appendix 4: exploring the future: review of scenarios defra ref:...
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Appendix 4: Exploring the Future: Review of scenarios
Defra ref: WC0794
David C. Howard
Centre for Ecology & Hydrology, Lancaster
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Introduction
Scenarios are developed and used for ‘what if’ modelling in policy development; they have a well
established track record and are widely used (see (Schultz 2011) for an inventory of scenarios). In
this project rather than use scenarios as a method of policy guidance or assessment, they are
needed to define the bounds and offer exemplars to demonstrate the application of spatial decision
support systems (sDST). It is important to recognise the origin and authority of scenarios; they are
generated through a plethora of methods, some with formal data analysis, others using expert
knowledge and vision. As they are not considered to be predictions of the future to be validated
against reality they have to be viewed with a fair amount of scepticism, however they do illuminate
a number of issues and lead to interesting questions.
Unfortunately, none of the land use scenarios or models reviewed for this project (or known by the
team) are spatially co-registered to the extent that scenarios can be interpreted at local scales.
Some scenarios present impacts in terms of changes to ecosystem services and may be suitable to
define the scope or domain of sDST, by linking national views of drivers and trajectories of change to
impacts on services; the sDST should identify the specific location and stock at risk to consistently
flesh out the consequences of a change.
There are other utilities that extend the value of scenarios, such as UKCP09. UKCP09 is a UK climate
projections tool that takes emissions scenarios (based on HadCM3) and then attempts to translate
them into geographic distributions of climate variables. Scenarios are translated over a 25 Km grid,
through Water Framework Directive (WFD) catchments or over administrative regions (modified
Government Office Regions). Forecasts are probabilistic and cover 7 overlapping 30 year time
periods set at decadal intervals extending from 2010 through to 2070 and are described as 2020s,
2030s, through to 2080s1.
UKCIP is the UK Climate Impacts Programme offers further application of climate projections such as
UKCP09. For previous projections there are regional interpretations of the impacts of the scenarios
e.g. UKCIP98 interpreted for NW England (Shackley et al. 1998), where the region was divided into 5
landscape types (urban core,urban fringe, coast, rural uplands, rural lowland) and interpretation by
expert opinion was made to amalgamations of Joint Character Areas.
UKCIP also offers tools for interpretation such as LCLIP (Local Climate Impacts Profile). It is targeted
at local authorities and organisations who need to reflect national climate change mitigation and
adaptation policy in their planning. It covers Climate Change Act (2008), UKCP09, Planning Policy
(PPS1, 2005) and Civil Contingencies Act (2004). The usual outputs of the exercise include a list of
weather events, interpretation and understanding of the impacts of those events and a headline
message to raise the profile. Detailed local maps are not provided for the UKCIP projections, but
may be generated by the local authority making their own interpretation of impacts, or more likely
identifying stock at risk.
The goal from this mini review will be to identify a small suite of scenarios that are relevant to both
the national agenda and local land managers and can be seen as exemplars to demonstrate the
1 http://ukclimateprojections.defra.gov.uk/content/view/12/689/
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value and use of scenarios in this project. They will then provide a framework to scope the
requirements of a sDST.
Objectives
To identify and characterise a range of scenarios so that a selection can be made that will be capable
of parameterising sDST to demonstrate the range and magnitude of changes in ecosystem services
that are both realistic and relevant to users.
Typology of scenarios
Models and scenarios have an important role in determining policy targets and constraints; in this
project they will contribute by identifying the characteristics to be modelled in developing sDST.
Models are often the basis of scenarios and will be briefly considered here. Unfortunately, terms are
commonly applied in many situations and can mean different things to different people. This review
will start by defining models and scenarios, then will establish their spatial, temporal and conceptual
limits before briefly reviewing models their instigation and drivers and use for generating scenarios.
It is important that the scenarios selected are relevant to users of sDST.
Models can be physical or abstract; abstract forms are predominantly used to describe the
environment. The term can be used to describe concepts, frameworks, logic or functions (the latter
usually mathematical). These are then applied in different situations with parameters that will
generate valuable insights. Models can be considered to underpin scenarios and define the
relationships between factors that will create different outputs as the suite of input parameters
vary. The model will determine if the output is relevant and appropriate for use in this project and
define its limitations; in many of the scenarios described here the underlying models are simple
expert opinion, sometimes supported by data; in a few cases there are formal mathematical models.
