Methodological Issues in Forestry
Mitigation Projects
Ken Andrasko
Office of Atmospheric Programs
U.S. Environmental Protection Agency, Washington, DC, USA
Jayant Sathaye, LBNL
at
Workshop on Climate Change Mitigation Forestry Projects in
India, Bangalore, July 10-12, 2003
I. Project Experience
• Projects -- IPCC Special Report LULUCF, 2000
• “Planned set of activities that are
– confined to one or more geographic locations in the
same country
– belong to specified time periods and institutional
frameworks, and
– allow monitoring and verification of greenhouse gas
(GHG) emissions or changes in carbon stock
”
• Much experience with LULUCF projects,
but the number for which GHG elements
have been explicitly evaluated is
limited: c. 20-30
GHG Project Experience
• About 3.5 million ha of land in about 30
projects in 19 countries being implemented
during the 1990s
• For 21 projects w/ sufficient data available:
– Estimated accumulated carbon uptake over
the project lifetime in 11 forestation
projects on 0.65 Mha amounts to about 30
Mt C,
– Estimated accumulated emissions avoided
in 10 forest protection and management
over the project lifetime on 2.86 Mha
amounts to between 46 to 53 Mt C
– Several issues may affect these estimates.
Cost and carbon mitigation of 21 selected AIJ pilot phase
and other LUCF projects in some level of implementation.
(source: IPCC LUCF SR, 2000)
Project Type
Land Carbon
Area Mitigati
(Mha)
on
(Mt C)
Costs
$/t C
Carbon Mitigation
t C/ha
Emissions Avoidance via
Conservation:
2.9
0.06
40-108
5.6
0.1– 15
0.3 – 8
4 - 252
40 - 85
Reforestation and Afforestation (7)
0.10
12
1 – 28
26 – 328
Agroforestry (2*)
0.2
10.8
0.2-10
56-165
0.53
20-49
0.2 – 15
0.2 –165
Forest Protection (7)
Forest Management (3)
Carbon Sequestration
Multi-Component and Community
Forestry (2*)
Examples: The Nature Conservancy
Climate Action Projects
Location
Cost
Funders
Activities
Rio Bravo
Belize
$5.6 million
WEPCO, 3
other utilities,
2 oil co.
Protection, sustainable
forestry, community
development
Noel Kempff
Bolivia
$9.6 million
AEP, BP,
Pacificorp
Protection, community
development
Guaraquecaba 1
Brazil
$5.4 million
AEP
Reforestation, protection,
community development
Guaraquecaba 2
Brazil
$10.0 million
General Motors
Reforestation, protection,
community development
Guaraquecaba 3
Brazil
$3 million
Texaco
Reforestation, protection,
community development
Midwest
restoration
Ohio/Indiana
$500,000
Cinergy
Reforestation
Nachusa
Grasslands
Illinois
$50,000
Natsource
Grassland restoration
Example: Reforestation Forest Restoration
• Brazil - Atlantic Forest Projects





Since 1999, purchased over 20,000 ha of lands now
managed for Asian water buffalo
Project = improved buffalo management, reforestation
& natural regeneration w/ native species, agroforestry.
Land is owned and managed by Brazilian NGO
Sociedade de Pesquisa em Vida Selvagem (SPVS)
Total cost = $17 million, about 7.5 million tons of CO2
over 30 years
3 separate projects funded by American Electric Power,
General Motors, Texaco
Credit: Bill Stanley, TNC
Project-Based Activities Mechanism:
How it might work
Step I: International
and/or National
Sponsors Develop
Project
Step II: Submit to
Governments for
Approval/Registration
•Governments approve/
register/validate project
Step III: Submit to
UNFCCC for Registration
•Project developer provides evidence of
government approval/registration,
•Projected impacts demonstrate that
proj ect will provide measurable benefits
•Project satisfies additionality
requirements
Step IV:
Monitoring and
Reporting
•Project developer monitors
proj ect on a regular basis
Step V:
Verification
•Independent
auditor verifies
emissions reports
•Auditor needs to
be accredited
Step VI:
Certification of
emissions
reductions
Step VII:
Appeal Process
(if necessary)
Kyoto process: Sinks inclusion in Art. 3.3,
3.4 (Annex I) & CDM (non-Annex I)
limited by inadequate experience &
methods to address sinks technical issues
• LULUCF projects share most issues with energy projects
-- except duration of benefits (permanence).
