate in England is presented in the main report. The main report also includes illustrative maps of the results. The measure of extent is based upon the National Forest Inventory for Great Britain which includes areas which are used for forestry even if they have recently been felled. However the NFI excludes areas of woodland measuring less than 0.5 of a hectare. Hence there is a need to reconcile these accounts with any overarching estimates of woodland cover in the UK. It has not been possible under this project to develop estimates for some ecosystem characteristics that would normally be included in the stock account table, as the spatial data analysis that would be required has not been available (this is also the case for Table 4.3 on PFE). For example, information on the extent of population in close proximity to woodland is not included in the stock account despite being a key determinant of recreational value. However, this characteristic should be included in ecosystem asset accounts and is included (with two indicators) in the ONS asset account, using aggregate data (ONS, 2013b). The characteristics included in Table S1 (and Table 4.3 on PFE) are not completely aligned with those presented in the ONS initial accounts as the latter adhere to the framework adopted in the United Nations guidance on Ecosystem Accounting 2012 (SEEA-EEA) United Nations (2013). The reason for this is that the accounts presented in this report have focused on accounting for the condition of those characteristics that are important determinants of the capacity of woodland to produce benefits to society. For comparison, estimates from the Forestry Commission (FC) (from various sources set out in Table S1) are also reported alongside the independent estimates developed by the project team using National Forest Inventory (NFI) (2012) data. In comparison to the FC estimates reported in Table S1, a potential reason for the discrepancy in the estimated figures for woodland extent in this project may be that the FC report figures are based on field plot data. This means that the FC estimates provide a more reliable figure for the overall extent of conifer/broadleaved woodland. The NFI figures (from the NFI map) are based on an analysis of aerial photographs which have been adjusted by the Forestry Commission using the field plot data. There is also a discrepancy between the

4 Land cover/ecosystem functional unit, this is defined as a contiguous set of BSUs satisfying a pre-determined set of factors relating to the characteristics and operation of an ecosystem.

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estimated NFI volume figures and the FC reported figures. Again this is likely to be due to FC using field plot data whereas the study estimates are based on combining spatial data on the reported extent (hectares) and volume (cubic metres) of broadleaved/coniferous trees in each NFI region to provide a volume per hectare ratio for each region. This is then applied across the whole region to estimate for each BSU the extent of the areas of different species types.

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Table S1: Physical account of ecosystem condition and extent (stock) at the end of an accounting period for GB woodland Ecosystem: Woodland 2012

Ecosystem extent

Characteristics of ecosystem condition

Total Area Species Type (Extent and Volume)

Age (years)

Biomass Stock Carbon Stock Woodland in Flood Risk Areas10

Woodland SSSI

Broadleaved (BL)

Coniferous (C)

BL C 0-40 41-60 61-80 >80 Total Total Biomass

Total Soil FZ1 FZ2 FZ3

(million ha)

1 Extent (million ha) 2 Volume

(mill m3) 3 Age by Volume (mill m3)4

Million tonnes (Mt) oven dry5

MtCO26 MtCO27 Extent (mill ha)8 Extent (mill ha)9

Coverage (Countries/ regions)

GB GB GB GB GB GB SW England E&W E&W E&W GB

Closing Stock (2012)

2.78 1.27 1.51 239 375 163 251 105 109 426 780 133 2.61 0.094 0.075 0.243

(FC Estimates11) (2.64) (1.34) (1.31) (245) (355) (194) (193) (84) (109) (426) (780) - - -

1 FC, NFI Woodland Map (2012)

2 FC, NFI Woodland Map (2012); FC (2012d) 50 year forecast of softwood availability; FC (2012e) 50 year forecast of hardwood availability

3 FC, NFI Woodland Map 2012; FC (2012d) 50 year forecast of softwood availability; FC (2012e) 50 year forecast of hardwood availability

4 FC, NFI Woodland Map 2012; ; FC (2011c) Standing timber volume for coniferous trees in Britain; FC (2013c) NFI preliminary estimates of quantity of broadleaved species in broadleaved woodlands, with special focus on ash

5 FC (2014c)

6 Cantarello et al (2011) 7 FC, NFI Woodland Map 2012

8 Environment Agency, Flood Zone GIS layers

9 Natural England (http://www.gis.naturalengland.org.uk/pubs/gis/GIS_register.asp), Natural Resources Wales (http://www.ccgc.gov.uk/landscape--wildlife/protecting-our-landscape/gis-download---welcome.aspx), Scottish Environment Protection Agency (http://www.snh.gov.uk/publications-data-and-research/snhi-information-service/naturalspaces/) GIS layers 10. Flood risk zone 3 represents areas that could be flooded from a river by a flood that has a 1 per cent (1 in 100) or greater chance of happening each year. Flood Zone 2 represents outlying areas that are likely to be affected by a major flood, with up to a 0.1 per cent (1 in 1000) chance of occurring each year. Flood Zone 1 represents the remaining areas, where flooding is very unlikely (less than a 0.1 per cent (1 in 1000) chance of flooding occurring each year). 11. Some of the aggregate estimates provided in this report differ from those published by the Forestry Commission either because it has not been possible to replicate the FC adjustments to National Forest Inventory estimates at a geographically detailed level or because a more approximate methodology has been used.

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http://www.gis.naturalengland.org.uk/pubs/gis/GIS_register.asp

http://www.ccgc.gov.uk/landscape--wildlife/protecting-our-landscape/gis-download---welcome.aspx

http://www.snh.gov.uk/publications-data-and-research/snhi-information-service/naturalspaces/


Table S2 presents the estimated physical ecosystem service flows for British (GB) woodland. In the absence of spatial data on the location of timber harvests, it shows aggregate (GB) data on timber harvesting for Britain of 0.59 million m³ of timber for broadleaved and 11.78 million m³ for coniferous, as well as the estimated flow over 20 years of 11.74 million m³ of timber for broadleaved and 235.60 million m³ for coniferous. Mapping of the distribution of this harvest across GB is described in Section 4.2 of the main report. Table S2 also shows estimated annual carbon sequestration for broadleaved (6.01 MtCO2) and coniferous (6.55 MtCO2) woodland which have been estimated and mapped using FC published rates of carbon sequestration. It also accounts for estimated recreational visits (481 million) across GB woodland based on Sen et al (2014). The accompanying maps for these estimates are illustrated in Figures 2.4 and 2.5 of the main report and Annex 2.3. However it has not been possible to estimate volumes of water controlled by woodlands in GB. A similar table (Table 4.6) for woodland in the PFE (England) is presented in the main report. Table S2: Physical account of ecosystem service provision (flow) for GB woodland

Type of ecosystem

Woodland

Flow (Annual, 2012) Expected future Flows (‘20’ years) Provisioning Biomass for

Timber Broadleaved BL Coniferous C Broadleaved BL Coniferous C

- - - -

FC Estimates 0.587 million m3(overbark)

11.78 million m3

(overbark) 11.74 million m3 (20 yrs; 2012-2031)

235.60 million m3 (2012-2031)

Regulating Carbon Sequestration

6.01 MtCO2 6.55 MtCO2 120.20 MtCO2

131.00 MtCO2 (2012-2031)

FC Estimates1 10.3 MtCO2 (2010) -

Water flow regulation

Difficult to measure in physical terms

Difficult to measure in physical terms

Cultural Recreation 481 million visitors 9,620 million visitors (2010-2029)

1 Some of the aggregate estimates provided in this report differ from those published by the Forestry Commission either because it has not been possible to replicate the FC adjustments to National Forest Inventory estimates at a geographically detailed level or because a more approximate methodology has been used.

The estimated monetary account for woodland is set out in Table S3 for GB woodland. A similar table (Table 4.10) for woodland in the PFE in England is presented in the main report. It shows the estimated recreational value of GB woodland to be in the order of £1.7 billion a year, carbon sequestration by broadleaved trees of £341million a year and coniferous of £372 million a year as well as biomass for timber of broadleaved being valued at £9million a year and coniferous trees at £165 million. Monetary estimates are derived using the physical flows estimated for woodland in Table S2 multiplied by the following unit values: • For timber, broadleaved timber unit value is taken from the Nix handbook (2013) of £14.74/m³

and for coniferous the unit value used is £14.03/m³. • For carbon, the DECC (2014) non-traded price of carbon is used (£56.78/tCO2e in 2012 price

terms, with real term increases in future years also taken into account). • For recreation, the willingness to pay per person per trip to woodlands and forests in 2010 is

used of £3.47, taken from Sen et al (2012). Further work is needed on methodologies for estimating the value of the reduction in risk of flooding from the water regulating services provided by woodland. Discussion concerning the application of these unit values is provided in Section 5.2 of the main report and Annex 5.

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Table S3: Monetary account of ecosystem stock and flow for GB woodland (2012, £ million) Type of ecosystem service

Biomass for Timber Carbon Recreation Water Regulation

Broadleaved Coniferous Broadleaved Coniferous

Value

Flow (Annual) 9 165 341 372 1,669 (2010)

Not modelled

Stock (PV of future flows over 20 years)

127 2,431 5,738 6,254 24,552 Not modelled

Recommendations A number of recommendations are outlined with respect to the development of ecosystem accounts: Cross cutting issues – across ecosystem accounts • The displacement of ecosystem service flows due to changing land cover should be captured by

developing accounts in a way where assumptions made in one account follow into others – the use of cross-habitat ecosystem service models (both for bio-physical and for cultural services) as a way of ensure consistency across accounts should be explored.

• The total value of ecosystem service provision can be identified and monetised in most cases

through application of valuation techniques to measures of quantity in absolute terms. Practical examples need to be developed to understand how ecosystem services providing a reduction in risk can be measured in relative terms (with an explicit counterfactual) within an accounting framework. The total value of such services is established through modelling of risk under the counterfactual (e.g. of agricultural land use) and the risk under the existing land cover (i.e. woodland).

• It may not necessarily be invalid to use evidence generated by economic valuation methods to

value ecosystem services for ecosystem accounting purposes. This is because the concept of an exchange value may not be appropriate for certain ecosystem services; some valuation methods for non-market ecosystem services are more consistent with the notion of exchange values than others. As stated in Day (2013) ‘There is nothing logically inconsistent with the conventions for pricing used in the national accounts to value the benefits derived from public goods by the surplus derived from their consumption’.

• Further work – via practical examples - is required to determine if the concept of resource rent

is suitable for ecosystem accounting given the assumption of appropriately-functioning markets and if so how this can be applied within spatially disaggregated natural (ecosystem) asset accounts.

• When accounting for biodiversity in ecosystem accounts it is important to identify the final

ecosystem service ‘flows’ that biodiversity is directly associated with (e.g. charismatic species and other cultural services) as well as the ‘stock’ of biodiversity that underpins natural processes and hence supports the provision of other services. Whilst this ‘stock’ of biodiversity is partly measuring the abundance and variation in species, the need to capture the ‘role’ of these species within a functioning ecological system is what is desirable in terms of providing a comprehensive account.

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• Further research is needed into the issue of using land use based data to populate these

accounts and how to ensure comprehensive accounts of the different land cover types are established.

To inform policy • Ultimately a determination may need to be made by Defra and ONS as to the purpose and

application of ecosystem accounts – whether it is possible both to compile a high level aggregated view of ecosystem service provision and ecosystem assets as part of a wider system of national accounts, as well as a dataset and coherent framework for monitoring and understanding ecosystem service provision and the value of ecosystem assets at lower spatial scales in order to make more informed decisions as to the conservation and enhancement of the natural environment.

Using existing aggregate data on stock characteristics that are important determinants of the capacity of woodland to produce ecosystem services (e.g. using the Accessible Green Space Standard (ANGSt) as a proxy of value of woodland for recreation as done by ONS, 2013) is likely to be sufficient to account for and monitor the stock condition and service flows from ecosystems at a national level and for comparison with the national accounts. However, if establishing the variation in the value of woodland areas (or other ecosystems) across the nation and assessing trade-offs between ecosystem services are of interest, then the conceptually correct method is to pursue a spatially disaggregated approach.

• With respect to further development of a spatially disaggregated approach to ecosystem accounts:

- Defra should consider undertaking a scoping study on the use of spatially sensitive

‘ecosystem service models’ to explore further how current models and their potential short – medium term development can be applied to ecosystem accounts, including data requirements, cost and technical knowledge.

- This could be coupled with a study at a specific sub-regional level (e.g. catchment,

national park) to explore further how an account can be applied at this scale.

- Defra and ONS should work closely with the FC to establish how forthcoming NFI data can be used to update, develop and refine the initial woodland ecosystem accounts.

Conclusion Overall there is a decision to be made as to the purposes of ecosystem accounts, as they can serve multiple purposes at multiple levels. If the purpose is only for high level monitoring and comparison to the traditional National Accounts, then the use of relatively coarse aggregated indicators (national/regional scale) is likely to be sufficient. If the ambition is for a broader application to improve evidence and decisions concerning ecosystem service provision and the value of different assets, then spatial disaggregated accounts should be developed at a scale that is commensurate with the information required for a decision to be made. The extent to which this is feasible is dependent upon spatial data availability and robustness at different scales. These accounts can then be used for a range of policy applications, such as: spatially explicit prioritisation (e.g. through planning); targeting of habitat creation/restoration (e.g. through strategic policy decision); communication; grant allocation; and problem identification (especially in relation to synergies and

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trade-offs among different ecosystems and between different ecosystem services. The disaggregated accounts could also be used to improve the estimates at the aggregate level.

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1. INTRODUCTION

Background 1.1 This report is concerned with the measurement and reporting of ecosystems and ecosystems service provision as part of the development of UK ecosystem accounts for the eight UK National Ecosystem Assessment (UKNEA) broad habitat types. It follows from the ONS Roadmap published in December 2012 for the development of natural capital (ecosystem) accounts (ONS, 2012). The ONS Roadmap sets out key proposals and timings to produce experimental national ecosystem accounts within 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 2013 System of Environmental-Economic Accounting framework for Experimental Ecosystem Accounting (SEEA-EEA). In June 2013 the Office for National Statistics (ONS) published an initial set of physical ecosystem accounts for UK woodland, along with related discussion papers, based on high level national statistics. The purpose of this study is to examine the potential to develop UK national ecosystem accounts beyond the tabulated collation of high level statistical estimates of ecosystem condition and service flows (i.e. UK, national and/or regional level). It tests the application of spatially disaggregated (mapped) data as the basis for estimating the distribution of woodland ecosystem stock and service flows across the UK. The study also considers ‘cross-cutting’ issues for the development of ecosystem accounts across different habitat types. This includes how the accounts capture changes in one ecosystem (e.g. woodland) and subsequent changes in the quantity of others, as well as changes to ecosystem service provision, at a national level. The output of this study will inform an interim evaluation of progress on the ONS Roadmap, which is planned for the end of 2014. The outputs will also contribute to the commitment made in the UK Government’s Forestry and Woodlands Policy Statement (Defra, 2013) of working with the Natural Capital Committee and ONS to develop a set of natural capital accounts for UK forestry. The methodological development, outputs and practical experience from this study will also help inform the UK’s continuing contribution to the development of international standards in ecosystems accounting, such as the SEEA-EEA.

Study objectives 1.2 This report focuses on the development of ecosystem accounts for UK woodland. The specific objectives for the study are to: 1. Develop a set of physical and monetary natural capital accounts for the Public Forest Estate

(PFE) in England, drawing on all relevant data;

2. Develop physical and monetary natural capital accounts for woodlands in the UK, built up from spatially disaggregated data and taking into account where relevant the data sources, methodologies and the framework developed for the PFE;

3. Establish the rationale for the structure and coverage of the accounts and identify ways in which these accounts can be improved in terms of their potential to inform policy; and

4. Make recommendations on cross-cutting methodological issues that arise.

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The scope of the study and objective to develop ecosystem accounts for UK woodland and the PFE in England is ambitious. It represents the first attempt to develop a set of UK ecosystem accounts via an explicit spatially disaggregated approach. This requires addressing both conceptual and practical challenges in the process of mapping and reporting the extent, condition and distribution of woodland ecosystems and the associated service flows. Inevitably the present work is subject to gaps in both scientific understanding of ecosystems and the availability of data. Overall the key contribution of the study is to demonstrate and test the feasibility of spatially disaggregated ecosystem accounts, and assess how they could be refined in the future.

Report structure 1.3 The remainder of this report is structured as follows: Section 2

Overview: this sets out the background to the project, the guiding principles that have informed the development of the ecosystem account, and the basis of the spatially disaggregated approach to the study.

Section 3

Method: this describes the practical steps undertaken to compile the initial woodland ecosystem accounts, covering the selection of ecosystem services, development of logic chains, and measurement and valuation of stocks and flows.

Section 4

Results: this presents the physical stock account, physical flow account and monetary account for the initial UK woodland and PFE (England) ecosystem accounts.

Section 5

Cross cutting issues: this discusses a number of key issues common to the wider development of ecosystem accounts, including the integration of ecosystem accounts for multiple habitats, monetary valuation, the isolation of resource rent, accounting for biodiversity, and land cover versus land use as the basis for an account.

Section 6

Conclusion: this summarises the application (purpose) and feasibility of developing spatially disaggregated accounts along with acknowledging the current limitations of data and future refinement of the accounts.

The content of the report is accompanied by a number of supporting annexes. Annex 1 provides a comparative overview of the various conceptual frameworks that have informed the development of the approach to compiling a spatially disaggregated ecosystem account. Annex 2 presents accompanying ‘Method Notes’ that describe the approach taken with respect to the carbon, water flow regulation, recreation, and biodiversity components of the account. Annex 3 reports a comparison of the Land Cover Map 2007 (LCM 2007) and the National Forest Inventory (NFI, 2012). Annex 4 provides a review of ecosystem service models that could be used to further develop spatially disaggregated estimates of service flows. Annex 5 reports on issues concerning the valuation of ecosystem service flows for accounting purposes.

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2. OVERVIEW

Scope of ecosystem accounts 2.1 The guiding framework for the project is the System of Environmental-Economic Accounting – Experimental Ecosystem Accounting (SEEA-EEA), (United Nations, 2013). This is an evolving initiative that aims to produce international standards in ecosystems accounting. The SEEA-EEA framework defines ‘ecosystem accounting’ as a coherent and integrated approach to the assessment of the environment through the measurement of: (i) ecosystems; and (ii) flows of services from ecosystems into economic and other human activity. Although SEEA-EEA is not a formal international standard for environmental-economic accounting, producing ecosystem accounts that are consistent within this framework will ensure that developing methods in the UK are aligned with, and can inform, the broader international momentum for greater understanding of national wealth and improved measurement and accounting for natural capital and its benefits e.g. TEEB (2010), WAVES (World Bank, 2012), Wealth of Nations reports (World Bank, 2006; 2011), CICES (European Environment Agency, 2013) and MAES (EC, 2013) A key distinction to make for the scope of this study is between ‘natural capital accounts’ (the formal title for this study) and ecosystem accounting (the actual focus of this study). Natural capital accounting represents a broader scope which incorporates the wider elements of environmental accounting covered by the System of Environmental-Economic Accounting Central Framework (SEEA-CF, 2012). This 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’ (e.g. timber, subsoil assets, and fossil fuels) and expected flow of benefits in basic resource accounts. Only a subset of natural capital assets are measured and reported within an ecosystem account. This focuses on ‘ecosystem assets’ only, which include, for example, species, ecological communities, soils, rivers and land. Natural capital assets not covered by ecosystems accounts include atmosphere, minerals, sub-soil assets and oceans (Defra/ONS, 2014). The principles for measuring and accounting for ecosystem assets are consistent with those applied to natural capital assets. This is demonstrated in a number of recent domestic initiatives such as the UK National Ecosystem Assessment follow-on (UKNEAFO, 2014) and the current discourse by the Natural Capital Committee (NCC, 2012). Inevitably there are differences in the terminology, definitions and emphasis of principles set out in the SEEA-EEA and other initiatives. A comparative review of the various terminology and definitions demonstrates, however, that there is overall alignment (see Annex 1). In practice there are no fundamental differences in the conceptual approaches that have been developed. This is to be expected since all are grounded in a common scientific understanding of ecological functioning and economic outcomes. There are however semantic differences in the terminology used to describe the respective conceptual approaches.

Key principles for ecosystem accounting 2.2 Defra/ONS (2014) details the key principles to be followed when developing ecosystems accounts in the UK as part of the ONS Environmental Accounts. These principles are consistent with the SEEA-EEA, which takes an ‘ecosystem approach’, considering 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.

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A formal way to describe the ecosystem approach to accounting for ecosystem stock and flows in this way is provided by the ‘logic chain’ relationship (or alternatively, ‘chain model’). This characterises – in a structured manner – the ‘pathway’ by which ecosystem assets generate benefits for society, in terms of the goods and services that are consumed from the provision of ecosystem services. As an example, Figure 2.1 illustrates the logic chain for the provision of fibre from woodland, from which timber is produced. Here the pathway requires a combination of an ecosystem asset (woodland) along with management actions and other forms of capital input (e.g. for harvesting) in order to derive timber from the provision of a final ecosystem service (fibre).

Figure 2.1: Illustrative logic chain for the provision of fibre from woodland

The starting point for the logic chain relationship is ‘ecosystem characteristics’, which represent the primary factors that describe the ecosystem and determine the provision of ecosystem services. Figure 2.1 lists a series of characteristics relevant to the production of fibre and consequently timber, including (woodland) extent, tree species, age structure and yield class of trees. The characteristics are not necessarily independent in their effect on the productivity of an ecosystem; indeed relationships are complex, multi-dimensional, and dynamic. For instance soil type, precipitation, solar exposure and land aspect/ slope will all influence yield class. By implication, ecosystem characteristics need to be appropriately identified and measured to usefully inform on the state/condition and productive capacity of an ecosystem. In the case of fibre (timber), yield class can be interpreted as a proxy for multiple biotic and abiotic components on an ecosystem along with other properties (e.g. solar exposure). In addition, to account for some types of ecosystem service provision, the relevant characteristics may not relate directly to the ecological functioning of an ecosystem but rather to its spatial configuration (e.g. proximity to population), which is an important determinant of the value derived by society for some ecosystem services (e.g. recreation). Overall, the logic chain provides the fundamental basis for developing an ecosystem account in that it explicitly provides the link between the qualities or characteristics of an ecosystem asset and the provision of services. In particular it provides a comprehensive structure for the measurement of ecosystems and flows of services into economic and other human activity. Importantly the approach

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recognises that ecosystem service provision and the economic benefits that are generated are dependent on the underlying status of ecosystems. Given this, the practical use of the logic chain therefore is to establish the appropriate data and information needed to measure and report on the status of the various links within pathway from ecosystem assets to economic goods and benefits. SEEA-EEA (2013) complements this by setting out the composition of an ecosystem account in compatible terms to the logic chain relationship via the four types of accounts and accompanying reporting statements: 1. Physical account of ecosystem condition and extent (stock account): reporting bio-physical data

on the condition of ecosystem assets through key ecosystem characteristics;

2. Physical account of ecosystem service provision (flow account): reporting data on the physical flow of ecosystem services linking ecosystems assets to economic and other human activity;

3. Monetary accounts of ecosystem stocks and flows: reporting the values of ecosystem services, and from these deriving values for the assets themselves; and

4. Within the flow accounts, describing the generation and use of ecosystem services: reporting on

the beneficiaries of ecosystem service provision distinguishing between the (spatial) area within which the ecosystem services are generated and the areas in which ecosystem services are used.

