SAHRA Steve StewartHydrology and Water Resources, University of Arizona Potential applications of...
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Transcript of SAHRA Steve StewartHydrology and Water Resources, University of Arizona Potential applications of...
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Potential applications of economic tools to weather and
society interactionsSteve Stewart
Presented at WAS*IS
Boulder, CO July 2006
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Topics
• Non-market valuation – Riparian restoration– Weather forecasts– Impact assessment
• Economic experiment– Behavior under extreme
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Value of improved weather/climate information
• Many ways to value (Katz & Murphy 97)– http://www.isse.ucar.edu/HP_rick/esig.html
• Many values to assess
• Better information can lead to social gains or losses (what is the metric for value?)
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
From Serreze et al., Water Resources Research 35(7), July 1999
Current approaches for estimating SWE and forecasting runoff are highly inaccurate.
Sierra Nevada: 67%
Colorado: 63%
Utah: 60%
Arizona/New Mexico:
39%
Snow Contributions to Annual Precipitation
Uncertain Supply – Estimating snow
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Uncertain Supply – Measuring precipitation
Source: Maddox, et. al. Weather and Forecasting, 2002.
Sparse rain gauge distribution Mountain blockage of radar
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Dscn1084.jpgDouglas “Lake”, East Tennessee
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Dillon Reservoir, Summit County, CO
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Blue Ridge Parkway, VA
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Elk, Yellowstone National Park
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRABuffalo, Yellowstone National Park
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Bald Eagle. Near Muddy Gap, WY
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA 11 endangered mussels, Clinch River Valley, TN-VA
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Environmental Economics: Valuing Changes
Hope to Improve Efficiency/effectiveness of Restoration:
About $1 Billion Spent per year (National River Restoration Science Synthesis)
System Changes
HydrologyBiology
Valuing River Restoration – integrating biology, hydrology and economics
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Different Riparian Resources • Rio Salado de Oeste in Phoenix• San Pedro National Riparian Conservation Area• Rio Grande Bosque in central New Mexico
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Sources of Riparian Value expanded from Postel & Richter, 2002
Use and Non-Use Values
• Natural resource supplies• Recreation opportunities• Property value• Biodiversity conservation• Flood control• Scenic value & inspiration• Air and water quality
enhancement• Tourism (regional)• Long-term community impact
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Instream Flow • Changing Preferences• Scarcity of Riparian
Corridors• Restoration vs. Extraction • Legal Barriers• Valuation Difficulties
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Non-Market Goods: Public Goods
• Non-exclusive• Non-rival
• Where is the market?– River Restoration– National Defense– Weather/climate information
Pric
eQuantity
Demand
Consumer Surplus
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Non-Market Valuation Methods
• Stated Preference– Hypothetical Market
• Contingent Valuation Method• Choice Experiment
• Revealed Preference– Observed Behavior, Related Market
• Hedonics• Travel Cost Method• Avoidance behavior
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
+ = $ ???
Valuation of restoration of the Albuquerque Rio Grande Bosque
Stewart and Weber
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Albuquerque Bosque
• US Army Corps – Study Partner• Policy Context - Actual Ongoing Restoration• Public Input Needed – Survey
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Survey Development
• How do people use and perceive the bosque?
• What is bosque restoration worth?
• What are recreation amenities worth?
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Describing Bosque Restoration• Define Attributes & Levels• Focus Group Meetings
– What hits the public radar• Research Partner Meetings
– What work is planned
TrailsTrails
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Metrics of Bosque Restoration
1. Bird and Wildlife Habitat (Habitat Suitability Index) (5, 7, 8)
- HSI Scale 0-102. Vegetation Density (Full, Moderate, No Thinning)
- Appearance- Water Use- Fire Risk
3. Native vs. Non-Native Trees (Native Dom, Equal, Non-Native Dom)
- Cottonwood, New Mexico Olive- Tamarisk, Russian Olive
4. Natural River Processes (Some, none)
- Naturalized Flooding- Removing Bank Stabilization
5. Cost: Increase in sales tax ($125 - $0)
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Survey Instrument
• Bosque Usage Patterns• Travel Cost Model• Willingness to Pay
Restoration State Avoid Loss
• Choice Experiment Ecosystem services
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Variable Coeff. St.Err. t-stat. WTP
Constant -1.102 0.661 -1.667
Restoration Cost -0.015 0.005 -2.938*
Habitat Score = 7 -0.768 0.508 -1.513 - $51
Habitat Score = 8 0.046 0.495 0.093 $3
Moderate Thinning 1.675 0.626 2.676* $111
Fully Thinned 1.322 0.647 2.044 $88
Equally Native & Non 2.876 0.741 3.88* $191
Native Dominant 2.600 0.720 3.612* $173
Natural River Processes 0.702 0.506 1.388
Native_Process -0.102 0.654 -0.155
Mix_Process -1.289 0.669 -1.927
Hab7_Process 1.41 0.534 2.644
Hab8_Process 0.544 0.546 0.997
FullThin_Native -1.675 0.755 -2.219
FullThin_Mix -1.433 0.716 -2.00
ModThin_Native -1.256 0.707 -1.776
ModThin_Mix -2.251 0.724 -3.108
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Whither Homo Economicus?
