Roy Haines-Young & Marion Potschin Centre for Environmental Management, School of Geography, University of Nottingham

Transcription

1 Roy Haines-Young & Marion Potschin Centre for Environmental Management, School of Geography, University of Nottingham

2 Elites Rational, evidence-based policy? Consent? Science, or embedded rationalism Experts Policy = framework for action Trust? Engagement? Understanding? Post-normal science Publics 2

3 A new paradigm? 3

4 Core 5 principles involves: 1. taking a more holistic approach to policy-making and delivery, with the focus on maintaining healthy ecosystems and ecosystem services 2. ensuring that the value of ecosystem services is fully reflected in decision-making 3. ensuring environmental limits are respected in the context of sustainable development, taking into account ecosystem functioning 4. taking decisions at the appropriate spatial scale while recognising the cumulative impacts of decisions 5. applying adaptive management of the natural environment to respond to changing pressures, including climate change. (Defra, 2007: Action Plan, 2010: update) 4

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6 Increasingly dominant way of looking at the world Driven by factors from outside science It challenges (some) conventional wisdoms Poses some puzzles that seem to have attracted a lot of research and policy interest... Nature Has Has value 6

7 Increasingly dominant way of looking at the world Driven by factors from outside science It challenges (some) conventional wisdoms Poses some puzzles that seem to have attracted a lot of research and policy interest... Nature...and we can tell you what it is!! Has Has an an economic value 7

8 Increasingly dominant way of looking at the world Driven by factors from outside science It challenges (some) conventional wisdoms Poses some puzzles that seem to have attracted a lot of research and policy interest......and we can tell you what bits of nature are important! Ecosyste m Services Has Has an an economic value...and we can tell you what it is!! 8

9 Paradigms (Theories too?) give us insight and make us blind 9

10 Critical levels of natural capital? Biophysical structure or process (e.g. woodland habitat or net primary productivity ) Function (e.g. (e.g. slow slow passage passage of of water, water, or or biomass) biomass) Limit pressures via policy action? What is the nature of this relationship? Σ Pressures Service Service (e.g. (e.g. flood flood protection, protection, or or harvestable harvestable products) products) Benefit Benefit (e.g. contribution to (e.g. contribution to aspects of well-being aspects of well-being such as health and such as health and safety) safety) Value Value (e.g. willingness to pay (e.g. willingness to pay for woodland for woodland protection or for more protection or for more woodland, or woodland, or harvestable products) harvestable products) 10

11 Critical levels of natural capital? Biophysical structure or process (e.g. woodland habitat or net primary productivity ) Function (e.g. (e.g. slow slow passage passage of of water, water, or or biomass) biomass) Limit pressures via policy action? Σ Pressures Its all ecosystem, a social-ecological system!! Where is the ecosystem? Service Service (e.g. (e.g. flood flood protection, protection, or or harvestable harvestable products) products) Benefit Benefit (e.g. contribution to (e.g. contribution to aspects of well-being aspects of well-being such as health and such as health and safety) safety) Value Value (e.g. willingness to pay (e.g. willingness to pay for woodland for woodland protection or for more protection or for more woodland, or woodland, or harvestable products) harvestable products) 11

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13 New insights and puzzles? 13

14 Biophysical structure or process Function Service Service Benefit Benefit Value Value 14

15 Biophysical structure Function Service Benefit Value Apple tree >100, e.g. Carbon storage Sequest Carb Help fight gw Pest and enemy predation Pest control Increased yield and inc pesticide Soil Purification processes Clean drinking water Reducing climate Avoid damage change costs 52 Replacement costs joy 15

16 Is there a simple direct relationship between biodiversity and service output? What in fact do we mean be biodiversity? Can the results of manipulative experiments be scaled up to whole landscapes? Ecosystem function B A Biodiversity Schwartz et al. (2000) Balvanera et al. (2006) EASA (2009) and others C 16

17 Service providing units (SPUs) & Ecosystem Service Providers (ESP) Luck et al. (2009) BioScience 59:

18 Ecosystem services are the outputs of ecosystem functioning that directly contribute to human well-being. Biophysical structure or process (e.g. woodland habitat or net primary productivity ) Function (e.g. (e.g. slow slow passage passage of of water, water, or or biomass) biomass) Service Service (e.g. (e.g. flood flood protection, protection, or or harvestable harvestable products) products) Benefit Benefit (e.g. contribution to (e.g. contribution to aspects of well-being aspects of well-being such as health and such as health and safety) safety) Value Value (e.g. willingness to pay (e.g. willingness to pay for woodland for woodland protection or for more protection or for more woodland, or woodland, or harvestable products) harvestable products) 18

19 There is a pressing need to understand how these production systems work And how the outputs change in response to the pressures upon these systems and our management interventions But empirical, observational data are often lacking and or difficult to collect.. Production systems ~ production functions 19

20 These production systems cross disciplinary divides and require us to combine quantitative and qualitative information Moreover, they are socially constructed and depend fundamentally on how different groups (experts, decision makers, publics) construct the world They also should capture the uncertainty that exists in the insights we have about the way the world works 20

