ORE Open Research Exeter
TITLE
Bringing ecosystem services into economic decision-making: land use in the United Kingdom
AUTHORS
Bateman, IJ; Harwood, Amii R.; Mace, Georgina; et al.
JOURNAL
Science
DEPOSITED IN ORE
28 January 2016
This version available at
http://hdl.handle.net/10871/19382
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Submitted Manuscript: 1234379
Title: Bringing ecosystem services into economic decision making:
Land use in the UK
,
3
, Robert T. Watson
2
, Georgina M. Mace
1
, Amii R. Harwood
1*
Ian J. Bateman Authors:
,
1
, Brett H. Day
6
, Andrew Crowe
1
, Amy Binner
1
, Barnaby Andrews
4,5
David J. Abson
Mark ,
9
Young-Haines Roy ,
1,8
David Hadley,
7
, Jo Foden
1
, Carlo Fezzi
1
Steve Dugdale
,
11,12
, Unai Pascual
1
, Paul Munday
1
, Andrew A. Lovett
11
, Andreas Kontoleon
10
Hulme
, Daan van
10
, Gavin Siriwardena
1
, Antara Sen
1,14
, Grischa Perino
13
James Paterson
16
Mette Termansen,
15
Soest
Affiliations:
1
Centre for Social and Economic Research on the Global Environment (CSERGE),
School of Environmental Sciences, University of East Anglia (UEA), Norwich Research
Park, Norwich, NR4 7TJ, United Kingdom.
2
Department of Genetics, Ecology and Environment, University College London, London
WC1E 6BT.
3
Chief Scientist, Department for Environment, Food and Rural Affairs (Defra), London.
4
FuturES Research Center, Leuphana Universität Lüneburg, Germany.
5
School of Earth and Environment, University of Leeds, UK.
6
The Food and Environment Research Agency, Department for Environment, Food and
Rural Affairs, H.M. Government, London.
7
Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, UK.
8
UNE Business School, University of New England, Armidale, New South Wales.
9
Centre for Environmental Management (CEM), School of Geography, University of
Nottingham.
10
British Trust for Ornithology, Thetford, UK.
11
Department of Land Economy, University of Cambridge, UK.
12
Basque Centre for Climate Change (BC3) and IKERBASQUE, Basque Foundation for
Science, Spain.
13
School of Geosciences, University of Edinburgh, Edinburgh, UK
14
School of Economics, University of East Anglia, UK.
15
Department of Spatial Economics and IVM, VU University Amsterdam, and
Department of Economics, Tilburg University, Netherlands.
16
Department of Environmental Science, Aarhus University, Denmark.
*i.bateman@uea.ac.uk
Abstract: Landscapes generate a wide range of valuable ecosystem services, yet land use
decisions often ignore the value of these services. Using the example of the UK, we show
the significance of land use change not only for agricultural production but also for
emissions and sequestration of greenhouse gases, open-access recreational visits, urban
green space and wild species diversity. We use spatially explicit models in conjunction
with valuation methods to estimate comparable economic values for these services,
taking account of climate change impacts. We show that, while decisions which focus
solely upon agriculture reduce overall ecosystem service values, highly significant value
increases can be obtained from targeted planning incorporating all potential services and
their values, and that this approach also conserves wild species diversity.
One Sentence Summary: Valuation of ecosystem services within land-use planning
creates significant gains relative to current, market-dominated, decision making.
Main Text:
The Millennium Ecosystem Assessment (1) provided important evidence of the ongoing
global degradation of ecosystem services and highlighted the need to incorporate their
value into the economic analyses which underpin real-world decision-making. Previous
studies have shown that the overall values of unconverted natural habitats can exceed the
private benefits following conversion (2, 3), that knowledge of landscape heterogeneity
and ecological processes can support cost effective land planning (4-7), that trade-offs in
land-use decisions affect values from ecosystem services and biodiversity at local level
(8, 9), and that current land use is vulnerable to the impacts of global change (10, 11). In
the UK National Ecosystem Assessment (NEA) (12), a comprehensive assessment of the
UK’s ecosystems was linked to a systematic, environmental and economic analysis of the
benefits they generate. Here we show how taking account of multiple objectives in a
changing environment (including, but not restricted to, climate change) fundamentally
alters decisions regarding optimal land use. The NEA analyses are based upon highly
detailed, spatially-referenced environmental data covering all of Great Britain. These data
supported the design and parameterization of models of both the drivers and
consequences of land use decisions, incorporating the complexity of the natural
environment and its variation across space and time (13). Model outputs provide inputs to
economic analyses which assess the value of both marketed and non-marketed goods
(Table 1).
<Table 1 here>
The NEA specifically addressed the consequences of land use change driven by
either just agricultural or a wider set of values, all within the context of ongoing climate
change. To assess this, raw data on land use and its determinants were drawn from
multiple sources to compile a 40 year dataset, spatially disaggregated at a resolution of
2km grid squares (400ha) or finer across all of Great Britain, forming more than ½
million sets of spatially referenced, time specific, land use records. Data on the
determinants of that land use were assembled from multiple sources and included the
physical environment (both spatially variable factors such as soil characteristics, slope,
etc. and spatio-temporal climate variables such as growing season temperature,
precipitation, etc.); policy (both agricultural and relevant environmental measures
including subsidies, taxes, activity constraints, etc.); market forces (prices, costs, etc.);
and technology (reflected as changes in costs).
