4.1

CGEGreenhouse Gas Inventory

Hands-on Training Workshop

LAND-USE CHANGE ANDFORESTRY SECTOR

(LUCF)

4.2

Background COP adopted guidelines for preparation of initial National

Communications at its second session (10/CP.2) IPCC guidelines used by 106 NAI Parties to prepare National

Communications. New UNFCCC guidelines adopted at COP8 (17/CP.8) UNFCCC User Manual for the Guidelines on National

Communications to assist NAI Parties in using latest UNFCCC guidelines

Review and synthesis of NAI inventories highlighted several difficulties and limitations of using IPCC 1996GL (FCCC/SBSTA/2003/INF.10)

GPG2000 and GPG2003 have addressed some of the limitations and provided guidance for reducing uncertainty

4.3

Purpose of the Presentation

GHG inventory in biological sectors such as LUCF is characterized by:

methodological limitations lack of data or low reliability of existing data high uncertainty

Presentation aims at assisting NAI Parties in preparing GHG inventories using the IPCC 1996GL, particularly in the context of UNFCCC decision 17/CP.8, focusing on:

the need to shift to GPG2003 and higher tiers/methods to reduce uncertainty

overview of the tools and methods review of AD and EF and options to reduce uncertainty use of IPCC inventory software and emission factor database (EFDB)

4.4

Problems Addressed and Approach

The presentation addresses many of the problems encountered by NAI experts in using IPCC 1996GL

Problems are reviewed and categorized into: methodological issues, AD and EF/RF

Approach adopted includes: GPG2003 approach Strategies for improvement in methodology, AD and EF GPG2003 strategy for AD and EF/RF – 3-Tier approach Sources of data for AD and EF/RF, including EFDB

4.5

Organization of the Presentation

IPCC 1996GL and GPG2003; Approach and Steps Key source/sink category analysis and decision trees – GPG2003 Reporting framework for LUCF sector -IPCC 1996GL-GPG2003 Choice of methods – Tier structure and Features Review of the problems encountered in using IPCC 1996GL and how

these are addressed in GPG2003 Methodological issues Activity data (AD) Emission/removal factors (EF/RF)

IPCC 1996GL category-wise assessment of problems and GPG2003 options to address them

Review and assessment of AD and EF/RF; data status and options Uncertainty estimation and reduction and EFDB

4.6

Background Resources

Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories http://www.ipcc-nggip.iges.or.jp/public/gl/invs1.htm

GPG2000 – Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories

http://www.ipcc-nggip.iges.or.jp/public/gp/english/ GPG2003 – Good Practice Guidance for Land Use, Land-Use Change and Forestry

http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm EFDB – Emissions Factor Database

http://www.ipcc-nggip.iges.or.jp/EFDB IPCC Inventory Software – Revised 1996 IPCC Guidelines; Software for the Workbook

http://www.ipcc-nggip.iges.or.jp/public/gl/software.htm Subsidiary Body for Implementation (SBI)

http://maindb.unfccc.int/library Subsidiary Body for Scientific and Technological Advice (SBSTA)

http://maindb.unfccc.int/library

4.7

Definition of Key Terms

LUCF (Land-Use Change and Forestry) – Land use is the type of activity being carried out on a unit of land, such as forest land, cropland and grassland. The IPCC 1996GL refers to sources and sinks associated with GHG emissions/removals from human activities, which:

Change the way land is used (e.g., clearing of forest for agriculture, conversion of grassland to forest)

Affect the amount of biomass in existing biomass stocks (e.g., forest, village trees, savanna) and soil carbon stocks

LULUCF (Land Use, Land-Use Change and Forestry) – This includes GHG emissions/removals resulting from managed land (involving no change in use, such as forest remaining forest land) and land-use changes (involving changes in land-use, such as grassland converted to forest land or forest land converted to cropland).

4.8

Definitions…

Source – Any process or activity that releases a GHG (such as CO2 and CH4) into the atmosphere. A

carbon pool can be a source of carbon to the atmosphere if less carbon is flowing into it than is flowing out of it.

Sink – Any process, activity or mechanism that removes a GHG from the atmosphere. A given pool can be a sink for atmospheric carbon if during a given time interval more carbon is flowing into it than is flowing out of it.

4.9

Definitions… Activity data – Data on the magnitude of human activity,

resulting in emissions/removals taking place during a given period of time (e.g., data on land area, management systems, lime and fertilizer use).

Emission factor – A coefficient that relates the activity data to the amount of chemical compound, which is the source of later emissions. Emission/removal factors are often based on a sample of measurement data, averaged to develop a representative rate of emission or removal for a given activity level under a given set of operating conditions.

Removal factor – Rate at which carbon is taken up from the atmosphere by a terrestrial system and sequestered in biomass and soil.

