Methodological Issues in Methodological Issues in Forestry Mitigation ProjectsForestry Mitigation Projects

Ken Andrasko Ken Andrasko Office of Atmospheric ProgramsOffice of Atmospheric Programs

U.S. Environmental Protection Agency, Washington, U.S. Environmental Protection Agency, Washington, DC, USADC, USA

Jayant Sathaye, LBNLJayant Sathaye, LBNLatat

Workshop on Climate Change Mitigation Forestry Workshop on Climate Change Mitigation Forestry Projects in India, Bangalore, July 10-12, 2003Projects 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 phaseand other LUCF projects in some level of implementation.

(source: IPCC LUCF SR, 2000)

Project Type LandArea

(Mha)

CarbonMitigati

on(Mt C)

Costs$/t C

Carbon Mitigation t C/ha

Emissions Avoidance viaConservation:

Forest Protection (7) Forest Management (3)

2.90.06

40-1085.6

0.1– 150.3 – 8

4 - 25240 - 85

Carbon Sequestration

Reforestation and Afforestation (7)

Agroforestry (2*)

0.10

0.2

12

10.8

1 – 28

0.2-10

26 – 328

56-165

Multi-Component and CommunityForestry (2*)

0.53 20-49 0.2 – 15 0.2 –165

Location Cost Funders Activities

Rio Bravo Belize $5.6 million WEPCO, 3other utilities,2 oil co.

Protection, sustainableforestry, communitydevelopment

Noel Kempff Bolivia $9.6 million AEP, BP,Pacificorp

Protection, communitydevelopment

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

Midwestrestoration

Ohio/Indiana $500,000 Cinergy Reforestation

NachusaGrasslands

Illinois $50,000 Natsource Grassland restoration

Examples: The Nature Conservancy Climate Action Projects

• 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

Example: Reforestation - Forest Restoration

Credit: Bill Stanley, TNC

Project-Based Activities Mechanism:How it might work

Step I: Internationaland/or NationalSponsors DevelopProject

Step VII:Appeal Process(if necessary)

Step V:Verification

•Project developer provides evidence ofgovernment approval/registration,

• Projected impacts demonstrate thatproject will provide measurable benefits

•Project satisfies additionalityrequirements

• Governments approve/register/validate project

Step IV:Monitoring andReporting

Step III: Submit toUNFCCC for Registration

Step II: Submit toGovernments forApproval/Registration

Step VI:Certification ofemissionsreductions

•Independentauditor verifiesemissions reports

•Auditor needs tobe accredited

•Project developer monitorsproject on a regular basis

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

GHG project

Without-project Baseline (B)

GHG emissions

Time

Estimated GHG Benefit

Adjusted for leakage (P)

Project Concepts: Monitoring GHG Benefit

During Project Implementation

P (estimated)

Measured GHG Emissions

B (estimated)

GHG emissions

Time

Estimated GHG Benefit

Monitored GHG Benefit

P (Measured and Adjusted for leakage)

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

Project Baseline ScenariosAtlantic ForestProjects, Brazil

Ongoing Asian water buffaloranching or other agriculture

Clearing of additional forests forpasture and agriculture

Noel Kempff Project,Bolivia

Conventional timber extraction

Land clearing for agriculture

Rio Bravo Project,Belize

Logging followed by clearing ofupland forests for agriculture

Conventional timber extraction onupland forestsFrequent burning and timberharvest on pine savanna

Examples of Project Baseline Scenarios

Credit: Bill Stanley, TNC

Methods matter: Comparison of 5 Methods matter: Comparison of 5 baseline-setting approaches: baseline-setting approaches: Rio Bravo Rio Bravo

project, Belizeproject, Belize

(Draft: Winrock Intertl-EPA analysis, in prep.)(Draft: Winrock Intertl-EPA analysis, in prep.)

