Scenario workshop honduras zamorano irbio 24 may 2011 wv r
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  • 25/05/11
  • In 2001 – we produced an OECD Environmental Outlook to 2020 to identify the key challenges facing environmental policy makers. The results were summarised using a “traffic lights” analogy: green lights for issues that are being well managed, yellow for ones where there has been improvement but are not yet on the right track, and red for issues that need to be urgently addressed. You can see from this table the “ red light ” issues which need the most action from policy makers. These were established based on extensive analysis for the Outlook, and consultation of experts. Greenhouse gas emissions  despite the recent introduction in many OECD countries of carbon or energy taxes, tradable permits, and the growing use of the Kyoto flexibility mechanisms – only one-third of OECD countries have stabilised or reduced GHG emissions since 1990. Those that have, was the result of economic or structural changes, not successful climate policies. Motor vehicle and aviation air pollution – major source of local air pollution, GHG emissions, congestion, accidents, etc. Some local air pollutants from transport decreased (e.g. sulphur dioxide and nitrogen oxides), but overall pressures continuing to increase. Agricultural pollution - Agriculture continues to have significant pressures on the environment – it is responsible for 40% of nitrogen emissions and 30% of phosphorous emissions to surface waters and contributes to groundwater pollution – an increasing concern for many OECD countries. Over-fishing - 28% of major marine stocks are overexploited or recovering; 47% are fully exploited. Global biodiversity  increasing the are of natural parks (now 14.6% of OECD land area), but less success outside the parks. The percentage of known species that are endangered is continuing to increase. Chemicals in the environment - Releases of chemicals during manufacturing and from products continues, and they are now widespread presence in the environment, causing environmental and human health and problems. Of particular concern are chemicals that are persistent, bioaccumulating and/or toxic.
  • Example: MedAction: Land use change scenarios at various scales  To better understand the driving forces leading to land degradation and desertification in the Northern Mediterranean and to contribute to policy-making to address these issues 25/05/11
  • 25/05/11
  • 25/05/11
  • Backcasting starts with defining a desirable future and then works backwards to identify policies and programs that will connect the future to the present 25/05/11
  • Ensure focal issue matches the purpose identified in phase 1 Ad 2: inductive …. Deductive ….. incremental
  • Ad 1: Even when scenarios will not be quantified you should continue with steps 2 and 3
  • Ad 2 as a scenario should fit the context there is a strong link with Phase 2
  • Based on National socio-economic development plan
  • The future land use map generated by Clue is used as input map for Globio3 to generate the MSA_lu map and also used in combination with (future) road map to generate future MSA_infra and MSA_frag maps.

Scenario workshop honduras zamorano irbio 24 may 2011 wv r Scenario workshop honduras zamorano irbio 24 may 2011 wv r Presentation Transcript

  • Land use scenario development Workshop Regional changes of land use for climate change adaptation and mitigation 2 4-25 May 2011, Zamorano By Wilbert van Rooij Netherlands Environmental Assessment Agency (PBL) seconded to Aidenvironment
    • Multi-scale
    • Multi-theme
    • Multi-sectoral and thus
    • Multi-disciplinary
    Environmental science – a complex issue An assessment of the current status is complex, the assessment of the future status even more
  • What is a scenario? Scenarios are credible , challenging, and relevant stories about how the future might unfold that can be told in both words and numbers. Scenarios are plausible descriptions of how the future may develop, based on a coherent and internally consistent set of assumptions about key relationships and driving forces. Scenarios are not forecasts , projections , or predictions .
  • Scenarios – overview
    • Scenarios:
    • Have the ability to address complex issues in an integrated manner.
    • Have the ability to deal with surprises , system changes , alternatives.
    • Are an excellent tool for communication
    • Possibilities for participation are large.
  • Purposes of using (participatory) scenarios
    • Environmental scientists:
    • Scenarios are a good tool for an integrated analysis of a complex problem. Scenarios provide in-depth insight in complex societal problems.
    • Focus on results
    • Social scientists:
    • Scenarios are a good tool for communication, conflict management, and long-term participation. Scenarios provide an excellent tool for communication.