21 Tools 21

22 These are many modelling approaches but Bayesian Networks (BN) are a good way of constructing narratives and of combining different kinds of knowledge They can help build up this rich picture of the problem from the stakeholder/client perspective, by: Helping capture stakeholder's views of the world Modelling Service Providing Units (SPUs), etc. [The production cascade] By helping constructing scenarios and identifying values 22

23 Software: NETICA Many other systems available HUGIN BayesBuider... 23

24 Cain (2001) defines a BN as a graphical tool for building decision support systems. Cain (2001) argues that they are mainly used to help make decisions under uncertain conditions. Basically they are a way of structuring our beliefs about the way systems work in terms of a set of relationships that have probabilities associated with them. 24

25 Slugs State of lettice Buy slug stuff 25

26 Slugs State of lettice Buy slug stuff Peacock 26

27 Abundant Rare Slugs Peacock Present 50.0 Absent 50.0 State of lettice Leafy 32.5 Holey 67.5 Buy slug stuff Yes 23.3 No 76.7 An influence diagram consisting of: nodes (variables that can be in different states relationships (edges) that define the dependencies A Conditional Probability Table, CPT 27

28 Abundant Rare Slugs Peacock Present 50.0 Absent 50.0 State of lettice Leafy 32.5 Holey 67.5 Buy slug stuff Yes 23.3 No

29 Abundant Rare Slugs Peacock Present 50.0 Absent 50.0 State of lettice Leafy 10.0 Holey 90.0 Buy slug stuff Yes 41.0 No

30 Abundant Rare Slugs Peacock Present 0 Absent 100 State of lettice Leafy 20.0 Holey 80.0 Buy slug stuff Yes 82.0 No

31 Abundant Rare Slugs Peacock Present 100 Absent 0 State of lettice Leafy 0 Holey 100 Buy slug stuff Yes 0 No

32 Abundant Rare Slugs Peacock Present 100 Absent 0 Why is there a link between peacock and buy slug stuff? State of lettice Leafy 10.0 Holey 90.0 Buy slug stuff Yes 1.00 No 99.0 Abundant Rare Slugs State of lettice Leafy 10.0 Holey 90.0 Peacock Present 100 Absent 0 Basically we need two pieces of information otherwise we could be wrong if we base the decision on holes alone. Compare the CPTs for our model and this simpler one Buy slug stuff Yes 91.0 No

33 Perhaps it is a bit clearer that Marion uses two pieces of information in her decision if we draw up a network like this. Abundant Rare Slugs Peacock Present 50.0 Absent 50.0 State of lettice Leafy 32.5 Holey 67.5 Marion buys slug stuff Yes 26.0 No 74.0 Marion Hears Peacock Rarely 50.0 Frequently

34 We use evidence and beliefs to set up the CPTs. Cain (2001) suggests the following hierarchy: Information Type 1: Raw data collected by direct measurement (e.g. groundwater depth measured by piezometer, population measured by census, income measured by accounting). Information Type 2: Raw data collected through stakeholder elicitation (e.g. stakeholder perceptions of groundwater depth, population and income). Information Type 3: Output from process-based models calibrated using raw data collected by direct measurement. Information Type 4: Academic expert opinion, based on theoretical calculation or best judgement. Type 1 is is to to be be preferred, then then type type 2, 2, 3 and and Often we we only only have have opinion! But But opinions and and values count 34

35 P.A. Aguilera et al. (2011) Bayesian networks in environmental modelling. Environmental Modelling & Software xxx, 1-13 Newton A et al. (2007) Bayesian Belief Networks as a tool for evidencebased conservation management. Journal for Nature Conservation 15(2): Stelzenmüller V et al. (2010) Assessment of a Bayesian Belief Network- GIS framework as a practical tool to support marine planning. Marine Pollution Bulletin 60(10): Smith CS et al. (2007) Using a Bayesian belief network to predict suitable habitat of an endangered mammal - The Julia Creek dunnart (Sminthopsis douglasi). Biological Conservation 139(3-4): Zorrilla P, et al.(2010) Evaluation of Bayesian networks in participatory water resources management, upper Guadiana Basin, Spain. Ecology and Society 15(3) [online]. Haines-Young R. (2011) Exploring ecosystem services issues across diverse knowledge domains using Bayesian Belief Networks. Progress in Physical Geography, in press 35

36 Biophysical structure or process (e.g. woodland habitat or net primary productivity ) Limit pressures via policy action? Σ Pressures Function (e.g. (e.g. slow slow passage passage of of water, water, or or biomass) biomass) Service Service (e.g. (e.g. flood flood protection, protection, or or harvestable harvestable products) products) Marginal values- How sensitive are benefit outputs to changes in ecosystem function? Benefit Benefit (e.g. contribution to (e.g. contribution to aspects of well-being aspects of well-being Value such as health and Value such as health and (e.g. willingness to pay safety) (e.g. willingness to pay safety) for woodland for woodland protection or for more protection or for more woodland, or woodland, or harvestable products) harvestable products) (after Haines-Young and Potschin, 2010) 36