Land use change
Land use in the UK is dominated by agriculture which accounts for some 18.3
million hectares or 74.8% of the total surface area (14), including not only cropland but
also the majority of grassland, mountain, moor and heathland habitats. Agricultural land
use was analyzed using integrated environmental-economic models developed to capture
spatial and temporal variation in determinants (15). These models start from the premise
that farmers seek to arrange land use so as to maximize long run profit, subject to the
physical-environmental, policy and price conditions they face in a given location and
time (13). Even within the relatively small area of Great Britain, variation in
environmental conditions is sufficient to yield very substantial differences in agricultural
productivity and hence land use. These differences are captured by the model along with
the variation due to other drivers; the models being verified using rigorous out-of-sample,
actual versus predicted, testing (13).
The focus of the analysis concerned the consequences of alternative land use
futures up till 2060. To assess this, information was needed regarding how drivers of land
use change might alter over that period. Some physical environment factors can be
treated as fixed (e.g. soil type) but others, most notably climate change, vary temporally
and spatially. For these, modeled outputs of variables such as growing season
temperatures and precipitation (16) were included in our land use models. Certain market
drivers were kept constant due to extreme uncertainties (e.g. food prices may well rise
due to increased demand from higher population and other pressures; but this may be
mitigated by technological advance and behavioral change). Policy-induced changes,
such as the consequences of stronger or weaker environmental regulation on both
agricultural and other land, were addressed through an expert-based, deliberative process
consistent with (1). This process generated six plausible future scenarios, each described
in terms of changes in regulations, these being either generally applied or spatially
focused (Table 2). A rule-based approach was used to generate probabilities for each land
cover transition in each cell under each scenario (e.g. transfers of land out of intensive
agriculture to support the enhancement of areas of conservation importance, as per (17,
18)). Resultant scenarios are summarized in Table 2 and discussed in detail in (13).
<Table 2 here>
Response of market-priced goods to land use change
An initial analysis demonstrates the outcome of conventional land-use decision
making, which emphasizes market values (e.g. agricultural produce) and ignores non-
market ecosystem services. Figure 1 provides maps of the change in the market value of
agricultural output from the present day (2010 baseline) to 2060 under alternative climate
change and policy scenarios (ignoring any effects from inflation). In Fig. 1A, climate
change follows a low greenhouse gas emission path (taken from (16)), therefore having
relatively little impact on farming during this period, but relatively stronger
environmental regulations are imposed (the NW scenario from Table 2), restricting high
intensity farming in many areas which results in declines in market agricultural values
across much of the country. These relatively strong environmental regulations are
maintained in Fig. 1B but climate change now follows a high emissions scenario. While
climate change is expected to have mixed consequences for agriculture at a global scale
(18, 19), comparison of Figs. 1A and 1B shows that farming in the UK will largely
benefit from warmer temperatures. Figure 1C maintains that high emission assumption
but now weakens environmental regulations (the WM scenario). This allows land use
changes such as the conversion of some currently protected, conservation areas into
higher intensity farming, resulting in substantial further increases in agricultural
production and corresponding market values.
<Fig. 1 here>
Figure 1 shows that, irrespective of climate change projections, if land use
decisions are based on market priced goods alone then a reduction in environmental
regulations must always appear justified. Land use change, however, alters not only
market-priced agricultural outputs but many other important (but typically non-market)
ecosystem services as well.
Response of non-market ecosystem services to land use change
The analysis was extended to include the consequences of land use change for
greenhouse gas balance (GHG), open-access recreation, urban greenspace, and wild
species diversity (each modeled as per Table 1 and (13)). Economic values were
estimated for each of these additional impacts (ibid.) with the exception of wild species
diversity which is difficult to measure accurately using standard economic tools (15) and
was accordingly assessed using a diversity index (13, 20).
Land use change was then modeled for all scenarios, embracing a variety of
combinations of environmental regulation and climate change, with the consequences for
all market and non-market ecosystem services (including agricultural outputs) and their
value or indices being assessed. Figure 2 presents changes in value from the 2010
baseline under either the weaker environmental regulations of the WM scenario (upper
row of Fig. 2) or the stronger regulations of the NW scenario (lower row); with high
emission climate change projections being assumed in both cases. Considering
agricultural values alone, results are (as per Fig. 1B and 1C) that the weaker
environmental regulations of the WM scenario yield higher market values. However, the
non-market impacts of land use change illustrated in the rest of Fig. 2 show that, across
much of the country, strong environmental policies yield gains in the value of ecosystem
services resulting from reduced GHG emissions and enhanced recreation and urban
greenspace as well as improvements in species diversity. Temporarily setting aside the
non-monetary wild bird diversity index and summing across all other values shows that
weaker (stronger) environmental regulations lead to net losses (gains) nationally; a result
which reverses the restricted, market value assessment of Fig. 1. It is clear that
considering market prices alone can drive decisions for land use that would deprive