    • Focus on process
  • Types of scenarios A Project goal - exploration vs decision support: I. Inclusion of norms? : descriptive vs normative II. Vantage point: forecasting vs backcasting III. Subject: issue-based, area-based, institution-based IV. Time scale: long term vs short term V. Spatial scale: global/supranational vs national/local B Process design – intuitive vs formal: VI. Data: qualitative vs quantitative VII. Method of data collection: participatory vs desk research VIII. Resources: extensive vs limited IX. Institutional conditions: open vs constrained C Scenario content - complex vs simple: X. Temporal nature: claim vs snapshot XI. Variables: heterogeneous vs homogenous XII. Dynamics: peripheral vs trend XIII. Level of deviation: alternative vs conventional XIV. Level of integration: high vs low
  • Scenarios, models, and participation Traditional approach Integrated approach
  • Example scenario for Global assessment 1: The Millennium Ecosystem Assessment (full Storyline-And-Simulation approach)
  • Millennium Ecosystem Assessment
    • An international scientific assessment of the consequences of ecosystem changes for human well-being:
      • Modeled on the IPCC
      • Providing information requested by:
        • Convention on Biological Diversity (CBD)
        • Convention to Combat Desertification (CCD)
        • Ramsar Convention on Wetlands
        • Convention on Migratory Species (CMS)
        • other partners including the private sector and civil society
      • With the goals of:
        • stimulating and guiding action
        • building capacity
  • MA Conceptual Framework
  • MA Scenarios
      • Not predictions – scenarios are plausible futures
      • Both quantitative models and qualitative analysis used in scenario development
  • Scenario Storylines
      • Global Orchestration Globally connected society that focuses on global trade and economic liberalization and takes a reactive approach to ecosystem problems but that also takes strong steps to reduce poverty and inequality and to invest in public goods such as infrastructure and education.
      • Order from Strength Regionalized and fragmented world, concerned with security and protection, emphasizing primarily regional markets, paying little attention to public goods, and taking a reactive approach to ecosystem problems.
  • Scenario Storylines
      • Adapting Mosaic Regional watershed-scale ecosystems are the focus of political and economic activity. Local institutions are strengthened and local ecosystem management strategies are common; societies develop a strongly proactive approach to the management of ecosystems.
      • TechnoGarden Globally connected world relying strongly on environmentally sound technology, using highly managed, often engineered, ecosystems to deliver ecosystem services, and taking a proactive approach to the management of ecosystems in an effort to avoid problems.
  • Changes in indirect drivers
    • In MA Scenarios:
      • Population projected to grow to 8–10 billion in 2050
      • Per capita income projected to increase two- to fourfold
  • Changes in direct drivers Crop Land Changes in crop land and forest area under MA Scenarios Forest Area
  • Changes in direct drivers
    • Habitat transformation:
      • Further 10–20% of grassland and forestland is projected to be converted by 2050
    • Overexploitation, overfishing:
      • Pressures continue to grow in all scenarios
      • Invasive alien species:
      • Spread continues to increase
    • Observed recent impacts of climate changes on ecosystems:
      • Changes in species distributions
      • Changes in population sizes
      • Changes in the timing of reproduction or migration events
      • Increase in the frequency of pest and disease outbreaks
      • Many coral reefs have undergone major, although often partially reversible, bleaching episodes when local sea surface temperatures have increased
    Changes in direct drivers: Climate Change
    • Potential future impacts
      • By the end of the century, climate change and its impacts may be the dominant direct driver of biodiversity loss and changes in ecosystem services globally
      • Harm to biodiversity will grow worldwide with increasing rates of change in climate and increasing absolute amounts of change
      • Some ecosystem services in some regions may initially be enhanced by projected changes in climate. As climate change becomes more severe the harmful impacts outweigh the benefits in most regions of the world
    • Net harmful impact on ecosystem services
      • The balance of scientific evidence suggests that there will be a significant net harmful impact on ecosystem services worldwide if global mean surface temperature increases more than 2 o C above preindustrial levels ( medium certainty). This would require CO 2 stabilization at less than 450 ppm.
    Changes in direct drivers: Climate Change
  • Changes in ecosystem services under MA Scenarios
      • Demand for food crops is projected to grow by 70–85% by 2050, and water withdrawals by 30-85%
      • Food security is not achieved by 2050, and child undernutrition would be difficult to eradicate (and is projected to increase in some regions in some MA scenarios)
      • Globally, the equilibrium number of plant species is projected to be reduced by roughly 10–15% as the result of habitat loss over the period of 1970 to 2050 ( low certainty )
    Child undernourishment in 2050 under MA Scenarios
  • 2 nd example Global assessment: OECD ENVIRONMENT OUTLOOK: TRAFFIC LIGHTS
    • Some air pollutants (lead, CFCs, NOx, SOx)
    • Forest coverage in OECD regions
    • Water use
    • Surface water quality
    • Hazardous waste & toxic emissions from industry
    • Energy production & use
    • Forest quality in OECD regions
    • Waste management
    • GHG emissions
    • Motor vehicle & aviation air pollution
    • Municipal waste gen.