The role of economic experiments and behavioral economics in
weather/climate research
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Whither Homo Economicus?
• Rationality• Expected utility• Individuals may pursue goals other than
maximization of expected utility provided that those goals are self-consistent
• Problem in the lab is measurement of these goals
• Rationality is usually a useful approximation
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Why Experimental Economics?
• EE examines the fundamentals of economic behavior
• With EE, we are committing to models that are behavioral in character
• Complements traditional methods– EE is or will eventually become a traditional
method
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Economic Experiments
Tests of predictions of existing theory
• Test inclusion of variables on which the theory is silent
• Speak to policy issues/support a position
• Demonstration (forensic economics)
• How? Remove context/Induce value
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Recipe for Behavioral Game Theory (Camerer)
• Begin with a game or situation in which standard game theory makes a bold prediction
• If behavior is different than predicted, seek explanation
• Extend formal game theory to incorporate the results
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Individual Value Formulation
• What information do individuals and groups use to make decisions?
• How do risk, uncertainty, and ambiguity affect those decisions?
• How individuals make decisions vs. how humans interact
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Lessons from Cognitive psychology
• Evaluability (Hsee, et al)– Framing of low probability events– individuals don’t relate well to probabilities stated
as “the probability of a major storm event is .005”
• Loss aversion (Kahneman & Tversky 1979)– Gains and losses evaluated wrt reference point– Value losses more than equivalent gains
• Loss avoidance (Cachon & Camerer 1996)– Rule out certain behaviors on the part of others
(competitors, colleagues, EM agencies, etc)
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Experiment: Information as Insurance
• Ganderton, et al 2000 JRU• Examine value of improved geologic mapping
information to prevent earthquake losses• Natural disasters as low probability/high loss
events• In many areas natural disasters are
inevitable, yet individuals don’t take measures to avoid, prepare, or insure from loss.
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
P
N
E
P
N
Buy insurance?
YES
NO
E
Large E loss
Small P loss
Large P loss
Small E loss
$0
E = episodic eventP = periodic eventN = no event
Nature
$0
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Experimental Parameters Values
Period Income 200
Cost of Policy 5,10, 25, 50, 99
Event probability (N, P, E) (.89, .1, .01) (.95, .04, .01) (.5, .4, .1)
Loss Probability (small, large) (.9, .1) (.7, .3)
Magnitude of Loss
(small P, large P, small E, large E)
(0, 100, 100, 500) (0, 200, 100, 500)
(0, 200, 200, 1000)
32*2*5 = 90 possible combinations
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Maximize Expected Utility?
• EU theory: buy information (insurance) if: E(utility w/information) >= E(utility w/o info)
• Buy Policy if: Cost <= E(loss) + risk premium
• May not hold for low probability events
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Expected Utility and Low probability/high loss events
• Evidence that individuals do not maximize expected utility when probability of loss is low or magnitude of loss is very large
• Camerer and Kunreuther (1989); Thaler (80,83)– Conjunction fallacy– Optimism– Threshold effects
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Analysis
• Logit Prob(purchase policy)• Buy = const* -cost* -wealth* +exposure* -
experience* +smallloss +medloss –smallprob* -medprob* + lowprob* -riskindex*
• Value of a policy E(WTP) = $22.4
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
WTP/E(loss)
0
5
10
15
20
25
0.1 0.3 0.5 1 2 4 9 18
Whole
Risk Averse
Risk Seeking
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Analysis (2)
• Value of policy to insure against loss is greater for risk averse individuals than for others. There is a significant risk premium.
• Suggests that the value of information to reduce uncertainty is higher for risk averse subjects as well.
• If theory breaks down with low prob/high loss events, how can we determine whether reductions in uncertainty (improved forecasts) have value?
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Thanks!
Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA
Cropping choices in conjoint
Attribute Best forecast
Better forecast
Existing forecast
Value of production
$200 $275 $250
Episodic event
P*$100 P*$200 $P*225
Periodic Event
P*$10 P*5 P*$5
Cost $30 $15 $0