There are a plethora of scenarios with different purposes (see Figure 1). Examination of the
literature has Identified four dominant uses (Sparrow 2000) namely:
Sensitivity analysis, usually focussing on cash flow management, risk assessment, or project
management i.e. for making practical decisions.
Tactical contingency planning as used in military or civil emergency planning; the goal is to
define who is to do what during a particular event.
Strategic contingency planning as applied to decision-making in corporate or national
Government policy i.e. for broader longer term planning.
Coherently structured speculation. Sparrow argues that planners advising decision makers
use a fourth interpretation, regarding scenarios as more exploratory so that a scenario is less
a strategic speculation; the goal is to improve understanding.
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Figure 1 Scenario space after (Carter et al. 2007)
Our use is for structured speculation that may lead to strategic contingency planning that is to
improve our knowledge in a way that can benefit decisions of what is to be done rather than how to
do it. There have been numerous models constructed and scenarios developed describing aspects of
land use change in Britain reflecting different drivers. They can be focussed on specific business
sectors (e.g. transport (Curry et al. 2006)), regions (e.g. London (PB Power 2006)), technologies (e.g.
hydrogen infrastructure (Eames & McDowall 2006)), specific timeframes (e.g. through to 2050),
investigating specific policies and mechanisms (e.g. permits and taxation) or focusing on wider
impacts (e.g. (UK National Ecosystem Assessment 2011)). As outputs, scenarios are not intended to
be definitive statements, but rather informative illustrations of the way components may interact.
One thing that is universally agreed is that scenarios are not predictions, consequently they may be
inconsistent and not contain sufficient information to interpret through a comprehensive range of
ecosystem services.
Table 1 provides an indication of the characteristics of different scenarios. The individual scenarios
are usually presented to contrast with other scenarios from the same project or programme and
there may be unseen inconsistencies if those from different studies are compared. This is a problem
with the Foresight Land Use Futures where a large number of experts drafted reports on specific
topics. Rather than presenting rounded scenarios, the project attempted to draw an integrated
perspective of the major drivers of change in the UK over the next 50 years. The drivers they
identified were:
Demographic change
Economic growth and changing global conditions
Climate change
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New technologies
Societal preferences and attitudes
Policy and regulatory environments
Some of the Foresight Land Use Futures impact areas can be scaled to local levels (e.g. flooding
(Wheater & Evans 2009)), but some of the data have inconsistent coverage for the UK, with many
simply reflecting the lack of availability of data for any of the home nations other than England or
England and Wales. Where data are available for all UK, the data characteristics may differ between
nations (e.g. soil which is mapped using a different classification for England & Wales (Avery 1980) to
that for Scotland (MISR 1984) and with different protocols including depths). Expertise or support
would be needed to use individual components at scales other than those presented.
From the Foresight Land Use Futures drivers three cross sectoral challenges were highlighted,
namely demand for land in south east England, climate change and land use, and delivery of public
goods and services. They also identified pressures on different sectors (water (supply & flooding),
land use, conservation, agriculture, forestry, energy, residential & commercial land, transport and
recreation). The reports (Newbery et al. 2010) and accompanying papers can provide detailed
information and contacts, but there are no coherent scenarios that can be easily used.
The Foresight Land Use Futures (see Table 1) were constructed through a series of meetings using an
inductive ‘bottom-up’ approach. Groups of stakeholders were brought together and issues
discussed covering impacts, uncertainties and potential change that were then clustered iteratively
and related to extensive land use systems maps in order to develop narratives. The approach is
generalised and cannot be effectively scaled.
UK Research Council’s Rural Economy and Land Use (RELU) programme also developed and applied
scenarios covering different geographic regions (e.g. uplands) and sectors (e.g. flooding). Scenarios
were developed usually through participatory methods (Reed et al. 2009) although sometimes
through more formal mathematical approaches (e.g. (Chapman et al. 2009)). As with the Foresight
Land Use Futures, the approach can provide information and ideas, but unless a sDST is only going to
be demonstrated in specific locations matching those in RELU or on specific topics the approach is
not capable of providing appropriate information; it could define datasets that are needed, but they
may not be extant.