37 Scenarios Opinions about probability of Impacts of scenarios on indicators Opinions on limits of acceptable change Change in utility (Value) Opinions on importance of indicator 37

38 Scenario Connect for life Go for Growth Keep it local Succeed through Science 2.5 ± Overall Value Value Defined Limit for Engagement ± 1.4 People's engagement indicator ± 1.3 Can capture some aspects of ± 1.6 participatory processes Defined Limit for Landscape Defined Limit for Biodiversity Eng_impact -2 0 Landscape_impact -2 Above Above Below Below ± ± 1 Landscape indicator ± ± 0.79 Biodiversity indicator ± 1.9 Biodiversity impact Above 43.4 Below ± 0.99 Engagement importance V Important 41.2 Important 23.5 L Important ± 0.87 U_People Landscape importance V Important 40.0 Important 26.7 L Important ± 0.85 U_Landscape Biodiversity importance V Important 53.8 Important 46.2 L Important ± 0.5 U_Biodiversity 38

39 A sub-global assessment following the framework of the Millennium Ecosystem Assessment Sponsored by Central Government Started June 2009 ended January 2011 Involved over 300 researchers, users and clients 39

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41 This scenario is essentially a projection based on current trends and results in a future UK that is roughly based on today's ideals and targets... This is a future where society is more concerned with the immediate surroundings and strives to maintain a sustainable focus on life within that area Going with the flow Local stewardship A preservationist attitude arises because the UK can afford to look after its own backyard without diminishing the ever-increasing standards of living... Green and Pleasant Land UK NEA scenarios National security work World markets Under this scenario climate change results in increases in global energy prices forcing many countries to attempt greater self-sufficiency (and efficiency) in many of their core industries... The belief that the promotion of ecosystem services through the creation of multifunctional landscapes is essential for maintaining the quality of life in the UK is widely accepted.... High economic growth with a greater focus on removing barriers to trade is the fundamental characteristic of this scenario... 41

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43 Structure of Bayesian Belief Network used to make land cover projections for scenarios using Example output for projections of arable areas under the World Markets scenario for high and low climate change versions of the storyline. Map shows % difference in arable area between them for 2060; the differences between scenario outcomes are greatest in south where climate impacts are projected to be greatest. 43

44 A: Scenario Structure Function Service Significant Moderate None J: SeaLevel I: Flood risk GPL NW WM NS LS GF Input None Low Moderate Significant C1: AW_Density B: Altitude 0 to to 100 Unassigned Rural NearUrban Urban InnerCity Designated NotDesignated ± 30 D1: RuralUrban E1: Designation F1: ALCCode ± 2 Upland Lowland C: WoodlandPotential ASNWHigh ASNWLow D: UrbanInfluence Near Far F: AgriculturalLandClass High Med Low X: Cover2060 Arable 8.06 ImGrass 6.53 BL 13.2 Conif 9.21 Urban 9.96 SemiNat 10.7 Upland 10.0 Water 10.4 Coast 9.22 Sea ± 20 K: Cover2000 Arable 10.0 ImGrass 10.0 BL 10.0 Conif 10.0 Urban 10.0 SemiNat 10.0 Upland 10.0 Water 10.0 Coast 10.0 Sea ± 18 C3: carbon storage to 1 1 to 2 2 to to to 110 E2: Storage Change -110 to to to -1-1 to 0 0 to 1 1 to to to ± ± 20 C2: carbon storage 0 0 to 1 1 to 2 2 to to to ± 18 LC1 LC2 LC3 LC4 LC5 LC6 LC7 LC8 LC9 LC10 LC11 LC12 LC13 LC14 LC15 LC16 LC17 LC18 LC19 LC20 LC21 LC22 LC23 LC24 LC25 LC26 LC27 LC28 LC29 LC30 LC31 LC32 G: LandClass 44

45 a) Variation of vegetation carbon in 2000 predicted by BBN model b) Variation of vegetation carbon in 1990 estimated by Milne and Brown (1997). 45

46 Key: Change in average vegetation carbon 2000 to 2060 (T/ha) c) Green and Pleasant Land d) World Markets 46

47 GWF 47

48 They allowed us to combine different sorts of data into a single meta model. They allowed us to express scenarios systematically and understand the implications of our different assumptions They allowed the plausibility of (spatial) assumptions to be made more explicit They allowed rapid prototyping of ideas 48

49 They can help us describe how we believe the world is structured.. They allow us to combine different sorts of data They can help bridge different knowledge domains They help us bring evidence into the decision making arena, and express the uncertainties we have about it They are not dynamic models. They do no include feedback Data loss (categorisation of states) Challenges: How do we make them spatially explicit and testable? 49

50 A new paradigm? How do these production systems work? Analysis poses particular challenges because of their trans-disciplinary nature BBNs offer one way of linking different knowledge domains... 50