    • Agricultural pollution & groundwater quality
    • Over-fishing
    • Biodiversity & tropical forest coverage
    • Chemicals in the environment
  • Approach to quantifying scenarios in IMAGE model IMAGE 2 model Global change WaterGAP model World water resources
  • Calculate environmental changes Modelling course ITC-MNP: GLOBIO 3.0
    • Land use (incl forestry)
    • Climate
    • N-deposition
    • Infrastructure
    • Fragmentation
    Use GLOBIO to calcultate MSA
  • Land-use change
    • Changes in population and economy
    • Changes in diet: more or less meat; more or less animal products like milk
    • Changes in agricultural trade assumptions
    • Changes in food requirements for animals (more or less residues, more or less grass)
    • Changes in feed efficiency per animal
    Bas Eickhout, IMAGE: from global to local
  • Where does the food and feed demand come from?
    • Using a macro-economic agricultural trade model
    • Inputs like capital, labour and land determine amount of production per region
    • Price elasticities determine type of agricultural product and technology developments
    Bas Eickhout, IMAGE: from global to local
  • Taking environmental constraints into account Source: Van Meijl et al., 2005
  • Outlook
    • Increase in food and feed demand
    • Global trade regimes will change
    • Other service will be required from land (bio-energy)
    • Competition for land both globally and locally
    Bas Eickhout, IMAGE: from global to local
  • But this was very global How can this be used for national scenario development?
    • Use global model results as a context (what food feed, bio-energy and timber demand is required in your region)
    • Use global results as an input where possible (climate change, nitrogen deposition)
    • Use local experts to assess region-specific aspects (land-use change)
  • The MA is a multi-scale assessment with multiple layers of nesting e.g. Central America e.g. Honduras e.g. El Paraíso
  • Methodology of multi scaled assessment:Eururalis
  • Examples Sub Global Assessments (SGAs). Multi-scale assessments
  • Story of the present: Writing post-its
  • Discussing relationships between factors
  • Final product Climate Water Land use change Population, Migration Environmental education Regional Policies Agrarian Policies Desertification
  • Creating the scenarios
  • Presenting the scenarios
  • Backcasting exercise: Multifunctional sustainable agriculture (BiB)
  • Quick reference scenario exercise Phase 1 Phase 2 Phase 3 Phase 4 Scenario exercise Source: Ecosystems and human well-being: A manual for Assessment Practitioners Neville Ash et all, 2010
  • Phase 1: How to set up a scenario exercise Understand context, aim of scenario exercise Identify and agree on type of support to be given Agree on expected outcome in terms of process and product Define scope: Budget and time frame Geographical scale and time horizon Type of scenarios and analysis Set up a project team and environment: Establish authorising environment Decide who to involve in the process and when Define role of stakeholders 1 2 3 4 5
  • Phase 2: How to develop scenarios Identify main concerns and stakeholder questions and understand how past changes have come about Establish scenario development procedure and decide on method( inductive, deductive or incremental Analyse main drivers of change in future Discuss possible trends for each driver Identify the main uncertainties for the future Develop a set of scenario logics Describe scenario assumptions and story lines based on identified drivers and scenario Optional: use models to quantify main trends and assumptions Stage 1 2 3 4 1 Stage 2 Stage 3
  • Phase 3: How to analyze scenarios Determine whether, what and how to quantify. Check: Need and role quantitative information Availability of quantification tools Availability of budget and time Time horizon of analysis Which need to be assessed To what extent models need to be coupled Analyze implications of individual scenarios Optional: Quantify driving forces and impacts Assess ecosystems and human well being implications Optional: Analyze specific response options Analyze across the set of scenarios: Identify reasons for differences across scenarios Identify differing, similar and offsetting trends Optional: Analuze response options in scenarios 2 3 1 Policy options: e.g. climate adaptation and mitigation
