John B. Braden University of Illinois at Urbana-Champaign

John B. Braden University of Illinois

at Urbana-Champaign

Economic Modeling for Water Resources

NSF Interdisciplinary Modeling Workshop – July 2005


Thanks:

Laurel Saito Heather Segale Xiaolin Ren


Contributions of Economics Understand Behaviors

– Responses to institutions & policies– Market power (size, information)– “Positive” analysis

Design Institutions & Policies– Benefit/cost analysis– Planning for behaviors– “Normative” analysis


Limitations of Economics

Anthropocentric Utilitarian Statistical Allocational (efficiency) Material


Economic Modeling

Theory – generate hypotheses

Econometrics – test hypotheses

Operations Research – simulate outcomes– optimize complex systems


Outline of Presentation

1. Basic Economic Models2. Pricing Aquatic Ecosystems3. Hydro-Economic Models4. Bio-Economic Models5. Benefit-cost Analysis6. Risk and Uncertainty7. Summary Remarks


Resources for Lecture

Griffin, R.C. Water Resource Economics. MIT Press (forthcoming)

Young, R.A. Determining the Economic Value of Water. Resources for the Future (2005)

Other books & articles on website


1. Basic Economic Models


Agent Models

Consumers Maximize Utility Max u(Y,w) , uy, uw > 0

uyy, uww < 0

s.t. PYY + pww < B

Producers Maximize ProfitMax π = p1y1 – Σi cixi – cww

s.t. y1 = f(X, w) , fx, fw > 0;

fxx, fww < 0


Marginal Analysis

Marginal benefits = incremental demand price

Marginal costs = incremental supply price

Operating returns vs. fixed costs


Supply Model – Input Choice

W

X

CSlope

C

01y

21y

11y

W

X

X

W


Supply Model – Output

1P

1y 1y

1P1

1

( , )X W

CS C C

y


Aggregate Supply

1y

1P

1pS 1

qS 1AggS


Demand Model

1 11

( , , )D

d P P BP

1y

1P


Aggregate Demand

1y

1P

1bd1

ad 1Aggd


Nonrival (“Public”) Goods

Rival – Ordinary goods that only one person can consume

Nonrival – Goods that can be consumed by many simultaneously– Excluability allows pricing


“ Public Goods” & Economic Value

azd

bzd Agg

zd

zP

z


Markets

Producers offer good & buy inputs

Consumers bid for goods & supply labor

Prices coordinate producers & consumers– Output markets (py, pw)

– Input markets (ci, cw)

– Parametric to individuals


Market Model

1y

1P1aggS

1aggd

1My

1MP


Welfare Analysis (normative)

Maximize Net Benefits– “Consumer surplus”– “Producer surplus” [returns to

owners & fixed inputs]

Competitive Equilibrium Social Optimum


Welfare Analysis – Economic Surplus

Consumer Surplus

Producer Surplus

WP

W

WS

WD

*W

*WP


2. Pricing Aquatic Ecosystems


The Diamond-Water Paradox

Diamond fetch very high prices, although they have limited usefulness. Water is essential to life, but fetches very low prices.

WHY?


Total vs. Marginal Value -- Water

W

Value

WMV

WTV

WP

AggWS


Total vs. Marginal Value -- Gems

G

Value

GMV

AggGS

GP

GTV


Answering the Paradox

Water: Adequate supplies produce low marginal value (even though basic water needs are highly valued).

Diamonds: Limited supplies

produce high marginal value.


Pricing Aquatic Ecosystems

Whole vs. components

Value vs. supply cost

Use vs. nonuse


Models for Valuing Ecosystems

Market-based (Revealed Preferences): – Expenditures on services – fish & fishing;

whale watching – Opportunity cost of laws –Lagragian

multipliers on constraint functions – Replacement cost

Experiment-based (Stated Preferences): – Trade-offs between service levels & prices– Willingness to support tax referenda– Expressed willingness to pay


Example: Value of ∆ Fishery Quality

fP

f

2( , )fD P Q

1( , )fD P Q

*fP


Example: Value of Wetlands (Earnhart, Land Econ., 2001)

Hedonic housing value – price differentials for homes adjacent to restored wetland vs. not adjacent to any distinct features– Proximity to L.I. Sound, river, stream ~ + 3%– Proximity to restored marsh ~ +16%– Proximity to disturbed marsh ~ -13%

Conjoint choice – selecting between hyp. homes differing in amenities & price – All values ~ 80 – 120%


Example: “The Value of the World’s Ecosystem Services & Natural Capital” (Costanza et al., Nature, 1997)

Benefits transfer – borrow marginal values from literature and apply them to increments to env. quality or natural resources

Multiply by total quantity of natural resources

Total value ~ $33 trillion


Example: “The Value …” Critique

“Serious underestimate of infinity.”

