1. 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

2. 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.

3. 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

4. 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)

5. 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.

6. 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

7. 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

8. 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.

9. 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

10. 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

11. 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

12. 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.

13. 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%

14. 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.)

15. 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

16. 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

17. Calculate Net Project Carbon Benefits (Gross – Baseline – Leakage)

18. 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

19. Impacts on Economic Returns
Economic returns under different baseline stringency levels (Confidence intervals from regression results)

20. Impacts on Economic Returns
Economic returns with and without baseline adjustments

21. Impacts on Economic Returns (cont.)
Commercial Forestry vs. Forest Preservation

22. Program-Specific Issues: CA Registry
Baseline guidance - additionality
Eligibility: Pools - above ground only
Secondary effects – leakage not required in CCAR

23. 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