Aggregator Perspective on Carbon Credits Presented by AgraGate Climate Credits Corp David Miller, Chief Science Officer July 18, 2007.

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Presentation transcript:

Aggregator Perspective on Carbon Credits Presented by AgraGate Climate Credits Corp David Miller, Chief Science Officer July 18, 2007

Items to be Covered Current opportunities in US carbon credit markets Carbon market expectations Dealing with uncertainty in agricultural carbon credits Characteristics of cost-effective aggregation programs

Carbon Credit Program Greenhouse Gas Emission Reductions Carbon Credit Program Eligibility Assessment Protocol Development Monitoring Reporting Verification Registration Carbon Credits (certified, tradable, $$) Chicago Climate Exchange protocols Achieved via qualifying GHG emission reduction projects Sell on CCX through an aggregator

CCX Reduction Timetable : Reduce emissions to 1%, 2%, 3%, 4% below baseline 2006 – 2010: Reduce emissions to 6% below baseline

Meeting CCX Reductions Allowances (x% less than baseline) Own reductions Industry credits from excess reductions Offsets (no more than 50% of reduction requirement) Soil Offsets No-till New Grass Rangeland Forestry New Plantings Enhanced Working Forest Ag Methane Industrial Fuel Switching Biofuels Landfill Methane

Carbon Offset Prices

Price forecasts for US carbon credits Figure 1. Projected price curves for US carbon credits ($US per metric ton). Sources: Carbon Finance, August 2004; EIA/DOE Analysis of S. 1844, the Clear Skies Act of 2003; S. 843, the Clean Air Planning Act of 2003; and S. 366, the Clean Power Act of Energy Information Administration, USDOE, SR/OIAF/ , May 2004; EIA/DOE Impacts of Modeled Recommendations of the National Commission on Energy Policy. Energy Information Administration, USDOE, SR/OIAF/ , April 2005; AEP An assessment of AEP’s actions to mitigate the economic impacts of emissions policies. American Electric Power, August

How Our Land is Used

Carbon Sequestration Carbon sequestration can be defined as the capture and secure storage of carbon that would otherwise be emitted to or remain in the atmosphere. What are Carbon Credits? –Carbon credits encompass two ideas: (1) Prevention/reduction of carbon emissions produced by human activities from reaching the atmosphere by capturing and diverting them to secure storage. (2) Removal of carbon from the atmosphere by various means and securely storing it.

SOIL CARBON DYNAMICS IN RESPONSE TO TILLAGE SOIL CARBON (% OF ORIGINIAL) IN RESPONSE TO CULTIVATION 1 50 SOIL CARBON PLOWING PERENNIAL VEGETATION years CONSERVATION TILLAGE 50

Soil Offset Credit Zones No-till Credit Rates (Metric Tons CO2/Acre/Year) CCX Zone DrylandOther A 0.60 B, E, G & LRR(K) 0.40 LRR-G & H Irrigated 0.60 C 0.32 MLRA 52, 53A, 54 Chem Fallow 0.32/0.00 D & F 0.20 LRR-G & H Irrigated 0.60

New Grass Plantings Southwestern US: 0.4 ton; Rest of US: 1.0 ton Western Canada: 1.0 ton

Tillage Equipment Full width inversion –Moldboard plow –Chisel plow –Field cultivator –Tandem disk –Offset disk –Row crop cultivator Okay to use –No-till/strip-till planter –No-till drill –Rolling harrow –Tools with wide knives Subsoiler/ripper Anhydrous applicator Manure knife applicator General Guideline: After the implement has been through the field, there must still be a substantial amount of surface residue present and the soil disturbance must not be full width. If use of the implement would require that a leveling or smoothing activity follow, it would probably result in too much soil disturbance. No credits earned during year if residue is removed or burned

Dealing with Uncertainty What is the goal? –Assurance of a minimum amount? –Minimization of variation around the mean? Two approaches –Pooling with discount –Specific plot measurement

Pooling with Discount Define eligible practices Set rates for geographic areas –Peer-reviewed literature –Long-term studies (Reference sites) Apply discounts from the mean rate –Cumulative probability distribution function Law of large numbers

Specific Site Measurement Illusion of accuracy –Sampling error –Sampling bias If aggregated, pool average will match true mean Does not change carbon being sequestered

Cost-effective Aggregation Standardized contracts Easy to understand forms & reports Common, established practices in similar geographic areas Reference sites with long-term data sets Self certification with random spot checks Penalties for dishonesty or fraud Low transaction and verification costs

Mechanics of Trading CCX Registry CCX Trading Floor

Contact Information David Miller Chief Science Officer AgraGate Climate Credits Corp University Ave West Des Moines, IA Ph: