Polasky decision making under great uncertainty
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Polasky decision making under great uncertainty






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  • Should you get rid of one of these – confusing.. Does not change relative ranking across scenarios
  • Actual prices in red versus no price change in green. No price change reflects the change from land use Prices dropped and cost rose between 96-01 for agriculture products. Urban and forestry prices rose.

Polasky decision making under great uncertainty Polasky decision making under great uncertainty Presentation Transcript

  • Decision-Making under Great Uncertainty: EnvironmentalManagement in an Era of Global Change Stephen Polasky University of Minnesota
  • Introduction Two large related issues of ecosystem management: Management of ecosystems to provide multiple ecosystem services  Spatially-explicit integrated ecological-economic analysis Management under uncertainty in an era of global change  Global change clouds our ability to accurately predict future conditions
  • Current state of affairs Ecosystems provide a wide array of goods and services of value to people (“ecosystem services”) Human actions affect ecosystems and the services they provide The provision of ecosystem services often is not factored into important decisions that affect ecosystems As a result, we often do a poor job of ecosystem management
  • The MillenniumEcosystemAssessment (2005)Ecosystems andbiodiversity areessential for humanwell-beingEcosystem servicesas a centralorganizing principle
  • Current state of affairs New global era – “anthropocene” Human actions driving global environmental conditions (“global change”)
  • If humanity is driving –where are we headed?
  • Introduction Global change issues are complex and the consequences of decisions are often highly uncertain Large spatial and temporal scales Large stakes involved Vitally important to try to take account of present and potential future consequences in decision-making But very difficult to do so
  • Introduction How can we best guide decision-making to meet present and future human needs* in an era of global change given pervasive uncertainty? Note: could substitute alternative objectives for human needs  Biodiversity conservation  Environmental health  Evolutionary process
  • Outline An approach to ecosystem management taking account of multiple ecosystem services at landscape scales  Examples from Minnesota and Oregon Approaches to decision-making under uncertainty:  Decision theory  Scenario planning  Thresholds approach  Resilience thinking Thoughts on integrated assessment of ecosystem management under great uncertainty
  • Assessing multiple ecosystemservices at landscape scales
  • A research agenda for ecosystem services Policy (1) Incentives Decisions by firms decisions and individuals (3) Non- anthropocentric (2) Actions approaches Other (5) Biophysical (7) Economic tradeoffs Ecosystems considerations efficiency (4) Ecological production functions Benefits Ecosystem and costs services (6) ValuationPolasky & Segerson Annual Review of Resource Economics 1: 409-434.
  • The Natural Capital Project:Mainstreaming ecosystem services
  • “InVEST” Integrated Valuation of Ecosystem Services and Tradeoffshttp://www.naturalcapitalproject.org/InVEST.html Frontiers of Ecology and Environment Feb 2009
  • Modeling multiple ecosystem services and tradeoffs at landscape scalesNelson et al. 2009. Frontiers in Ecology and Environment 7(1): 4–11.