0

10

20

30

40

50

60

70

80

90

100

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035

YEAR

% D

EFO

RE

STE

D (C

umul

ativ

e)

SIMPLE MODEL high

GEOMOD regional

GEOMOD project

SIMPLE MODEL low

Original project

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 Currentland use & location

Identify key baseline drivers

Simpler case:Reforestation Degraded or Burned

forestNaturalregrowth

Demand foragriculturalland orfuelwood

Complexcase:Protection

Undisturbed forestin Chiapas, Mexico

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.

426278>30 hab/km2

345067>15 a 30

243855>0 a 15

725430 hab/km2

> 2000 m1000 - 2000 m

0 -1000 mRoads, by Population

Baseline Driver Example: % Land-Use Change, Forest to Non-

forest: 1975-96. Chiapas, Mexico. Factors: Distance from Roads, by Population

Density

Source: ECOSUR, & de Jong et al., 2000

Source: ECOSUR, Chiapas, Mexico; & de Jong et al, 2002.

Vegetation cover in 1975, Chiapas, Mexicofrom 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

Total emission

119, 465,774 tCSource: ECOSUR, Chiapas, Mexico; & de Jong et al, 2002.

Carbon emissions from land use change, 1975 - 1996

Predicted Deforestation: Noel Kempff Project, Bolivia -- GIS model

projections

Deforestationprojection,2000 - 2040

Baseline drivers =nearness to road& ag croplands;populationchanges

Source: Bill Stanley, TNC from GEOMOD by M. Hall, 2002

1986 1992

Project-Scale Land Use Analysis for Baseline:Jambi, Indonesia: 1986-92

Rizaldi et al, 2003:

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

Project-Scale Land Use Analysis: Real vs. PredictedLand Use. : Jambi, Indonesia: 1986-96

Mitigation Scenario-1

70500000

71000000

71500000

72000000

72500000

73000000

73500000

74000000

1999 2001 2003 2005 2007 2009 2011 2013

Year

C-Sto

ck (to

nnes)

BaselineAdjusted BaselineC-Project

Mitigation Scenario-2

70500000

71000000

71500000

72000000

72500000

73000000

73500000

74000000

1999 2001 2003 2005 2007 2009 2011 2013

Year

C-Sto

ck (to

nnes)

BaselineAdjusted BaselineC-Project

Jambi Case: Mitigation Scenario Results, and Effect of Adjusting Baseline.

Rizaldi et al, 2003

Example: EPA Mississippi Case:

Testing 4 coarse to fine resolution data approaches to Baseline Setting &

Additionality

• Green: Green: national national forestsforests

•Brown: Brown: marginal marginal croplands,, croplands,, flooded every flooded every 2 yrs.2 yrs.

•Project: Project: restore restore wetland wetland hardwood hardwood speciesspecies

• 4 counties4 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 (pixel-level GIS) methods?

– Can existing national data sources be used to allow for transferable methods across the U.S.?

Mississippi case: Approach & Mississippi case: Approach & FindingsFindings

• 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

BenefitAfter some years of project implementation

P (estimated)

P (measured and adjusted for leakage)

B (estimated)

GHG emissions

Time

B (adjusted)

Estimated GHG Benefit

Monitored GHG Benefit

Adjusted GHG Benefit

Note: B (adjusted) can be below B (estimated) -- Adjusted GHG benefit would then be less than monitored amount

Baseline validfor 5-10 years? Influenced by policy?

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 bottom-

up estimates from project

Leakage Example: Mississippi, US, Case:• Brown lands Brown lands

= Project: = Project: marginal marginal cropland into cropland into afforestration. afforestration.

•If retire cropland, but new cropland cleared outside project, = activity shifting.

•If afforestation produces wood products traded on market, = market effect.

Does the project include an alternative livelihoods programme?

Primary leakage likely to occur

NO

Were the baseline agents previously engaged in commercial activities?

Secondary leakage due to market effects

possible.

Have baseline agents engaged in alternative livelihoods options?

Secondary leakage due to ‘super-acceptance’ possible.

Is there evidence of ‘super-acceptance’ of the options programme by either the

original baseline agents or external actors?

