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Alternative Approaches to Quantifying and Reporting Carbon Sequestration Projects: The Case of Afforestation. Allan Sommer and Brian Murray (RTI) Third USDA Symposium On Greenhouse Gases and Carbon Sequestration in Agriculture and Forestry, March 21-24, Baltimore MD
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Outline of Presentation and Analysis
Overview of the role mitigation projects and quantification protocols play in GHG policy Application of a generalized WRI/WBCSD GHG Protocol to a hypothetical mitigation project Implications that variation in quantification procedures and protocols may have on quantified project benefits In an effort to create transparency and consistency across current and future programs implementing GHG policies and accounting principles, the WRI/WBCSD collaboration have been working on the developing GHG Protocol for project quantification standards. In this analysis we use the WRI draft protocol as a guide for establishing baselines for a hypothetical afforestation case study in the MS River valley in west central MS. In this analysis we explore two approaches to establishing baselines, the performance standard and project specific approach. The performance standard is a top-down approach based on the historical activities in a region and tracking the performance of a reference group over time. The project specific approach is conversely a bottom-up approach that completes a detailed evaluation of the circumstances pertaining to a specific project.
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Project Based Approaches to GHG Mitigation
Projects involve intentional activities or actions to reduce GHG’s The product of these projects may (may not) be used to produce GHG emission offsets Mitigation projects are voluntary, not required by law Development of mitigation projects contain nuances that are location and sector specific
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GHG Mitigation Project Programs/Registries
Domestic US Federal Section 1605(b) of the Energy Policy Act of 1992: GHG Registry State California Climate Action Registry Oregon Climate Trust Other emerging state programs Private Chicago Climate Exchange International Kyoto Mechanisms (JI and CDM)
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The Role of “Protocols”
Emergence of different project-based GHG mitigation projects has created some confusion and demand for quantification/reporting standards Protocol guidance on methods for quantifying and reporting GHG emission and sequestration effects at the project level Current Efforts Program-specific: e.g., CA registry protocol, 1605(b), Kyoto Broad/harmonization: WRI/WBCSD GHG offsets generated from activities or projects have the potential to reduce atmospheric concentrations considerably. However to achieve the highest level of reduction efficiency in a project, there needs to be an accounting system that ensures that every reduction or credit issued as an emission offset is in fact additional to what would have happened absent the project. Establishing the guidance methods for reporting and quantifying projects are critical to this process. Although there are many complicated issues surrounding project quantification, in this analysis we focus on setting baselines to determine the GHG reductions that are additional to the baseline or BAU.
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General Framework for Project Quantification
If No, Revise as needed Yes Estimate Secondary Effects Bottom-up (Project-specific) Approach Top-down (Performance Standard) Approach Set Project Baseline Calculate Net GHG Effects Benefit-cost Screening Define Project Dimensions Initial Assessment Define primary and secondary effects Determine eligibility Perform initial screening Additionality Leakage Assess Costs Revise as needed Report Estimates resulting from Baseline Approach Estimate Project GHG Effects
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Project Baselines and Additionality
General Definitions Baselines – activity and GHG effect that would occur without the project Additionality – GHG mitigation relative to the baseline Two options/methods to setting baselines exist Project specific approach – bottom-up approach, detailed evaluation of the circumstances pertaining to a specific project Performance standard approach – top-down approach, based on the historical activities in a region and tracking the performance of a reference group over time
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Case Study Application of Bottomland Hardwoods in the Lower Mississippi Valley
Project Description Afforestation of marginal croplands in Miss. River Valley Frequently flooded (2-year floodplain) Issaquena County 13,784 acres in total; ,000 met selection criteria The case study consists of an afforestation project of marginal croplands within a four county region of MS. The project defines marginal lands as those that are frequently flooded or fall within the two year floodplain. The project is located in Issaquena county and covers total area of 13,784 acres, 2,000 of which met our selection criteria.