  • Modeling multiple services under alternative scenarios Three scenarios of land use / land cover change for the Willamette Basin developed by the Willamette Partnership for 1990 – 2050 • Plan trend • Development • Conservation
  • Modeling multiple services under alternative scenarios Model outputs: service provision and biodiversity  Water quality  Storm peak mitigation  Soil conservation (sediment retention)  Climate stabilization (carbon sequestration)  Biodiversity (species conservation)  Market returns to landowners (agricultural crop production, timber harvest and housing values)
  • Outputs through time
  • Ranking of scenarios depends on set of ecosystem services considered
  • The Impact of Land Use Change on EcosystemServices, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota Photo by Raymond Gehman, National GeographicPolasky et al. Environmental and Resource Economics 2011
  • Introduction Use InVEST to analyze how changes in land use in Minnesota affect ecosystem services and biodiversity conservation Compare the impact on ecosystem services & biodiversity from:  Actual land use change from 1992- 2001  Alternative land use change scenarios
  • Land use scenarios Use National Land Cover Database (NCLD) for 1992 to 2001 for data on actual land use change in Minnesota Alternative land use scenarios:  No agricultural expansion  No urban expansion  Agricultural expansion into highly productive soils  Forestry expansion into highly productive forest parcels  Conservation: low productivity ag land and ag land within a 100 m buffer of waterways in MN River watershed were converted to pre-settlement vegetation
  • InVEST outputs Ecosystem services  Carbon sequestration  Water quality (phosphorus exports in the Minnesota River Basin) Biodiversity  Grassland bird habitat  Forest bird habitat  Overall biodiversity (all natural habitat) Returns to landowners  Value of agricultural production  Value of timber production  Value of urban/suburban development
  • Change from 1992 to 2001 by scenario: carbon sequestrationMg C
  • Change in phosphorus exports to mouth of Minnesota RiverMg P/yr
  • Percentage change in habitat quality for grassland breeding birds
  • Percentage change in habitat quality for forest breeding birds
  • Change from 1992 to 2001 by scenario: market returns to agriculture, forestry, urban Agriculture Forestry UrbanMillion 1992 US $
  • Annual value from land use change scenarios 1992-2001 Actual land No ag No urban Ag Forest Conser- use expansion expansion expansion expansion vationChange in total value:carbon, water quality, ag& forest production, $3,328 $3,407 $3,040 $2,742 $3,300 $3,380urban using actualprices (M1992 $)Change in returns tolandowners: ag & forestproduction, urban using $3,320 $3,343 $3,027 $3,418 $3,292 $3,221actual prices (M1992 $)
  • Summary The failure to incorporate the value of ecosystem services in land use planning can result in poor outcomes  Low level of ecosystem services  Low value of total goods and services from landscape Agricultural land use change had a bigger effect on ecosystem service value and biodiversity than urbanization  Result is largely due to the fact that there is far more agricultural land than urban land  Urban land: generates negative externalities but the direct value of urban land use is high  Agriculture: generates negative externalities but with lower direct land use value
  • Challenge Analysis assumes we know the links between  Action and ecosystem impact  Ecosystem functions and ecosystem services  Ecosystem services and human well-being Each link is subject to considerable uncertainty Global change means that future cause-effect relationships may look quite different than current relationships
  • Decision-making under uncertainty
  • Decision theory Standard approach for rational choice under uncertainty Specify objective function (e.g., maximize expected human well-being) Define uncertainty: probability of alternative states Use best available information to specify how states and actions combine to form outcomes (probability of outcomes) Define the net benefits of different outcomes Choose action that maximizes the objective function
  • Example: guilty or not guilty? Actual condition Guilty Innocent Guilty Correct False conviction (Type I error)Decision decision Innocent False release Correct (Type II error) decision
  • Example: guilty or not guilty? Version 1 Actual condition (Unknown) Guilty (p) Innocent (1-p) Guilty 20 -40Decision Innocent -5 10
  • Guilty or not guilty? How high does the probability of guilt need to be before you issue a guilty verdict? Expected value of guilty verdict: 20P+(-40)(1-P) = -40+60P Expected value of innocent verdict: -5P+10(1-P) = 10-15P As long as the probability of guilty is at least 2/3, then issue a guilty verdict (otherwise not)
  • Example: guilty or not guilty? Version 2 Actual condition (Unknown) Guilty (p) Innocent (1-p) Guilty 20 -400Decision Innocent -5 10
  • Guilty or not guilty? As long as the probability of guilty is at least 0.9425 then issue a guilty verdict (otherwise not) If the cost of false imprisonment is high then you must have a very high probability of guilt before issuing a guilty verdict “Innocent until proven guilty” – high cost of false imprisonment (US criminal law)
  • Application to climate change and mitigation actions Actual condition (Unknown) Low sensitivity High sensitivity to climate to climate change (p) change (1-p) Business-as- 20 -400 usualDecision Reduce GHG -5 10 emissions
  • Decision theory Would only choose to continue on business-as-usual path if you placed a probability of 0.9425 of low sensitivity to climate change
  • Decision theory Decision theory is NOT hypothesis testing  Do not need to be 95% sure that climate change is real before we act  Decision theory applied here minimizes expected losses  Even if potential loss is uncertain, the optimal action is often to avoid large potential losses
  • Risk versus uncertainty Critics of decision-theory say that you may not know what the probabilities are “True uncertainty”  Unknown probabilities (P)  Unknown outcomes (don’t know possible states) Former Defense Secretary Donald Rumsfeld:  “As we know, there are known knowns. There are things we know we know. We also know there are known unknowns. That is to say we know there are some things we do not know. But there are also unknown unknowns, the ones we dont know we dont know.”
