4.6. Carbon accounting: Monitoring

4.6. Carbon accounting: Monitoring Markku Kanninen, CIFOR

Carbon accounting 5: Monitoring • USAID project monitoring for performance • Data needed for Forest Carbon Calculator • Detailed project monitoring • Monitoring plans • Sampling • Collecting and analyzing data • General concepts and guidance • National monitoring systems • Forest carbon inventory of India • Australian national carbon accounting system • US Forest Service carbon inventory

USAID’s standard climate change indicator • USAID’s standard climate change CO2 indicator : • “Quantity of greenhouse gas emissions, measured in metric tons of CO2 equivalent, reduced or sequestered as a result of USG assistance in natural resources management, agriculture, and/or biodiversity sectors” • This indicator can be used at the project level with USAID’s Forest Carbon Calculator • If the project is to influence national level policy, the USAID indicator will be a policy indicator, not CO2 • If USAID engages in large scale attempts to change a country’s emissions trajectory, then national GHG inventory done with host gov’t provides CO2 impact measure

Data needed for project monitoring with USAID’s Forest Carbon Calculator • Locations of project in terms of administrative unit like state or district • Hectares of the activity type (area of forest protected, area reforested or regenerated, area now under reduced impact logging, area now under agroforestry) • Measure of project effectiveness: • % reduction in deforestation, • % of trees that survive at end of the year, • % of logging stopped or % of logging that is being done with reduced impact • Documentation of how you estimated project effectiveness measures

Detailed project monitoring More site-specific monitoring may be desired for project performance or required for carbon finance. Requires a monitoring plan and approach Monitoring that seeks to measure actual carbon accumulation in soils may be outside the timescale of USAID funding, so measures of activity adoption may be more practical.

Manuals and guidebooks • MacDicken (1997) • IPCC GPG (2003) • Pearson et al. (2005) • GOFC-GOLD (2008)

What to monitor?

How to proceed? Define monitoring boundaries (national, project etc.) Stratify the area to be monitored Decide which carbon pools to measure (5 pools) Determine type, number and location of measurement plots Determine measurement/monitoring frequency

Measuring and monitoring plan for a project-based activity Source: IPCC GPG 2003:

General approach to monitoring • Monitoring carried out through sampling • Monitoring should produce estimates of carbon stocks that are both precise and accurate • These will affect the monitoring costs • It is important to design a monitoring system (using stratification, etc.) that produces the desired precision and accuracy with minimal costs Accurate but not precise Precise but not accurate Precise and accurate

Sample size • Calculate the sample size n (number of plots) – based on pre-sampling • Where • n = number of plots to be measured • Syx = estimation error • t = Studet t value • S = variance • X = mean value

Stratification • Allows to obtain a certain precision of estimations with lower cost than without stratification • Steps: • Divide heterogeneous population into homogenous groups • Apply monitoring (sampling & calculations) to each strata and compile results at the end

Field plots The schematic diagram below represents a three-nest sampling plot in both circular and rectangular forms: Source: Pearson et al. 2006

Frequency of monitoring • It is recommended that for carbon accumulation, the frequency of measurements should be defined in accordance with the rate of change of the carbon stock • Forest processes are generally measured over periods of five year intervals • Carbon pools that respond more slowly, such as soil, are measured every 10 or even 20 years • See the graph in the next slide Source: IPCC GPG 2003; Pearson et al. 2005

Detecting the difference • Two means (time 1 and time 2) • RME = Reliable Minimum Estimate • When number of observations (plots) increases -> variability of the data (standard deviation) decreases • RME 1 is smaller than RME 2 Standard deviation explained A data set with a mean of 50 (shown in blue) and a standard deviation (σ) of 20. Source: IPCC GPG 2003 15

Tons/cell = (tons/ha)*0.0001*(30^2) Noel Kempff Project (Bolivia): 625 permanent sample plots were measured in 640,000 ha Vegetation classes Tons of carbon/cell

Noel Kempff (Bolivia) carbon inventory Results based on 625 permanent plots

Leakage (displacement) • Carbon leakage is the result when interventions to reduce emissions in one geographical area lead to an increase in emissions in another area • Example: if curbing the encroachment of agriculture into forests in one region results in conversion of forests to agriculture in another region • In the context of REDD, leakage is also referred to as ‘emissions displacement’ • In the Noel Kempff project: • Leakage for the stop-logging component was thoroughly screened and found to be in the 2-42% range • Deforestation among local communities actually increased initially, which was hoped to be transitory, related to the creation of new land use systems

Quality assurance and quality control QA/QC elements: • Reliable field measurements • Re-check measurements with independent crew (10-20% of plots re-measured) • Verify laboratory procedures • Re-analyse 10-20% of samples • Verify data entry and analysis techniques • Check 10-15% of the data entries • Adequate data maintenance and archiving • Make sure that data (including computer files, imagery etc.) is adequately archieved

National forest carbon inventory of India • Stratification • The country is stratified into 14 physiographic zones • In each strata, districts are considered first sampling units. Ten percent of districts are being inventoried every year • Field measurements • National grid and sub-grids are marked as the center of the plot at which a square sample plot of 0.1 ha area is laid out to conduct field inventory of trees • Soil, litter, and humus samples are collected in sub-plots • Carbon calculation • Based on stem volumes obtained in forest inventory • Using expansion factors for conversion from volumes to carbon

Australian National Carbon Accounting System (NCAS) Components: • Remotely sensed land cover change (including mapped information from thousands of satellite images) • Land use and management data • Climate and soil data • Greenhouse gas accounting tools, and • Spatial and temporal ecosystem modeling

US Forest Service Carbon Inventory • Using USDA Forest Inventory & Analysis (FIA) inventory data coupled with a modeling approach • Data from many field plots, collected by FIA beginning in 1950s • Area data from remote sensing • Where FIA data are limited – models are used - such as equations to estimate non-tree carbon • System (model) can track carbon through harvested wood products

Large-scale field inventories include remote sensing for area estimation For example, sample “points” located systematically over the “effective area” and land cover determined at the “point”

USDA Forest Inventory Program Evolution • In recent past, FIA periodically (5-14 years) measured all plots in a state in a 1-2 year timeframe • FIA recently adopted annualized inventory, with a subset of plots measured throughout the state each year (5-7 years) • Soil and litter layer carbon measured on subset of plots in new system

US National GHG reporting to UNFCCC • Annual Greenhouse Gas (GHG) Emissions and Sinks Inventories (1990-present) (US Environmental Protection Agency)

Discussion • How should a USAID project set up its monitoring? What fits within its timescale and funding? • Is the accuracy of good measures worth the cost?

Thank you for your attention

4.6. Carbon accounting: Monitoring