Tracking these accounts over time therefore enables assessments of changes in the extent and condition of ecosystem assets, along with associated changes in the level of provision of ecosystem services. For example, a general deterioration in the condition of characteristics as reported in the stock account implies degradation in the ecosystem stock (i.e. a reduction in the quality of the forests) and hence a reduced capacity to produce ecosystem services (e.g. lower potential future flows of timber). Such changes might be a result of natural changes (e.g. pests and diseases), deliberate actions management actions (e.g. harvesting of biomass for timber) or impacts from other human activities (e.g. negative externalities such as agricultural fertiliser run-off impacting water quality). Although these pressures are not recorded in the stock account itself (except for management practices which it might be appropriate to record in some cases), the impact of changes in these pressures are reflected in the stock account through changes in the characteristics which describe the condition of the stock. The ability of an ecosystem account to record such impacts is, though, both data dependent and subject to scientific understanding. This is particularly the case with respect to establishing which ecosystem characteristics should be included within a stock account, and the availability of data (metrics and indicators) to adequately represent the condition of ecosystems and estimate ecosystem service flows. These practical issues have been encountered in the development of the initial ecosystem accounts for UK woodland and the Public Forest Estate (PFE) and are addressed subsequently (Sections 4 and 6).

Spatially disaggregated ecosystem accounts 2.3 2.3.1 Spatial units in ecosystem accounts The SEEA-EEA (2013) defines three types of spatial units which may be aggregated for different scales of analysis:

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• Basic spatial units (BSU): this represents a small spatial-scale area (tessellations) formed either by a standard grid (e.g. 1km²) - the most likely approach for UK accounts - or by land parcels delineated by the cadastre (e.g. variably shaped polygons defined by an official register of land ownership and extent). A BSU will have certain characteristics attributed to it, such as land cover, soil type, elevation.

• Land cover/ecosystem functional unit (LCEU): this is defined as a contiguous set of BSUs

satisfying a pre-determined set of factors relating to the characteristics and operation of an ecosystem (such as land cover type, water resources and soil type). It is defined in such a way that the set of BSU within a LCEU operate in a relatively joint manner and independently from neighbouring LCEUs. The LCEU represents the basis for measuring the geographical coverage of each ‘ecosystem’ (i.e. the ecosystem extent).

• Ecosystem accounting unit (EAU): this is a larger scale and rather fixed area taking account of

administrative/management boundaries or large scale natural features (e.g. river basins), which there is interest in understanding and managing change. This, for example, could be a National Park or other administrative area where reporting of ecosystem accounts could inform management decisions. The EAU may also be built from an addition of BSUs, although the concordance may not be exact.

In this, the complete set of LCEUs add together to provide comprehensive coverage of the UK, as does the complete set of EAUs. The EAUs and LCEU will generally overlap, and can be approximated by the BSUs. Figure 2.2 provides an illustration of the relationship between BSUs, LCEUs and EAUs. The BSU is defined by grid squares, whilst the EAU, here presented as a national park, contains three distinct types of LCEU; type A which is woodland, type B which is enclosed farmland and type C which is urban, with one type (type A woodland) occurring in two locations. Figure 2.2: Stylised depiction of BSU, LCEU and EAU

LCEU type A (woodland)

Ecosystem Accounting Unit (National Park)

BSU

LCEU type C (urban)

LCEU type B (enclosed farmland)

LCEU type A (woodland)

Alternatively, the BSU’s may be cadastre-based (i.e. land parcels built up from variably shaped polygons). Hence there may be issues associated with reconciling data that is ‘grid-based’ with that which is ‘cadastre-based’ because of the different scales at which data is presented. For example, considering trade-offs between ecosystem services is complicated if the estimate of timber is at 1km2 and carbon is on a cadastre basis. Another challenge for the measurement of physical stock/flow and monetary valuation of ecosystem service provision is that the LCEU classes proposed in SEEA-EEA (2013) are highly

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aggregated. For example, all forest land would be classed under ‘Forest tree cover’. SEEA-EEA does note that some cross-classification may be needed to reflect the extent of human activity, and that LCEU classifications may need to reflect variations in climatic conditions, geophysical conditions, and land use. But for most services, the high level classifications used in the SEEA are too aggregated to use as categories for measurement and valuation. A bottom-up approach with a much more detailed and spatially disaggregated land cover classification can be better matched with appropriate valuation data - noting in particular that proximity to human populations and economic activities is a key determinant of the value of many ecosystem services. These values can then be aggregated to totals that match the SEEA-EEA classification. Therefore there can be a clean mapping from the ecosystem characteristics used in the logic chains to define service flows and values on to the aggregates presented in the ecosystem accounts reporting tables. 2.3.2 Aggregate versus disaggregate ecosystem accounts The choices for the spatial level of the analysis depend on the purposes of the ecosystem accounting exercise. A highly aggregated approach would likely be sufficient for national-level monitoring and integration with the SNA. For this purpose alone, it would be disproportionate to develop highly spatially disaggregated estimates of ecosystem service provision, even where this is sensitive to location and quality. For example aggregate data on timber harvests/sales may be sufficient to account for this service in this case.

However, ecosystem accounting holds great potential for structuring information, communication and decision-support at a variety of scales and governance levels. To achieve these ends, and to take proper account of trade-offs across different services at local scales, spatially explicit accounts are needed. Spatially dis-aggregated ecosystem accounts map the spatial distribution of an ecosystem, its condition and service flows. An account is populated by building up geo-coded data via geographic information systems (GIS) at defined spatial units (e.g. 1km²) to a national/regional level. Where direct spatially-explicit measurements of services and stocks are available, these are ideal. However, this is rarely the case. Rather, the approach required is to use available spatially explicit data on ecosystem characteristics and modelling of some form to estimate service flows on the basis of these characteristics to supplement these spatially explicit measurements. Fundamentally, this is what the logic chain model described in Section 2.2 is intended to do; i.e. to establish the key parameters of ecosystem assets (i.e. the characteristics) that determine the level of ecosystem service provision. In the absence of directly observed data on ecosystem provision, modelling permits the estimation (prediction) of ecosystem service flows based on empirical methods5. The spatially disaggregated approach can be considered to be a ‘grid/cadastre-based’ methodology to compiling an ecosystem account. In this way a contrast can be made to ‘aggregated’ ecosystem accounts which report at a defined level (e.g. UK/national/regional) but do not have the property for breaking down to smaller spatial scales since data is not built up on a grid/cadastre-based system of spatially disaggregated estimates. The distinction between aggregated and disaggregated (‘grid/cadastre-based’) accounts can however be blurred, since in practical terms both directly measured data and estimates of ecosystem service provision are provided at different spatial levels. Moreover, higher level aggregated estimates can be used to calibrate estimates of the spatial distribution of an ecosystem, its condition or service flows across spatial units (e.g. 1km²). The point in using national aggregates to calibrate spatial estimates is to improve statistical properties. A spatially-sensitive model is best

5 See Annex 4 for examples of ecosystem service models.

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suited to derive spatially explicit estimates - see for example, the trip generating function applied in this study for estimating recreation flows associated with woodland. National estimates are statistically better for estimating totals but give no spatial information - for example, use of the original Monitor of Engagement with the Natural Environment (MENE) data to estimate total numbers of recreation trips. Calibration therefore ensures that the bottom-up estimates are consistent with the national aggregates. 2.3.2 Relevance of spatially disaggregated ecosystem accounts Where the purpose of ecosystem accounts is to analyse trade-offs between ecosystem services due to changes in the extent or condition of woodland or another ecosystem within a specific area, then it is relevant to develop spatially disaggregated accounts for all ecosystem services. Beyond this the importance of a spatially disaggregated approach to ecosystem accounts is based on the extent to which ecosystem service provision is spatially sensitive. Stocks and flows of ecosystem services and associated economic benefits are not spatially uniform. The value of ecosystem services is dependent on quality, quantity and spatial configuration of the ecosystem which varies across locations. This is the fundamental reason for a spatially explicit approach. However, the significance of the spatial dimension is not the same for different ecosystem services. For example, it is the case that the (marginal) value of carbon sequestration does not vary depending on woodland location, but only according to other ecosystem characteristics. This is because the nature of these benefits is not constrained by the juxtaposition of the beneficiary population. This is also true to some extent for timber, however access and distance to processing facilities do have an effect on the value. In contrast, though, the specific location is critical to adequately representing the level of ecosystem service provision for some other types of benefit. Services that are highly spatially specific As an example, Figure 2.3 maps the distribution of PFE woodland within Environment Agency flood zones for the New Forest (an example of a National Park EAU). Explanation of the method and data sources used to develop this mapping is provided in Section 4.1. Whilst this does not formally establish the level of flood risk reduction associated with the water quantity regulating function of woodland, it is reasonable to assume that this service is more important in the woodland adjacent to/or within higher risk flood zones. The EA flood risk zones are defined according to estimated frequency of flooding; in the context of ecosystem services provided by woodland it is the coincidence of woodland, flood risk zone and population at risk that is of interest. Therefore, identifying woodland that is adjacent to high flood risk zones that is also in close proximity to large population centres will identify the woodland area that provides greatest water quantity regulating services. The relationship between woodland location, water flow regulation, flood risk, and at risk economic assets (and people) can only be assessed via spatially-explicit analyses; it cannot be assumed that all woodland provides the same level of water flow regulation or flood risk protection.

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Figure 2.3: Distribution of PFE Woodland in EA Flood Zones6 in the New Forest Region

Source: EA flood map (2014); NFI woodland map (2012) Similarly, recreation service of woodlands is crucially dependent on proximity to user populations, as examined by Sen et al. (2014). Figure 2.4 shows an estimated 3.5 million visits per year to woodland in the New Forest providing a total of £12.1 million year in terms of recreation value. A comparison can be drawn to an equivalently sized EAU in Dartmoor. In Figure 2.5, the value of woodland recreation services is estimated to be lower, with approximately 1.5 million visits per year with an estimated value of £5.3 million per year. The average recreation value of woodland is approximately £214/ha/year in the New Forest, but only £55/ha/year for Dartmoor7. The main reason for this is the difference in the extent of woodland in the area and the proximity to large population centres. But the maps also show the great variability within these areas – again, largely due to differences in presence of woodland and proximity to users.

6 Flood risk zone 3 represents areas that could be flooded from a river by a flood that has a 1 per cent (1 in 100) or greater chance of happening each year. Flood Zone 2 represents outlying areas that are likely to be affected by a major flood, with up to a 0.1 per cent (1 in 1000) chance of occurring each year. For information, Flood Zone 1 represents the remaining areas, where flooding is very unlikely (less than a 0.1 per cent (1 in 1000) chance of flooding occurring each year) and woodland in this area has not been included in the analysis as this woodland area is assumed not to provide flood risk benefit. 7 Monetary values are expressed in 2012 prices. Further information on the derivation of these values is given in the recreation method note (Annex 2.5).

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Figure 2.4: Estimated annual woodland recreation visits to the New Forest based on Sen et al. (2014)

Source: Sen et al (2014); LCM2000; Natural England (2010) Figure 2.5: Estimated annual woodland recreation visits to Dartmoor based on Sen et al. (2014)

Source: Sen et al (2014); LCM2000; Natural England (2010) Beyond accounting for ecosystem service provision in terms of flows and values, the spatially disaggregated approach also permits mapping of key underpinning characteristics that should be captured within a physical stock account. For recreation, this largely concerns accessibility in terms of proximity to areas of population, access permissions, and the availability of facilities8. Note that

8 In referring to the proximity of recreational areas to populations as a characteristic of the stock account, a link can also be made to the proximity of recreational areas to residential properties which is an attribute of the value of the latter. This can be assessed using the hedonic pricing method (see Annex 5). This aspect of recreation service provision would relate to the monetary flow account if assessed. However, in this study, the

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none of these characteristics are ecological in nature, but that the latter two are instead related to the management of woodland. Figure 2.6 shows the variation in recreational facilities across the Public Forest Estate within the New Forest. Figure 2.6: Distribution of PFE recreational facilities within the New Forest region

Source: FC PFE sub-compartment database (2014) Spatially disaggregated accounts can also identify and account for the variation in woodland condition (e.g. through the availability of recreational facilities - Figure 2.6), as well as the variation in the value of woodland due to service flow provision (Figures 2.4. – 2.5) across the nation. They can also be used to assess risks to ecosystems productive capacity. For example, an assessment of the distribution of risk (potential cost) to UK timber provision from diseases that infect particular tree species can be achieved by combining spatial data on ecosystem service provision across UK woodland with information on disease risk. Such information can be taken from the published UK Plant Health Risk Register (FERA, 2014).

This can potentially be used to inform decision making at national and sub-national (e.g. New Forest) levels, in terms of planning (e.g. green infrastructure) and strategic policy decisions. Importantly, the spatially disaggregated basis of an account can ensure consistency of data and assumptions across these scales, avoiding the potential for ad-hoc and incomparability that could arise otherwise from separate analyses.

aspect of recreation that is focused on is active recreation which is explained by trips to recreational woodland sites, hence the use of a trip-generating function to predict the number of recreational trips to woodland.

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Services that are highly periodic Many services can be considered as more-or-less constant flows from the ecosystem stock/asset – though in fact most vary considerably over a year, such intra-annual variation can be ignored for annual accounts. However some services may be highly periodic – the main example being timber extraction, which may occur only once or twice a century for any particular woodland stand, with other examples such as protection from flooding. If the spatial scale is great enough (e.g. at the regional or national scale) then the whole woodland asset can often be considered in “steady state” and most service flows can be assumed to be constant (e.g. cutting down one stand of trees does not make a difference to timber provision because of growth elsewhere). However, at lower spatial scales (e.g. accounting at the BSU level - here 1km2 - or at the level of a small EAU, such as a single protected area) such ‘averaging out’ assumptions do not hold. It then becomes important to consider the uses of the accounts, and whether it is more appropriate to consider a service as arising only at the specific moment at which it is used (extraction of timber; reduced damage during actual flood conditions) or instead whether the service should be considered as spread more evenly over time (annual increment of timber growth; reduction in risk of flood damage in any given year). Where the main objective is reconciliation with monetary flows, the former approach may be preferable, but for any purposes associated with understanding ecosystem services, trade-off among them, and the implications of land-use and management decisions, the latter may be better suited. 2.3.3 Data sources As implied in Section 2.3.2, spatially disaggregated accounts can be developed using a range of data sources and methods:

• Estimates based on observed spatially disaggregated data of physical stocks and service flows at

the BSU/LCEU/EAU level (e.g. from ecological surveys, management data);

• Estimates based on spatially-sensitive models (e.g. ecosystem service models) and auxiliary spatially disaggregated data to approximate the distribution of physical stocks and service flows at the BSU/LCEU/EAU level; and/or

• Scientifically grounded assumptions and auxiliary spatially disaggregated data to estimate the

distribution of physical stocks and service flows at the BSU/LCEU/EAU level. The initial woodland accounts developed for this study – as described in Section 3 and 4 – primarily use spatial data from the Forestry Commission (FC) to test the feasibility and application of compiling data at the BSU level to enable the reporting of accounts at either the national LCEU level (e.g. all woodland in the UK) or for a defined sub-national EAU level. Whilst the SEEA-EEA recognises that direct measurement could be made at either the LCEU or EAU level without the need for aggregating from BSU level data, the flexibility of the bottom-up approach means that reporting of ecosystem accounts is not limited to pre-specified boundaries. BSU data can be aggregated to report for any given EAU or spatial extent of LCEU. This avoids the need for applying ad-hoc assumptions to more top-down/direct measured data, particularly with respect to the spatial distribution of ecosystem service flows at a sub-national EAU level.

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The National Forest Inventory (NFI) details the distribution of some key UK woodland characteristics across Britain at what is termed the ‘interpreted forest unit’9 level. In addition, spatial data on woodland in the PFE in England is recorded at the ‘sub-compartment-level’. Both the interpreted forest unit level and sub-compartment level could be candidates for a BSU for accounting purposes (as defined in the SEEA-EEA), as they are small (variably shaped) polygons (spatial areas) for which certain information is held, which includes data on ecosystem characteristics including species type and standing stock volume. Alternatively, BSUs can be defined based on a standard grid (e.g. 1km2) superimposed on the NFI data. It should be noted that BSUs could potentially be of different sizes for different services, either for reasons of data availability, or for reasons associated with the service itself (for example, flood protection services may need to be considered at quite a relatively larger scale). As an example, Figure 2.7 illustrates the distribution of woodland across Britain by species type (broadleaved and coniferous species) mapped via BSU data. This represents a key ecosystem characteristic for understanding ecosystem service provision such as fibre for timber. Regional or sub-regional level (e.g. national parks) estimates of woodland extent and service provision would represent the EAU as defined by the SEEA-EEA, since these are ‘socio-ecological’ boundaries that contain a range of ecosystem types.

9 Within each woodland, internal parcels of land with a minimum area of 0.5 hectare are classified as one of the following: Conifer, Broadleaved, Mixed (predominantly conifer), Mixed (predominantly broadleaved), Coppice, Coppice with standards, Shrub, Young Trees, Felled and Ground prepared for planting. These categories are referred to as interpreted forest units, as they result from the interpretation of aerial photographs.

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Figure 2.7: Distribution of Broadleaved and Coniferous Tree Species in Great Britain

Source: FC NFI woodland map (2012).

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2.3.4 Summary A spatially disaggregated approach to ecosystem accounts provides an essential basis for establishing the importance of the provision of many ecosystem services at the BSU level. Hence this provides a greater level of sensitivity for establishing variation in – for example – the relative value of woodland stands, which would not be evident if ecosystem service provision were assessed in more aggregated terms by applying national average values. This also holds for smaller EAUs (e.g. local authorities, protected areas). Regional or national average values could be used for creation of purely regional/national accounts, however the use of spatially explicit accounts, building up to national accounts in a bottom-up fashion, does not in theory compromise the national accounts while enabling a broader range of uses for the accounts at sub-national levels. Moreover, for spatially disaggregated accounts, the logic-chain approach has the advantage of developing a typology bottom-up, based on clear linkages from underlying ecosystem characteristics to the provision of services. The logic chains build up an internally consistent representation of the ways in which ecosystem condition, location and extent determine the level of ecosystem services and the value of natural capital to human society, and therefore ground the accounts not merely in observations of service flows, but in scientific understanding of how these flows arise. This greatly increases the usefulness of accounts, because the implications of changed management, climatic conditions or other scenarios can be explored via the logic chains, in a way that is simply impossible for a purely statistical approach.

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3. METHOD

Practical methodology 3.1 Figure 3.1 outlines the practical methodology followed in the study to develop the initial UK and PFE (England) woodland ecosystem accounts using spatially disaggregated (geo-coded) data. The approach is framed around the logic chain model described in Section 2.2 and the tasks of identifying, measuring and quantifying the ecosystem stock and service flows that make up the account. For each ecosystem service of interest, a step-wise process is undertaken that follows the SEEA-EEA structure comprised of a physical stock account, a physical flow account, a monetary account, and identification of the generation and use of ecosystem services. Figure 3.1: Outline of practical methodology for compiling an ecosystem account

The basic process outlined in Figure 3.1 is to: 1. Select the ecosystem services to include in the account;

2. For each ecosystem service, develop (scientifically-grounded) logic chain models to establish

the key characteristics that are relevant to determining the productivity of the ecosystem and underpin the provision of ecosystem services;

3. Identify and compile relevant physical data on the key characteristics for mapping ecosystem stock extent and condition to populate the physical stock account;

4. Identify and compile physical data and/or ecosystem service models to estimate flows of ecosystem service provision to populate the physical flow account; and

5. Identify relevant valuation evidence to estimate the monetary value of ecosystem service flows from which to populate the monetary stock and flow accounts.

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These five steps are described in further detail with reference to the development of the UK woodland and PFE accounts in the following sub-sections.

Developing an account – step-by-step 3.2 Developing an account over multiple ecosystem services implies that it can be built-up in stages, with relevant information and data incrementally added for each additional service that is included. This means that the analysis underpinning the stock account can inform the estimation of multiple ecosystem service flows; for example, ensuring that estimates of carbon sequestration are consistent with assumptions concerning timber provision and the growth of trees. Hence, the logic chain basis for developing an account can ensure not only consistency in the basis for estimating the provision of a given ecosystem service, but also consistency across multiple ecosystem services where the same key characteristics are essential to understanding the flow of benefits. 3.2.1 Selection of ecosystem services (Step 1) As set out in Table 3.1, the selection of ecosystem services to include in the initial woodland accounts was informed by the Common International Classification of Ecosystem Services (CICES) and previous work on woodland ecosystem service provision (eftec, 2010). Table 3.1: Potential ecosystem services for initial woodland accounts CICES Classification Woodland ecosystem services (eftec, 2010)

Prov

isio

ning

se

rvic

e

Nutrition Wild food Materials Fibre (timber)

Ornamental goods (e.g. Christmas trees)

Energy Fibre (fuel)

Regu

lati

ng

serv

ices

Maintenance of physical, chemical and biological conditions

Carbon sequestration Water purification Air quality Pest control

Mediation of flows Flood protection

Cult

ural

se

rvic

es

Physical and experiential interactions Recreation (walking, biking, riding, camping, field sports)

Intellectual and representative interactions

Education, aesthetics, scientific, heritage/cultural

Other cultural outputs (existence) Biodiversity (partial only concerning the cultural value associated with existence of species)

Given the developmental nature of the woodland ecosystem account, the main driver for the selection of ecosystem services was the general availability of spatial data and (reliable) methods for quantifying and valuing the stock and service flows. The selection also, however, considered the potential to inform on how ecosystem accounts could be further developed, to ensure that in the future they can include a range of provisioning, regulating and cultural services that have high policy relevance and are also of environmental concern.

