Analysing scenarios using models

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1 Analysing scenarios using models Examples with IMAGE and Eururalis Bas Eickhout

2 Where do I come from 2 Chemist and environmental scientist from background Modeller at IMAGE-group for almost 10 years; many years responsible for integration Involved in several scenario studies Specialist in land-use studies; focusing more and more on European issues: Eururalis

3 The IMAGE model 3 Old name: Integrated Model to Assess the Greenhouse Effect Simulating consequences of land-use change for greenhouse gas emissions New one: Integrated Model to Assess the Global Environment Applied in several global studies: IPCC s Special Report on Emission Scenarios UNEP s Global Environment Outlook Millennium Ecosystem Assessment Eururalis

4 The IMAGE-model: an (over)used work horse 4 Why models? Enriching storylines by providing insights in a complex system Checking consistency and plausibility Provide relevant numerical information But: What s relevant for the study? (usually not well thought-through) Source: MNP, 2006

5 Energy use in 26 world regions 5 Source: MNP, 2006

6 Translate storylines into scenario assumptions: MA example 6

7 Qualitative / quantitative (population) High economic growth and education of women leads to drop of fertility Variable Fertility Mortality Migration Global Orchestration D: low I: medium D: low I: low high Order From Strength D: high I: low D: high I: high low Adapting Mosaic D: High I: low until 2010, deviate to medium by D: high 2050 I: high until 2010, deviate to medium by low 2050 TechnoGarde n D: medium I: medium D: medium I: medium medium 7 High economic growth and investment into health services leads to drop of mortality Globalised world has high migration rates

8 Energy demand Energy supply Climate policy Global Orchestration Order from Strength lifestyle regionalized assumptions and assumptions energy efficiency investments based on current North American values regionalized assumptions market focus on domestic some preference liberalization; energy resources for clean energy selects least-cost resources options; rapid technology change no no no Adapting Mosaic TechnoGarden lifestyle assumptions and energy efficiency investments based on current Japan and West European values preference for renewable energy resources and rapid technology change yes, aims at stabilization of CO 2 -equivalent concentration at 550 ppmv 8 Depletion Technology development Depletion Technology development Population GDP Income Elasticity Energy demand Costs of different energy sources Preferences Energy supply Climate policy (Carbon tax) Costs of different energy sources Trade restrictions Price Elasticity Prices

9 In detail: Coal Oil Natural gas Trad. biofuel Mod.biofuel NTE Hydropower

10 10

11 The rationale 11 A discussion-support tool was designed to visualize long-term future challenges (2030) for the European rural areas taking into account global context and the heterogeneity of Europe s rural areas. Moreover, possible policy options are evaluated on their effects for people, planet and profit.

12 For this 12 A multi-dimensional, multi-modeling tool was designed consisting of a global trade model (LEITAP), an environmental integrated assessment model (IMAGE) and a detailed European land-use model (CLUE-s). To take into account future uncertainties and different world views, four scenarios are applied as well.

13 Methodology of Eururalis 13

14 14 No CAP support CAP: green/blue services CAP: no change CAP: export subs. abolished

15 Assumptions storylines 15 Population Solidarity Economy Technology Globalizatio n Governmen t regulation Global economy Global cooperation Continental Markets Regional Communities

16 Focus on agricultural policies per scenario 16 Base situation Global Economy Global Co-operation Continental Markets Regional Communities Export subsidies EU Agenda Abolished Abolished No change Abolished 2000 Import tariffs Abolished Abolished No change No change Domestic support Abolished -/-50% linked to env. and social targets No change +10%, linked to env. and social targets Trade blocks Turkey, FSU accede EU Turkey, FSU accede EU EU-USA FTAA (North + South America) Sugar and milk Abolished Abolished Self sufficient Self sufficient EU quota (EU25) Non trade concerns situation 1997 Global SPS & TBT leading to 2.5% price increases in all countries except EU and Japan EU Different SPS and TBT between trade blocks: increase non tariff barriers with 10% Source: Eickhout et al., 2004

17 Eururalis 1.0 results 17 EU 25 arable and pasture land # kha A1 A2 B1 B year

18 Criticism on Eururalis To preserve Multi model and multi scale approach PPP and land use Global context Long term and spatially explicitness Common language Interactive tool Scenario approach

19 Criticism on Eururalis To improve Black box Lack of explicit policy options Causal tracing Impact of biofuels Regional level Baseline versus multi narratives

20 Opening the black box - model framework 20

21 Complex modelling: Technical background report 21 21

22 22 22

23 Exogenous 23 23

24 24

25 25

26 26

27 Policy responses 27 In many scenario studies policy responses are integrated in the scenario development Example: Millennium Ecosystem Assessment Usually not well thought-through what policy responses need to be analysed What are external drivers? What can be influenced by stakeholders? Often: scenario development is a mixture

28 Always be clear in the target of your analysis. This will prevent hot debates 28 Probalistic Scenario development A set of storyline based scenarios There is no value in providing users a set of lines that go all over the place. It is the moral task of experts to determine what are the most likely assumptions and therefore the most likely outcomes. Scientists deal with uncertainty by indicating the most likely outcome and an uncertainty range. Future emissions are the product of a large range of very uncertain factors such as population, technology, socio-economic development etc. Storylines are used to define a consistent set of assumptions. Scenarios can help exploring some of these futures; they are not predictions.

29 And a hot debate it can be 29 Probalistic Scenario development A set of storyline based scenarios Decision-makers need to address risks. Risks are determined by impacts AND probability. For instance, information that an asteroid can destroy the earth can only be properly assessed, if one knows that changes are 1 in a billion. The probabilistic approach only attempts to assign subjective probabilities in a situation of ignorance forms a dismissal of uncertainty in favor of spuriously constructed expert opinion.

30 Attempts to combine 30 Unfortunately not many good examples are available We tried in the follow-up of Eururalis

31 Table 5: Default settings of the four scenarios (shaded) and its available policy variants (WUR/MNP, 2007) CAP Market support Global Economy 1 Global Co-operation Continental Markets Regional Communities CAP Income support Ambition Biofuels Less Favoured Areas

32 32

33 33

34 Policy makers are interested in concrete policy options 34 Example of biofuels: Theoretically large potential of biofuels However, not all forms of biofuels deliver this potential Deforestation and loss of biodiversity Negative GHG balance Competition for food Therefore, discussion on sustainability criteria

35 Shift towards a reference scenario (being the A1 scenario) 35 20% 15% 10% 5% Impact of Biofuel Directives on World Prices, in %, 2020 relative to % -5% -10% -15% Cereals Oilseeds Sugar Crude oil Reference Biofuel policies, EU Biofuel policies, global Bron: Banse et al., 2008 (Eururalis)

36 Trade-offs to land use 36 50% 40% 30% Impact of Biofuel Directives on Agricultural Land Use, in %, 2020 relative to % 10% 0% -10% Africa Asia C&SAmer EU27 HighInc World Reference Biofuels, EU Biofuels, global Bron: Banse et al., 2008 (Eururalis)

37 Current discussion on Eururalis 37 Discussion support tool or policy support tool? How to deal with scenarios? Using narratives or focussing on policy options How to use Eururalis-insights in broader European discussions on European policies for territorial cohesion?? Many aspects cannot be modelled So far; combination of stakeholders and modellers has not been successful

38 Thank you for your attention 38

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