The other studies presented within the table either focus on the whole of the UK or England or
represent global drivers therefore matching Government policy and national economic drivers.
Most (with the exception of Foresight Futures) are recent studies and so cover contemporary events
and forecast through to 2050 or 2060. The final date for forecasting is often vague, being described
as 40 or 50 years in the future; UKERC’s Energy 2050 model however is the only study here that does
model in 5 year time-steps and projects through to 2050 with intermediary data. For climate data
UKCP09 presents data as a moving average in decadal steps. The need for a timeline or trajectory
has not so far been identified, but may prove an important characteristic. The transition from the
current prevailing system to that described in a scenario has been described in terms of three
horizons moving from existing system (1st Horizon), through an unstable transitory phase (2nd
Horizon) to a new world (3rd Horizon). The impacts on biodiversity may well be greatest in the
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second phase irrespective of the capacity offered by the scenario. The time for the environment to
reflect the full impact of change (relaxation) is known to be long with estimates measured in
centuries (Terborgh 1976).
The important issue for use of scenarios in sDST is their ability to provide effective input. None of
the scenarios reviewed are capable of providing simple definitive spatially registered parameter
values to local systems. Even the UKCIP approach (LCLIP see http://www.ukcip.org.uk/lclip/) where
toolsets are provided only supplies newspaper type examples of events with vague spatial locations
(in part because the domain boundaries and heterogeneity of events are unknown and the dramatic
impacts have specific locations; for example the Boscastle flooding in 2004 happened because of a
coincidence of factors (including the remains of Hurricane Alex) created localised intense rain to
drive a flash flood).The scenarios can be used to generate the range of parameters and capture the
links between variables and drivers of change. The recommended approach is to use scenarios to
provide structured speculation (Sparrow 2000).
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Project App. Scenarios Spatial domain
Time-frame
Spatial resolution ES Driver Source
NEA EO Green & pleasant land
UK 2060 Generalised Conservation of biodiversity & landscape
(Haines Young et al. 2011)
EO Nature @ work UK 2060 Generalised
Population growth and new technology
EO World markets UK 2060 Generalised Economic growth - liberalisation
EO
National security
UK 2060 Generalised Self sufficiency & efficiency
EO
Local stewardship
UK 2060 Generalised Reduced consumption and intensity
EO
Go with the flow
UK 2060 Generalised Business as usual
HCHV EO Restoration UK 2030 generalised but limited regional interpretation
long-term governance, dematerialised consumption
(Hodge et al. 2006)
EO
Krypton Factor/ Alchemy
UK 2030 generalised but limited regional interpretation
long-term governance, material consumption
EO Survivor UK 2030
generalised but limited regional interpretation
short-term governance, dematerialised consumption
EO
Strike it rich/ Jeopardy
UK 2030 generalised but limited regional interpretation
short-term governance, materialised consumption
Table 1 Examples of scenarios that could be used to parameterise sDSS, presenting some of their characteristics. Project indicates the origin of the different
scenarios App. Shows the approach either EO – expert opinion or MM – mathematical model, ES indicates the level to which ecosystem services are
presented, Driver indicates the driving force(s) considered to cause change and Source is where additional information can be found
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Project App. Scenarios Spatial domain
Time-frame
Spatial resolution ES Driver Source
Energy 2050
MM Low carbon UK 2050 generalised 80% reduction in GHG emissions (Ekins & Skea 2010)
MM Resilience UK 2050 generalised Secure energy supply
MM Reference UK 2050 generalised Business as usual
MM
Low carbon resilience
UK 2050 generalised 80% ghg reduction and resilience
NE-STEEP or ScENE
EO Connect for Life
England 2060 generalised Multi functional land management
(Kass et al. 2011) or
EO Go for Growth England 2060 generalised
Rapid change driven by monetary value
(Schultz 2011)
EO Keep it Local England 2060 generalised Self sufficiency
EO
Succeed through Science
England 2060 generalised Biotechnology & technical control
Foresight Futures Scenarios
EO World markets UK 2020 generalised Personal independence, material wealth and mobility
(Berkhout & Hertin 2002)
EO
Global responsibility
