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


Topics

• Non-market valuation – Riparian restoration– Weather forecasts– Impact assessment

• Economic experiment– Behavior under extreme


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?)


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


Uncertain Supply – Measuring precipitation

Source: Maddox, et. al. Weather and Forecasting, 2002.

Sparse rain gauge distribution Mountain blockage of radar


Dscn1084.jpgDouglas “Lake”, East Tennessee


Dillon Reservoir, Summit County, CO


Blue Ridge Parkway, VA


Elk, Yellowstone National Park

Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRABuffalo, Yellowstone National Park


Bald Eagle. Near Muddy Gap, WY

Steve Stewart Hydrology and Water Resources, University of ArizonaSAHRA 11 endangered mussels, Clinch River Valley, TN-VA


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


Different Riparian Resources • Rio Salado de Oeste in Phoenix• San Pedro National Riparian Conservation Area• Rio Grande Bosque in central New Mexico


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


Instream Flow • Changing Preferences• Scarcity of Riparian

Corridors• Restoration vs. Extraction • Legal Barriers• Valuation Difficulties


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


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


+ = $ ???

Valuation of restoration of the Albuquerque Rio Grande Bosque

Stewart and Weber


Albuquerque Bosque

• US Army Corps – Study Partner• Policy Context - Actual Ongoing Restoration• Public Input Needed – Survey


Survey Development

• How do people use and perceive the bosque?

• What is bosque restoration worth?

• What are recreation amenities worth?


Describing Bosque Restoration• Define Attributes & Levels• Focus Group Meetings

– What hits the public radar• Research Partner Meetings

– What work is planned

TrailsTrails


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)


Survey Instrument

• Bosque Usage Patterns• Travel Cost Model• Willingness to Pay

Restoration State Avoid Loss

• Choice Experiment Ecosystem services


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


Whither Homo Economicus?

The role of economic experiments and behavioral economics in

weather/climate research


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


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


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


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


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


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)


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.


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


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


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


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


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


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


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?


Thanks!


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

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