• Perception: adequate methods & data exist for A, R, D
in Annex I.
• Perception: CDM sinks in non-Annex I difficult to
measure & high leakage. So: A & R; no forest protection
• Key technical issues:
– baseline setting by activity and location
– additionality of activities (envir. & financial)
– leakage of GHG benefits offsite
– duration (permanence) of LUCF benefits.
– Envir. & sus. dev. (eg, avoid incentives for planting
monocultures).
II. Project Concepts:
Estimating Baseline and GHG Benefit
Prior to project implementation
Estimated
GHG Benefit
Without-project
Baseline (B)
Adjusted for
leakage (P)
GHG
emissions
GHG project
Time
Project Concepts:
Monitoring GHG Benefit
During Project Implementation
Estimated
GHG Benefit
B (estimated)
GHG
emissions
P (estimated)
Monitored
GHG Benefit
P (Measured and
Adjusted for
leakage)
Measured GHG
Emissions
Time
Note: P (measured and adjusted for leakage) can be above P (estimated);
Monitored GHG benefit would then be less than the estimated amount
Evolving Steps for Estimating Baseline
and Project GHG Reductions:
WRI/WBCSD draft 7/03
1. Identify project and its primary GHG effect
2. Check project eligibility
3. Undertake preliminary evaluation of
secondary effects
- Leakage and life-cycle effects
4. Check if project is “surplus” (additional) to
regulation
Evolving Steps Baseline and Project: 2
6. Select an approach and set a baseline for
each primary effect:
- Project-specific, or GHG performance
standard baseline
7. Identify and assess relevance of secondary
effects
8. Calculate project emissions reductions
9. Classify emissions reductions into direct and
indirect: ( Under control of the developer or not).
II. Methods for Setting Baselines, & Examples
from Case Studies
• Project-specific
– Baselines are set specific to each project
– Concern: Project baselines set strategically to
maximize credits
• Multi-project Baselines or Emissions Factors
– Generic baselines may reduce transaction costs,
be transparent, and provide consistency across
projects
– Setting minimum performance benchmarks would
avoid rewarding projects with poor practices
• Fixed vs. Adjustable Baselines
– Fixed baseline would reduce uncertainty
– Adjustable baselines would ensure more realistic
offsets, but would increase cost and uncertainty
– Periodic adjustments may be one solution
Examples of Project
Baseline Scenarios
Project
Atlantic Forest
Projects, Brazil
Baseline Scenarios
Ongoing Asian water buffalo
ranching or other agriculture
Clearing of additional forests for
pasture and agriculture
Noel Kempff Project,
Bolivia
Conventional timber extraction
Land clearing for agriculture
Rio Bravo Project,
Belize
Logging followed by clearing of
upland forests for agriculture
Conventional timber extraction on
upland forests
Frequent burning and timber
harvest on pine savanna
Credit: Bill Stanley, TNC
Methods matter: Comparison of 5 baselinesetting approaches: Rio Bravo project, Belize
(Draft: Winrock Intertl-EPA analysis, in prep.)
SIMPLE MODEL high
100
% DEFORESTED (Cumulative)
GEOMOD regional
90
GEOMOD project
80
SIMPLE MODEL low
Original project
70
60
50
40
30
20
10
0
1990
1995
2000
2005
2010
2015
YEAR
2020
2025
2030
2035
Source: Brown et al. 2002, Winrock International analysis
Baseline Steps: Identify spatial and
temporal boundaries for baseline, & project.
• Step 1: Determine Baseline Afforestation,
Reforestation, or Other LU Rates
– Assess land-use trends and changes in C stocks
for candidate area and activities
• Step 2: Determine Likely Locations of Future
Af/Reforestation, Deforestation, etc.