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As a result, the following ecosystem services were selected in agreement with Defra for inclusion in the initial woodland accounts: • Fibre (timber); • Carbon sequestration (climate regulation); • Water quantity regulation (flood protection); and • Recreation. Consideration is also made for the function of biodiversity through its ‘supporting service’ to other final ecosystem services. This is a critical component of an ecosystem and is only partially represented by the inclusion of biodiversity in terms of the value associated with the existence of species as a cultural service. Annex 4.5 considers further how biodiversity can be incorporated within ecosystem accounts. It is also recognised that the selection of final services for the initial woodland account excludes the provision of a number of significant ecosystem services, such as fibre (fuel), air quality and water purification, and aesthetic (landscape) benefits, and others. Discussion of the potential for these to be developed, including data sources and methods for stock and flow accounting is set out in Section 6. 3.2.2 Review evidence and develop logic chains (Step 2) The logic chain approach provides a structured representation of the pathway by which ecosystem assets generate benefits for society. Linking ecosystem characteristics to ecosystem service provision is reliant on scientific and economic evidence and understanding of ecosystem functioning. This includes determining the ecosystem characteristics (e.g. species, soil, climate, etc.) that influence the productive potential of the ecosystem and establishing the role of management actions in ecosystem service provision. The distinctions made between final ecosystem services, goods/services and benefits produced are particularly relevant in relation to the attribution of value between natural capital and other (man-made) capitals through isolating resource rent. These also help inform on links to the System of National Accounts (SNA), and the potential for double-counting if ecosystem accounts are presented alongside the SNA. Logic chains for each ecosystem service considered in the initial woodland accounts are set out in Annex 4. Their development is based on a review of scientific literature. In practice these logic chains provide the basis for establishing the data and information needed to populate the account. While the ecosystem characteristics highlighted in the logic chains are non-exhaustive, they are taken to cover a number of the key factors that determine the provision of ecosystem services. In certain cases it may be relevant to include the type and extent of management practices as proxies for the condition of the ecosystem (e.g. controlled burning of heathland) or relate to the attaining of benefits by society (e.g. access to woodland). The inclusion of the impact of human pressures/management in the ecosystem account is consistent with an ‘ecosystems approach’, which considers humans as an integral part of ecosystems (Defra, 2010). It is also consistent with conventional notions of natural capital which define it as something that is productively valuable. This productivity may only arise through the combination of other capitals. For example, fibre is the final ecosystem service in the logic chain set out in Figure 2.1. This is the service closest to the point of direct consumption by economic actors, which is the tree immediately before harvesting. Once harvested, through the use of other capital it becomes a good, timber, that provides benefits to society. It should be noted that what are sometimes referred to as ‘supporting services’, such as pollination, would be captured as characteristics that support the provision of final ecosystem services.

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3.2.3 Physical stock account (Step 3)

The physical account reports the opening and closing stocks of the ecosystem as well as the reconciliation of these stocks by recording intervening (net) changes to assets over the accounting period. In this study, the scope of the initial woodland accounts is concerned with measuring the current extent and condition of physical stock and not changes over time. This means that only a closing stock is reported. These results therefore would represent the opening position for a future account (i.e. in the next time period) such that changes in ecosystem provision can be measured over time. Much of the learning associated with ecosystem accounting comes from constructing both the opening and closing stocks and determining what caused the changes. Net changes in the physical stock tend to be due to differences in physical quantity/extent (e.g. land use change) or the quality/condition (e.g. different species types for timber, volume of biomass etc.) which includes the spatial configuration (e.g. proximity of woodland areas to population) of the asset over time. These changes can be due to deliberate actions by society (e.g. management actions), accidental or non-deliberate actions (e.g. forest fires) or due to natural changes. However, the headline account does not distinguish between causes of change. The condition of an ecosystem is reported in the stock account through evidence on the condition of the key ecosystem characteristics identified in the logic chains. This is the basis for monitoring the condition of the stock in terms of its capacity to deliver ecosystem services. Ideally characteristics should therefore be selected for inclusion in the stock account based on evidence of their importance in determining an ecosystem’s capacity to produce service flows10, however it may be difficult to source data sets which fit these requirements and which also provide national coverage. The final selection of characteristics to include in a particular stock account is therefore dependent upon data availability. Each ecosystem characteristic (e.g. species type) is reported in an account using a specific metric (e.g. ha) and categorisation (e.g. broadleaved and coniferous or more detailed species breakdowns). The categories chosen should aim to capture the variation in the condition of the ecosystem and specifically its capacity to produce services. For example broadleaved and coniferous species command different prices of timber and differential growth rates mean that they have different carbon sequestration outputs (tonnes) and values (£). Table 3.2 illustrates the spatially explicit (geo-coded) data sources identified to populate a stock account for woodland with respect to fibre (timber). The spatially disaggregated data identified for the provision of other ecosystem services accompanies the reporting of the logic chains in Annex 2.

10 “The key objective of the asset account is to enable us to monitor changes in the stock in terms of its capacity to deliver services” (Defra/ONS, 2014).

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Table 3.2: Spatial (geo-coded) data sources for potential use in accounting for characteristics Ecosystem service

Characteristic Data source Indicative source Fi

bre

(tim

ber)

Extent FC PFE data and NFI PFE data held in internal

FC data base/ http://www.forestry.gov.uk/datadownload

Species composition

FC PFE data and NFI http://www.forestry.gov.uk/datadownload

Local survey data will include detailed species data but amount and quality will vary across the country. May also be difficult extracting it from 'owner' organisations

No current source

Age structure FC PFE data and NFI PFE data held in internal FC data base

Yield class FC PFE data and NFI PFE data held in internal FC data base

Soil type Magic / NE GI Toolkit http://www.magic.gov.uk/

NERC Soil Portal http://maps.bgs.ac.uk/soilportal/wmsviewer.html

LandIS Digital dataset http://www.landis.org.uk Rainfall Met office data http://data.gov.uk/datase

t/rainfall-1km-grid Solar exposure Solar GIS http://solargis.info/doc/a

bout-solargis Land aspect / slope

Ordnance Survey topographic layer

http://www.ordnancesurvey.co.uk/business-and-government/products/terrain-50.html

Management practices

FC PFE data and NFI PFE data held in internal FC data base

Woodland grant scheme / other RDPE funding

http://www.forestry.gov.uk/datadownload

Harvesting FC PFE data and NFI (but not annual from the NFI)

http://www.forestry.gov.uk/forestry/INFD-7AQL5B (provides statistics at country level only)

Timber FC PFE data and NFI PFE data held in internal FC data base/ http://www.forestry.gov.uk/datadownload

Percentage of woodland cover harvested

FC PFE data and NFI Private forestry operations and other landowners (e.g. NT) may provide this information for sites within specific geographical locations

No current source

Whilst logic chains are constructed for specific ecosystem services, the intention is that this forms part of a single comprehensive ecosystem stock account which has the key characteristics for the whole range of services (some of which are more important for some services than others). The development of the ecosystem stock account can therefore be incremental, and as noted, built up as the provision from further ecosystem services is added.

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http://www.magic.gov.uk/

http://www.magic.gov.uk/

http://maps.bgs.ac.uk/soilportal/wmsviewer.html

http://maps.bgs.ac.uk/soilportal/wmsviewer.html

http://www.landis.org.uk/

http://data.gov.uk/dataset/rainfall-1km-grid

http://data.gov.uk/dataset/rainfall-1km-grid

http://solargis.info/doc/about-solargis

http://solargis.info/doc/about-solargis







http://www.forestry.gov.uk/forestry/INFD-7AQL5B

http://www.forestry.gov.uk/forestry/INFD-7AQL5B




3.2.4 Physical flow account (Step 4) Physical service flows from an ecosystem stock can be estimated using a range of approaches, depending on data and method availability for specific ecosystem services. It is likely that in producing an account that covers multiple ecosystem services, a number of strategies will be needed to measure and/or estimate the provision of ecosystem services. Indeed whilst it is possible to observe the annual flow of some ecosystem services at an aggregate level (e.g. total timber provision), there is no comprehensive mapping of the spatial distribution of ecosystem service provision across the UK/GB. Hence, for the most part, some form of modelling is required to estimate the spatial distribution of ecosystem service flows for a given year. The combination of approaches considered in this study with respect to the initial woodland accounts can be described as: • Estimates of physical service flow based on observed/directly measured spatially disaggregated

data: this involves summation of direct measurement data from independently sampled woodland areas (e.g. for timber harvest). Whilst this data has not been made available for use in this study, the FC does hold data on expected timber harvest for specific stands (BSU) within its sub-compartment database (SCDB) for the PFE, England. It is recommended that this data source is used in the future refinement of the PFE accounts; the sampling underpinning the compilation of this data implies that estimates of service flow in terms of timber can be regarded as ‘statistically robust’.

• Estimates of physical service flow based on ecosystem service models and auxiliary spatially disaggregated data: this refers to the application of statistical/econometric methods to estimate the spatial distribution of ecosystem service flows across a LCEU at a specific resolution. Typically this entails the use of different datasets on ecosystem characteristics and service flows to estimate the empirical relationship between the two in order to predict ‘out of sample’ (i.e. the level of ecosystem service provision in locations outside of the sampled data). The accuracy of such approaches is dependent on the observed data that is sampled as part of the empirical analysis and how representative it is for wider application and/or whether variation between spatially explicit factors can be appropriately controlled for. Also, as noted in Section 2.3, UK/national/regional estimates may be used for calibration at smaller spatial scales. For example, this study uses a trip-generating function to estimate the distribution of recreational visits to UK woodland at 1km² resolution (BSU) with calibration to national level MENE data (i.e. the total number of trips annually at the national level).

• Estimates of physical service flow using scientifically-grounded assumptions and auxiliary spatially disaggregated data to estimate the spatial distribution (mapping) of UK/national/regional level estimates: this approach is necessary in the absence of available statistical data on expected provision on a spatially disaggregated basis (e.g. 1km²). In this study this approach has been applied across the FC’s NFI interpreted forest units (IFU) for UK woodland, or the FC’s sub-compartment-data-base (SCDB) for the PFE in England (e.g. only trees of a certain age are considered ‘mature’ for harvesting). Whilst total (in aggregate) figures for expected flows are robust, the assumptions applied in determining the spatial distribution of service flows imply a high level of uncertainty. When adopting this approach, all assumptions regarding the relationship between the ‘characteristics’ of the ecosystem and the provision of ecosystem service flows should be explicitly evidenced.

In the initial woodland account the (physical) flow of ecosystem services is estimated on an annual basis and over a relevant time period into the future. This follows from the perspective adopted in the monetary valuation of ecosystem stocks, which is estimated in terms of the discounted value of expected future flows over a relevant time period. The period that is deemed to be ‘relevant’ is

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not explicitly addressed within the scope of this study, but in practice should be dictated by the application of the account in terms of understanding the capacity of ecosystems to produce ecosystem services and how this may be change over time. Therefore the choice of relevant time period will relate to: (i) the time frame associated with asset renewal or recovery from natural or human induced disturbance (management or otherwise) to a productive state; and (ii) data availability which dictates knowledge of asset renewal and recovery. It is, though, difficult to obtain data on the timescales of recovery for woodlands. However, Broadbalk and Geescroft woods (at Rothamsted) were created on arable land and have been monitored for over 100 years (Harmer et al., 2001). They are currently mature, mixed deciduous woodland (dominated by ash and sycamore). Monitoring has shown the following developments over time since their creation: • Woody species colonised after 10 years (also found by Rebel and Franz in Germany); • 20-30 years to complete canopy cover (Harmer et al., 2001); • 20-40 years change in flora from light demanding to shade tolerant; • pH at Broadbalk changed from pH 8 to pH 7, and at Geescroft it changed from pH 7 to pH 4.2

over 100 years;, and • Ground flora: many characteristic woodland plants still had not established after 100 years. Other studies looking at the recovery of woodlands after logging found that: • Even 50-80 years after the disturbance, tree species richness, diversity and abundance had still

not recovered although many were on a recovery trajectory (Moola and Vasseur 2004, Duffy and Meier 1992). The same was true for ground beetle species composition and abundance after 27 years (Niemela et al. 1993) and ant species composition after 100 years (Palladini et al 2007);

• Nitrogen cycling had only just returned to pre-disturbance levels after 75 years (McLauchlan 2007);

• A number of soil parameters (Soil C, N, P, nitrification, infiltration) had not recovered 50 years (Harden and Matthews 2000) and 120 years (Compton and Boone 2000) after cultivation and abandonment;

• Flinn and Marks (2005) found that the pH and nutrients were slightly higher on woodland on restored agricultural sites than nearby un-cleared sites after 80-100 years, but that the variation was not as high as with other un-cleared woodland sites in alternate locations; and

• Soil compaction from machine logging at a forest in Belgium had not recovered after seven years (Rohand et al 2003).

This evidence suggests that a longer time period of between 50 and 100 years may be justifiable when considering physical stocks and flows. However, for the initial woodland accounts the estimation of service provision over time is taken to be consistent with a constant service flow assumption based in 20-year period. The chosen time horizon is largely illustrative, reflecting the fact that shorter time frames (e.g. less than 10 years) may not capture significant variation in ecosystem stocks and flows, whilst a longer time horizon (e.g. greater than 30 years) introduces greater uncertainty due to projection of future stocks and flows. In addition, given the discounting of future values, future benefits/cost occurring beyond 20-25 years represent relatively smaller incremental values; i.e. the greatest proportion of monetary benefit is captured within the first 20-25 years. Hence, even accounting for longer time periods for recovery, longer term future benefits will be significantly discounted and have a relatively marginal impact on reported estimates. For example, doubling the time horizon results in an only a 50% increase in stock value of woodland for the provision of timber from £2,558m (PV over 20 years) to £3,855m (PV over 40 years).

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3.2.5 Monetary Accounts (Step 5) The physical service flows estimates are quantities which are valued in monetary terms to produce the monetary flows account, which in turn (together with assumed future service flows and discounting factors) are used to compile the monetary stock account. In effect, a physical quantity is multiplied by a unit value (£) to produce a monetary estimate of the benefits from each ecosystem service provided by the ecosystem per year. For example, for timber from woodland, the estimated annual flow of timber reported in the accounts is based on FC harvesting rates (FC, 2012) and the value of this flow is estimated based on standing stock timber values. Once the estimates of physical flows have been valued, the monetary estimates are reported in the monetary flows account according to the type of ecosystem service: provisioning; regulating and cultural. Consistency between physical flow account and monetary valuation account is required with respect to the metrics that measure ecosystem service provision. For example, timber flows are typically valued in tonnes (i.e. price × quantity) and so physical accounts need to ensure the metrics are reported in this way. Additionally, timber value depends on the quality of the wood (i.e. price × quantity × quality) and so metrics need to capture the fact that broadleaved trees such as oak are worth more than coniferous trees such as pine because it is a better quality of wood. Accounting for, and valuing, the physical flow of other services such as water flow regulating services is less straightforward. Consideration of how such flows may be valued (e.g. reductions in risk) should therefore be undertaken in consultation with the development of the physical flow estimates. Some ecosystem service models, produce estimates of value in addition to the physical flow estimates (e.g. The Integrated Model, Bateman and Day, 2013). In other cases, identification of appropriate valuation evidence is required to apply to the physical flows in order to produce a monetary account. Valuation evidence can be sourced from a range of established valuation techniques and this is set out further in Section 5.4 and Annex 5. The discounted value of the profile of ecosystem service flows represents the value of the stock, for the provision of a given ecosystem service11. A point to note is that both the stock and flows from woodland are measured based on the physical flow account (i.e. the value of the stock is not directly linked to the ecosystem service characteristics recorded in the stock account). Discounting future flows of ecosystem service value requires the application of a discount rate. In line with Defra/ONS (2014), Green Book guidance for project appraisal (HM Treasury, 2003) is applied, including a discount rate of 3.5%.

11 Further work needs to be undertaken to consider how the use of markets for woodland areas might be used to value the stock, recognising the issues of market values of land not necessarily reflecting the (full) ecosystem service capacity of woodland.

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4. RESULTS – INITIAL WOODLAND ECOSYSTEM ACCOUNT

Physical stock account 4.1 The physical stock account compiles data on the characteristics that relate to the physical quantity, quality and spatial configuration of the ecosystem (woodland) that are relevant to the provision of ecosystem service flows. The species composition of woodland is a key characteristic in the logic chain models for woodland and provides a useful example of the variability of data quality throughout the UK. The Forestry Commission, in collaboration with Natural Resources Wales, collects and maintains a detailed sub-compartment database (SCDB) on the PFE (England) which represents 17% of English woodlands (7% of UK woodland). The database includes the species composition of the PFE woodlands recorded at the 'sub compartment' unit, and is presented in a spatially explicit form. A range of other information is also recorded for PFE forests at the sub compartment unit, such as yield class, planting year, felling year, management (including thinnings) and recreational facilities. The availability of this data at this resolution permits the exploration of a separate account (following the same methodology but with different data) for the PFE (England). However, the PFE account is not additional to the UK woodland account; rather it is a subset.

Much less information is currently available on the remaining 93% of UK woodlands, although an up-to-date national dataset, the National Forest Inventory (NFI) does exist and is updated. The extent of all woodlands >0.5 ha is captured in the NFI woodland map. The NFI programme also includes field survey work which enables the estimation of woodland metrics on a regional scale through the sampling of 15,000 statistically representative 1ha forest sample squares. Data collected in the survey squares is multiplied up to NFI woodland map areas (i.e. Britain coverage). In this way similar information can be reported for non-PFE forest as that directly measured for PFE forest, such as standing volume data. 4.1.1 UK (GB) woodland Table 4.1 presents the physical ecosystem stock account (closing stock) showing total extent of woodland (2. 78 million ha); extent of species type (broadleaved 1. 27 million ha and coniferous 1. 51 million ha) and volume (broadleaved 239 million m³ and coniferous 375 million m³), age (e.g. 251 million m³ aged 41-60 years old), biomass stock (426 Mt oven dry biomass), carbon biomass stock (780 MtCO2) and woodland SSSI extent for GB woodland (243 thousand ha), as well as woodland in flood risk areas in England and Wales (94 thousand ha in flood zone 2 and 75 thousand ha in flood zones 3) and soil carbon stocks in south west England (133MtCO2), using ‘grid/cadastre-based’ data. It has not been possible under this project to develop estimates for some characteristics set out in the logic chain as the spatial data analysis required has not been available (this is also the case for Table 4.3 on PFE woodland). For example, information on the extent of population in close proximity to woodland is not included in the stock account despite being a key determinant of recreational value. However, this characteristic should be included in ecosystem asset accounts and was included (with two indicators) in the ONS asset account, using aggregate data. The characteristics included in Table 4.1 (and Table 4.3 on PFE) are not completely aligned with those set out in ONS (aggregated) ecosystem accounts which adhere to the indicative accounts set out in the SEEA-EEA. This is because the initial woodland accounts prepared in this report have

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focused on accounting for the condition of characteristics that are important determinants of the capacity of woodland to produce benefits to society (via the logic chains). For comparison, estimates from the FC (from various sources set out in Table 4.1) are also reported alongside the independent estimates developed by the project team using NFI (2012) data. In comparison to the FC estimates reported in Table 4.1, a potential reason for the discrepancy in the estimated figures for woodland extent in this project may be that the FC report figures are based on field plot data. This means that there is a greater reliability in determining extent of conifer/ broadleaves. The IFU figures (the NFI map) are based on analysis of aerial photographs which do not always correspond to what is actually on the ground. There is also a discrepancy in the estimated NFI volume figures and the FC reported figures. Again this is likely due to FC using field plot data whereas the study estimates are based on combining spatial data on the reported extent (ha) and volume (m3) of broadleaved/coniferous trees in each NFI region to provide m3/ha figure per region. This is then applied across the whole region to conifer, mixed mainly conifer and the conifer-allocated assumed woodland and young trees, and similarly for broadleaves.

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Table 4.1: Physical account of ecosystem condition and extent (stock) at the end of an accounting period for GB woodland Ecosystem: Woodland 2012

Ecosystem extent



Age (years)

Biomass Stock Carbon Stock Woodland in Flood Risk Areas10

Woodland SSSI

Broadleaved (BL)

Coniferous (C)

BL C 0-40 41-60 61-80 >80 Total Total Biomass

Total Soil FZ1 FZ2 FZ3

(million ha)

1 Extent (million ha) 2 Volume

(mill m3) 3 Age by Volume (mill m3)4


MtCO26 MtCO27 Extent (mill ha)8 Extent (mill ha)9

Coverage (Countries/ regions)

GB GB GB GB GB GB SW England E&W E&W E&W GB


2.78 1.27 1.51 239 375 163 251 105 109 426 780 133 2.61 0.094 0.075 0.243

(FC Estimates11) (2.64) (1.34) (1.31) (245) (355) (194) (193) (84) (109) (426) (780) - - -

1 FC, NFI Woodland Map (2012)

2 FC, NFI Woodland Map (2012); FC (2012d) 50 year forecast of softwood availability; FC (2012e) 50 year forecast of hardwood availability

3 FC, NFI Woodland Map 2012; FC (2012d) 50 year forecast of softwood availability; FC (2012e) 50 year forecast of hardwood availability

4 FC, NFI Woodland Map 2012; ; FC (2011c) Standing timber volume for coniferous trees in Britain; FC (2013c) NFI preliminary estimates of quantity of broadleaved species in broadleaved woodlands, with special focus on ash

5 FC (2014c)

6 Cantarello et al (2011) 7 FC, NFI Woodland Map 2012

8 Environment Agency, Flood Zone GIS layers

9 Natural England (http://www.gis.naturalengland.org.uk/pubs/gis/GIS_register.asp), Natural Resources Wales (http://www.ccgc.gov.uk/landscape--wildlife/protecting-our-landscape/gis-download---welcome.aspx), Scottish Environment Protection Agency (http://www.snh.gov.uk/publications-data-and-research/snhi-information-service/naturalspaces/) GIS layers 10. Flood risk zone 3 represents areas that could be flooded from a river by a flood that has a 1 per cent (1 in 100) or greater chance of happening each year. Flood Zone 2 represents outlying areas that are likely to be affected by a major flood, with up to a 0.1 per cent (1 in 1000) chance of occurring each year. Flood Zone 1 represents the remaining areas, where flooding is very unlikely (less than a 0.1 per cent (1 in 1000) chance of flooding occurring each year). 11. Some of the aggregate estimates provided in this report differ from those published by the Forestry Commission either because it has not been possible to replicate the FC adjustments to National Forest Inventory estimates at a geographically detailed level or because a more approximate methodology has been used.

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The characteristics reported in the UK woodland stock account are based on the following sources of spatial data and are robust to the spatial levels stated: • Ecosystem extent (ha): the Forestry Commission maintain spatial data on UK woodlands as a

whole through the National Forestry Inventory (NFI) and is reported in the stock account. Note that this relates to the total extent of woodland with the capacity to produce ecosystem services, but not necessarily the extent that it is producing them, which is established in the measurement of ecosystem service flows.