  • Phase 4: How to use and communicate scenarios Map target audience and context conditions: Do a network analysis regarding actors, relationships, information needs and habits Map purpose to context: Check consistency ,credibility, saliency and legitimacy Assess what can reasonably be done with resources Develop outreach and communication strategy Develop clear success criteria Ensure steady high level support and backing Resent scenarios to target audience(s) Discuss implications, response options and lessons learnt Evaluate and monitor outreach action against your success criteria 1 2 3 4 5
  • Practical steps: Storyline And Simulation approach Narrative storylines Model runs
  • Practice: What are the scenario archetypes? IPCC SRES A1 GEO-3 Markets First OECD Reference MA Global Orchestration MedAction Big is Beautiful Solidarity/Pro-active Self-interest/Reactive Regional Global IPCC SRES B1 GEO-3 Sustainability First MA Techno Garden MedAction Knowledge is King IPCC SRES A2 GEO-3 Security First MA Order from Strength MedAction Big is Beautiful? IPCC SRES B2 MA Adaptive Mosaic
  • Practice: What are the scenario archetypes? Global Markets Global Sustainability Continental Barriers Regional Sustainability Solidarity/Pro-active Self-interest/Reactive Regional Global
  • Example: Characteristics of Global Markets scenario Main drivers Population growth: low increase quality of life Economic development: very rapid Technology development: rapid new inventions, but no magic Environmental attitude: reactive no environmental laws and policies Trade increase (globalisation) Institutional strength policies help economy State of environment very poor Main objective economic growth
  • Wilbert van Rooij, March 2009
    • Determine local consequences climate change on main drivers E.g. on Agriculture :
    • Change of productivity because of floods, drought, salinization ;  migration of land use to other areas
    •  extensification vs intensification  import of foodcrops?
    • E.g. on Biodiversity :  Habitat change
    •  Increased pressure on natural resources because of migration
      • Etcetera
    Effects of climate change on land use scenario
  • Link with policy alternatives for climate change adaptation and mitigation + biodiversity conservation
    • Land use scenario should be as specific as possible on policy options or alternatives for climate adaptation and mitigation and biodiversity conservation.
    • Example: Possible options in relation to biodiversity conservation:
    • Extension of protected area system
    • Conversion to organic type of farming
    • Investing in intensification of agriculture (new technologies)
    • Reforestation programmes
    • Avoiding land abandonment
    • Sustainable use of natural ecosystems
  • Link with CLUE-s model
  • Demand: How much will be the change (in ha) of all major land uses? Spatial policies: New national parks, restricted areas, agricultural development zones Location characteristics: Infrastructure (new roads?) = change in accessibility Soil (soil degradation?) Population (migration because of globalisation?) Etc. Conversion settings Transition possibilities from one land use type to another Link with CLUE-s model
  • National scenarios – essential characteristics The scenario should be: 1. Consistent with the assumptions of a selected archetype scenario 2. Consistent with current national trends 3. Creative! (do not use archetype as straitjacket) 4. As specific as possible on policy options for conversation 5. Linked with CLUE-s where possible
  • National scenarios – essential elements Your scenario could have information on: Factors: Sectors: Actors: Economic development Agriculture * Government Population growth Tourism Businesses Consumption pattern Energy NGOs Technology Water Environmental Policies Forestry Scientists Protected area Institutions Urban area State of environment etc. (Biodiversity!) * Share intensive / extensive agr. area
  • Translating storylines
    • Quantitative differences in land use areas (demand)
      • Agricultural demand
      • Intensification
      • Reforestation
      • Focus on export crops (perrennials)
    • Spatial differences (conversion matrix/region file)
      • Parks
      • Restricted areas
    • Behavioural differences (suitability maps/elasticities)
      • Subsidies
      • Awareness (erosion?)
      • Learning
      • Farming system change
  • Quantifying demand changes for input CLUE
    • Arable land / Grassland
      • Compare with trend from FAO and IMAGE simulations
      • Only use trend (e.g. % change between 2000 and 2030)
      • Divide by perennial/arable crops depending on scenario conditions
    • Nature
      • Agricultural expansion mostly at cost of nature?