– Identify 2-5 key baseline drivers
– Assess historical trends and projection into future
• Step 3: Estimate Net Emissions or
Sequestration for Each Unit of Baseline
Deforestation/Reforestation
Baseline: Quantify probability of current
land use changing without project
Identify Current
land use & location
Identify key baseline drivers
Simpler case:
Reforestation Degraded or Burned Natural
forest
regrowth
Complex
case:
Protection
Undisturbed forest
in Chiapas, Mexico
Demand for
agricultural
land or
fuelwood
Pop. growth Land tenure
Closeness .
to roads
Additionality: Can determine relative
additionality of proposed project activities
• Land transformation matrices can project
probability of future land change without
project.
• Method steers developers & regulators
towards areas and activities with high
likelihood of being additional.
• Method reflects heterogeneity of land uses, &
avoids binary, yes/no additionality.
Baseline Driver Example:
% Land-Use Change, Forest to Non-forest:
1975-96. Chiapas, Mexico.
Factors: Distance from Roads, by Population Density
Roads, by
Population
0 hab/km2
>0 a 15
>15 a 30
>30 hab/km2
0 -1000 m
1000 - 2000
m
> 2000 m
43
25
7
55
38
24
67
50
34
78
62
42
Source: ECOSUR, & de Jong et al., 2000
Vegetation cover in 1975, Chiapas, Mexico
from Remote Sensing data
Source: ECOSUR, Chiapas, Mexico; & de Jong et al, 2002.
Vegetation cover in 1996, Chiapas, Mexico:
rate rate of land cover and use change
Source: ECOSUR, Chiapas, Mexico; & de Jong et al, 2002.
Carbon emissions from land use change, 1975 - 1996
Total emission
Source: ECOSUR, Chiapas, Mexico;
& de Jong et al, 2002.
119, 465,774 tC
Predicted Deforestation: Noel Kempff
Project, Bolivia -- GIS model projections
Deforestation
projection,
2000 - 2040
Baseline drivers =
nearness to road
& ag croplands;
population
changes
Source: Bill Stanley, TNC from
GEOMOD by M. Hall, 2002
Project-Scale Land Use Analysis for Baseline:
Jambi, Indonesia: 1986-92
1986
1992
Rizaldi et al, 2003:
Project-Scale Land Use Analysis: Real vs. Predicted
Land Use. : Jambi, Indonesia: 1986-96
Real Land Use: 1992
Predicted Land Use: 1992
Figure 6. Comparison between real and
predicted land use/cover of Jambi province
and Batanghari district using the logit
regression equations. Rizaldie et al, 2003
Jambi Case: Mitigation Scenario Results,
and Effect of Adjusting Baseline.
Rizaldi et al, 2003
Mitigation Scenario-2
74000000
74000000
73500000
73500000
73000000
72500000
72000000
71500000
71000000
Baseline
Adjusted Baseline
C-Project
70500000
1999 2001 2003 2005 2007 2009 2011 2013
Year
C-Stock (tonnes)
C-Stock (tonnes)
Mitigation Scenario-1
73000000
72500000
72000000
71500000
71000000
Baseline
Adjusted Baseline
C-Project
70500000
1999 2001 2003 2005 2007 2009 2011 2013
Year
Example: EPA Mississippi Case:
Testing 4 coarse to fine resolution data approaches
to Baseline Setting & Additionality
• Green: national
forests
•Brown:
marginal
croplands,,
flooded every 2
yrs.
•Project: restore
wetland
hardwood
species
• 4 counties
Mississippi Case Study: Test 4 different
baseline-setting approaches
– Can generic or ‘multi-project’ baselines be established
that could apply to any project within a large region?
– Or, are county-level or sub-county baseline needed to
capture land-use dynamics at project scale?
– What are the tradeoffs between cost and accuracy
when moving from coarse (county-level) to fine (pixellevel GIS) methods?
– Can existing national data sources be used to allow for
transferable methods across the U.S.?
Mississippi case: Approach & Findings
• Baseline: county-level land-use change using national
NRI data: all cropland has same baseline rate afforestn.
• If add 2-3 baseline drivers (frequency of cropland
flooding, crop type), have 5-7 baseline rates.
• if use remote sensing/GIS, have dozens of baseline
rates. But: requires ground truthing; data issues.
• Allows “relative additionality” if baseline varies by
category. Vs. single, binary yes/no additionality.
• Leakage: Testing bottom-up approach by land category,
& comparing with US national ag/forest FASOM model
default values for activity shifting & market leakage.