• Extent of species (ha): calculated for broadleaved and coniferous woodland at interpreted forest unit level for each NFI region from NFI Woodland Map 2012. NFI woodland classes were allocated to broadleaved/coniferous as detailed in Table 4.2.

• Standing stock volume (m3): the FC provide standing volume for both broadleaved woodland

and coniferous woodland at the NFI region level from the NFI (2012) field survey (FC, 2011; FC, 2013). These estimates of volume were applied to the extent of woodland in each region to provide estimates of distribution of volume for both broadleaved and coniferous woodland. The NFI classes include Young Trees and Assumed Woodland which in the NFI maps do not distinguish between conifer and broadleaved. In order to allocate these to broadleaved or coniferous the proportion of total woodland in each NFI region which is broadleaved or coniferous was calculated, and then Assumed and Young Trees were allocated according to that proportion.

• Age by volume (m3): the NFI regional averages as reported from the 2011 NFI data (FC, 2013; FC, 2011).

• Biomass stock (Mt): FC published figures for oven dry tonnes are available to NFI region level.

• Carbon biomass stock (MtCO2): FC estimates based on NFI spatially disaggregated (IFU) data (note this was not available to the study and hence the published aggregate figure is reported).

• Carbon soil stock (MtCO2): this is estimated for the South West of England only using Cantarello et al. (2012).

• Woodland in flood risk zones (ha): the method note for water quantity regulation (Annex 4.3)

sets out the large number of characteristics (e.g. soil type, nutrient status of soil) that ecosystem service models use to estimate the impact of woodland on mitigating the impacts of flooding. However, it is judged that the broad characteristic of ‘woodland extent in flood risk areas’ is sufficient to provide a high-level indication of the condition of woodland for regulation of water flows in the stock account. This is estimated by overlaying the spatial data on flood risk for EA flood risk zones 2 and 3 onto the spatial data on the extent of woodland from the NFI woodland map.

• Woodland sites of special scientific interest (ha): this has been mapped by overlaying the SSSI data from NE onto the NFI woodland map.

For national woodland analysis, data on certain characteristics is available at the interpreted forest units level which is the resolution of the NFI woodland map (e.g. extent of broadleaved and coniferous species). This is only available for Great Britain (GB) not for the entire UK. For other characteristics, the data is either not available at this level but at regional scale such as age structure or is not available from the NFI such as yield class and standing volume. Other

eftec March 2015 27


characteristics have not been sourced for this study include precipitation, solar exposure and land aspect. In other cases, it is possible to estimate the extent of a woodland ‘characteristic’ by overlaying datasets (e.g. EA flood zones 2 and 3) onto the NFI interpreted forest units, to give the extent of woodland in these flood zones, but only for England and Wales. An estimate of soil carbon was available based on a paper by Cantarello et al. (2012) for the South West of England only (as estimates are regionally specific). This analysis needs to be replicated across the whole UK. Table 4.2: Allocation of NFI woodland classes to broadleaved and coniferous NFI Woodland Class Broadleaved Coniferous

Broadleaved X - Mixed Mainly Broadleaved X -

Coppice X - Conifer - X Mixed Mainly Conifer - X

Assumed Woodland X* X*

Young Trees X* X* Notes: * Both ‘Assumed Woodland’ and ‘Young Tree’ extents are allocated to broadleaved/coniferous using the proportion of total woodland in each NFI region calculated for Broadleaved and Mixed Mainly Broadleaved/Coniferous and Mixed Mainly Coniferous. NFI classes such as Felled, Bare, Ground Preparation have relevance in that they have potential to provide flow of timber in the future, however as this use cannot be assured they are excluded from the account. Other NFI classes were not used in the account as they were not relevant (e.g. roads, rivers, quarry). 4.1.2 PFE woodland (England) For the PFE woodland analysis, the accounts are compiled at the level of FC sub compartments which is the PFE woodland map scale. For certain ecosystem characteristics identified in the logic chains the data is available at this level (extent of broadleaved and coniferous species, age structure, yield class). Other data has not been sourced for this project (precipitation, solar exposure, land aspect). In other cases – as with the UK account - it is possible to estimate the extent of a woodland ‘characteristic’ by overlaying datasets (e.g. EA flood zones 2 and 3) onto the PFE sub compartment map, to give the extent of woodland in these flood zones. Table 4.3 presents the physical ecosystem stock account (closing stock) for PFE woodland showing total extent (180 thousand ha)12 extent by species type (broadleaved 40 thousand ha and coniferous 140 thousand ha) and volume (broadleaved 4 million m³ and coniferous 11 million m³), age (e.g. 4 million m³ aged 41-60 years old), yield class (e.g. 8 million m³ yield class 12 to 22), biomass stock (24 Mt oven dry biomass), carbon stock in biomass (46 MtCO2), woodland in flood risk zones (2 thousand ha in flood zone 2 and 2 thousand ha in flood zones 3), woodland SSSIs (47 thousand ha) and woodland with different recreational facilities for PFE (e.g. 1,146ha or 194km² cells with advanced facilities which includes accommodation, building, concert, ponds/lakes, and/or sports as well as any other facilities), for PFE England woodland as well as soil carbon (10.3 MtCO2) for PFE woodland in south west England.

12 This estimate excludes areas within the PFE which are not wooded.

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Table 4.3: Physical account of ecosystem condition and extent (stock) at the end of an accounting period for PFE woodland, England Ecosystem: Woodland 2012

Ecosystem extent



Age (years)

Yield Class Biomass Stock

Carbon Stock Woodland in Flood Risk Areas

Woodland SSSIs

Extent of woodland with recreational facilities9

Number of 1 km2 cells with recreational facilities

Broadleaved (BL)

Coniferous (C)

BL C 0-40 41-60 61-80 >80 0-10 12-22 24-32 Total Total Biomass

Total Soil1

FZ1 FZ2 FZ3 Advanced (A)6

Intermed. (I)7

Basic (B)8

A I B

(thousand ha)1

Extent (thousand ha)1 Volume (mill m3)1

Age by Volume2 (mill m3)1

YC by Volume (mill m3)


MtCO22 MtCO23 Extent (thousand ha)4

Extent (thousand ha)5

Hectares of woodland (ha)1 Number of 1 km2 cells1

Coverage England England England England England England England South West England

England England England England


180

40 140 4 11

6 4 3

1 7 8 0.2 25 46 10.3 177 2 2 47 1,146 4,582 4,667 194 48 99

(FC estimates)

(180)

(50) (130) (9) (27) (-) (14) (6) (6) (-) (-) (-) (25) (46) (-) (-) (-) (-) (-) (-) (-) (-)

(-) (-) (-)

1 Forestry Commission, National Forest Estate Sub compartment shapefile (2012); Forestry Commission, sub compartment data base (2013). Excludes non-wooded areas of the Public Forest Estate.

2 FC (2014)

3 Cantarello et al (2011)

4 Forestry Commission, National Forest Estate Sub compartment shapefile (2012); Environment Agency, Flood Zone GIS layers

5 Natural England (http://www.gis.naturalengland.org.uk/pubs/gis/GIS_register.asp), Natural Resources Wales (http://www.ccgc.gov.uk/landscape--wildlife/protecting-our-landscape/gis-download---welcome.aspx), Scottish Environment Protection Agency (http://www.snh.gov.uk/publications-data-and-research/snhi-information-service/naturalspaces/) GIS layers

6 Advanced facilities covering accommodation, building, concert, ponds/lakes, and/or sports

7 Intermediate facilities covering play and/or visitors areas

8 Basic facilities covering vehicles

9 In addition, any area of woodland which is not served by ‘intermediate’ or ‘basic facilities’ (i.e. having any other combination of facilities than basic or intermediate facilities) is counted as having advanced facilities.

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The following characteristics are reported in the PFE (England) woodland stock account based on the sources of data given and are robust to the spatial levels stated: • Ecosystem extent (ha): the FC maintain spatial data on the subset of woodland they own known

as the Public Forest Estate. Note that this relates to the total extent of woodland with the capacity to produce ecosystem services, but not necessarily the extent that it is producing them, which is established in the measurement of ecosystem service flows.

• Species type extent (ha): the FC SCDB provides a variety of detailed information on species growing in each sub-compartment, including 'habitat' level which identifies coniferous and broadleaved woodland.

• Standing stock volume by species (m3): the publicly available portion of the SCDB does not provide volume data, however the FC extracted standing volume data and made this available for use in the account13. Reporting the volume of woodland that exists in the physical account is important as this will dictate its value for timber; i.e. how much timber will be produced.

• Age by volume (m3): the FC SCDB identifies the date of planting of all stands and this information was utilised to provide age data for the account. Age is important in the context of timber flows because not all woodland will be felled for timber. Instead age is used to estimate the total stock of woodland that is likely to be used for timber provision; i.e. whether a stand is considered ‘economically mature’ and is likely to be felled. Age is also important to report because older woodland has a greater biodiversity value than younger woodland.

• Yield class (m3): the FC SCDB identifies the yield class of all stands and this information is used to provide yield class data for the account. The yield class of a woodland informs on how productive it is per year (the average annual increment over a rotation). It varies by stands and by species according to the underlying conditions of the land area (e.g. soil type, aspect, altitude etc). It is therefore a proxy for the other underlying characteristics that determine productivity of a woodland as identified in the logic chains.

• Biomass stock (Mt): this is taken from FC estimates which are based on NFI spatially disaggregated (IFU) data for FC woodland in England (meaning that they have not been produced as an output of this study).

• Total soil carbon (MtCO2) this is estimated for the South West of England only using Cantarello et al. (2012).

• Carbon biomass stock (MtCO2): this is taken from FC estimates which are based on NFI spatially

disaggregated (IFU) data for FC woodland in England (but have has not been available for this study).

• Woodland in flood risk zones (ha): in line with UK (GB) woodland, this is estimated by overlaying the spatial data on flood risk though EA flood risk zones 2 and 3 onto the spatial data on the extent of woodland from the PFE, England woodland map.

13 The standing volume per stand data which was provided did not include a figure for volume for every stand due to internal data anomalies, so while the figures used in this account they will not match with FC reported figures which are corrected for the data anomalies.

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• Woodland sites of special scientific interest (ha): this has been mapped by overlaying the SSSI data from Natural England onto the PFE SCDB.

• Recreational facilities: using the PFE sub-compartment data, the number of 1 km2 cells of woodland and the corresponding area in hectares served by recreational facilities is estimated14,15. The number of cells and the corresponding area of woodland served by recreational facilities are broken down into three categories: (i) Advanced facilities covering accommodation, building, concert, ponds/lakes, and/or sports; (ii) Intermediate facilities covering play and/or visitors areas and (iii) basic facilities covering vehicles. In addition, any area of woodland which is not served by ‘intermediate’ or ‘basic facilities’ (i.e. having any other combination of facilities than basic or intermediate facilities) is counted as having advanced facilities.

Physical flow account 4.2 4.2.1 UK (GB) woodland The estimated expected physical flows of ecosystem service provision for the national (GB) woodland analysis is set out in Table 4.4. In the absence of spatial data on the location of timber harvests, it shows aggregate (GB) data on timber harvesting for Britain of 0.59 million m³ of timber for broadleaved and 11.78 million m³ for coniferous, as well as the estimated profile of flow over 20years of 11.74 million m³ of timber for broadleaved and 235.6 million m³ for coniferous. Mapping of the distribution of this harvest across GB is as described in Section 4.2. Table S2 also shows estimated annual carbon sequestration for broadleaved (6.01MtCO2) and coniferous (6.55MtCO2) woodland which have been estimated and mapped using FC published rates of carbon sequestration. It also accounts for estimated recreational visits (481million) across GB woodland based on Sen et al analysis (2014), the accompanying maps of which are illustrated in Figures 2.4 and 2.5 and Annex 2.3.

14 Facilities that are considered are: accommodation (e.g. camp site, cabin site, caravan site, wild camp, etc.); building (e.g. visitor centre, toilet, cycle hire, catering, shelter, etc.); ‘concert’ defined as an area designated for concert activities (e.g. stage, backstage, viewing area); play defined as an area designated for play activities (e.g. play area, paddling, etc.); ponds/lakes defined as a natural or man-made feature (e.g. fishing, model boats, etc.); sports defined as an asset built and installed by the forestry commission (or sub-let) (e.g. archery, bike park, ropes course, etc.); vehicle defined as an asset built and installed by the forestry commission, or a regularly used natural feature (e.g. car park, layby, etc.); and visitor areas (e.g. bbq area, picnic, education, events, arboretum, etc.). 15 Ponds/lakes are classified as a facility in the list above as the spatial data for this feature is available in the recreation database. It is however arguable that ponds/lakes constitute another habitat separate from the woodland habitat. They are though unlikely to be included in any other ecosystem accounts (except large ponds/lakes which may be included in a ‘freshwater’ ecosystem account), so are included here as a feature of woodland which is in good condition to provide recreational benefits.

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Table 4.4: Physical account of ecosystem service provision (flow) for GB woodland

Type of ecosystem

Woodland

Flow (Annual, 2012) Profile of Flows (‘20’ yrs) Provisioning Biomass for

Timber BL C BL C

- - - -

FC Estimates 0.587 million m3(overbark)

11.78 million m3


235.60 million m3 (20 yrs; 2012-2031)

Regulating Carbon Sequestration

6.01 MtCO2 6.55 MtCO2 120.20 MtCO2 (20 yrs; 2012-2031)

131.00 MtCO2 (20 yrs; 2012-2031)

FC Estimates 10.3 MtCO2 (2010) -


Difficult to measure in physical and monetary terms


Cultural Recreation 481 million visitors 9,620 million visitors (20 yrs; 2010-2029)

Timber Aggregate data exists on timber harvesting for Britain of 0.59 million m³ of timber for broadleaved and 11.78 million m³ for coniferous. These are based on FC administrative records for the FC estate and for private woodland on volumes reported by harvesting companies for coniferous trees, and deliveries to saw mills for broadleaved trees. Broken down by England, Wales and Scotland this can be mapped across the 3 countries as shown in Figure 4.1 with a high level of robustness. The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. Figure 4.1: Harvesting volume of broadleaved and coniferous trees by country across Britain

Source: FC (2014) Timber Statistics

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These aggregate figures can also be broken down into each of the 14 NFI regions across England, Scotland and Wales as shown in Figure 4.2 by spreading the national harvest evenly across the NFI regions in proportion to woodland volume in the region. However, this requires an assumption that there is equal harvesting to standing stock volume ratios across each region. This is unlikely to be the case and introduces some uncertainty into the estimates of harvesting at this regional level and therefore the robustness of the account if reporting at this level is reduced. Given the limited application of these regional estimates of ecosystem service flows (in the case of harvesting), performing such analysis (even if robust) is not likely to be a requirement. Figure 4.2: Estimated harvesting volume of broadleaved and coniferous trees by NFI region across Britain

Source: FC (2014) Timber Statistics There is no available spatial data on the distribution of timber provision across UK woodland level at a sub-national level. Therefore it is not possible to sum individual accounts from independently sampled areas. Instead assumptions need to be made in order to sub-divide the national aggregated estimates. This increases the level of uncertainty around each reported measure unless accompanied by an increase in sampling intensity. Therefore there is a trade-off between the degree of sub-division and the robustness of evidence to inform decision making. For this study, the potential to sub-divide national estimates of timber has been examined through the use of ad-hoc assumptions. In order to ‘distribute’ the aggregate harvesting figures, an assumption is made as to the point in the forestry cycle when trees have reached ‘economic maturity’ and are ready for harvesting. It is assumed that trees at ‘economic mature’ age are those that are harvested. For conifers this is usually when they are 40 to 60 years old (FC, 2013). Broadleaves are generally slower-growing and maturity may take 80 to 120 years (UPM Tilhill, 2014). This is the point where the greatest return on the investment in planting and growing the

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trees can be achieved through selling the timber. Maximum timber production occurs where the average annual increment peaks (this is the yield class); the economic maximum occurs slightly earlier due to the impact of discounting. The total observed harvested volume estimates for broadleaved and coniferous trees across England, Scotland and Wales (FC, 2012) have been distributed evenly across the total volume of all ‘mature’ trees (considered to be +80 years for broadleaved and 41-60 years for coniferous) within the 14 regions at IFU level (BSU) by randomly allocating mature forest blocks to harvesting. No NFI data exists for current standing volume of trees at the IFU level, therefore the NFI regional average volume for broadleaved and coniferous trees has been applied across the IFU’s within the 14 regions. The output of this is illustrated for the New Forest region in Figure 4.3. Figure 4.3: Estimated harvesting volume of broadleaved and coniferous trees for the New Forest area

Source: FC (2014) Timber Statistics; NFI woodland map Whilst the total harvest figures are robust at national level, there is a very high level of uncertainty associated with the estimated distribution of flows below this level. In other words the estimated outputs of the distribution of ecosystem service provision may differ greatly from reality, especially at local level. In order to produce robust results at this scale, observed data (e.g. on date of felling as for the PFE) is needed to estimate the expected flow or ecosystem service models should be used (for services particularly sensitive to changes in ecosystem location or where consideration of trade-offs is relevant).

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Carbon The annual carbon sequestration for broadleaved/coniferous woodland is estimated using FC published rates of carbon sequestration16 as shown in Table 4.5. This gives an estimated total carbon sequestration per year for broadleaved trees of 6.01MtCO2 and coniferous trees of 6.48MtCO2. The 'Adult' figures are based on the average sequestration rates for trees to 200 years under all management/felling regimes. In reality sequestration rates vary vastly depending on the growth phase trees are in however as the age of each stand in the UK woodland is not recorded in the published Woodland Map, the account has used an overall average. The Young Trees estimate is based on sequestration rates for trees 0-10 years and is applied to the extent of forest classed as 'Young Trees' (see Section 4.1.1). The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. For example, current evidence indicates that the rate of carbon accumulation from UK woodland is expected to peak at around 2030 (Centre for Ecology and Hydrology (CEH), 2013)17. This work by CEH should be reviewed in order to establish how the constant flow assumption can be refined and applied within the context of spatially disaggregated accounts and if this is useful given the purpose of these accounts. Table 4.5: Average annual carbon sequestration for broadleaved and coniferous species Species Average annual carbon sequestration Adult Trees

(tCO2e/ ha/yr) Young Trees (0-10 Years)

(tCO2e/ ha/yr) Broadleaved 4.71 2.20

Coniferous 4.47 2.64

A simplifying assumption adopted is that all coniferous/broadleaved species have same sequestration rates at all points in life cycle, other than other than 0-10 years (young trees). Average carbon sequestration rates were therefore estimated for standing conifer and broadleaved trees across all coniferous/broadleaved species for all spacing/thinning/yield class/age categories in tCO2e/ ha/yr apart from 0-10 years. In reality rates vary extensively and are much higher during vigorous growth periods and will become negative at later life stages. Different species will have differing rates which will vary in different regions. CEH estimates (2013) of carbon sequestration are 10.3MtCO2 in 2010 and 10.7 MtCO2 in 2015 (FC, 2014; CEH, 2013). These figures are net carbon dioxide removals attributed to UK forestry which were produced for input into the DECC 2012 UK Greenhouse Gas emissions figures. Water flow regulation Estimating water quantity regulating service of UK woodland has not been possible as this requires the use of ecosystem service models beyond the scope of this study. Specifically the data and resource requirements to run the reviewed models were too demanding. This means that it has not been possible to estimate the water regulating services provided by UK woodlands for input to these initial ecosystem accounts. Further detail on how ecosystem service models can be used to

16 The Woodland Carbon Code Carbon Lookup Tables (v1.5), 2013. 17 FC reported figures based on Centre for Ecology and Hydrology (CEH) derived data produced for the final DECC UK Greenhouse Gas Emissions (2014).

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estimate the flow of water quantity regulating services from woodland is though provided in Annex 2.3. Recreation

The methodology for accounting for recreational flows (i.e. visits/trips to woodland) is detailed in the separate recreation method note (see Annex 2.3). The estimation of recreational flows from woodland is carried out using a trip-generating function (TGF) which predicts the total number of recreational trips to woodland and maps them spatially. This modelling is undertaken by the University of East Anglia based on the Sen et al. (2014) study. The TGF predicts the number of trips made to woodland in Great Britain in 2010 based on land cover type, visitor demographics and population at the outset area of a trip. This applies data from the MENE survey for 2009-2010, which is then further applied to calibrate results from the TGF. The number of outdoor trips to woodland in England is estimated to be 262 million in 2009/10 based on the MENE survey18. This equates to 13% of outdoor visits based on the MENE survey (= 262m/ 1,950m = 13%). The Sen et al. (2014) trip-generating function estimates the predicted number of recreational trips to woodland to be 481 million trips in Great Britain in 2010 which is equivalent to 12% of TGF-predicted visits to all UK NEA habitats. There is therefore a 2% difference between the proportion of visits which are made to woodland in the MENE survey and as predicted by the TGF. The predicted number of visits to woodland based on the TGF is used in the physical flow account for woodland. The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used as well as predictions regarding demographic change (especially ageing) and the impact that this may have on the number of recreational visits in the future. Future work could usefully review the literature on this to establish how the constant flow assumption can be refined and applied within the context of spatially disaggregated accounts and if this is useful given the purpose of these accounts. Note that while the Sen et al. trip generating function is specified using data from the MENE survey which is distributed in England, the function is transferred to Great Britain to estimate visits in 2010. An advantage of the Sen et al. (2014) estimate is that it can allow visits to all NEA habitats to be mapped spatially, as depicted in Figure 4.4.

18 The reported estimate here (262 million visits) is a subset of the 317 million visits to a woodland or forest reported in Natural England (2010) and reflects outdoor visits to ‘other seaside coastline (including beaches and cliffs)’ and ‘in the countryside (including areas around towns and cities)’. Only outdoor visits are reported here so that a comparison to the predicted visits by the TGF (which only considers outdoor recreational visits) can be made.

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Figure 4.4: Annual number of visits to all NEA habitats in 2010 (based on Sen et al. (2014) trip generating function)

Source: Sen et al (2014); LCM2000; Natural England (2010). The difference between the Sen et al. (2014) estimates and the MENE survey estimates of visits to woodland as well as to other habitats is likely due to the following: • The Sen et al. (2014) TGF covers GB habitats whereas the MENE survey covers England which, in

part, explains the latter estimates being lower than the latter.