UK 2020 generalised High levels of welfare within communities with shared values
EO
National enterprise
UK 2020 generalised
Material wealth within a nationally rooted cultural identity
EO
Local stewardship
UK 2020 generalised Sustainable levels of welfare in local communities
Table 1(contd) Examples of scenarios that could be used to parameterise sDSS, presenting some of their characteristics. Project indicates the origin of the
different scenarios App. Shows the approach either EO – expert opinion or MM – mathematical model, ES indicates the level to which
ecosystem services are presented, Driver indicates the driving force(s) considered to cause change and Source is where additional
information can be found
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Project App. Scenarios Spatial domain
Time-frame
Spatial resolution ES Driver Source
ALARM MM BAMBU Europe 2080 Mixed, NUTS2 for drivers
Business as usual baseline (Reginster & Rounsevell 2006)
MM GRAS Europe 2080
Growth strategy with liberal, globalised, deregulated growth
MM SEDG Europe 2080
Backcasting scenario to represent integrated social, environmental and economic sustainability
CH Food Supply
EO Just a Blip Global 2100 Global translated to UK
Short term price increase long term trend remains stable
(Chatham House Food Supply Project 2008)
EO Food inflation Global 2100 Global translated to UK
High food prices for a protracted period but system continues
EO Into a new era Global 2100 Global translated to UK
Per capita production falls so yields increase sustainably
EO Food in crisis Global 2100 Global translated to UK
Disrupted system high prices famine and panic.
Table 1(conts.) Examples of scenarios that could be used to parameterise sDSS, presenting some of their characteristics. Project indicates the origin of the
different scenarios App. Shows the approach either EO – expert opinion or MM – mathematical model, ES indicates the level to which
ecosystem services are presented, Driver indicates the driving force(s) considered to cause change and Source is where additional
information can be found
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Project App. Scenarios Spatial domain
Time-frame
Spatial resolution ES Driver Source
Foresight Land use Futures
EO Leading the Way
UK 2060 Generalised
High adaptation to change with dispersed population and activity
(Foresight land Use Futures Project 2010)
EO Valued Service UK 2060 Generalised
Concentrated but flexible activity
EO
Competition Rules
UK 2060 Generalised Little adaption and resistant to change
IPCC MM/EO
A1 Global 2100 Generalised Market lead growth and increased mobility
Nakicenovic , N. and Swart, R. (Eds.) (2000)
MM/EO
A2 Global 2100 Generalised Slower growth more regionalism
MM/EO
B1 Global 2100 Generalised
Sustainable development with social and environmental conscientiousness
MM/EO
B2 Global 2100 Generalised
Sel f supporting sustainable development with regional and sub-regional diversification
Table 1(contd) Examples of scenarios that could be used to parameterise sDST, presenting some of their characteristics. Project indicates the origin of the
different scenarios App. Shows the approach either EO – expert opinion or MM – mathematical model, ES indicates the level to which
ecosystem services are presented, Driver indicates the driving force(s) considered to cause change and Source is where additional
information can be found
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Assessment against users’ needs
The questionnaire provided valuable information about the ways that users are currently using or
are interested in using sDSTs. It is interesting that over half those questioned already consider that
they are using systems and from discussions at the workshop they are most commonly used to guide
practical or tactical decisions. Scenarios are familiar to and valued by all those questioned and
should be included within sDST. The respondents clearly indicated that they were familiar with the
notion of scenarios to help formulate effective decisions, although the variation in their definition of
the term or their use (practical, tactical, strategic or improving understanding) could not be explicitly
discerned.
The questionnaire highlighted the fine resolution that most people are applying sDSTs, which, with
the notable exceptions of climate change and socio-economics are at on units of less than 1 km2 and
for many issues looking at individual patches of less than 1 ha. The scenarios reviewed here as
representative of those available generate much broader brush images at national to global scales.
They are already being loosely linked in providing a frame or background against which to examine
future spatial decisions. Stronger links between scenarios and sDST are needed.