  • Typology of uncertainty
  • Risk versus uncertainty With known probabilities of all possible events – maximizing expected utility is a reasonable rule With “unknown unknowns” what do you do? Almost all important global change issues have some element of “unknown unknowns”
  • Decision theory response to uncertainty challenge Assign subjective probabilities then proceed to maximize expected utility Subjective probabilities – combination of available information and best guesses (or opinion) Predicting future mean global temperature in 100 years with triple the greenhouse gas concentration from pre- industrial times • With some basic physics we can probably “narrow” the possible range • Could assign equal probability to every temperature in this range • Or use best available information to assess probabilities, for example, a normal distribution with a mean of +3C from today and standard deviation of +/- 2C
  • Other approaches to decision- making under uncertainty Social-ecological systems are complex systems The future trajectory of social-ecological systems under global change is subject to considerable uncertainty Hard to understand system behavior and harder still to predict the likely impacts of decisions Complexity has led some to think the decision theory approach is not well suited to provide guidance for managing social-ecological systems under global change
  • Scenario Planning Natural Capital Project
  • Scenario planning Scenario planning is a method for thinking creatively and systematically about complex futures Scenarios: sets of plausible stories, supported with data and simulations, about how the future might unfold from current conditions under alternative human choices Scenarios can address many important uncertainties and contrasting beliefs about the future of the system Example: IPCC scenarios of future emissions and consequences
  • Scenario planning Scenarios were first used in the analysis of global change during the 1970s More recent efforts:  Global Environmental Outlook  Special Report on Emissions Scenarios (SRES) of the Intergovernmental Panel on Climate Change (IPCC)  Millennium Ecosystem Assessment These studies explored widely contrasting alternative visions using quantitative models and a diverse set of quantitative indicators
  • Scenario planning Advantage of scenario planning is that it expands horizons (“blue sky thinking”) that gets people thinking about potential outcomes Weakness: difficulty of assessing the likelihood of alternative futures SRES presented six scenarios – all “equally sound”  High uncertainty prevented realistic assessments of probabilities  Criticism that the lack of probabilities limited the value of the scenarios to decision-makers Scenario planning can be combined with more quantitative decision theory analysis  Scenario planning as first stage – get universe of potential outcomes  Robust decision-making to analyze more and less desirable decisions in light of potential outcomes
  • Thresholds approach Rockstrom et al. Nature 2009
  • Thresholds Approach Social–ecological systems are complex adaptive systems that can exhibit nonlinear dynamics, historical dependency, have multiple basins of attraction and limited predictability When crossed, thresholds between multiple basins of attraction can lead to fundamental transformations in system feedbacks and dynamics
  • Thresholds Approach Focus attention on critical boundaries that have major consequences if crossed Examples of the application of thresholds in global change:  Planetary boundaries (Rockstrom et al. 2009)  Limits on emissions to avoid dangerous climate change (e.g. cap of 450 ppm CO2e)  Thresholds are often used in regulatory or legal contexts to distinguish permissible from impermissible activities Thresholds can be used as a screen to rule out actions thought to have too high a risk of crossing a threshold or to rank actions based on risk
  • Thresholds approach There is often uncertainty about the exact level of a threshold Decision-making involves choices about what risks are acceptable: putting more stress on the system can increase current benefits but at a cost of having a higher probability of crossing a critical threshold Thresholds have been criticized as giving a false impression that degradation below the threshold level is ‘safe’ and improvements beyond a threshold are of no value
  • Resilience thinking Resilience Alliance
  • Resilience thinking Resilience thinking focuses on critical thresholds for system performance If current situation is desirable then manage in ways to increase resilience  Resilience: ability to withstand shocks/perturbations and remain within one basin of attraction (Hollings resilience) Build capacity to recognize and respond to emerging transformations before they occur Build capacity to adapt should transformation occur
  • Resilience Multiple basins of attraction and hysteresis Source: Scheffer et al. Nature 2001
  • Resilience Ways to shift between stable equilibria Source: Scheffer et al. Nature 2001