Total value vs. marginal value– Tools best applied to small changes from

status quo

Double - counting


3. Hydro-Economic Models


Hydro-economic Topics

Dam management balancing hydropower, recreation, ecological benefits

Administered water allocation Policy-simulation, e.g.,

– Auctioned access to locks– Targeted NPS abatement– Instream flow management– Economic forecasting of land

use/hydrologic change


Example: Downstream Impacts of Development (Johnston et al. JWRPM, 2006)

Determine the downstream economic value of low-impact development:

Identify impact categories (flooding, water quality,…)

Use weather series & HSPF to compute stage, flow, and flood frequencies for different development scenarios

Attach typical “prices” to impacts Calculate economic impact of each scenario Engineering costing of each scenario


Example: Spatial Management of Ag. Pollution (Braden et al., AJAE, 1989)

Max π = Revenues – Costss.t. Crop production functions

Spatial pollution transport functions < T*Identifies actions (crop, tillage)

by location that minimize economic losses


Hydro-economic Challenges

Scale: Markets vs watersheds Time: Water cycles vs

Economic cycles


4. Bioeconomic Models


Bioeconomic Topics

Fisheries management Floodplain & wetlands

management Forecasting landscape change

and effects on ecosystems


Example: Efficient Protection of Fish Habitat (Braden et al., WRR, 1989)

Max π (crops, tillage, pesticides)

s.t. Prob {HSI (sed., chem.) > H*} > R


Example: Economic/Runoff/Fish/Model

[Braden et al., WRR, 1989]


Example: Cost/Habitat Suitability

[Braden et al., WRR, 1989]


Fish Habitat: Discharges vs. Impacts (Braden et al, AJAE,

1991)

Impact Targets:Min C(x) s.t. Pr{q(x,h[x],ε)>Q} > A

Q = Habitat Qual., A = reliability

ε = stochastic factorDischarge Standards (Proxy):

Min C(x) s.t. Pr {h(x) > H} > Bh intermed to q; H linked to Q


Example: Habitat Impacts vs Discharges

[Braden et al., AJAE, 1991].


Example: Floodplain Management for Crops and Fish in Bangladesh (Islam & Braden, Env. Devel. Econ., 2006)

MaxHi Σc,t φtNRcit*Hcit + Σf,t φtNRfi(qfit)*Hfit

s.t. ΣcHci + Σf Hfi < H all t [area]

qfit = gfit(Hfit) [nonlinear production]

Differentiates production functions by land capability, crop, and species types


Floodplain Model Implementation

Fourier analysis (econometric) simulation of flood levels

Monthly average water levels -> flood coverages w/ digital elevation model

Land capabilities identified Capabilities changeable with levees Optimize land allocations to

activities by max economic returns


Bioeconomic Modeling Challenges

Matching spatial and temporal scales

Model complexity Simplifications that lose

information (e.g., averaging)


5. Benefit-Cost Models


Policy Analysis

Maximum Net Benefits– Potential Pareto Optimality – costs not

actually compensated– Function of existing distribution

Discounting– Opportunity cost of time

Max NPV =

Σt { (Benefits)t - (Costs)t} (1 + r)t


6. Risk and Uncertainty


Sources of Variability

Weather Ecological dynamics Geology/geography/

topography Technology Households Culture Economy


Modeling Variability

Statistical confidence intervals Monte Carlo simulation


Challenges

Interactions of systems Differences in scale & detail Structural change Pure uncertainty

– Precautionary principle


7. Summary Remarks

Economics adds people -- systematically

Total value vs. price & cost Integrating role Different disciplinary scales

and time-frames challenge integration

Documents

John B. Braden University of Illinois at Urbana-Champaign