Future scenario development should be encouraged to consider spatial linkage and translation
across scales. The issues are not simple and will require underpinning scientific research. For
existing scenarios, the styles of additional information or resource needed to link them to sDST are
indicated in Table 2. Many of the comments could be read across several of the scenarios.
Just as scenarios are used to provide narratives to draw in people and make ideas of future planning
accessible, the sDST need to be developed with the same objectives. A hidden core covering
complex analysis and ensuring that spatial and temporal co-registration is effectively carried out
could be developed through a hub and spoke infrastructure; it is essential that any output is properly
validated and auditable.
Users can see future scenarios being needed to cover issues that are minor or not present and they
do recognise the need for understandable confidence to be presented as part of the output of a
sDST, but a stronger driver is the ability to express options in ways that are comprehensible by
different stakeholders and groups. The outputs of sDST will be as important as the inputs.
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Project Issues covered Additional needs
NEA Biodiversity & ecosystem
services
Consistent datasets describing
the stock at risk to change e.g.
agreement between national
datasets and those held by local
organisations
HCHV Environmental pressures How to link to specific
ecosystem services, although
some (e.g. Hydrology) are
included.
UKERC Energy 2050 Climate change mitigation,
energy security
Increased geographic
resolution or guidance on how
to apply results to smaller
localities
NE STEEP/ ScENE Biodiversity & ecosystem
services
Extension to cover rest of UK
Foresight Futures Scenarios Economic growth Links through to ecosystem
services
ALARM Land use change driven by
economic issues
Measures of confidence and
directions on how to co-register
information
CH Food Supply Agriculture (Food Provision) Description of the local
heterogeneity within different
scenarios and finer
UKCP09 Climate Change (Regulatory
and support)
Translation of climate data to
finer spatial resolution so that
opportunities and impacts can
be better assessed
Foresight land Use Futures Many Easy and well supported access
to underpinning data.
Comprehensive and even
coverage across UK
IPCC Atmospheric emissions Spatial and temporal
heterogeneity; variations in
forms of emission
Table 2 Issues scenarios have been used to cover and indication of the additional information or
resources needed to employ scenarios in sDSTs.
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Draft recommendations/Conclusions
Recommendation 3.1: Explicitly incorporate scenarios in decision tools through the operation of the
hub. Users recognised that the twin pressures of land-use and climate change are likely to
increasingly affect their decision-making (pg 15). There is a clear requirement for easier access to
scenarios and to simplify their use and interpretation (pg 11, user questionnaire report). Sectors of
particular interest to users are water resource management & flooding, biodiversity & conservation,
agriculture, woodland & forestry, recreation (pg 15).
Recommendation 3.2: Press for spatially and geographically explicit scenarios that can be translated
across scales. As most scenarios are non-spatial all they can provide are descriptions of pressures,
these have to be spatially co-registered with the stock at risk to identify impacts. Even UKCP09 is
essentially a series of spatially explicit modelled impacts driven by non-spatial emissions scenarios.
Hence there is a pressing need for better ways of translating non-spatial storylines of plausible
environmental change across England into plausible local impacts. Biodiversity models as
components of new sDST capability have a clear role to play. These could be inputs to or outputs of
sDST
Recommendation 3.3: Develop and deploy tools to encourage wider participation in decision
making. Use of visualisation tools to help local communities discuss and imagine local impacts of
demographic, land use or climate change should be considered but the novelty is using them in such
a way that such participatory sessions can generate possible impacts that are captured and returned
to systems such as InVEST and Polyscape as new datasets of variables that then set new boundary
conditions for carrying out a new habitat connectivity or ES trade-off and land-use optimisation
analysis.
Recommendation 3.4: Recognise that tools for different scales will represent different magnitudes of
effect. At smaller scales, it may well be the rare but increasingly probable extreme weather events
that stimulate changes in local planning and ecosystem management. People maybe generally more
affected by drought or flood than imperceptible rise in mean temperature. Therefore visualisation
and estimation of climate change impacts might beneficially focus on prediction of weather
extremes and on their impacts. The two are separate. Extremes may be inherently unlikely and hard
to predict but severity is high. So even if extremes cannot be usefully forecast then it may still be
worth envisioning their impact given their increasing likelihood in the future
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