4.10

Contribution and Role of LUCF sector to NAI GHG emissions

Examination of National Communications (examples) – Argentina, Indonesia and Zimbabwe for 1994

GHG inventories show that LUCF sector has a significant impact on national net CO2 equivalent emissions in developing countries

Could be a significant source or sink of CO2 LUCF sector is a net sink for Argentina and Zimbabwe Net source for Indonesia, which experienced forest land conversion of

over one Mha Inclusion of LUCF sector in the inventory had the following impact on

GHG emissions: Argentina: Emissions of 119 Tg CO2 when LUCF excluded, but 84 TgCO2

when LUCF included Zimbabwe: Source of 17 Tg CO2 when LUCF excluded, but a net sink of 45

TgCO2 when LUCF included Indonesia: Emissions of 189 Tg CO2 when LUCF excluded, but 344 Tg CO2

when LUCF included.

4.11

Revised 1996 IPCC Guidelines

Fundamental basis for inventory methodology rests upon two linked themes

Flux of CO2 to/from atmosphere assumed to be equal to changes in C-stocks in existing biomass and soils

Changes in C-stocks can be estimated by establishing rates of change in land use and practices that bring about change in land use

Estimating C-stocks in land-use categories: that are not subjected to change that are changed

4.12

Default Categories in IPCC 1996GL

5A. Changes in forest and other woody biomass stocks due to commercial management harvest of industrial roundwood (logs) and fuelwood establishment and operation of forest plantations planting of trees in urban, village and non-forest locations

5B. Forest and grassland conversion the conversion of forests and grassland to pasture, cropland etc.

can significantly change C-stocks in vegetation and soil

5C. Abandonment of cropland, pasture, plantation forests, or other managed lands

5D. CO2 emissions and removals from soils cultivation of mineral soils cultivation of organic soils liming of agricultural soils

4.13

Steps in Preparing Inventory Using IPCC 1996GL

Step 1: IPCC 1996GL does not provide key category analysis approach. However, inventory experts are encouraged to conduct key category analysis using GPG2003 approach. Estimate the share of LUCF sector to national GHG inventory

Step 2: Select the land-use categories (forest/plantations), vegetation types subjected to conversion (forest and grassland), changes in land-use/management systems (for soil carbon inventory)

Step 3: Assemble required AD, depending on tier selected, from local, regional, national and global databases, including EFDB

4.14

Steps (IPCC 1996GL)…

Step 4: Collect EF/RF, depending on tier level selected, from local/regional/national/global databases, including EFDB

Step 5: Estimate GHG emissions and removals

Step 6: Estimate uncertainty involved

Step 7: Report GHG emissions/removals

Step 8: Report all procedures, equations and

sources of data adopted for GHG inventory

estimation

4.15

GPG2003 LULUCF Land-use Categories and Methods

GPG2003 adopted two major advances overIPCC 1996GL, namely:

Three hierarchical tiers of methods they range from use of default data and simple equations to use

of country-specific data and models to accommodate national circumstances

Land-use-category-based approach for organizing methodologies land-use categories: Adopted six land categories to ensure consistent

representation, covering all geographic areas of a country. Forest land, cropland, grassland, wetland, settlements and others

Each land-use category is further disaggregated to reflect the past and the current land use

Forest land remaining forest land Lands converted to forest land

4.16

CO2 Pools, Non-CO2 Gases and Sources of Non-CO2 Gases

CO2 and non-CO2 trace gases

CO2 emissions and removal are estimated for all the C-pools namely: Above-ground biomass Below-ground biomass Soil carbon Dead organic matter and woody litter

Non-CO2 gases estimated include: CH4, N2O, CO and NOx

Sources of non-CO2 gases: N2O and CH4 from forest fires

N2O from managed (fertilized) forests

N2O from drainage of forest soils

N2O and CH4 from managed wetland

Soil emissions of N2O from land-use conversion

4.17

Broad Approach and Steps in Adopting GPG2003 LULUCF

Accounts for all land-use categories and sub-categories, all carbon pools and non-CO2 gases, depending on key source/sink category analysis

Select nationally adopted land-use classification system (categories and sub-categories) for inventory estimation. Each land category is further subdivided into:

land remaining in the same category (e.g. forest land remaining forest land)

other land category converted to this land category (e.g. grassland converted to forest land)

Select appropriate land classification system most relevant to country Conduct key source/sink category analysis to identify the key:

land categories and sub-categories non-CO2 gases carbon pools

4.18

Steps to Adopting GPG…

Select appropriate tier level for key land categories and sub-categories, non-CO2 gases and carbon pools, based on key category analysis as well

as resources available for the inventory process Assemble required AD, depending on tier selected, from regional, national

and global databases Collect EF/RF, depending on tier selected, from regional, national and

global databases, forest inventories, national greenhouse gas inventory studies, field experiments and surveys and use of EFDB

