Estimating the Costs, Capacity (Supply), and Tradeoffs of Ecosystem Services in a Resource

Assessment

R. Bernknopf University of New Mexico

S. Faulkner USGS Leetown Science Center

B. Chivoiu USGS National Wetlands Research Center

ACES December 12, 2012

Key Policy Questions

What are the ecosystem effects from likely terrestrial carbon sequestration management actions? How do we incorporate social and economic sciences that enhance our ability to quantitatively evaluate the full range of effects of proposed terrestrial carbon sequestration strategies? How does it affect the benefits of the policy?

B = P ´DQ( )

There are many analyses about the economic uncertainty of values for ecosystem services that is reflected in the price or nonmarket value. Few, if any estimates of the effect of uncertainty of the quantity of ecosystem services? Reliability of the policy - Do we get all the benefits from a management activity that analysis tells we should? Objective is to maximize benefits and minimize uncertainty simultaneously

Motivation for research

Mitigation Actions:

Land Use/ Cover

Management

Policy Portfolios

and Mitigation Options

Ecosystem Structure, Processes

and Function

Ecosystem Goods

and Services

Ecological evaluation

Policy/Management strategies and ecosystem services approach

Portfolio Analysis

Valuation Benefits / Costs

Quantitative Spatially Explicit Metrics

+ Economic evaluation

-0.50

0.00

0.50

1.00

Carbon Sequestration

Waterfowl

NitrateWater Supply

Sediment Retention

Baseline

Targeted Reforestation

Max Eucalyptus

Spider diagram of management scenarios for five ecosystem services

Policy scenarios and benefits

A policy scenario i, i=1,…,N scenarios is composed of a land portfolio in a region of k, k=1,…,K land units of land uses that produces market commodities Vi and ecosystem services benefits . For any scenario i there is a regional land grid of available agricultural lands that, if converted to forest in a scenario, has annualized net value of benefits Bi. A policy is weighted by the emphasis placed on the types of services provisioned.

Bi Z( ) = bz( )k

k=1

K

å , i =1,...,N

Bi Z( )

A land use / land cover (LULC) portfolio analysis of an afforestation

policy that maximizes the production of ecosystem services

(expected return on investment (ROI)) and minimizes the standard

deviation (risk) of the quantity of ecosystem services produced given

their values.

Research approach

r =Bt -B0

B0

s = BiB js i

i=2

N

åi=2

N

å s j

Multiobjective Optimization

Two objectives are to maximize B and minimize risk σ, which can be converted to a single objective to minimize the relative risk – coefficient of variation:

Min CV B( )N

s.t. Bi ³ B0

where CV =s N

rN

Expected return vs. standard deviation

B0

0

B1

B2

B*

Lower limit benefit constraint

Bi

rN

s N

Scenario 1: Baseline – no change in current land cover (forest, agriculture,

restored forested wetlands)

Scenario 2: Convert agriculture to forest through targeted afforestation

by selecting specific landscape locations

- hydric soils, soybean production, low elevation

- high hydrologic connectivity score

- watersheds with currently high sediment and nutrient loads

- near watershed outlet

- native Quercus/Carya species

- increase current forest area by 15% over 20 years

Carbon Management Strategy - Increase

biological carbon sequestration on the landscape

to mitigate increasing CO2/climate change effects

Scenario 3: Convert agriculture to forest through random afforestation

- fast growing Eucalyptus species

- increase current forest area by 15% over 20 years

- harvest every 10 years (biomass energy)

Scenario 4: Convert all agriculture to Eucalyptus forest through

random afforestation

Two Parishes in LA – Madison and Tensas

Carbon Management Strategy - Increase

biological carbon sequestration on the landscape

to mitigate increasing CO2/climate change effects

Current Practice in MAV: random conversion of cropland to forests

Targeting afforestation to enhance other ecosystem services

Sink

Source

Connector

“Prices” and quantities for ecosystem services

Scenario Baseline Eucalyptus (15%)

Targeted reforestation

(15%)

Eucalyptus (max)

P(carbon)1 $10.09 $10.09 $10.09 $10.09

P(waterfowl)2 $7.90 $7.90 $7.90 $7.90

P(Water quality – nitrate)3 $624.00 $624.00 $624.00 $624.00

P(Water quality – sediment)4 $2.76 $2.76 $2.76 $2.76

P(Water quantity)5 $0.0003 $0.0003 $0.0003 $0.0003

Q (carbon) mg/ha 173 191 186 351

Q (waterfowl) in 000 36,308.6 36,191.4 42,949.0 35,876.1

Q(Water quality – nitrate) kg/ha 7.8 7.7 9.7 8.0

Q(Water quality–sediment) metric tons 12,851 12,426 7,253 4,908

Q(Water annual flow) m3/s 2,133 2,096 1,997 1,614

1 California cap and trade market 2012 2 Gascoigne et al 2011 3 Jenkins et al 2010 4 Hansen and Ribaudo 2008 5 delivery cost price for a ft3

Forest establishment and maintenance costs

Land prices: Madison Parish - $1,675/ha - $4,586/ha Tensas Parish - $4,700/ha - $7,384/ha Hectares converted to forest – 9,632 (15%); 86,747 (MAX) Total hectares – 167,745 ha

Afforestation activity Establishment costs/ha ($2009) Establishment and maintenance costs/ha ($2009)

Eucalyptus Land preparation 252 Weed control 264 Plantation establishment 112 Seedlings 416

Fertilization 151 Fire and safety 2 Weed control 142

Hardwood Land preparation 145 Saplings and planting 209 Supervision 109

Weed control 72 Boundary 8 Management 32

Gonzalez et 2011 Zhu et al 2010

Ecosystem service expected return and standard deviation

Scenario Baseline Eucalyptus (15%)

Targeted reforestation

(15%)

Eucalyptus (max)

r(carbon) 0.10 0.08 1.03

r(waterfowl) -0.003 0.18 -0.01

r(Water quality – nitrate) -0.013 0.24 0.03

r(Water quality – sediment) 0.03 0.44 0.62

r(Water quantity) -0.02 -0.06 -0.24

σ(carbon) 162 160 157 292

σ(waterfowl) 16,923,356 16,630,274 17,668,619 17,509,519

σ(Water quality – nitrate) 14.1 13.9 14.9 14.4

σ(Water quality – sediment) 14,478 14,413 7,599 5,503

σ(Water quantity) 2,888 2,834 2,581 2,161

Management scenario return, uncertainty, and coefficient of variation

Scenario CV Eucalyptus (15%) – B1 -0.02 48,707 2,435,350

Targeted reforestation (15%) – B2 0.16 43,081 269,256

Eucalyptus (max) – B3 1.43 51,998 36,362

r s

Summary

We have integrated natural and economic sciences in a multiobjective optimization analysis to better understand the impacts and tradeoffs involved in comparing reforestation management scenarios with different objectives in a risk – return analytical framework. From our analysis the targeted hardwood scenario has a lower coefficient of variation that indicates it is a better management option.