• The Sen et al. (2014) TGF estimates the total number of trips to all habitats and apportions this estimate (and monetary estimates) evenly based on the proportion of each habitat within 1 km2 cells. This assumption could potentially explain the difference between this estimate and other studies’ findings given that the approach to determining habitat-specific visits in Sen et al. (2014) does not account for differences in the attractiveness of habitats to visitors or the impact this may have on visits to different habitats in Great Britain. This results in an under-estimation of visits to ‘linear’ habitats such as coastal zone habitats. The assumption was made

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in the context of the original study which forms part of the UK NEA and was primarily interested in total visits rather than habitat-specific recreational visits.19

• Habitat types between different studies may not overlap. For example, the different types of

locations in the MENE survey do not overlap with all the habitat types used in the Sen et al. (2014) TGF based on the UK NEA habitat types. A like-for-like comparison is therefore not possible across all different habitats.

While the TGF developed by Sen et al. (2014) does not take any account of site-specific variation (i.e. facilities etc.), the approach can still be appropriate for broad-scale assessment in ecosystem accounts. The function approach has the advantage of providing a consistent basis for applying values to all recreation types in comprehensive ecosystem accounts and also takes account of substitution effects across different habitat types. It is particularly relevant in the context of the long term objectives for comprehensive natural capital accounting. It, however, has the limitation of apportioning the number of visits to habitats in a simple spatially-proportioned manner. In light of this, further research effort is needed to develop a TGF which can predict visits to different habitats while accounting for more than the proportion of each habitat within spatial units. 4.2.2 PFE woodland (England) The estimated expected physical flows for the PFE (England) analysis is set out in Table 4.6. In the absence of spatial data on the location of timber harvests, it shows aggregate (England) data on timber harvesting 0.06 million m³ of timber for broadleaved and 1.4 million m³ for coniferous, as well as the estimated profile of flow over 20years of 1.2 million m³ of timber for broadleaved and 28 million m³ for coniferous. Table 4.6 also shows estimated annual carbon sequestration for broadleaved (0.27 MtCO2) and coniferous (2.36 MtCO2) woodland which have been estimated and mapped using FC published rates of carbon sequestration. It also accounts for estimated recreational visits (15million) across PFE, England woodland based on Sen et al analysis (2014). Table 4.6: Physical account of ecosystem service provision (flow) for PFE, England woodland

Type of ecosystem

Woodland

Flow (Annual, 2012) Profile of Flows (‘20’ yrs) Provisioning Biomass for Timber BL C BL C

- - - -

FC Estimates 0.06 million m3 (overbark)

1.4 million m3


28.00 million m3 (5 yrs; 2012-2031)

Regulating Carbon Sequestration 0.27MtCO2 2.36MtCO2 5.40 MtCO2(20 yrs; 2012-2031)

11.5MtCO2(20 yrs; 2012-2031)

Water flow regulation Difficult to measure in physical and monetary terms


Cultural Recreation 15 million visitors 300 million visitors (20 yrs; 2010-2029)

Timber The Forestry Commission has a timber production forecast model for the PFE in England, which provides statistically robust estimates of expected timber provision at the sub-compartment (BSU) unit level based on observations. This is because the FC has estimates of the date of felling for each stand for management purposes. In effect this means that a map such as Figure 4.3 can be

19 Pers. comm. University of East Anglia (October, 2014).

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created for the PFE (England) with a high level of robustness. However, this model has not been available to the study team for similar reasons to those given above on the availability of ecosystem service models (i.e. large, technically and resource demanding and/or or still under development). In the absence of available spatial data on the distribution of provision of timber across PFE woodland at the sub compartment level, FC reported figures for softwood and hardwood timber production are used. These are based on FC administrative records for the FC estate and for private woodland on volumes reported by harvesting companies for coniferous trees, and deliveries to saw mills for broadleaved trees, along with certain assumptions to map the distribution of fibre (timber) service. This has meant that the total observed harvested volume figures for PFE of 0.06 million m³ broadleaved trees and 1.4 million m³ coniferous trees has been distributed evenly across all ‘mature’ trees in the PFE (considered to be +80 years for broadleaved and 41-60 years for coniferous) based on standing volume data under each yield class. Taking the assumption on distributing harvest across these ‘age’ categories as given, it is unlikely that there will be an even distribution of harvesting across all yield classes nationally. Moreover, assuming ‘mature trees’ are only harvested results in the proportion of trees harvested in the 41-60 age range of 45%. Therefore, whilst the total harvest figures are robust at a national level, there is a high level of uncertainty associated with the robustness of the estimated distribution at a sub-national level. Modelled outputs of the distribution of ecosystem service provision may differ greatly from reality, especially at local level. The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. Carbon The same approach was adopted for the PFE (England) as had been followed for all UK woodland as reported above. This gives an estimated total carbon sequestration for broadleaved trees of 0.27MtCO2 and coniferous trees of 2.36MtCO2. The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. Water flow regulation As with all UK woodland it has not been possible to estimating water quantity regulating service of PFE (England). Further detail on how ecosystem service models can be used to estimate the flow of water quantity regulating services from woodland is provided in Annex 2.3. Recreation The Sen et al. (2014) model is comprehensive in that it estimates visits to all GB woodland given the existence of substitute sites. This means that estimating the flow of visits to PFE woodland in isolation is not appropriate because not all substitutes would be factored in. Therefore visits to the PFE have been estimated by taking the Sen et al. analysis and isolating the spatial area covered by the PFE using FC spatial data on PFE woodland. However, the model does not include specific characteristics of woodland (e.g. recreational facilities) as an explanatory factor for recreation visits. This means that data on recreational facilities that are included in the PFE stock account are not used in the estimation of visits to the PFE. As a result, the estimated recreational visits to PFE woodland of 15 million (see Table 4.6) will be an underestimate. Future refinement of the Sen et

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al. (2014) work should seek to incorporate such characteristics of ecosystems in order to more accurately estimate the distribution of visits across the country. The profile of flows over 20 years is based on a ‘constant flow’ assumption (Defra/ONS, 2014). In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used.

Monetary account of ecosystem stock and flow 4.3

The valuation of the flows of service provision over time provides the basis for the third component of the ecosystem accounts, the monetary account. The valuation of the various ecosystem services that are provided by the woodlands is undertaken using unit values obtained from secondary sources. The evidence available for different services ranges from exchange values to welfare values, which incorporate measures of consumer surplus. The rationale for using one or the other depends on multiple considerations including: (i) the purpose of the account being developed (the woodland ecosystem account); and (ii) a combination of the extent of unit values that are actually available and the services being valued (see Annex 5).

With respect to (i), the SEEA-EEA states that if the purpose of the account is the comparison of the values of ecosystem services and assets to existing national accounting values, it is appropriate to use a consistent valuation basis for all entries. This means the value of ecosystem service provision should be based on the concept of exchange values. It may also be possible to develop an account that incorporates welfare values of ecosystem services but this would require a re-estimation of any relevant elements of the SNA which use exchange values to render them consistent with or comparable to the total welfare-based values of ecosystem services. However the purpose of the accounts being developed within this study potentially goes beyond a comparison with the SNA and stems from the broader commitment to recognise and value the benefits of nature (see for example Natural Environment White Paper, HM Government 2011). This implies that a strict adherence to exchange values – which do not exist for the provision of many ecosystem services – is not necessarily essential. Moreover the distinction between what represents an ‘exchange value’ and a ‘welfare-based’ value may not always be clear-cut (e.g. Day, 2013; see also below and Annex 5). With respect to (ii) and the extent of unit values that are actually available and the services being valued: • Where there is a legitimate price that reflects the value of an ecosystem service in

exchanges, this price can be used and is considered an exchange value. This is the case for timber, a provisioning ecosystem service, which is a market good. Observed prices are used to estimate the benefits from this service. For timber from coniferous woodland (i.e. softwood), the Forestry Commission’s Coniferous Standing Sales Price Index for Great Britain in 2012 (£14.07 per m3 overbark in 2012 prices20) is used. The value is applied to both GB (UK) and PFE (England) estimates of biomass of timber from coniferous woodland. Note that, although the price reflects FC sales only, it is used in the accounts for all GB woodland. This is justified because, firstly, the price is similar to prices reported in other sources (e.g. the price of timber from coniferous woodland is £13.75 per m3 standing in 2012 prices based on the Nix Farm Management Handbook (2013)), and,

20 This is the average of two prices which are recorded in 2012: £14.10 per m3 overbark on 31/03/2012 and £14.03 per m3 overbark on 30/09/2012.

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secondly, using this price ensures a consistent approach in valuing timber from the same type of woodland. For timber from broadleaved woodland (i.e. hardwood), FC estimates are not available. The price of timber from broadleaved woodland is therefore sourced from the Nix Farm Management Handbook (2013). It is reported as a range (£14.74 - £245.74 in per m3 standing in 2012 prices). The higher value (£245.74) is not used because it is likely to represent the price of specialty products. Since most timber from broadleaved woodland is used for woodfuel21 (a non-specialty product), the lower value (£14.74) is used. The same value is used for all GB woodland including PFE woodland to ensure consistency in the approach applied. Whilst this lower value is chosen based on the data available, it may, however, be considered an under-estimate of the value of timber. Further, the use of annual unit values for timber from broadleaved and coniferous woodland ignores the fact that the price of timber varies over time. In principle, average prices over longer periods are preferable to capture structural changes which drive the price of timber rather than fluctuations due to external factors.

• Where 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, then such a price is not considered an exchange value.

In general, the use of prices which are more a reflection of market institutions than the value of exchanges between consumers and producers will bias the development of ecosystem accounts as a measure of the value of ecosystem assets and their contribution to human welfare. SEEA-EEA (2013) states that, in using market-price equivalents, an assumption that must be noted (and therefore must hold when adopting this approach) is that the prices being used to approximate missing prices are themselves formed in a manner that can be considered incentive compatible. In other words, the market/institutional setting of markets which provide ‘missing prices’ must be such that the revealed prices reflect the truthful responses of the market participants.

These considerations inform the selection of value for the carbon sequestration service22. Existing carbon markets such as the EU ETS (European Union Emissions Trading Scheme) or the UK Woodland Carbon Code produce prices which reflect the institutional setup of carbon markets rather than the true value of carbon sequestration if it were to be exchanged. For example, the drop in the price of EU carbon allowances at the end of 2007 (the pilot phase of the EU ETS) was a response to information that the market was oversupplied with allowances rather than a reflection of decreased benefits from carbon sequestration. The ensuing increase in the price of carbon allowances beyond 2007 was a reflection of information that a tighter cap on carbon emissions would be imposed in future years rather than a reflection of increased benefits from carbon sequestration. While the benefits of carbon abatement may have been constant during this time of institutional change, the market price would have given a false impression by reflecting institutional factors and speculative behaviour rather than the true exchange value of carbon sequestration.

21 Since 2009, 75% of deliveries for UK-grown hardwood (i.e. timber from broadleaved woodland) have been for use as woodfuel. See the Forestry Commission’s Forestry Statistics 2014 – UK Grown Timber: http://www.forestry.gov.uk/website/forstats2014a.nsf/LUContents/187E23791CE53F068025735200491AFF [Accessed November 2014] 22 For woodland carbon sequestration, different valuation approaches are explored in more detail in Annex 2.1.

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http://www.forestry.gov.uk/website/forstats2014a.nsf/LUContents/187E23791CE53F068025735200491AFF


The Department of Energy and Climate Change (DECC, 2014) central non-traded price of carbon, adjusted to 2012 prices, is therefore used to value carbon sequestration from woodland. This price reflects carbon mitigation and meeting the UK’s short and long-term greenhouse gas emissions. 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 existing market prices which are misleading due to institutional factors. The price of carbon in existing markets may become more representative of the value of carbon sequestration in the future as the institutional setup of markets becomes more established. Given this, average prices traded under the Woodland Carbon Code may act as a potential future source of valuation estimates of woodland carbon sequestration. The decision of which price to use will need to be re-evaluated in the future.

• Where the only available evidence to value an ecosystem service is the expenditure on goods

that are complements to the service, and these are already recorded in the System of National Accounts, a case can be made to use welfare estimates. For example it is arguable that there are ‘markets’ for recreation, where people pay for access to sites and complement goods (e.g. food and drink, gifts, souvenirs, etc.). However, the benefits from recreation are not accurately represented by these expenditures because they do not measure the actual use value associated with a recreational visit, either in exchange price terms or welfare (instead they represent the exchange price for the complementary goods). Rather it would require isolating the contribution of ‘woodland recreation’ as an ecosystem service from the contribution of other capital inputs. Noting that further work is required to establish how this isolation should be undertaken if expenditure by visitors is to be used in ecosystem accounts, the valuation of recreational benefits from woodland is based on welfare values. In particular, the Sen et al. (2014) value per trip for woodland recreation visits (£3.47 per trip in 2012 prices) is applied to represent the average value for a visit to a woodland site in GB23.

The use of welfare estimates can be valid depending on the purpose of the accounts. For this study, the Sen et al. (2014) meta-analysis function which produces the recreation value has the disadvantage of not being specific to woodlands, but woodland and forest cover are a component of the function. Further, the model does not take full account of site-specific variation (in facilities, etc.) but it is appropriate for broad scale assessments such as in natural capital accounting. In particular the function has the advantage of providing a consistent approach for applying valuation estimates to recreation from all habitats in comprehensive natural capital accounts and is also based on a wide range of source studies. Note that while the use of average unit values for recreation from woodland ignores the fact that the value of recreational benefits varies over time and space, the major source of uncertainty in most recreation valuation is the estimation of visits not the unit value applied to those visits. This relates to the concern identified by Bateman et al. (2003) that economics research has tended to focus on estimation of robust unit values for recreation, whereas the most important determinant of changes in values of recreation are changes in the number of

23 While this valuation is surplus based, it does not estimate the total consumer surplus from visits to woodland. The valuation is based on a meta-analysis of recreation valuation studies, which for the most part are based on the use of revealed preference methods (e.g. individual travel cost) which estimates consumer surplus as the excess benefit over and above costs (typically travel expenditure and time) incurred for a visit (i.e. the net surplus).

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visitors, and this has been relatively overlooked. More generally, value transfer studies have demonstrated that the sensitivity of results to beneficiary populations is very high, and therefore requires increased attention which had previously been focussed on unit values. Further information of the methods and valuation evidence relating to recreation is provided in Annex 2.3.

• Where the only available evidence to value an ecosystem service is welfare-based (versus market-based exchange values), then a case can be made to use welfare estimates or the ‘simulated exchange value approach’. The latter aims to measure the income that would occur in a hypothetical market where ecosystem services are bought and sold by estimating a demand and a supply curve for the ecosystem service in question and then making further assumptions on the price that would be charged by a profit-maximising resource manager under alternative market scenarios. The method then takes the hypothetical revenue associated with this transaction (excluding the associated consumer surplus) as a measure of value of the flow of ecosystem services (UN SEEA-EEA, 2013: 131; see Oviedo et al., 2010 for an example). These considerations are likely to inform the selection of valuation method for some regulating and cultural services which are not valued in this study.

Table 4.8 summarises valuation evidence for each of the ecosystem services assessed for woodland. These are used in the development of the monetary accounts for woodland. Note that valuation evidence for water quality and quantity regulation is excluded from Table 4.8 as this ecosystem service is not assessed. Table 4.8: Summary of valuation evidence for woodland ecosystem services (2012, £)

Ecosystem service

Study/ source

Valuation method

Description of good Unit Monetary values

Lower Central Upper

Timber

Forestry Commission data

Market price

Coniferous Standing Sales Price Index for Great Britain in 2012

2012 £/m3 overbark

- 14.03 -

Nix Farm Management Handbook (2013)

Broadleaved timber unit values of standing stock to be harvested in 2013

2012 £/m3 standing

14.74 - -

Carbon

DECC (2014) non-traded price of carbon

Non-traded market price

Cost of ‘non-traded’ measures to mitigate carbon emissions for 2012-2031 (2012 price shown here)

2012 £/tCO2e

- 56.78 -

Recreation Sen et al. (2012) & Sen et al. (2014)

Meta-analysis function of different valuation studies

Willingness to pay per person per trip to woodlands and forests in 2010a

2012 £/person/trip

- 3.47 -

Notes:a Estimate of willingness to pay (WTP) per person per trip represents the average value attributed to a visit and has been applied to obtain the value of recreational benefits from woodland. This does not, however, imply that this or any amount is charged. The extent to which the variation in the value of ecosystem service flows is captured in monetary accounts is dependent upon the extent to which the reporting of physical flows reflects this. In the case of timber for the initial woodland account, the physical flow is reported in terms of broadleaved and coniferous species, since data exist on the harvest for these and applying average unit values to these is judged to sufficiently capture the variation in value. The estimated monetary account for woodland is set out in Table 4.9 for UK (GB) woodland. It shows the estimated recreational value of GB woodland to be in the order of £1.7 billion a year,

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carbon sequestration by broadleaved trees of £341 million a year and coniferous of £372 million a year as well as biomass for timber of broadleaved being valued at £9 million a year and coniferous trees at £165 million. Monetary estimate are derived using the physical flows estimated for woodland in Tables 4.4 multiplied by the unit values in Table 4.8. Table 4.9: Monetary account of ecosystem stock and flow for GB woodland (2012, £) Type of ecosystem service

Biomass for Timber Carbon Recreation Water Regulation

BL C BL C

Value

Flow (Annual) £ 9m (2012) £165m (2012)

£341m (2012)

£372m (2012)

£1,669m (2010)

Not modelled

Stock (PV of future flows over ‘20’ years)

127m £2,431m £5,738m £6,254m £24,552m Not modelled

The estimated monetary account for woodland is set out in Table 4.9 for PFE woodland in England. It shows the estimate recreational value of PFE, England woodland to be in the order of £52 million a year, carbon sequestration by broadleaved trees of £15 million a year and coniferous of £134 million year as well as biomass for timber of broadleaved being valued at £1 million per year and coniferous trees at £20 million. Monetary estimate are derived using the physical flows estimated for woodland in Tables 4.6 multiplied by the unit values in Table 4.8. Table 4.10: Monetary account of ecosystem stock and flow for PFE woodland (England) (2012, £) Type of ecosystem service

Fibre (Timber) Carbon Recreation

Water flow regulation BL C BL C

Value

Flow (Annual) £ 1m (2012) £20m (2012)

£15 m (2012)

£134m (2012)

£52m (2010) Not modelled

Stock (PV of future flows over ‘20’ years)

£13m £289m £258m 2,253m £766m (2010)

Not modelled

Generation and use of ecosystem services for an accounting area 4.4 The final component within the set of ecosystem accounts records the generation (or provision/ownership) and use (or beneficiary) of ecosystem services (Table 4.11). This recognises that the use of services generated within an accounting area may not take place within that accounting area. For example, if the accounting area (EAU) is an urban area, this will benefit from the air filtration services provided by nearby woodland. Whilst, spatial data on ownership does exist for PFE, England, beyond this it is difficult to identify spatially and to map ownership or ‘generation’ of ecosystem services. Furthermore, there are also issues with identifying ‘users’ meaning that identifying the spatial distribution is difficult. In these initial woodland accounts, a distinction is made between PFE (England) and non-PFE (England) areas of woodland but no attempt is made to populate this ‘generation’ and ‘use’ of ecosystem services accounting table. Nevertheless, how this account might be developed theoretically has been explored below. The SEEA suggests that ecosystem services that are embodied in traded products such as timber should not be recorded in the ‘generation’ and ‘use’ accounting table. This stems from the definition of final ecosystem services and the desire to account for the resource rent they provide, as opposed to the total market value of goods and services which are recorded in the SNA. For

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example, it is suggested that fibre immediately before harvesting is the service flow that should be accounted for, through recording the flow from the ecosystem to the enterprise undertaking the logging. Subsequent flows of products are recorded in the SNA. However, if this is the case then the distinction between ‘generation’ and ‘use’ of ecosystem services is less apparent and the SEEA-EEA is unclear on this separation. This distinction is harder for cultural services, such as for recreation because the point of ecosystem service ‘generation’, as separate from ecosystem service ‘use’, which in turn is distinct from consumption of recreation benefit, is unclear. The distinction is easier for some regulating services such as climate regulation because the ‘generation’ of this good occurs from woodland in-situ and the ‘user’ is the global population. For water quantity regulating services, it is distinct that users are located separately (i.e. in another LCEU or EAU) from those generating the services and so this is worthwhile exercise, but requires modelling to identify and quantify users and the flow of services across space. Given that the isolation of resource rent from ecosystem services has not been identified in this project, Table 4.11 has been populated qualitatively assuming that the ‘generation’ of ecosystem services is attributable to the owners of woodland, and ‘users’ of ecosystem services are the beneficiaries from either the final ecosystem service (e.g. climate regulation) and/or market good (e.g. timber provision). Further work is needed to determine how to isolate resource rent from market goods in order to be consistent with the SEEA-EEA aspirations.