• Permanence: comparing insurance and discounting
Project Concepts:
Adjusting Baseline and GHG Benefit
After some years of project implementation
Adjusted
GHG Benefit
Estimated
GHG Benefit
B (adjusted)
B (estimated)
GHG
emissions
P (estimated)
Monitored
GHG Benefit
P (measured and
adjusted for
leakage)
Baseline valid
for 5-10 years?
Time
Influenced by policy?
Note: B (adjusted) can be below B (estimated) -Adjusted GHG benefit would then be less than monitored amount
III. Methods for Estimating Leakage
• Definitions
• Examples from different approaches
– Philippines, Indonesia, Mexico, US
• Which approaches to use in India?
Discussion?
Leakage = Unintended Change in GHG Flux
Outside the Boundaries of Project, as Result of
Project Activities
• Types: 1) Activity shifting, 2) market leakage (from
changes in traded products)
• Assess likelihood of leakage for project activities &
location: Decision trees help identify land & product
markets affected, & activity shifting.
• Option 1: Avoid via Project Design or Location:
– Project components supply fiber/ land demanded
• Option 2: Estimate Leakage, & Include in GHG
accounting
– Use top-down models, default values, or bottomup estimates from project
Leakage Example: Mississippi, US, Case:
• Brown lands =
Project: marginal
cropland into
afforestration.
•If retire
cropland, but
new cropland
cleared outside
project, =
activity shifting.
•If afforestation
produces wood
products traded
on market, =
market effect.
Bottom-Up
Leakage
Decision Tree
for
Deforestation
Projects in
Tropics
Identify the baseline drivers
(e.g. deforestation)
Project intervention selected
(e.g. forest conservation)
Does the project include an
alternative livelihoods programme?
NO
Primary leakage likely to
occur
YES
NO
Have baseline agents engaged in
alternative livelihoods options?
YES
Were the baseline agents previously
engaged in commercial activities?
NO
Is there evidence of ‘super-acceptance’ of
the options programme by either the
original baseline agents or external actors?
NO
Source: Aukland, Moura Costa, Brown
2002
No further analysis needed:
no leakage expected.
YES
Secondary leakage
due to market effects
possible.
YES
Secondary leakage due to
‘super-acceptance’ possible.
Jambi Case: Effect of considering Leakage in Mitigation Scenarios.
Rizaldi et al, 2003
Mitigation Scenario-1
Mitigation Scenario-2
1200000
Without Leakage
With Leakage
700000
200000
-300000 2002-2008 2008-2012 2002-2012
-800000
C-Stock (tonnes)
C-Stock (tonnes)
1200000
Without Leakage
With Leakage
700000
200000
-300000 2002-2008 2008-2012 2002-2012
-800000
Chiapas, Mexico Leakage Study: BottomUp Approach on Farmer Small Holdings
• Plan Vivo: farmer-made drawing of his
baseline & project land use (c. 1-5 ha).
• Survey instrument: questions re Plan Vivo
intended vs. actual land use & adjacent plots.
• Survey: technicians visit 10% of 450
farmers, and 3 community Plan vivo projects.
• Questions identify specific types of leakage:
activity shifting; market (wood products).
• Hypothesis: minimal leakage. In progress.
Leakage: Top-Down Modeling Approach:
EPA
Work w/ National Ag/Forest Economic Model May Allow Projects to
Target Low-Magnitude Regions in U.S.
Preliminary leakage estimates for large regional programs,
using FASOM national-scale model
Afforestation Program Leakage Results, as %
Northeast
Lake States
Corn Belt
Southeast
South-central
23
18
30
40
42
Avoided Deforestation Leakage Results, as %
No Harvesting
Harvesting
Allowed
Pacific Northwest-east side
Northeast
Lake States
Corn Belt
South-central
8
7
43
92
31
28
41
73
–4
21
Source: Murray, McCarl, Lee 2002
IV. Permanence: Adjust GHG accounting
for duration, saturation, other factors
• Duration: reversibility of carbon storage.
– Options: Use insurance, take discounts, use project
portfolios to spread risk.
• Saturation: biological limit to carbon storage.
• Saturation may reduce value of forestry and ag
offsets relative to permanent emission offsets.
• 1 - 49% discount forestry options for U.S.. (50 yr.)