    • Quantification of land use scenarios essential for model to allocate future land use  production of future land use map
  • Example FAO statistics
  • Example IMAGE simulation output
  • Wilbert van Rooij, May 2011 Bridging gap between numeric and geographical data: Baseline scenario 1: Extract numeric data from available resources: A: Global: FAO (website), Global Assessments (MA, OECD, GBO, IPCC) B: National: Development reports (agricultural + forestry department Outlooks (Vietnam: Agenda 21, MDG report, etc) Census data from statistical department Specialists (Socio-Economists. Agronomists, Forestry planners, Environmentalists, etc) 2: Aggregate land use classes So that you can compare spatial and numeric data and for which you have future data 3: Create trends, historical and for planned time horizon 4: Compare geographical areas aggregated land use classes of the land use map with the areas derived from non spatial sources 5: Interpret reasons for difference, adjust numeric data and use relative differences for creation of demand table
  • Wilbert van Rooij, March 2009 Bridging gap between numeric and geographical data: Baseline scenario + policy option
    • Determine different trends in land use change because of implementation of selected policy option(s)
    • Analyze national policies / plans with respect to climate adaptation and mitigation
    • Example adaptation: Support to farmers who want to change land use type: for example to more drought resistant crop types
    • Example mitigation: Increasing imports reduces pressure on natural resources
    • And finally quantify these changes in the demand table for this policy option. Per policy option 1 demand table.
  • Example scenario: Vietnam
    • 1: Baseline scenario :
    • Cropland demand: IMAGE OECD baseline scenario + 25% for 2030
      • - Cropland diversification: extensive (80%) and intensive (20%)
    • Plantation demand: increment of 500 km 2 /year.
    • Primary forest assumed to remain constant
    • 2: Biodiversity conservation policy option:
    • Primary forest: total forest cover (plantation + primary class) in 2030 will reach 40% of country land area
    • Protected areas (PAs) increase from 7% to 10% of the land:
      • - existing parks and primary forests above 1000 m
    • Strict law enforcement (no Land use change inside PA’s)
  • Forest scenario Vietnam used in Clue Heavily disturbed forest Slightly disturbed forest Regrowth shrub and bushes Shifting cultivation (ext.agr) Degraded lands Intensive agriculture Residential and urban land Year 2000 is the baseline derived from the current land use map. The rest are projections from a scenario     Primary forest Plantation Nature Others Area 2000 0 26020 20763 36142 19691 13963 17846 47162 87488 11634 18229 28525 32,7463 2001 1 26124 21563 38226 21115 15830 15704 43870 86619 11657 18229 28525 32,7463 2002 2 26232 22411 40257 22375 17698 13820 40743 85493 11681 18229 28525 32,7463 2003 3 26344 23305 42238 23479 19565 12162 37772 84141 11704 18229 28525 32,7463 2004 4 26460 24244 44169 24434 21433 10702 34950 82590 11727 18229 28525 32,7463 2005 5 26581 25227 46052 25247 23300 9418 32269 80863 11751 18229 28525 32,7463 2006 6 26708 26253 47617 26254 24513 8288 30049 79254 11774 18229 28525 32,7463 2007 7 26839 27312 49143 27150 25727 7293 27939 77508 11798 18229 28525 32,7463 2008 8 26975 28404 50631 27940 26940 6418 25936 75644 11821 18229 28525 32,7463 2009 9 27117 29527 52081 28630 28153 5648 24032 73674 11845 18229 28525 32,7463 2010 10 27265 30682 53495 29225 29367 4970 22224 71612 11869 18229 28525 32,7463 2011 11 27418 31866 52255 29730 30580 4374 20506 72088 11893 18229 28525 32,7463 2012 12 27578 33013 51045 30148 31793 3849 18874 72492 11916 18229 28525 32,7463 2013 13 27743 34124 49865 30485 33007 3387 17324 72834 11940 18229 28525 32,7463 2014 14 27913 35200 48715 30745 34220 2981 15851 73120 11964 18229 28525 32,7463 2015 15 28089 36242 47594 30931 35433 2623 14452 73357 11988 18229 28525 32,7463 2016 16 28271 37250 46501 31047 36647 2308 13123 73551 12012 18229 28525 32,7463 2017 17 28457 38227 45435 31096 37860 2031 11860 73708 12036 18229 28525 32,7463 2018 18 28648 39171 44395 31083 39073 1787 10660 73831 12060 18229 28525 32,7463 2019 19 28844 40085 43382 31009 40287 1573 9520 73924 12084 18229 28525 32,7463 2020 20 29044 40970 42394 30878 41500 1384 8438 73993 12108 18229 28525 32,7463
  • Interpolation of land use area data Select regression type: liner, logarithmic, polynomial, etc.   FRA2005 1990 1995 2000 2010 Prim. for 118 100 93 Sec. for 90 100 105
  • Scenario information is used for the geographical allocation of future land use and to determine its pressure on biodiversity + + + + Lu model + Globio MSA_lu2020 MSA_infr2020 MSA_frag2020 MSA_nitr2020 MSA_clim2020
    • Thank you for your attention!
    • Time for Questions .