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Table 4.11: Generation and use of ecosystem services for an accounting area Generation of ecosystem services Use of ecosystem services

Enterprises Households Non-Profit Institutions

Government Rest of World Enterprises Households Government Non-Profit Institutions

Rest of World

Provisioning (fibre for timber)

Commercial loggers harvest

Privately owned forest harvest

Non-profit institution owned woodland areas harvested (e.g. by National Trust; Woodland Trust)

Forest Enterprise harvest and from other government bodies

Extent of woodland harvest owned by non-UK residents

Private sector consumption of timber

Household consumption of timber

Government procurement of timber products

NGO use of wooded products

Export of UK harvested wood

Regulating (carbon)

Extent of commercially owned woodland

Extent of privately owned woodland

Extent of woodland owned by non-profit institution

Extent of woodland owned by government bodies

Extent of woodland owned by non-UK residents

Global population

(water quantity)

Extent of commercially owned woodland located upstream of beneficiaries in (high) flood risk zones

Extent of privately owned woodland located upstream of beneficiaries in (high) flood risk zones

Extent of woodland owned by non-profit institution located upstream of beneficiaries in (high) flood risk zones

Extent of woodland owned by government bodies located upstream of beneficiaries in (high) flood risk zones

Extent of woodland owned by non-UK residents located upstream of beneficiaries in (high) flood risk zones

Avoided flood damage costs to businesses located downstream of woodland and in (high) flood risk zones

Avoided flood damage costs to households located downstream of woodland and in (high) flood risk zones

Avoided flood damage costs to government associated with optimal location of woodland

Avoided flood damage costs to non-profit institutions located downstream of woodland and in (high) flood risk zones

Export of flood regulation benefits across country/EAU borders (e.g. from N.Ireland to Ireland)

Cultural (recreation)

Extent of commercially owned recreational woodland

Extent of private recreational woodland

Extent of recreational woodland owned by non-profit institution

Extent of recreational owned by government bodies (e.g. PFE, England)

Extent of recreational owned by non-UK residents

Visits to commercially owned woodland

Visits to private woodland

Visits to woodland owned by non-profit institution

Visits to woodland owned by government bodies (e.g. PFE, England)

Visits to UK woodland owned by non-UK residents

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5. CROSS-CUTTING ISSUES FOR ECOSYSTEM ACCOUNTS

Scope and interpretation 5.1 Ecosystem accounts provide a consistent framework in which to organise and analyse statistical evidence on the stock and condition of ecosystem services and the provision of ecosystem services. This provides a coherent structure for compiling disparate data on ecosystems, which may already be available to inform policy development, but in less of a systematic and organised way. The ONS Roadmap sets a clear direction for the development of ecosystem accounts for each of the eight UKNEA broad habitat types. Reporting on the extent of these habitats using spatially explicit (geo-coded) datasets is important to ensure that an integrated set of accounts will (eventually) enable the relationships and trade-offs between different ecosystems and ecosystem services to be analysed in a coherent way. In particular, the sum of the area of ecosystems under these eight habitat types should equal the total UK land and marine area. Changes in the extent of one ecosystem should therefore be offset by an equivalent change in others, such that the portfolio of ecosystems always sums to this total area. There are also trade-offs between ecosystem service provisions over time for a habitat/ecosystem account under changing land use management objectives that can be identified and analysed in spatially explicit accounts. For example, increasing/changing the condition of one ecosystem stock characteristic to ensure the delivery of a certain ecosystem service may come at the expense of another ecosystem service which requires different stock characteristics/conditions (e.g. monoculture in woodlands for provisioning services at the expense of biodiversity). Integrated accounts should also enable the condition of cross-cutting characteristics that span more than one ecosystem (e.g. pollinator condition) to be assessed comprehensively (i.e. as opposed to reporting in individual ecosystem accounts). Since the intention for ecosystems accounts is that they are integrated and comprehensive (i.e. across all habitat types), the displacement of ecosystem service flows due to changing land use are picked up as assumptions made in one account follow into others to capture this. For example, ensuring that the consequence of a change in the extent of one habitat feed through into consequential changes in other habitats. In this way, the purpose of integrated accounts is not report the marginal difference between value of ecosystem service provision under different land uses in a way that is consistent with the notion of opportunity cost; i.e. the next best alternative land use representing what is traded-off for the woodland land use (e.g. agriculture). Instead, the purpose of an account is to report the total (‘gross’) value of ecosystem capital, with displacement effects (i.e. opportunity costs) picked up through changes in other (individual habitat) ecosystem accounts. The total value of ecosystem service provision can be identified and monetised in most cases through the application of valuation techniques to measures of quantity in absolute terms (e.g. tonnes of biomass harvested for timber or tonnes of carbon sequestered). However, some services are measured in relative terms and so an explicit counterfactual must be stated to identify the total value of woodland land cover. This is the case for ecosystem services which provide a reduction in risk or changes to the quality of a good. For example the regulation of water quantities (quality) by woodland results in a benefit to downstream populations through reduced risk of flooding (deteriorated water quality). In order to estimate the reduction in risk (improvement in water quality), some initial level of risk (water quality) must be identified and this necessitates an explicit counterfactual, or initial state which might be assumed to be non-natural land cover or the most likely natural land cover if woodland were not there (e.g. agriculture). ADAS and eftec (2014)

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propose a baseline of potential agricultural productivity and cropping flexibility of the land based upon climatic, soil and topographic variables based on agricultural land classifications. It is proposed that the total value of woodland water regulating services is established through modelling of risk of flooding (and/or water quality) under counterfactual (e.g. agricultural land use) and the risk of flooding (and /or impact on water quality) under woodland land cover.

Valuation of ecosystem service flows 5.2 Monetary valuation of stocks and flows of ecosystems is discussed in a number of recent publications, including SEEA EEA (2013) and the ONS’ natural capital monetary estimates for the UK (ONS, 2014). Further discussion is provided by contributions to the Valuation for Natural Capital Accounting seminar (November 2013) and the Defra and ONS paper on the Principles of Ecosystem Accounting (Defra/ONS, 2014). Much of the available evidence for valuing ecosystem service flows is based on economic valuation methods which are applied in the context of (non-market) unpriced benefits provided by ecosystems. Notably these methods estimate welfare values (e.g. they measure benefits including consumer surplus), typically in terms of individuals’ willingness to pay (WTP) for environmental goods and services. This may be distinct from the price that consumers would actually pay to secure a good or service in a real market which is dictated by the interplay between what producers are able/willing to supply and what consumers are willing to pay, along with factors relating to the nature of competition in the market and institutional influences (e.g. competitive markets, monopoly suppliers, regulated industries, etc.). The System of National Accounts (SNA) is, though, concerned with capturing economic activity that is demonstrated by the presence of an ‘exchange value’ (i.e. a market price). Accounting with exchange values balances the production and consumption sides of the economy thereby allowing the accounts to balance. Hence, whilst economic valuation methods are the primary source of monetary value evidence for ecosystem accounting, the measurement of economic welfare/surplus may be considered to be inconsistent with the application of exchange values from an accounting perspective. Obst and Vardon (2014), for example, point out the need to recognise the requirements of alternative applications of valuation evidence, contrasting the welfare-based perspective of cost-benefit analysis (CBA), which is the traditional application for economic valuation, to the SNA perspective. However, the distinction in monetary valuation evidence is not necessarily so clear-cut. Table 5.1 summarises the methods that can be used in the monetary valuation of ecosystem service flows based on commentary in recent studies (Day, 2013; Mangos et al., 2010; ONS, unpublished24).

24 Office for National Statistics (unpublished), Monetary Woodland Ecosystem Account. Working paper.

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Table 5.1: Valuation methods – exchange values versus welfare based estimates* Notes: *A brief description of the valuation methods presented in this table is provided in a glossary at the end of this report. Following Table 5.1 (and the accompanying discussion in Annex 5), the hedonic pricing method, defensive/avertive expenditure, and the replacement cost methods are most consistent with the notion of an exchange value. However, the use of these methods with respect to valuing ecosystem flows is limited by the following considerations: • Hedonic pricing method: this estimates implicit prices for environmental goods based on market

transactions, where the environmental good is an attribute (i.e. feature) of a market good. The typical example is the demand for local environmental quality as reflected in house/property market exchange prices. While the method can disentangle the contribution of environmental goods for the demand for market traded goods, there is an implicit double counting with the SNA which already records this contribution to the exchange value.

• Defensive/avertive expenditure: this method can be applied in cases where an environmental good can be substituted by a form of defensive expenditure incurred in avoiding damages from reduced environmental quality (e.g. expenditure on substitutes for water supply; the value of which related to environmental/health characteristics can be established by, for example, application of a discrete choice model using revealed preference data). Expenditure on substitute goods may, however, already be included in the national accounts, making determining overlaps increasingly challenging if integrating ecosystem accounts to the SNA.

• Replacement cost method: this approach approximates the value of an ecosystem service from the cost of mitigating actions required if the service is lost or if its productivity decreases (e.g. the cost of man-made flood defences). The approach faces a number of practical challenges

Day (2013) Mangos et al. (2010) ONS (unpublished)

Exchange values / market prices



Reference prices (e.g. set by industry regulator)

Hedonic pricing method

Revealed preference methods

Defensive / avertive expenditure and replacement cost method

Replacement cost method

Travel cost method

Stated preference methods Stated preference methods

Social opportunity costs (e.g. Tinbergen, 1954) or shadow prices (Dasgupta et al.,1972)

Exchange values

Welfare values

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which include establishing the baseline for the ‘sustainable level’ of a natural capital asset and establishing the current status of an asset and the scope and scale of the actions needed to reach the target level of ecosystem service provision. This is complicated by the required understanding of the interactions between a natural capital asset and other capital inputs (e.g. human and physical) and the specific management actions required to bring the asset back to sustainable use. These drivers may also already be included in national accounts.

Overall, whilst the requirements of the SNA are clear, and hence the implied basis for monetary valuation of flows in ecosystem accounts is that of exchange values (SEEA EEA, 2013), there are several considerations – as set out in Section 4.3 with respect to the initial woodland accounts. Exchange prices are not available for many flows of ecosystem service provision because these are not traded in markets (i.e. the exchange price is zero). However, an aim of ecosystem accounts is to record the values of benefits from such ecosystem services, because while the exchange price does not signify the importance of the service, the values derived from such services are recognised as being substantial. This is especially the case for a number of other regulating and cultural ecosystem services (in contrast to provisioning ecosystem services which tend to be traded in markets). Hence the concept of an exchange values may not be appropriate for certain ecosystem services especially regulating and cultural services where the exchange value of such services is equal to zero. In addition, though, as with the task of measuring physical ecosystems and flows, the practical task of estimating the monetary value of ecosystem service flows is limited by data availability. Suitable source studies for applying in spatially explicit ecosystem accounts, which establish the change in value across characteristics (e.g. value of different species of trees for timber) for ecosystem services that are sensitive to woodland quality and/or location (e.g. recreation and water regulation) are limited. Indeed, most progress in spatially explicit valuation has been made in valuing non-market recreation benefits (e.g. Sen et al., 2013), yet even these are poorly aligned with the underlying ecosystem characteristics (e.g. availability of recreation facilities such as visitor centre, toilets etc.) and conceived logic chains, representing a notable disconnect between the physical and monetary sides of an account. In this case, average unit values are applied to the estimation of physical flows (e.g. number of recreational visits) as opposed to distinguishing between the value of visits to woodland with different recreational facilities. Hence further work is required to appropriately establish the specific requirements for valuing service flows in ecosystem accounts, addressing both the estimation of exchange values from current valuation methods, and the coherency of measurement in the physical and monetary flow accounts.

Isolating resource rent from ecosystems 5.3 Ecosystem accounts are set out according to an asset balance sheet approach, which includes estimates of the value of natural (ecosystem) capital that can be tracked over time. The value of environmental goods such as timber (that depend on ecosystem service flows) is currently captured in the SNA. Ecosystem monetary accounts can be incorporated into the SNA accounting framework to generate ‘environmentally-adjusted’ national accounts aggregates (such as the Genuine Savings Index, depletion-adjusted net saving, etc. as set out in ONS, 2013). These indicators are useful in demonstrating the importance of natural capital and progress in maintaining it which can be important to inform strategic policy decisions. Integrating an ecosystem account into the SNA, with both using the value of timber for example, results in double-counting of value.

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Avoiding double-counting would require the identification of goods and services in the SNA that are dependent upon woodland ecosystems and then isolating resource rent (i.e. distinguishing the value of natural (ecosystem) capital from the value of other capital inputs in providing service flows). Note that identifying and separating resource rent is considered here, but the overlaps and double-counting with the SNA is not. The logic chain perspective applied in developing the ecosystem accounts explicitly recognises that the provision of ecosystem services is typically dependent upon the interaction of different forms of capital together (e.g. human, physical and natural capital). The contribution of other forms of capitals therefore need to be ‘isolated’ from the value of final goods and services to capture the value of final ecosystem services. In particular, the concept of ‘resource rent’ refers to the contribution of natural capital to a final good in isolation of the contribution of other factors of production. Resource rent is relevant where ecosystem services are valued using market prices or exchange values; i.e. for provisioning services and consequent traded goods. This follows from SEEA-EEA (2013) which establishes that the value of final ecosystem services should be measured rather than the value of final goods (i.e. fisheries before they are combined with other inputs to become a final market good). There are though certain market conditions that must be satisfied for the calculation of resource rent to produce a meaningful and accurate result (Defra and ONS, 2014; Edens and Hein, 2013). In particular: (i) the resource must be harvested in a sustainable way; and (ii) the owner of the resource must seek to maximise resource rent over the long term. To date, studies that have attempted to apply the concept and calculate it have tended to under-estimate the natural capital resource rent because of imperfect market structures and data limitations (ONS, 2014). These are summarised in Annex 5. SEEA (2013) highlights that the applicability of resource rent is dependent on the access conditions of resources. For example open-access resources will tend to have ecosystem services ‘prices’ which are equal to zero based on a nil marginal unit resource rent. In this respect and in the context of ecosystem accounting, the ONS (2014) result for fisheries resource rent leads to the conclusion that that the concept of resource rent is of limited applicability (at best) for ecosystem accounting, especially as it falls short in an area where it is supposed to be most suitable (i.e. provisioning services). In addition, there is the consideration of the scale at which resource rent can be estimated. Most studies tend to estimate resource rent at the aggregate level following a top-down approach. Isolating resource rent at a spatially disaggregated scale is challenging given that investments in other capitals often occur at different scales. Attempting to reconcile these two different scales by scaling aggregate resource rent down to a more granular level introduces a further dimension of uncertainty to the analysis due to the varying spatial configuration of many ecosystem services. Hence further work is required to determine if the concept of resource rent is suitable for spatially disaggregated ecosystem accounts.

Accounting for biodiversity 5.4 SEEA-EEA (2013) states that a basic resource account for biodiversity focusing on the measurement of changes in species provides information suited for assessing ecosystem condition. Physical accounts showing the area of different ecosystems in protected areas is a straightforward first step, as land used for conservation usually has the express purpose of protecting biodiversity. Spatial data on the extent of woodland under protected area designations is available for Sites of Special Scientific Interest (SSSIs) and has been included in the initial woodland account. Other possible datasets that could have been used include Special Protection Areas (SPAs), Special Areas of

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Conservation, National Nature Reserves and Local Nature Reserves. It is advisable to exclude any where the designation is not dependent on the existence of woodland – e.g. geological SSSIs or SPAs for golden eagles. Although, it has not been possible to distinguish these areas under this project. However, accounting for ecosystems outside of protected area (i.e. the rest of the country) is also important since this represents the greater proportion of land and biodiversity. In addition, it is important to identify the final ecosystem service ‘flows’ that biodiversity is associated with – for example charismatic species and other cultural services - from the ‘stock’ of biodiversity that underpins natural processes. Whilst this ‘stock’ of biodiversity is partly measuring the abundance and variation in species, the capturing of the ‘role’ of these species within a functioning ecological system is what is desirable in terms of providing a comprehensive account. Accounting for biodiversity in ecosystem accounts should therefore be through consideration of characteristics that are indicative of the stock of biodiversity. The specific characteristics will differ depending on the ecosystem of interest and should be identified by ecologists. Whilst the initial woodland account does not report the characteristics of the biodiversity ‘stock’, forthcoming work by the Forestry Commission can inform such an account if it is developed. This includes the following stock ‘characteristics’ that are indicative of the ‘stock’ of biodiversity that exists within forests: • Volume of deadwood: dead and dying trees play a key role in the functioning and productivity

of forest ecosystems through effects on biodiversity (Forestry Enterprise, 2002).

• Invasive species: alien species that become invasive are considered to be main direct drivers of biodiversity loss across the globe (CBD, 2014).

• Number of native species: woodland biodiversity is affected by whether the main tree species making up the woodland are native or not.

• Vertical structure: the dimension from ground to canopy includes layers of foliage, gradients of microclimate and a diversity of plans and animals that respond to that vertical structure (Brokaw and Lent, 1999).

Annex 4.5 sets out a more detailed consideration of how biodiversity might be considered within ecosystem accounts.

Land cover vs land use 5.5

The main difference between land use data (e.g. NFI) and land cover data (e.g. Land Cover Map 2007) for woodlands is that felled areas are considered as part of woodland as a land use, but will not be included in woodland as a land cover. Defra/ONS (2014) proposes that ecosystem accounts should be based on the Land Cover Map (LCM, preferably LCM2007) in the first instance but that if more relevant data exists on land use (such as the National Forest Inventory) this should be used instead, with the results reconciled with the LCM at higher levels of aggregation. The need for reconciliation relates to the consistency with the approach adopted in the UK Roadmap25 which aims to build the accounts on an underlying land

25 Accounting for the valuation of nature in the UK.

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cover account, working through the broad habitat categories used in the National Ecosystem Assessment. The LCM represents the geographical coverage of the UK for each ‘ecosystem’, meaning that together they should cover the entire UK and should be mutually exclusive (although the extent of each may change over time). The initial woodland account utilises the NFI information on land-use for the account of UK woodlands. An assessment of the differences between the LCM and the NFI land use data is provided in Annex 5. It concludes that for the purposes of these initial accounts, reconciliation of NFI data with LCM2007 data is deemed to be too resource intensive and a crude reconciliation would result in significant information loss meaning its value would be questionable. The NFI has therefore been used where possible and is deemed to be appropriate without any reconciliation. It has been necessary to use the LCM in the estimation of the distribution of recreational visits/value because the methodology requires that other habitats are accounted for as substitute recreation sites. Further research into this issue is needed.

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6. CONCLUSIONS

Summary 6.1 This study tests the development of ecosystem accounts for woodland. The specific focus is to establish and apply a methodology using existing spatially explicit datasets for UK (GB) woodland and the Public Forest Estate (England) in a manner that is consistent with framework set out in SEEA-EEA (2013). This facilitates the mapping of the distribution of stocks and flows across and within UK woodland and the PFE. The task is to assess the application (potential use) and feasibility (including robustness) of a spatially disaggregated approach given currently available data and that data that may be available in the future. It is intended the outputs of this study will inform the development of further ecosystem accounts (i.e. beyond woodlands) and recommendations are made on cross-cutting methodological issues in this respect. The basic process for developing an ecosystem account is outlined as: 1. Select the ecosystem services to include in the account; 2. Develop (scientifically grounded) logic chain models for each service; 3. Identify relevant physical data sources for mapping ecosystem stock extent and condition; 4. Identify relevant physical data sources and/or ecosystem service models to estimate flows of

ecosystem service provision; and 5. Identify relevant valuation evidence to estimate the monetary value of ecosystem service flows

and the ecosystem stock. The study focuses on four ecosystem services – timber (provisioning service example), carbon and water flow regulation (regulating service examples), and recreation (cultural service example). It tests the different ways that these steps can be implemented in order to assess the application and feasibility of spatially disaggregated accounts.

Application of ecosystem accounts 6.2 6.2.1 Role for spatially disaggregated accounts Action 5 of the EU Biodiversity Strategy to 2020 calls on Member States to map and assess the state of ecosystems and their services in their national territory and identifies a ‘a strong link between Action 5, the work of “Mapping and Assessment of Ecosystems and their Services” (and) the work on natural capital accounts’ (EC, 2013). However, beyond this requirement the need for mapping of ecosystem services – for example via spatially disaggregated accounts – depends largely on the intended uses of measurements of ecosystem service provision. For national accounting, if the purpose is only for high level monitoring and comparison to the SNA then the use of coarse aggregated indicators (at the national/regional scale) for service provision is likely to be judged to be sufficient. If the wider application of accounts in establishing variation in the condition and the relative importance (value) of woodland stands (or other ecosystems) across the nation is of interest, then spatial disaggregated accounts should be developed. For services which are spatially dependent, this may also have the added benefit of leading to improvements in the national estimates. Spatially disaggregated accounts represent a conceptually consistent foundation for understanding ecosystem service provision. Mapping can then potentially be used for spatially explicit prioritisation (e.g. through planning), targeting of habitat creation/restoration (e.g. through strategic policy decision), communication of the significance of service provision,

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grant allocation, and risk identification (e.g. of ‘hotspots’ for pests and disease), especially in relation to synergies and trade-offs among different ecosystem services. For example, changes in the quantity of one habitat will lead to changes in others at a national level; e.g. a change in the woodland coverage will change the broader portfolio of ecosystem assets as well as aspects such as the potential for carbon stored in the environment, which will be accounted for in a cross-cutting carbon balance sheet. Beyond the analysis of trade-offs across ecosystem services, the case for pursuing disaggregated accounts lies in the fact that both stocks of ecosystem assets and the provision of ecosystem services and associated economic benefits are not uniform over locations. Overall service provision is dependent on spatial variation in quality, quantity and spatial configuration of the ecosystems. Hence if ecosystem accounts are to be applied to inform decisions at lower EAU levels, then a spatial approach should be preferred over attempts to breakdown highly aggregated (national/regional) level reporting. It should be recognised however that the significance of the spatial dimension is not the same for different ecosystem services. The examples highlighted in this study include importance of the specific location of woodland for adequately representing the level of ecosystem service provision for recreation and water regulation services. Hence it is important that ecosystem accounts are constructed in a way that accommodates these sensitivities. The way that this is achieved is through developing spatially disaggregated accounts at the BSU (for example 1 km2) or at a relevant LCEU level (for example, a forest site) for decision making at the EAU level (e.g. a catchment or National Park). This means accounts can identify (through mapped estimates) and report differences in the value of areas of woodland for these services, which can be substantial. In contrast it is broadly the case that the (marginal) value of carbon sequestration and timber does not vary depending on woodland location, but only according to other ecosystem characteristics. For these services, it may be judged to be acceptable to manage without explicit spatial disaggregation and retain coarse aggregate estimates for national ecosystem accounts. 6.2.2 Alignment with Defra-ONS principles Table 6.1 sets out a comparison of the approach and initial woodland ecosystem accounts developed in this study with the Defra-ONS principles (Defra/ONS, 2014) for ecosystem accounting. This provides a useful basis for cross-checking the outcomes of the study with the wider ambition for developing ecosystem accounts.

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Table 6.1: Comparison of Defra-ONS principles for ecosystem accounting with initial woodland ecosystem accounts Defra/ONS Principles Initial woodland account

P2.1 Subsoil assets are not part of the ecosystem accounts.

Excluded, but soil carbon included. Need for clarification of principle: abiotic sub-soil assets excluded, but ecologically-mediated carbon sequestration in soils should be included.

P3.1 Ecosystem accounts should be constructed around the categories of the Land Cover Map. However, where there is more detailed and relevant data available on land use, this should be used instead, with the results reconciled with the LCM.

NFI information on land-use is used to develop accounts. An assessment of the differences between the Land Cover Map 2007 and the NFI land use data is set out in Annex 5.

P4.1 The ecosystem accounts should continue to be developed for each of the NEA Broad Habitats, with a formal link where possible to the classifications used in the Land Cover Map.

NFI woodland classes split across ‘broadleaved’ and ‘coniferous’. The split is appropriate. although it is recognised that this spans the actual Broad Habitats - broadleaved, mixed and yew woodland; coniferous woodland.

P5.1 Accounts should be compiled initially at UK level.

For data reasons accounts are report for GB, and in more detail for PFE (England). Better NFI data shortly available.

P6.1 The standard format for asset accounts in physical terms should be adopted.

Yes.

P6.2 The reference condition should not be adopted and changes should simply be measured as differences between opening and closing stocks.

Generally this is followed. It is noted that for some service flows a reference condition is essential (e.g. water flow regulation, which is a reduction in risk compared to a counterfactual and cannot be measured in absolute terms). This is appropriate, though the actual accounts presented do not include this flow, and the associated stock (of woodlands in flood zones) uses no reference condition. Only closing stocks are presented with no measurement of change over a period, but this is normal (i.e. change can be measured for the next period of accounts).