• 45 - 62% for ag soil options for U.S. (saturate: 20 yrs.)
Source: McCarl et al. 2001 in press
Comparison Of Costs To Developer For Addressing Durationa
(as a percentage of carbon value). (Source: S. Subak, 2003)
Forest
Insurance
(Oceania)
TCERc
(5-year
expiry)
TCER
(20-year
expiry)
Project Cumulative
Length Project
Premium
50
20-35%
years
(5-9%)
50
years
100%
(60%)
Indefini 100%
te
(21% for initial
20-year period)
Assumptions
0.4%-0.7%/year
(if discount rate is 8%)
Replacement of TCER with
CERd at expiry, finite time
commitment
(if discount rate is 8%)
Replacement of TCER with
CER at expiry
(if discount rate is 8%)
Carbon
20
Reserve
years
(Costa Rica –
Protected
Areas Proj.)
20% b
(but in
temporary
reserve land,
not cash)
Linear removal of reserve from
40% to 0% over 20 years
Ton-Year
(te=100)
59%
(91%)
Would accrue 100% credit after
100 years
(if discount rate is 8%)
50
years
a. Assumes that the project sequesters one ton of carbon in year one and that carbon prices are
constant over time, unless otherwise stated. Probability of release assumed to be 1%/yr.
b. UNFCCC, 2000
c. Temporary Certified Emissions Reduction (TCER).
d. permanent
Certified Emissions Reduction (CER)
Source: Susan Subak, in press, 2001
DRAFT EPA Framework for Project Guidance
Step 1. Feasibility screening
Step 2. Establish and apply
baseline
Coarse-scale
baseline
credible?
Identify project
activity and region
Use fine-scale,
sub-county
baseline method
NO
YES
NO
Passes
institutional,
regulatory
additionality?
YES
Estimate project
GHG benefits
(unadjusted)
NO
Baseline-adjusted
project GHG benefits
viable?
YES
Step 3. Final adjustments, monitor,
verify, report
Other adjustments
if necessary
• leakage (default, specific)
• duration
HIGH
Review leakage
defaults
LOW
Adjusted
project GHG benefits
remain viable?
NO
YES
Monitor, verify, report
final, adjusted GHG
project benefits
Measuring Changes in
Carbon Stocks of Forestry Projects
• Carbon pools -- Live and dead biomass, soil, and
wood products
• Techniques and tools exist to measure carbon stocks
in project areas relatively precisely depending on the
carbon pool
• Monitoring cost between $1-5 per hectare and
US$0.10-0.50 per t C have been reported by a few
projects
Carbon Measurement Needs by Project Type
Project Type
Trees
Roots
Dead
Biomass
Soil
Products
Avoided
Emissions
Sequester
Carbon
Carbon
Substituttion
Re
Red:
Gree:
Yellow
needs to be measured;
recommended
may be necessary
Brown et al, 2000
Associated Impacts and Sustainable
Development
• Site-specific experience exists documenting the
socioeconomic and environmental impacts of
LULUCF projects
• Critical factors that affect contributions of
LULUCF projects to sustainable development
include:
– Extent and effectiveness of local community participation
– Transfer and adoption of technology
– Capacity to develop and implement guidelines and procedures
• Addressing factors can alleviate project
permanence
Issue 5: Can We Identify Co-Benefits and Co-Effects of
Mitigation Options, and Design Policies to Promote them?
Case study: Lower Mississippi River Basin:
Water Quality Changes due to Sequestration Activities
• Initial analysis by
RTI/Texas A&M for
EPA on water quality
implications of
sequestration
activities.
• Delta states show
largest water quality
improvement per
unit GHG reduced.
• Significant (~9%)
reductions in N
loadings entering
Gulf at $25 & $50/tC
incentive prices.
Change in WQI
from Baseline
-40 to -1
0
1 to 5
6 to 100
Source: Pattanayak et al. 2002
Ideal case study to explore
approaches to project issues
• Data richness and availability
• GIS images at fine resolution
• Large enough scale to test ‘spatial approach’ (e.g.,
low 1,000s of ha)
• Key baseline drivers that affect land-use change
and management well known (incl. policies)
• Area includes croplands and forest lands
• Targets area where options look promising: ability
to quantify issues & generate co-benefits