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).

http://www.ukcip.org.uk/lclip/

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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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Time-frame


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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Time-frame


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




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Time-frame


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




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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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