P7.1 Make full use of the UK NEA’s matrix of services and habitats for assessing the state / risk and significance / value of services within a habitat.

Not explicitly used as basis for selection of ecosystem service – see below.

P7.2 For each account, a brief transparent red-amber-green (RAG) assessment will be made of all relevant services against these criteria, which will identify which services to include and exclude.

Services are selected based agreement with Defra and scope of study.

P8.1 The Common International Classification of Ecosystem Services (CICES) should be adopted in a flexible way.

Service classifications from CICES and previous studies are used as basis for defining services.

P8.2 Use birds and other relevant indicators pro tem for biodiversity.

Not applied. SSSI designations (extent, not condition) are used. Forthcoming NFI condition

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Defra/ONS Principles Initial woodland account

reporting/ analysis tool for assessing Woodland Ecological Condition is suggested for future use.

P8.3 Adopt the standard structure for tables for ecosystem services in non-monetary and monetary terms.

Yes.

P9.1 Aim to reflect wherever possible the contribution of ecosystems to goods and services that benefit people.

‘Generation and Use’ table is not complete because further work is required to establish the link between the production and use of services and the beneficiaries that use woodland ecosystem services for woodland ecosystem services where it is diffuse/unclear (e.g. fibre (timber) provision).

P9.2 Our approach wherever possible will be to reflect actual use of services.

Yes, although the use of national figures for timber requires ‘constant flow’ assumptions and does not allow accurate disaggregation at sub-national scale. It would arguably be preferable to focus on potential service/increment, based on spatially-measurable characteristics, with calibration for actual use. It should be noted that SEEA explicitly cites forestry as a case in which the ‘area in use’ could be greater than the area from which harvest actually taken in any given year.

P9. 3 View the ecosystem as an asset in recording monetary flows

Yes, approach of valuing asset by present value of service flows. The time horizon is 20 years. Ideally an agreed time span would be standardised across all ecosystem accounts, for comparability.

P10.1 A range of established valuation techniques can be used to estimate exchange as well as welfare values, but the rationale for using particular techniques will be clearly explained within each account, and where possible breaking values down into their “price” and “quantity” elements.

Yes, discussion of the options for valuation and justification of values used.

P10.2 Not rule out stated preference methods but only use them where they are consistent with SEEA concept of exchange value, and where they can capture values that other methods cannot, in particular non-use values.

Recreation values based on meta-analysis that includes both revealed preference and stated preference sources. The value is welfare value rather than exchange value. This is judged to be appropriate.

P11.1 Derivation of values should be transparently set out in relevant annexes to accounts.

Yes, explained in accompanying annexes.

P12.1 In drawing up accounts we will seek explicitly to highlight conceptual and empirical overlaps between market and

Discussion of welfare and exchange values, of the separation of resource rent from expenditures and of possible double-counting

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Defra/ONS Principles Initial woodland account

non-market values, linkages and consistency with SNA within each account.

with SNA values is provided.

P13.1 We will adopt a net present value approach to estimating the accounting value of ecosystem assets in order to be consistent with SEEA asset valuation principles.

Yes, stocks valued at NPV over 20 years – see above.

P13.2 We will state explicitly the assumptions underlying asset valuation, and undertake sensitivity analysis to test the range of possible values.

Assumptions set out. No formal sensitivity analysis is undertaken, though uncertainty/robustness are addressed.

P14.1 Any departure from a constant service flow assumption needs to be justified and evidenced.

Constant flow assumptions are used. This is potentially problematic (e.g. data estimates carbon sequestration is set to decline) but proportionate and appropriate given the use-ability of current evidence (i.e. it is not easily incorporated into this analysis).

P16.1 We propose to use the recommended Green Book Discount Rate whilst allowing for sensitivity analysis to assess the effect of different rates.

Yes.

P17.1 We should adopt a flexible approach to periodicity, aiming for annual accounts wherever possible.

Method is consistent with this ambition.

P18.1 An assessment of uncertainty needs to be made against a range of quality criteria.

Yes – see Section 6.4 which presents an assessment of uncertainty against several criteria, with a red-amber-green scoring for each.

P18.2 We will indicate the degree of coverage of ecosystem services and total value clearly in the final presentation of the accounts.

The coverage is stated. Omissions of other services largely based on scope/resource constraints of study.

Feasibility of spatially disaggregated ecosystem accounts 6.3

The feasibility of producing ecosystem accounts at the scale required for meaningful analysis (e.g. BSU level or interpreted forest unit) is dependent upon the data and methods available. The present study has been constrained by data availability (including both gaps and data that is available but was not able to be provided) and the availability and resourcing requirements associated with ‘validated’ spatially sensitive ecosystem service models. However, the study does demonstrates the principles, structure and practical approach needed for developing spatially disaggregated accounts and provides a basis for future refinements. Establishing a ‘sufficient’ level of accuracy for the mapping of stock condition and service flow is dependent on the purpose of the analysis and what policy decisions this will informs.

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6.3.1 Stock characteristics If the use of spatially disaggregated accounts establishing variation in the condition of woodland areas is of interest, it is demonstrated that it is feasible to produce national/regional scale estimates based on the extrapolation/scaling up of site or local level evidence. Data used in this project is derived from the FC NFI woodland map and sub-compartments for the PFE, England. The NFI outputs are produced for all woodlands greater than 0.5 hectares in area and are dependent on a statistically representative sample of 15,000 sample squares. These sample squares enable forest metrics at the interpreted forest unit level to be scaled up to the relevant woodland area using statistical methods (to produce statistically reliable results at national and regional levels, rather than local levels). The FC also currently has robust spatial data at the sub-compartment unit level (BSU) on stock characteristics and some service flow (timber) evidence for the PFE woodland in England. 6.3.2 Service flows If the purpose of spatial disaggregation is to understand the variation in the relative importance (value) of ecosystem service provision across spatial locations, then – in the absence of directly observed measurements of service flows - modelling approaches are needed. With this measures of model confidence need to be provided to assess performance of the modelling and the reliability for informing decisions at different scales. Spatially sensitive models can be used to estimate the distribution of flows across an LCEU (e.g. GB woodland) at BSU level using data including some spatially disaggregated data. This is demonstrated by the use of a trip-generating function in this study to estimate the distribution of recreational visits to GB woodland at 1km² resolution (BSU) with calibration to MENE data. Models such as this, and other spatially sensitive models (ecosystem service and process models such as such as InVEST, ARIES and TIM) also have multiple applications beyond accounts. The use of GIS to estimate the distribution of ecosystem service flows is an evolving field and practice, and coverage and robustness will improve over time (ADAS and eftec, 2014; Tallis, et al. 2013;; Bateman and Day, 2013; UKNEAFO 2013; Bagstad, et al, 2011; Bateman et al 2003). Such refinement will likely benefit the future refine ecosystem accounts as modelling approaches become more established. For example, existing FC data on recreational facilities for the PFE, England and forthcoming data for GB woodland is not currently used in the estimation of recreational visits and value, but might be usefully incorporated into modelling in the future. There are also specific technical models developed and owned by Forestry Commission but these are for estimates of timber (e.g. the PFE production forecast) and carbon which are not as sensitive to variation in the location of woodland. Typically the sub-division of national level estimates (unless accompanied by an increase in sampling intensity) increases uncertainty around each reported measurement. If an account (or other analysis) were to be developed for a smaller scale area (e.g. a river basin district), using the same data in GIS and assumptions, then the underlying data quality is subject to broader assumptions about the applicability of the extrapolations to the local level analysis in question. Therefore such assumptions need to be clearly stated and reported to ensure that the appropriate caveats are recognised. This issue is illustrated in Figure 4.3 (estimated harvesting volume of broadleaved and coniferous trees for the New Forest area) where the estimated distribution of expected flows of timber is subject to a high level of uncertainty. This demonstrates the trade-off between the degree of sub-division and both the capacity to track change in service provision and the robustness of evidence to inform decision making. In practice accounts developed in this way are not ‘bottom-up’ but remain ‘top-down’ (although sub-divided to BSU level), and should be expected to be subject to considerable margins of error at the individual BSU level.

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To develop an account for a specific study area (e.g. a national park or catchment) by interpreted forest unit (BSU) for the purposes of high level monitoring and detection of change then coarse estimates can be used. If the NFI was to be used for this analysis, it would be necessary to ensure that the chosen study area encompasses enough NFI sample squares to enable robust outputs. In terms of the estimation of flows of services, consultation with the FC suggest that as a rule of thumb if the catchment contained >30k ha of woodland it is unlikely that additional sampling would be needed for coarse timber/carbon metrics to report at sub-national level. If the study area does not include sufficient number of NFI sample squares then the reported sampling standard error for estimates of stock characteristics such as standing volume, species composition, etc. will be significant, meaning interpretation of results will be subject to notable caveats26. However, these caveats need to be balanced with an understanding of wider ecosystem service provision; for example the relative importance of timber benefits to non-market benefits such as recreation. Hence sizeable margins of error may be acceptable in an account as a greater part of the purpose of interpreting an account is to understand the relative orders of magnitude of benefits.

Maintaining ecosystem accounts 6.4 With respect to maintaining the woodland account over time and the frequency with which this should be done (i.e. whether this should be a 1 year, 2 year or 5 year, etc. basis), there are two main factors to consider. First, the period over which the underlying datasets that are used to populate the accounts are updated needs to be determined. 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. Second, the purpose of the accounts needs to be addressed. If the purpose is for a comparison with SNA, then a longer time period 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. The purpose of this would be to establish a practice of regularly measuring (changes in) the condition of the stock so that this can be tracked and the potential impacts on future ecosystem service flows captured. For this tracking to be possible, there must be sufficient confidence in the underlying data and its ability to accurately detect changes in the condition of the stock over time at the spatial scale of interest. More work needs to be done to refine the accounts to achieve this and hence more frequent iterations provides the means for doing so. Given the development of these initial woodland ecosystem accounts and the data and method gaps identified, the subsequent recommendations and next steps do not relate to maintaining the estimates in the initial woodland account, but rather recognising how better data and methods can be employed within the framework developed in Section 3.2. These recommendations are set out in Section 6.5 and 6.6.

26 This could though be resolved by commissioning new sample squares at sufficient enough density to enable statistically robust outputs for the purposes of ecosystem accounting. While it may be possible to add more samples in one area at a reasonable cost, it is unlikely to be cost-effective to boost the sample size sufficiently to produce reliable local results across the country (in a ‘bottom-up’ manner).

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Future refinement of ecosystem accounts 6.5 Tables 6.4 and 6.5 provide an assessment of the practical scope for further developing and refining spatially disaggregated ecosystem accounts for UK woodland and PFE (England), respectively. This is based on a red-amber-green (RAG) rating of the extent to which future refinement of ecosystem accounts is likely to be: (i) feasible given data and methods available in the short – medium term (e.g. 10 years); and (ii) useful given potential applications. The RAG rating is based on the matrix criteria described in Table 6.2 and 6.3. Table 6.2 Criteria for assessing the scope of further refinement of spatially disaggregated accounts given potential uses Use – given potential applications

Very useful

Moderately useful

Limited use

Table 6.3 Criteria for assessing the scope of further refinement of spatially disaggregated accounts given feasibility Feasibility - given data and methods available

Data and methods available/low cost

Moderately challenging/costly

Very challenging/costly

Table 6.6 provides a similar assessment for ecosystem services omitted from the initial woodland accounts (see Section 3.2.1). RAG ratings are applied based on the potential ‘added value’ of a spatially disaggregated approach.

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Table 6.4: UK woodland account - overview of analysis Ecosystem service

Stock (characteristic) - modelling of spatial distribution

Flows - modelling of spatial distribution Valuation

Timber The extent (ha) of GB woodland is reported and is broken down by characteristics that determine condition. The characteristics relevant to timber provision, which are available from the NFI: − Species type (extent/area); − Standing stock volume − Age

Changes in these figures have implications in terms of the capacity of woodland to produce timber. Data on some relevant characteristics are not available (e.g. yield class).

The NFI woodland map will be updated following completion of the current NFI data collection cycle (the current outputs have utilised interim mapping). The stock account should be refined using this data. For timber, biomass, second cycle estimates of standing volume, increment and removals to give actual change in stocks are due in 2017.

Aggregated data exists on wood deliveries to pulp mills and sawmills for Britain of 0.587million m³ of timber for broadleaved and 11.78 million m³ and is broken down by England, Wales and Scotland. These aggregate figures can be broken down into each of the 14 regions across England, Scotland and Wales as shown in Figure 4.5 But to do this requires an assumption that there is equal harvesting to standing stock volume ratios across each region. In the absence of higher resolution spatial data (e.g. BSU level) on expected harvesting, assumptions have been made to distribute the timber production for broadleaves and conifers. Therefore, whilst the national level figures are robust, the distribution across the country of current (average over a number of recent years) harvesting flows is very uncertain. The figures are reported on a country scale only (excluding N.Ireland) and are based on figures submitted voluntarily by industry. Harvesting figures for NFI regions have been calculated using extent of mature trees in each region to allocate proportion of the total harvest volume for each country. In reality the harvesting effort is unlikely to be evenly spread. A constant flow assumption is used to estimate the profile of timber harvesting flows over time. In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. For timber (as for services not particularly sensitive to changes in the spatial positioning of woodland) the recommendation is that it is not a proportionate use of resources to develop spatially disaggregated accounts given limited policy use. Second cycle NFI estimates of removals to give spatial change in stocks are due in 2017. In order to produce robust results at localised scale, observed data (e.g. on date of felling as for the PFE) is needed to estimate the expected flow or ecosystem service models should be used. The absence of evidence from independently sampled areas means that it is not possible to sum individual accounts and there is no increase in the accuracy of expected flows at an aggregate level. However, if the use of the accounts is to assess trade-offs across ecosystem services then this sort of analysis would be useful.

For timber from coniferous woodland (i.e. softwood), the Forestry Commission’s Coniferous Standing Sales Price Index for Great Britain in 2012 (£14.07 per m3 overbark in 2012 prices) is used. The value is applied to both GB and PFE estimates of biomass of timber from coniferous woodland. For timber from broadleaved woodland (i.e. hardwood), FC estimates are not available. The price of timber from broadleaved woodland is sourced from the Nix Farm Management Handbook (2013) and is estimated at £14.74 per m3 standing in 2012 prices. The robustness in the distribution of value of timber harvest mirrors that of the physical flow estimates on which it is based. Therefore at national level these monetary values are robust, but become less so as estimates of distribution of value become more disaggregated.

RAG Feasibility Use Feasibility Use

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Ecosystem service



Recreation For non-PFE woodland it is not possible to map the characteristics that have been identified as important in determining the value of recreation, from the review of trip generating functions, as data does not exist. Forthcoming NFI data on recreation facilities can be used to update this. Spatial data on ‘individual parameters’ of recreation are not due to be released until 2014/2015.

The trip generating function developed by Sen et al. (2014) is used to predict the distribution of the estimated 481 million trips to GB woodland using the land cover map 2000.

The predicted number of trips to recreational trips to all habitats is adjusted to reflect the number of visits estimated in the MENE survey. The number of trip used in the accounts is based on the TGF rather than MENE because the TGF accounts for substitution effects between different habitats, visitor demographics and population at the outset area of a trip. For services such as recreation where their value is sensitive to the spatial positioning of woodland, accounting for service flows through maps adds value. Analysts can use this modelling to identify the variation in recreational visits and values provided by woodland and all other NEA habitats across GB. The proposed trip generating function does not account for the characteristics of woodland in determining visitor numbers. The extent to which other woodlands are actually substitutes is therefore uncertain (currently assumed all woodland and other habitats are a substitute). The Sen et al TGF has been proposed because it is applicable to mapping the provision of recreational value across all UKNEA habitat types. Although it does not take into account characteristics of woodland (e.g. recreational facilities), it is the currently best available model identified from review. This is because it fits with the wider development of UK ecosystem accounts, it is recent and is based on a large dataset (MENE) picking up substitution across all outdoor recreation, not just within woodlands. Further research effort is needed to develop a TGF which can predict visits to different habitats (i.e. woodland, coastal margins, marine etc) while accounting for the different recreation facilities of spatial units i.e. cells.

Total predicted trips is combined with an estimate of the value per trip (based on a review of the valuation literature) to derive the value of woodland recreation. The valuation evidence to use in the national account for recreation visits to woodland is that used in Sen et al (2012). This does not add any value to the aggregate estimate of monetary value of woodland recreation because the physical flow (visits) estimated in these spatial accounts does not pick up this variation in the quality of woodland according to the existence of recreational facilities for example.


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Ecosystem service



Carbon The extent (ha) of GB woodland is reported and is broken down by characteristics that determine condition. The characteristics relevant to carbon sequestration, which are available from the NFI (2012): − Species type (extent/area); − Standing stock volume − Age

In addition, NFI (2014) data exist for:

− Biomass stock − Stock of carbon in biomass of

broadleaved and coniferous trees

The stock of carbon in soils based on Cantarello et al (2011) for the south west of England only. Changes in these figures have implications in terms of the capacity of woodland to sequester carbon. The stock of carbon in soils is estimated based on Cantarello et al. (2011). The coefficients only apply to south-west, so replication of this analysis across the whole UK is required. Forthcoming NFI data can be used to update this. data For biomass carbon: second cycle estimates of standing volume, increment and removals to give actual change in stocks are due in 2017.

The work estimates the annual sequestration from the stock of trees in GB and its distribution within GB, based on average sequestration rates of broadleaved and coniferous trees as mapped in NFI woodland map (2012).

Existing estimates are available on woodland carbon sequestration from CEH at an aggregate level (Dyson et al. 2009).

The analysis of sequestration is rudimentary based on average rates of sequestration across broadleaved and coniferous. A constant flow assumption is used to estimate the profile of timber harvesting flows over time. In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. FC stock data does not include N.Ireland. Developing a localised (BSU level e.g 1km²) model using spatial data to estimate the flow of carbon sequestration associated with the stock of woodland at a localised level would require sample data from the NFI field survey. For carbon sequestration (as for services not particularly sensitive to changes in the spatial positioning of woodland) the recommendation is that it is not a proportionate use of resources to develop spatially disaggregated accounts given limited policy use. Instead, it is proportionate to use aggregate level estimates based on robust assumptions regarding the quality (e.g. species type) and quantity of woodland at a national level. However, if the use of the accounts is to assess trade-offs across ecosystem services then this sort of analysis would be useful.

Values for woodland carbon markets (e.g. REDD+) are much lower (£3.50 - £6) than UK (DECC, 2014) policy carbon values for traded (£23) and non-traded (£55) carbon. Rationale is provided for use of non-traded values. Although SEEA focus on exchange values, market failures exist meaning that exchange prices are not always suitable. The robustness in the distribution of value of carbon mirrors that of the physical flow estimates on which it is based. Therefore at national and regional level these monetary values are robust.


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Ecosystem service




The extent of woodland within catchments with different flood risks has been included in the stock account. Given it is not possible under this project to estimate water quantity regulating service flows using models, the approach reverts to estimate of extent (area) of trees in catchments with differing flood risks. This is a rudimentary approach that assumes that these woodland will provide some water quantity regulating function. A map of the extent of woodland in catchments with different flood risk across the country can be used by policy officials to prioritise woodland policy with a view to reducing flood risk. More complex ecosystem service modelling can establish the value of woodland. Stock - develop the woodland condition account to incorporate characteristics that are important determinants of the flow of water quantity regulating service from woodland, based on underlying spatial data. Based on a review of ecosystem service models used to estimate water regulation flows the following parameters/characteristics are important. These could be captured through a condition account using suitable metrics: − Coniferous vs deciduous woodland − Different stand ages of woodland − Upland vs. Lowland woodland − Soil type − Recently clear-felled woodland − Land management − Different densities of woodland − Nutrient status of soil − Height of shelterbelt for

interception of pesticide spray

The level of flood risk in an area is defined by characteristics such as slope and soil and the value of woodland in mitigating this risk is dependent upon characteristics such as the proximity to population and size of woodland. These characteristics may impact the value of woodland stands in intercepting/uptaking water and could be captured in ecosystem accounts. Information on these characteristics is relevant when undertaking complex modelling of ES flows (i.e. determining reduction in flood risk) as a result of situating woodland in catchments. The review of ecosystem service models concluded that all are too data intensive, requiring complex and technical knowledge and/or are not available. Trees reduce the risk of flooding and the aim is to capture the impact of the presence of trees. It has not been possible to estimate these flows under this project but the recommendation is for spatially explicit flows to be developed given the importance of the spatial positioning of woodland in providing this service. The impact of the woodland on water flow regulation (ES flows) is not captured as this requires complex modelling. However, the condition of woodland for these flows (i.e. extent of woodland in high/medium/low risk areas) is captured in a rudimentary way. Flow - use an ecosystem service modelling approach to estimate the water regulating service of woodland.

It has not been possible to estimate the value of water quantity regulation services of woodland as there are no physical estimates of the reduction in risk. Potential valuation evidence for use in the account includes: − Change in insurance premiums as a result

of differentials in risk

− Implicit prices from hedonic pricing associated with capitalisation of flood risk into house prices – by comparing prices in catchments with similar baseline conditions

Damage costs associated with flooding in catchments with similar baseline conditions

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drift − Geo-climatic region − Different N deposition rates − Buffer strip width − Buffer strip composition − Geology − Topography − Catchment water quality − Vulnerability to flooding


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Ecosystem service



Biodiversity In line with SEEA, the extent of protected areas has been mapped and reported through woodland SSSI extent. It is felt that SSSI extent is a poor indicator of the role of biodiversity in the functioning of ecosystems. Therefore, mapping SSSI’s does not add much to the aggregate indicators of biodiversity. Forthcoming NFI spatial data on biodiversity indicators will provide a detailed account of the condition of woodland across the UK on a disaggregated basis at stand level. Utilising the specific indicators related to the role of woodland biodiversity in supporting the ecological functioning of ecosystems is desirable. Forthcoming FC evidence on Woodland Ecological Condition from the NFI could be used as indicators of biodiversity: − Volume of deadwood; − Presence of invasive species. − Vertical structure/ storeys − No. of native tree &/or shrub

species NFI spatial data on ‘individual parameters’ of biodiversity ('forest condition 'is expected to be available in 2014/2015 with ‘condition scores’ index of woodland patches expected to be released 2015/2016. However, it has been possible to obtain a sample of this data in advance of its release in order to illustrate how this future analysis can be incorporated into the accounts as set out in Annex 2.4.

