antle j. trade off analysis minimum data july 2011

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Presentation for Smallholder Mitigation: Mitigation Options and Incentive Mechanisms - Expert Workshop 7 - 8 July 2011


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Modeling GHG Incentives Using TOA-MD

John AntleAgricultural and Resource Economics

Oregon State University

Smallholder Mitigation Options and Incentive Mechanisms Expert WorkshopJuly 7-8, 2011, Rome

Key issues for assessing GHG mitigation potential

- Heterogeneity in farm populations and ecosystems

- Uncertainties in key economic dimensions of systems

- productivity transitions

- variable costs

- size & timing of fixed costs (capital, transactions)

- Behavior: “willingness to adopt”

- Uncertainties in mitigation outcomes

- Payment mechanism design & verification costs

- Institutions and property rights

What is the TOA-MD Model?

• a unique simulation tool for multi-dimensional impact assessment and analysis of ES supply

• … that uses a statistical description of a heterogeneous farm population to simulate the adoption and impacts of a new technology or a change in environmental conditions such as climate change.

• a generic, “parsimonious” model of agricultural systems

• … designed for forward-looking assessments of agricultural technology adoption, ecosystem services supply and environmental change

What does the TOA-MD Model do?

TOA-MD simulates an experiment to measure the effects of technology adoption* or environmental

change under specified environmental and economic conditions.

TOA-MD parameters are means, variances and correlations of economic, environmental and social outcomes associated with production systems.

• the ideal data would come from a paired, stratified random sample of farms using each system

• …but such ideal data don’t exist!

• …so we combine available survey data with secondary, experimental, modeled data and expert knowledge

What kinds of data are needed?

It does not solve for market equilibrium prices determined by demand and supply.

It is not a decision support tool for management of an individual farm.

It does not predict the future, unless the future is a lot like the assumptions made in the simulated experiment!

What does TOA-MD not do?

TOA-MD simulates an experiment to compare two systems, referred to as System 1 and System 2. System 1 is the baseline case, or the control in an experimental design; System 2 is a new system, typically a modification of System 1, or the treatment in an experimental design.

• First, the model simulates the System 2 “adoption rate” –this can include a specified level of adoption incentive, e.g., a PES or GHG incentive payment

• Second, based on the adoption rate of System 2, it simulates economic, environmental and social impact indicators for adopters, non-adopters and the entire population (including the amount of GHG mitigation associated with adoption of system 2)

How does the TOA-MD Model work?

TOA-MD can be used to simulate many possible “experiments” for GHG mitigation and climate impact assessment:

• GHG mitigation without climate change

– System 1 = base climate, base technology

– System 2 = base climate, mitigation technology + incentive

• Climate change without mitigation payment

– System 1 = base climate, base technology

– System 2 = changed climate, base or adapted technology

• GHG mitigation with climate change:

– System 1 = base climate, base technology

– System 2 = changed climate, mitigation technology + incentive

• … and so on …

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0 10 20 30 40 50 60 70 80 90

Op

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ost

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Adoption Rate (%)

ROT Base +50% Productivity -50% Productivity + 50% Variable Costs

- 50% Variable Costs +50% Prod & Cost -50% Prod & Cost

Example: assessing productivity and cost uncertainties in rangeland soil C sequestration with adoption of rotational grazing (U.S. northern plains)

Example: Soil C supply curves for rotational grazingand improved pasture, U.S. northern plains

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0 5 10 15 20 25 30 35 40 45 50

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Carbon Sequestration (Million Mg CO2E/yr)

Improved Pasture Rotational Grazing

tradeoffs.oregonstate.edu

Key Issues in GHG Mitigation: system characterization and heterogeneity

Systems are being used in

heterogeneous populations

A system is defined in terms of household, crop, livestock and

pond sub-systems

(ω)

0

Map of a heterogeneous region

Opportunity cost, system choice and adoption

Opportunity cost follows distribution () for specified econ, environ conditions and techs

represents productivity and cost differencesbetween systems opportunity cost

()

100

0 < ω < a

a

Analysis of ES supply: Farms adopt system 2 if < a

a = mitigation incentive adjusted for “willingness to

adopt”

ω < 0

ω > a

r

adoption rate for

a = 0

adoption rate for a > 0

ES supply curve for specified prices, tech,

climate

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