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Data Sources Biophysical Data Economic Data
Land Use Characterization (National Resource Inventory) Geo-referenced Soil type, elevation etc Timber yields (Local Growth and Yield Functions) Carbon yields (FORCARB) Economic Data Timber prices and costs Agriculture prices and costs
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Preliminary Assessment
Generally involves a qualitative assessment of the following: Eligibility of project activities and GHG pools Initial screening for Additionality Leakage Assess Project Costs Assess Project Benefits
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Project GHG Quantification
Recall basic steps from general quantification framework Performance Standard Approach to setting baselines Estimate the baseline afforestation rate NRI Data and logistic regressions to calculate annual afforestation rates in MS counties Estimate Baseline Carbon Accumulation Combine county specific afforestation rates with carbon yield functions (time-dependent and dynamic), biophysical data, and forest carbon prediction model
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Quantification: Estimate Baseline Afforestation Rate Using Logistic Regression Analysis
Dependent Variable: Plot Conversion to Forest This table presents a lot of detailed statistical information, but the main point lies in the first two columns. The right-hand side variables in the regression are all dummy variables indicating which county the sample plots were located in and whether or not the plot was identified as flooding frequently. The coefficients present the likelihood that farmland acres are to convert to forestry in each of the counties relative the reference or county omitted from the regression. It is suggested from the regression with high statistical significance that if croplands are frequently flooded the likelihood that they will convert to forestry in the baseline increases. Additionally the regression analysis allows us to construct confidence intervals around each of the coefficients. I want to note there that originally we only included data from the four counties of the LYRB. The results from this initial regression produced large ranges around the coefficients in addition to large standard errors surrounding the predictions. The sample size was increased to include the entire state, while still controlling for county specific effects. The increased sample size reduced the standard errors associated with the coefficients and the predictions, and increased the support for our assumptions regarding flooding frequency. Full State Sample 82 Counties (4,299 observations) County coefficients show effects relative to omitted county. 81 of the 82 MS Counties were included in the regressions however only those in the LYRB used in the analysis are presented here.
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Baseline Afforestation Rate Confidence Interval Upper Bounds Derived from Regression Analysis
Calculated from confidence intervals, upper bound most conservative Issaquena Sharkey Warren Yazoo Mean 0.80% 0.18% 1.41% 0.52% Upper Bound of CI 1.58% 0.76% 2.38% 1.43%
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Baseline Quantification: Carbon Accumulation at Different Points in Time
Baseline carbon accumulation at year 10 and 60 (Baseline C 42,000 tons after 10 years.)
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Estimate Gross Project GHG: No Additionality or Leakage Adjustments
Estimated project carbon for year 10 and 60 Assume with project all trees planted in 1st year Quantities accumulated after 10 yrs, 60 yrs given below Soil Type Project Acres C Accumulation Projection by Year 10 (tC) C Accumulation Projection by Year 60 (tC) 1 1,506 30,554 170,751 2 149 3,254 18,232 3 345 8,130 45,664 Total 2,000 41,938 234,677
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Estimate Secondary Effects – Leakage
Leakage: Shifting of GHG emissions to outside project boundaries (undermines project GHG benefits) Estimates derived from study by Murray, McCarl and Lee (2004) Commercial forestry in South-Central USA is estimated to be ~20% Adjust project GHG benefits downward by 20% See Murray presentation (this session) for more details on leakage
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Calculate Net Project Carbon Benefits (Gross – Baseline – Leakage)
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Sources of Variation in Results
Choosing the project-specific (“case study”) approach to establishing the baseline would result in all project carbon being deemed additional in our example If timber harvesting is allowed, debits are imposed for carbon reversal Natural disturbances also produce the potential for carbon reversal and debiting These and other sources for variation in project results can affect project economic returns
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Impacts on Economic Returns
Economic returns under different baseline stringency levels (Confidence intervals from regression results)
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Impacts on Economic Returns
Economic returns with and without baseline adjustments
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Impacts on Economic Returns (cont.)
Commercial Forestry vs. Forest Preservation
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Program-Specific Issues: CA Registry
Baseline guidance - additionality Eligibility: Pools - above ground only Secondary effects – leakage not required in CCAR
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Summary and Recap Protocols are needed to ensure consistency of GHG project reporting Program-specific and cross-program protocols are now being developed Treatment of Baselines/Additionality and Leakage can substantially alter project benefits and economic returns More work is needed to create project-based empirical estimates
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