Aggregate data on the woodland bird index is sufficiently good measure of the flow of services associated with charismatic species. Spatially disaggregated data does not exist on woodland birds or equivalent measures of the cultural service associated with biodiversity (e.g. charismatic species) so spatial disaggregation has not been possible under this project. Given the importance of spatial positioning of this biodiversity relative to populations for use value, there is a case for developing spatial disaggregated accounts for the final (cultural) ecosystem services associated with biodiversity. However, there is also a considerable non-use (existence) value associated with these charismatic species, which would suggest an aggregate account is sufficient as the location of the species is not relevant to its value. On the other hand the widespread distribution of some charismatic species across the country is an element of the value and local extinctions would be seen as a loss of value.

The flow of cultural value associated with biodiversity as a final ecosystem services is not considered here. The stock of biodiversity acts as a supporting service through its functioning is not valued. Doing so would result in a double counting of ecosystem services.


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Table 6.5: PFE woodland account - overview of analysis Ecosystem service



Timber The extent (ha) of PFE, England woodland is reported and is broken down by characteristics that determine condition. The characteristics relevant to timber provision, which are available from the publically available subset of SCDB data obtained from FC: − Species composition (BL and C); − Standing stock volume (regional

level); − Age; and − Yield class. Changes in these figures have implications in terms of the capacity of woodland to produce timber. These characteristics are reported at the SCDB level, so the mapping of their distribution is robust. This makes it possible to infer condition of woodland for timber provision across specific geographical areas. Although the additional usefulness of this compared to aggregate figures of condition is low.

Aggregated data exists on wood deliveries to pulp mills and sawmills for PFE, England of 0.06 million m³ broadleaved trees and 1.4 million m³ coniferous trees. In the absence of more resolute spatial data (e.g. BSU level) on expected harvesting, assumptions have been made to distribute the timber production for broadleaves and conifers. Therefore, whilst the national level figures are robust, the distribution across the country of harvesting is very uncertain. An assumption has been made on the distribution of felling to be proportionate to the volume of mature trees in each yield class. In reality, many factors will determine the decision to fell a particular tree/stand. Full SCDB evidence includes date of felling, which was not available to project team. Production forecast modelling also includes breakdown across many species. A constant flow assumption is used to estimate the profile of timber harvesting flows over time. In the absence of this simplifying assumption the quantification and valuation of future service flows is highly dependent on the management assumptions/scenario used. The full SCDB data and forecast model owned by the FC could be incorporated into underpinning the UK natural capital account. However, timber (as for services not particularly sensitive to changes in the spatial positioning of woodland) the recommendation is that it is not a proportionate use of resources to develop spatially disaggregated accounts given limited policy use. This is of limited value given that the value of the service is not dependent upon its spatial positioning (e.g. relative to beneficiaries).

See UK Woodland

RAG Feasibility Use Feasibility Use Ecosystem service



Recreation The extent of PFE woodland accessible for recreation. Mapping of recreational facilities included in the SCDB as characteristics of woodland that are important in determining the value of woodland for recreation.

See UK Woodland See UK Woodland

RAG Feasibility Use

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Ecosystem service



Carbon See UK Woodland See UK Woodland See UK Woodland

Water Regulation

See UK Woodland See UK Woodland See UK Woodland

Biodiversity See UK Woodland See UK Woodland See UK Woodland

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Table 6.6: Potential for spatially explicit accounts for other ecosystem services Ecosystem service

Stock (characteristic) - modelling of spatial distribution of Flows - modelling of spatial distribution Valuation

Wild Food The creation of a logic chain and mapping of characteristics important for the provision of this service, which includes items such as game, wild berries and fruit FAO (2010), will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply of wild food) can be estimated.

There is generally a lack of data in this area, with minimal amounts extracted commercially in the UK. No spatial data on the presence of wild food exists and its location is not an important determinant of its value. Therefore it is suggested that spatially disaggregated accounts are not developed for this service. However, if the use of the accounts is to assess trade-offs across ecosystem services then this sort of analysis would be useful.

Forest Resource Assessment (limited); ONS Prices Data.


Stock (characteristic) - modelling of spatial distribution Flows - modelling of spatial distribution

Ornamental goods (Christmas trees)

The creation of a logic chain and mapping of characteristics important for the provision of this service, which includes Christmas trees, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply ornamental goods) can be estimated.

Information on the expected service flows of some ornamental goods such as Christmas trees does exist for the PFE, England from the FC sub-compartment database. However, its location is not an important determinant of its value. Therefore it is suggested that spatially disaggregated accounts are not developed for this service. However, if the use of the accounts is to assess trade-offs across ecosystem services then this sort of analysis would be useful.

Forestry Commission’s Coniferous Standing Sales Price Index for Great Britain (2012); Nix Farm Management Handbook (2013)



Woodfuel The creation of a logic chain and mapping of characteristics important for the provision of this service, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply woodlfuel) can be estimated.

The SEEA CF (section 5.372) says where data are available the energy from woodland could be recorded separately from the timber. Forestry Statistics (2013) have aggregated estimates of woodfuel deliveries. Its location is not an important determinant of its value. Therefore it is suggested that spatially disaggregated accounts are not developed for this service. However, if the use of the accounts is to assess trade-offs across ecosystem services then this sort of analysis would be useful.

Stumpage price; energy price per joule; FRA (2010) fuelwood price.


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Ecosystem service


Water purification

The creation of a logic chain and mapping of characteristics important for the provision of this service, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply water quality benefits) can be estimated. Understanding which characteristics are driving woodland capacity to provide water purification is important in order to determine the spatial distribution of flows, which is useful.

Woodland provide filtering and stabilising effects thereby improving water quality downstream. It is suggested that spatially disaggregated accounts are developed as woodland location is important determinant of the value of this service.

Avoided cost of water treatment.



Air quality The creation of a logic chain and mapping of characteristics important for the provision of this service, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply air quality benefits) can be estimated. Understanding which characteristics are driving woodland capacity to provide air purification is important in order to determine the spatial distribution of flows, which is useful.

Woodland provide filtering effects thereby improving air quality. It is suggested that spatially disaggregated accounts are developed as woodland location is important determinant of the value of this service (e.g. absorption of pollutants will be greater near urban areas).

Defra air quality impacts and Department for Transport and Department of Health values of life studies.


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Ecosystem service


Noise absorption

The creation of a logic chain and mapping of characteristics important for the provision of this service, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply noise absorption benefits) can be estimated. Understanding which characteristics are driving woodland capacity to provide noise absorption is important in order to determine the spatial distribution of flows, which is useful.

Trees can absorb noise, woodland can provide a noise regulation ecosystem service; this is based on the size and density of planting (Greenspace Scotland, 2008). It is suggested that spatially disaggregated accounts are developed as woodland location is important determinant of the value of this service (e.g. noise regulation will be greater near urban areas). A difficulty being that linear features (e.g. trees along railway lines) are not picked up in the NFI which measures woodland stands >0.5ha.

Hedonic pricing of noise reduction provided by trees in area.



Aesthetics (landscape)

The creation of a logic chain and mapping of characteristics important for the provision of this service, will add value to the aggregate estimates that exist because the condition of woodland for the provision of this service (i.e. the estimated capacity of woodland/supply air quality benefits) can be estimated. Understanding which characteristics are driving woodland capacity to provide aesthetic/landscape services is important in order to determine the spatial distribution of flows, which is useful.

Delineating what a ‘landscape’ is spatially is challenging. To some extent the value of aesthetics (landscape) will be captured in recreational value. Its location is an important determinant of its value but this is complex as it is location relative to people and can include pretty much any location where woodland can be seen. Therefore it is suggested that spatially disaggregated accounts are not developed for this service due to the complexity.

Landscape/aesthetic willingness to pay values.


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Recommendations 6.6

In conclusion, a number of recommendations are outlined with respect to the development of ecosystem accounts: 6.5.1 Cross cutting issues – across ecosystem accounts • The displacement of ecosystem service flows due to changing land cover should be captured by

developing accounts in a way where assumptions made in one account follow into others. The use of ecosystem service models as a way of ensure consistency across accounts should be explored.

• The total value of ecosystem service provision can be identified and monetised in most cases

through application of valuation techniques to measures of quantity in absolute terms. Practical examples need to be developed to understand how ecosystem services providing a reduction in risk can be measured in relative terms (with an explicit counterfactual) within an accounting framework. The total value of such services is established through modelling of risk under the counterfactual (e.g. agricultural land use) and the risk under land cover of interest (e.g. woodland).

• It is acceptable to use evidence generated by economic valuation methods to value ecosystem

services for ecosystem accounting purposes. This is because the concept of an exchange value may not be appropriate for certain ecosystem services; especially regulating and cultural services where the exchange value of such services is equal to zero. As stated in Day (2013) “There is nothing logically inconsistent with the conventions for pricing used in the national accounts to value the benefits derived from public goods by the surplus derived from their consumption”’.

• Further work – via practical examples - is required to determine if the concept of resource rent

is suitable for ecosystem accounting and if so how this can be treated within spatially disaggregated natural (ecosystem) asset accounts.

• When accounting for biodiversity in ecosystem accounts it is important to identify the final

ecosystem service ‘flows’ of which biodiversity is associated (e.g. charismatic species and other cultural services) as well as the ‘stock’ of biodiversity that underpins natural processes. Whilst this ‘stock’ of biodiversity is partly measuring the abundance and variation in species, the capturing of the ‘role’ of these species within a functioning ecological system is what is desirable in terms of providing a comprehensive account.

• Further research into the issue of using land use and land cover data in developing the accounts

is needed, specifically considering which datasets should be used and how overlaps in datasets can be reconciled.

6.5.2 To inform policy • Ultimately a determination may need to be made by Defra and ONS as to the purpose and

application of ecosystem accounts – whether it is possible both to compile a high level aggregated view of ecosystem service provision for comparison to the SNA, as well as a tool and coherent framework for measuring and understanding ecosystem service provision at lower spatial scales in order to make improved decisions as to the conservation and enhancement of the natural environment.

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For example using existing aggregate data on stock characteristics that are important determinants of the capacity of woodland to produce ecosystem services (e.g. using the Accessible Green Space Standard (ANGSt) as a proxy of value of woodland for recreation as done by ONS, 2013) is likely to be judged sufficient to fulfil a case of counting and monitoring the stock condition and service flows from ecosystems and for comparison to the SNA. Alternatively if establishing the variation in the value of woodland areas (or other ecosystems) across the nation and assessing trade-offs across ecosystem services is of interest, then the conceptually correct method is to pursue a spatially disaggregated approach.

• With respect to further development of a spatially disaggregated approach to ecosystem

accounts:

- Defra should consider undertaking a scoping study on the use of spatially sensitive ‘ecosystem service models’ to explore further how current models and short – medium term developments can be applied to ecosystem accounts, including data requirements, cost and technical knowledge.

- This could be coupled with a study at a specific sub-regional level (e.g. catchment,

national park) to explore further how an account can be applied at this scale.

- Defra and ONS should work closely with the FC to establish how forthcoming NFI data can be used to update, develop and refine the initial woodland ecosystem accounts.

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http://siteresources.worldbank.org/ENVIRONMENT/Resources/ChangingWealthNations.pdf [accessed August 2014] UK NEA (2010) UK NEA Economic Analysis Report: Valuation of services from Woodlands. Chapter 8: Valuation of ecosystem services provided by UK woodlands by Valatin & Starling. http://uknea.unep-wcmc.org/LinkClick.aspx?fileticket=TxLTiDHKooI%3D&tabid=82 [Accessed March 2014] UK NEAFO (2014) Reports from the UK National Ecosystem Assessment Follow-on Phase. http://uknea.unep-wcmc.org/Resources/tabid/82/Default.aspx United Nations et al (2012) System of Environmental-Economic Accounting: Central Framework. http://unstats.un.org/unsd/envaccounting/White_cover.pdf [accessed August 2014]. United Nations et al (2013) System of Environmental-Economic Accounting 2012: Experimental Ecosystem Accounting http://unstats.un.org/unsd/envaccounting/eea_white_cover.pdf UPM Tilhill (2014). Retrieved from: http://www.upm-tilhill.com/woodland-forest-for-sale/page-774.php [Accessed March 2014] Valatin, G. (2011). Forests and carbon: a review of additionality. Forestry Commission Research Report. Forestry Commission, Edinburgh. i–vi + 1–22 pp. http://www.forestry.gov.uk/pdf/FCRP013.pdf/$FILE/FCRP013.pdf [Accessed May 2014] Valatin, G. (2012) Additionality and climate change mitigation by the UK forest sector. Forestry, Vol. 85, No. 4, 2012. http://forestry.oxfordjournals.org/content/85/4/445.full.pdf+html [Accessed May 2014] World Bank (2012) Moving Beyond GDP http://www.wavespartnership.org/sites/waves/files/images/Moving_Beyond_GDP.pdf World Bank (2014) Wealth Accounting and the Valuation of Ecosystem Services. http://www.wavespartnership.org/waves/ [Accessed August 2014] Woodland Trust (2008) Space for People: Targeting action for woodland access. http://www.woodlandtrust.org.uk/mediafile/100083906/space-for-people.pdf [Accessed March 2014] Woodland Trust (2008) Woodland actions for biodiversity and their role in water management. http://www.woodlandtrust.org.uk/mediafile/100083927/Woodland-actions-for-biodiversity-and-their-role-in-water-management.pdf [Accessed March 2014] World Bank (2006) Where is the Wealth of Nations: Measuring Capital for the 21st Century World Bank (2011) The Changing Wealth of Nations: Measuring Sustainable Development for the New Millennium XE (2014) Currency Conversion. http://www.xe.com/currencyconverter/ [Accessed May 2014]

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GLOSSARY Basic spatial unit (BSU): A basic spatial unit (BSU) is a small area ideally formed by delineating tessellations (small areas e.g., 1 km2), typically by overlaying a grid on a map of the relevant territory. BSUs they may also be land parcels delineated by a cadastre or using remote sensing pixels. BSUs are the smallest unit in the model used to define areas for the purposes of ecosystem accounting. They can be aggregated to form Land Cover / Ecosystem functional Units (LCEU) and Ecosystem Accounting Units (EAU). Benefits: Benefits reflect the goods and services that are ultimately used and enjoyed by people and which contribute to individual and societal well-being. In SEEA Experimental Ecosystem Accounting framework, benefits are distinguished from ecosystem services (which contribute to the generation of benefits) and from well-being (to which benefits contribute). There are two broad types of benefits. (i) SNA benefits comprise the products (goods and services) produced by economic units (e.g. food, clothing, shelter, entertainment, etc.) within the production boundary defined by the SNA. SNA benefits include goods produced by households for their own consumption. (ii) Non-SNA benefits are not generated as a result of economic production processes defined by the SNA. Rather they comprise ecosystem services that do not contribute to the production of SNA goods and services. Biological resources: Biological resources include timber and aquatic resources and a range of other animal and plant resources (such as livestock, orchards, crops and wild animals), fungi and bacteria. Consumer surplus: Consumer surplus is the welfare generated by the fact that some consumers may be willing to pay a higher price than the market price which they pay for a good. Cultural services: Cultural services relate to the intellectual and symbolic benefits that people obtain from ecosystems through recreation, knowledge development, relaxation, and spiritual reflection. Defensive/ avertive expenditure: this method can be applied in cases where an environmental good can be substituted by a form of defensive expenditure incurred in avoiding damages from reduced environmental quality (e.g. expenditure on water filters and bottled water which is indicative of the value people place on clean water). Degradation: Degradation considers changes in the capacity of environmental assets to deliver a broad range of ecosystem services and the extent to which this capacity may be reduced through the action of economic units, including households. Discount rate: The discount rate is a rate of interest used to adjust the value of a stream of future flows of revenue, costs or income to account for time preferences and attitudes to risk. Economic benefits: Economic benefits reflect a gain or positive utility arising from economic production, consumption or accumulation. Economic rent: Economic rent is the surplus value accruing to the extractor or user of an asset calculated after all costs and normal returns have been taken into account.

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Ecosystems: “Ecosystems are a dynamic complex of plant, animal and micro-organism communities and their non-living environment interacting as a functional unit” (Convention on Biological Diversity, 2003) Ecosystem accounting unit (EAU): Ecosystem accounting units (EAUs) are large, mutually exclusive, spatial areas delineated on the basis of the purpose of accounting. Generally, they will reflect a landscape perspective. Factors considered in their delineation include administrative boundaries, environmental management areas, socio-ecological systems and large scale natural features (e.g. river basins). A hierarchy of EAU may be established building from a landscape scale to larger sub-national and national boundaries. EAU at the landscape level may be considered to reflect ecosystem assets. EAU are the highest level of the spatial model used to define areas for the purposes of ecosystem accounting. Ecosystem assets: Ecosystem assets are spatial areas containing a combination of biotic and abiotic components and other characteristics that function together. For ecosystem accounting purposes, the focus is on the functioning system as the asset. The term “ecosystem assets” has been adopted rather than “ecosystem capital” as the word “assets” is more aligned with the terminology employed by the SNA and also conveys better the intention for ecosystem accounting to encompass measurement in both monetary and physical terms. In general however, the terms “ecosystem assets” and “ecosystem capital” may be considered synonymous. Ecosystem condition: Ecosystem condition reflects the overall quality of an ecosystem asset, in terms of its characteristics. Measures of ecosystem condition are generally combined with measures of ecosystem extent to provide an overall measure of the state of an ecosystem asset. Ecosystem condition also underpins the capacity of an ecosystem asset to generate ecosystem services and hence changes in ecosystem condition will impact on expected ecosystem service flows. Ecosystem extent: Ecosystem extent refers to the size of an ecosystem asset, commonly in terms of spatial area. Ecosystem services: Ecosystem services are the contributions of ecosystems to benefits used in economic and other human activity. The definition of ecosystem services in SEEA Experimental Ecosystem Accounting framework involves distinctions between (i) the ecosystem services, (ii) the benefits to which they contribute, and (iii) the well-being which is ultimately affected. Ecosystem services should also be distinguished from the ecosystem characteristics of ecosystem assets. Exchange value: Exchange values are observed market prices which reflect actual transactions. The concept of using exchange values/prices for accounting purposes assumes that all consumers pay the same price for a good. This means that exchange prices exclude consumer surplus. Hedonic pricing method: this economic valuation method estimates implicit prices for environmental goods based on market transactions, where the environmental good is an attribute (i.e. feature) of a market good. The typical example is the demand for local environmental quality as reflected in house/property market exchange prices. Land cover: Land cover refers to the observed physical and biological cover of the Earth’s surface and includes natural vegetation and abiotic (non-living) surfaces. Land cover/ ecosystem functional unit (LCEU): A Land Cover/Ecosystem functional Unit (LCEU) is defined, in most terrestrial areas, by areas satisfying a pre-determined set of factors relating to the characteristics of an ecosystem. LCEU may be considered to represent ecosystem assets and LCEU may often reflect the common conception of ecosystems (e.g. forests, wetlands, deserts, etc.).

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Generally, LCEU represent the middle level of the model that is used to define areas for the purposes of ecosystem accounting. Thus, an Ecosystem Accounting Unit (reflecting a landscape perspective) will generally have a number of different LCEU types. Land use: Land use reflects both (i) the activities undertaken and (ii) the institutional arrangements put in place for a given area for the purposes of economic production, or the maintenance and restoration of environmental functions. Logic chain (or chain model): A logic chain or chain model is a means of linking ecosystem service provision to underlying ecosystem characteristics which influence their provision. Logic chains are developed based on scientific evidence and knowledge of human inputs to an ecosystem asset (e.g. management practices, harvesting, etc.). Market price: See ‘exchange value’. Natural capital: Natural capital is a configuration of natural resources and ecological processes, that contributes through its existence and/or in some combination, to human welfare Net present value (NPV): Net present value is the value of an asset determined by estimating the stream of income expected to be earned in the future and then discounting the future income back to the present accounting period. Producer surplus: Producer surplus is the welfare generated by the fact that some producers may be willing to accept a lower price than the market price they receive. Provisioning services: Provisioning services reflect contributions to the benefits produced by or in the ecosystem, for example a fish, or a plant with pharmaceutical properties. The associated benefits may be provided in agricultural systems, as well as within semi-natural and natural ecosystems. Regulating services: Regulating services result from the capacity of ecosystems to regulate climate, hydrological and bio-chemical cycles, earth surface processes, and a variety of biological processes. Rent: Rent is the income receivable by the owner of natural resources or putting the natural resource at the disposal of another institutional unit for use of the natural resource in production. Replacement cost method: this approach approximates the value of an ecosystem service from the cost of mitigating actions required if the service is lost or if its productivity decreases. Resource rent: Resource rent refers to the contribution of natural capital to a final good in isolation of the contribution of other factors of production. Stated preference methods: Stated preference methods can be used for environmental goods which are ‘final’ non-market goods. Stated preference methods include (i) contingent valuation (CV) and (ii) choice modelling. The CV approach entails the construction of a hypothetical, or ‘simulated’, market via a questionnaire methodology where respondents answer questions concerning their willingness to pay (or willingness to accept) for a specified environmental change. The principal outputs from CV studies are estimates of willingness to pay (WTP) or willingness to accept (WTA) for changes in the provision of non-market goods and services. In the choice modelling approach respondents are presented with a hypothetical, or ‘simulated’, market via a questionnaire (or ‘survey instrument’) for a specified non-market good which is described in terms

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of its ‘attributes’ (or characteristics). Choice experiments (CE) may be used as a stand-alone study or combined with a contingent valuation (CV) question, particularly in cases where packaging effects are investigated. CEs can also be used in conjunction with travel cost methods in relation to valuing benefits of environmental improvements that result in recreation and amenity benefits. Travel cost methods: these approaches are revealed preference methods. They use information on costs and time spent by individuals travelling to reach sites, and costs and time spent at sites, to estimate the value of recreation benefits. Different approaches can be used to analyse different aspects of individuals’ decisions concerning recreation sites including (i) the demand for recreation visits and (ii) the choice of which site to visit. Threshold: A threshold is a point at which going beyond will cause benefits from the environment to fall irreversibly (e.g. fish stock collapse). Thresholds are approached as the condition and extent of natural capital declines. They can arise from tipping points or chronic changes, and may be evident in increasing losses of productivity as the condition of natural capital declines, or as a restriction on the ability of natural capital to recover. Welfare economic value: Welfare economic values reflect the total (or gross) economic gain associated with the quantities of a product that are transacted. They include both the consumer and producer surplus and are different from exchange values to the extent of consumer surplus.

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Documents

DEVELOPING UK NATURAL CAPITAL ACCOUNTS: …sciencesearch.defra.gov.uk/Document.aspx?Document=...Developing UK Natural Capital Accounts: Woodland Final Report Where spatially sensitive