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Modeling GHG Incentives Using TOA-MD 
John Antle 
Agricultural and Resource Economics 
Oregon State University 
Smallholde...
Key issues for assessing GHG mitigation potential 
-Heterogeneity in farm populations and ecosystems 
-Uncertainties in ke...
What is the TOA-MD Model? 
•a unique simulation tool for multi-dimensional impact assessmentand analysis of ES supply 
•… ...
What does the TOA-MD Model do? 
TOA-MD simulates an experiment to measure the effects of technology adoption* or environme...
TOA-MD parameters are means, variances and correlationsof economic, environmental and social outcomesassociated with produ...
It does notsolve for market equilibrium prices determined by demand and supply. 
It is nota decision support tool for mana...
TOA-MD simulates an experiment to compare two systems, referred to as System 1 and System 2. 
System 1 is the baseline cas...
TOA-MD can be used to simulate many possible “experiments” for GHG mitigation and climate impact assessment: 
•GHG mitigat...
0 
20 
40 
60 
80 
100 
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0 10 20 30 40 50 60 70 80 90 
Opportunity Cost ($/Mg CO2E) 
Adoption Rate (%) 
ROT ...
Example: Soil C supply curves for rotational grazingand improved pasture, U.S. northern plains 
01020304050607080901000510...
tradeoffs.oregonstate.edu
Key Issues in GHG Mitigation: system characterization and heterogeneity 
Systems are being used in heterogeneous populatio...
(ω) 
0 
Map of a heterogeneous region 
Opportunity cost, system choice and adoptionOpportunity cost follows distribution...
() 
 
100 
0 < ω< a 
a 
Analysis of ES supply: Farms adopt system 2 if < a 
a= mitigation incentive adjusted for “will...
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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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Antle j. trade off analysis minimum data july 2011

  1. 1. Modeling GHG Incentives Using TOA-MD John Antle Agricultural and Resource Economics Oregon State University Smallholder Mitigation Options and Incentive Mechanisms Expert Workshop July 7-8, 2011, Rome
  2. 2. 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
  3. 3. What is the TOA-MD Model? •a unique simulation tool for multi-dimensional impact assessmentand analysis of ES supply •… that uses a statistical description of a heterogeneous farm populationto 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-lookingassessments of agricultural technology adoption, ecosystem services supply and environmental change
  4. 4. 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.
  5. 5. TOA-MD parameters are means, variances and correlationsof economic, environmental and social outcomesassociated 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?
  6. 6. It does notsolve for market equilibrium prices determined by demand and supply. It is nota decision support tool for management of an individual farm. It does notpredict the future, unless the future is a lot like the assumptions made in the simulated experiment! What does TOA-MD notdo?
  7. 7. 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?
  8. 8. 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 …
  9. 9. 0 20 40 60 80 100 120 140 160 0 10 20 30 40 50 60 70 80 90 Opportunity Cost ($/Mg CO2E) Adoption Rate (%) ROT Base +50% Productivity -50% Productivity + 50% Variable Costs - 50% Variable Costs +50% Prod & Cost -50% Prod & Cost Example: assessing productivity and cost uncertainties in rangeland soil C sequestration with adoption of rotational grazing (U.S. northern plains)
  10. 10. Example: Soil C supply curves for rotational grazingand improved pasture, U.S. northern plains 010203040506070809010005101520253035404550 Opportunity Cost ($/Mg CO2E) Carbon Sequestration (Million Mg CO2E/yr) Improved PastureRotational Grazing
  11. 11. tradeoffs.oregonstate.edu
  12. 12. 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
  13. 13. (ω) 0 Map of a heterogeneous region Opportunity cost, system choice and adoptionOpportunity cost follows distribution () for specified econ, environ conditions and techs represents productivity and cost differencesbetween systems opportunity cost
  14. 14. ()  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> 0ES supply curve for specified prices, tech, climate

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