Project intervention selected (e.g. forest conservation)

Identify the baseline drivers (e.g. deforestation)

YES

NO

YES

YES NO

NO YES

No further analysis needed: no leakage expected.

Bottom-Up Bottom-Up Leakage Leakage Decision Tree Decision Tree for for Deforestation Deforestation Projects in Projects in TropicsTropics

Source: Aukland, Moura Costa, Brown 2002

Mitigation Scenario-1

-800000

-300000

200000

700000

1200000

2002-2008 2008-2012 2002-2012C-Stoc

k (ton

nes)

Without LeakageWith Leakage

Mitigation Scenario-2

-800000

-300000

200000

700000

1200000

2002-2008 2008-2012 2002-2012C-Stoc

k (ton

nes)

Without LeakageWith Leakage

Jambi Case: Effect of considering Leakage in Mitigation Scenarios. Rizaldi et al, 2003

Chiapas, Mexico Leakage Study: Bottom- Up 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: Leakage: Top-Down Modeling Approach: EPA Work w/ National Ag/Forest Economic Model May EPA Work w/ National Ag/Forest Economic Model May Allow Projects to Target Low-Magnitude Regions in U.S.Allow Projects to Target Low-Magnitude Regions in U.S.

Afforestation Program Leakage Results, as %

Northeast 23Lake States 18Corn Belt 30Southeast 40South-central 42

Avoided Deforestation Leakage Results, as % No Harvesting Harvesting

AllowedPacific Northwest-east side 8 7Northeast 43 41Lake States 92 73Corn Belt 31 –4South-central 28 21

Source: Murray, McCarl, Lee 2002

Preliminary leakage estimates for large regional programs, using FASOM national-scale model

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)

ProjectLength

CumulativeProjectPremium

Assumptions

ForestInsurance(Oceania)

50years

20-35%(5-9%)

0.4%-0.7%/year(if discount rate is 8%)

TCERc

(5-yearexpiry)

50years

100%

(60%)

Replacement of TCER withCERd at expiry, finite timecommitment(if discount rate is 8%)

TCER(20-yearexpiry)

Indefinite

100%

(21% for initial20-year period)

Replacement of TCER withCER at expiry

(if discount rate is 8%)

CarbonReserve(Costa Rica –ProtectedAreas Proj.)

20years

20% b

(but intemporaryreserve land,not cash)

Linear removal of reserve from40% to 0% over 20 years

Ton-Year(te=100)

50years

59%(91%)

Would accrue 100% credit after100 years(if discount rate is 8%)

a. Assumes that the project sequesters one ton of carbon in year one and that carbon prices areconstant over time, unless otherwise stated. Probability of release assumed to be 1%/yr.

b. UNFCCC, 2000 c. Temporary Certified Emissions Reduction (TCER). d. permanentCertified Emissions Reduction (CER) Source: Susan Subak, in press, 2001

DRAFT EPA Framework for Project Guidance

Identify projectactivity and region

Coarse-scalebaselinecredible?

YES

Use fine-scale, sub-county

baseline method

Baseline-adjustedproject GHG benefits

viable?

NO

YES

Monitor, verify, reportfinal, adjusted GHG

project benefits

Other adjustments if necessary

• leakage (default, specific)• duration

Estimate projectGHG benefits(unadjusted)

LOW

NO

YES

Passesinstitutional, regulatory

additionality?

NO

Review leakagedefaults

HIGH

YESAdjustedproject GHG benefits

remain viable?

NO

Step 1. Feasibility screening Step 2. Establish and apply baseline

Step 3. Final adjustments, monitor, verify, report

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

Project Type Trees Roots Dead Biomass

Soil Products

Avoided Emissions

Sequester Carbon

Carbon Substituttion

Re

Carbon Measurement Needs by Project Type

Red: needs to be measured; Gree: recommendedYellow 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 • Biodiversity not yet assessed

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

Change in WQIfrom Baseline

-40 to -101 to 56 to 100

• 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.

Source: Pattanayak et al. 2002

Ideal case study to explore Ideal case study to explore approaches to project issuesapproaches 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