  • Resilience thinking Resilience thinking:  How can we remain within certain dynamic limits to avoid crossing thresholds and remain in a desirable state  In addition, how can be build capacity to cope with change should a change of regime occur Resilience thinking is more of a general framework for thinking than specific management prescriptions
  • Application to climate change and ecosystem management
  • Application to climate change Question: how much and how fast should we reduce GHG emissions? Dueling approaches to answering this question  Nordhaus Dynamic Integrated Climate Economy (DICE) Model  Stern Review of Climate Change Framing climate change as minimizing risk of crossing a threshold or maximizing expected utility
  • Stern Review on the Economics of Climate Change It is a “stern review” but in reality it is named for Nicholas Stern, an economist, who headed the study UK Treasury Department 2006 Created a splash  Proponents of strong action to address climate change trumpet the report  Critics say the report is fundamentally flawed and the conclusions are unwarranted
  • Stern Review on the Economics of Climate Change Main findings: “…the benefits of strong, early action considerably outweigh the costs.” “…if we don’t act, the overall costs and risks of climate change will be the equivalent to losing 5% of global GDP each year, now and forever. If a wider range of risks and impacts is taken into account, the estimates of damage could rise to 20% of GDP or more…” “Resource cost estimates suggest that an upper bound for the expected annual cost of emissions reductions consistent with a trajectory leading to stabilization at 550ppm CO2e is likely to be around 1% of GDP by 2050.”
  • Nordhaus DICE Model Strong action at present is at odds with most prior economic analyses of the climate change (e.g. Nordhaus analysis) Nordhaus policy recommendations: “climate change ramp” - low initial price of carbon that ramps up over time  Costs of moderate action are lower than rapid adjustment  Technological change lower future costs  Society will be richer in future and better able to afford costs associated with climate change This approach will not result in stabilization at 550 ppm CO2 – concentrations will continue to rise
  • Why do Nordhaus and Stern disagree? Discounting Estimates of damages Uncertainty – and general approach to climate change policy question
  • Uncertainty and general approachto climate change policy question Nordhaus: question of balancing costs and benefits  Takes estimates of benefits of avoided damages along with costs  Use integrated assessment model and inter- temporal optimization to find the efficient climate change policy
  • Uncertainty and general approach to climate change policy question Stern: risk assessment and cost  First do an analysis of risk and find a target for atmospheric concentrations (550 ppm of CO2e)  Second figure out cost-effective way of achieving the target On the standard cost-benefit analysis approach:  “As I have argued, it is very hard to believe that models where radically different paths have to be compared, where time periods of hundreds of years much be considered, where risk and uncertainty are of the essence, and where many crucial economic, social, and scientific features are poorly understood, can be used as the main quantitative plan in a policy argument.”  Modeling of costs and benefits to optimize emissions is “still less credible”
  • Application to ecosystem management Social-ecological systems: dynamic and interconnected Climate change problem is almost easy by comparison  Single dimension – CO2 concentration Ecosystem management – multiple dimensions  Water quality  Water flow (flood protection, drought mitigation)  Habitat and species  Agriculture & timber productivity  Livelihoods and jobs… Do we understand systems well enough to predict short- term and long-term consequences of management actions on services?
  • Application to ecosystem management Path forward in ecosystem management  Building systems models to improve understanding of potential system dynamics  Verification of models with data  Sensitivity analysis to probe uncertainty  Scenario thinking to broaden scope of analysis (reduce blindspots)  Focus on potential thresholds with dramatic consequences for system performance  Provide early warning to avoid thresholds and plan for adaptation in case thresholds are crossed
  • Conclusions Paul Krugman writing about the financial crisis (September 2009):  “…an all-purpose punch line has become ‘nobody could have predicted. . . .’ It’s what you say with regard to disasters that could have been predicted…”
  • Conclusions Essential role for conservation professionals  Provide better understanding of social- ecological systems  Highlight potential large-scale changes that could have detrimental impacts  Provide early warning signs of danger  Provide guide-rails to keep us from crossing thresholds
  • Conclusions We do not know enough BUT… We know enough to improve on current performance The long road rather than the quick fix:  Better science to improve understanding  Better institutions/policy that incorporates science and reduces risks  Adaptive process that learns through time