Select method of estimation (equations), based on tier level selected, quantify emissions/removals for each land-use category, carbon pool and non-CO2 gas. Adopt default worksheet provided in GPG2003

Estimate uncertainty Adopt QA/QC procedures and report results Report GHG emissions and removals using the reporting tables Document and archive all information used

4.19

Features of Land Category Based Approach – Forest Land

Estimates carbon stock changes and GHG

emissions/removals associated with changes in

biomass and soil organic carbon on forest land and

lands converted to forest land

Forest land remaining forest

Land converted to forest

Provides methodology for five carbon pools

Links biomass and soil carbon pools for the same land

areas (at higher tiers)

4.20

Features of Land Category Based Approach – Cropland

Provides methods for estimating carbon stock changes in living biomass, mineral soils and in organic soils

Provides methods for estimating annual N2O emissions

from mineral soils due to addition of N (in the form of fertilizer, manure and crop residue) and N released by soil organic matter mineralization

These categories are estimated and reported in agriculture sector in IPCC 1996GL

4.21

Features of Land Category Based Approach – Grassland

Provides methodology for estimating carbon stock changes in living biomass and soils in grassland and lands converted to grassland

Estimates annual change in carbon stocks in living biomass and soil carbon (mineral soils and cultivated organic soils) in grassland remaining grassland and lands converted to grassland

Provides methodology for estimating non-CO2 emissions from

vegetation fires based on: area of grassland burnt, mass of available fuel, combustion efficiency and emission factor for each GHG from grassland remaining grassland and land converted to grassland

4.22

Features of Land Category Based Approach – Wetlands

The GHGs estimated include CO2, CH4 and N2O

Methodology for estimating GHGs for ‘wetlands remaining

wetlands’ is given in the Appendix and for GHGs from ‘lands

converted to wetlands’ in the main text

Estimates changes in carbon stocks in lands converted to wetlands

due to peat extraction and land converted to flooded land

Estimates N2O emissions from peatland drainage and flooded land

and CH4 emissions from flooded land

4.23

Features of Land Category Based Approach – Settlements and Other Land

Settlements Provides methodology for estimating CO2 emissions and removals for

‘lands converted to settlements’ and methodology is given in Appendix for ‘settlements remaining settlements’

Methods for estimating Annual change in carbon stocks in living biomass in ‘forest lands converted to settlements’ based on area of land converted and carbon stock in living biomass immediately before and after conversion to settlements

 Other land Changes in carbon stocks and non-CO2 emissions/removals need not be

assessed for category of ‘other land remaining other land’ Methodology provided for estimating annual change in carbon stocks in

‘land converted to other land’ based on estimates of change in carbon stocks in living biomass and SOC

4.24

Key Source/Sink Category Analysis

“One that is prioritized within national inventory system because its estimate has significant influence on a country’s total inventory of direct GHGs in terms of absolute level of emissions (removals), the trends in emissions (or removals), or both”

A land-use system or C-pool or non-CO2 gas is significant if its

contribution to GHG emissions/removals is >25%–30% of overall national inventory or overall LUCF sector inventory.

The term key category is used to represent both sources and sinks

Key category analysis helps a country to achieve highest possible levels of certainty while using the limited resources available for the inventory process efficiently

4.25

Key Source/Sink Category AnalysisGPG2003 Approach

GPG2003 assists Parties in identifying the key: land categories (e.g. forest land, cropland, etc.) gases (CO2, CH4 and N2O) carbon pools (living biomass, dead organic matter

and soil organic carbon) The decision trees given in GPG2003 could be

adopted Decision trees at two levels of disaggregation

Land remaining in the same land-use category (e.g. forest land remaining forest land)

Land converted to another land-use category (e.g. grassland converted to forest)

4.26

Tier Structure: Selection and Criteria

GPG2003 provides users with three methodological tiers for estimating GHG emissions/removal for each source.

The three tiers defined in GPG2003 nearly correspond to the three levels of complexity given in IPCC 1996GL (not referred to as ‘tiers’)

Tiers correspond to a progression from use of simple equations or methods with default data to country-specific data in more complex national systems

Tiers implicitly progress from least to greatest levels of certainty in estimates as a function of:

Methodological complexity Regional specificity of model parameters Spatial resolution and extent of activity data

4.27

Combination of Tiers

NAI experts could adopt multiple tiers in the GHG inventory for LULUCF sector: for different land-use categories within a given land-use category for different carbon

pools within a carbon pool, for activity data and emission

factor

Adopt higher tiers for key categories and wherever possible use country-specific, climatic region-

specific emission/removal factors

4.28

Comparison Between IPCC 1996GL and GPG2003

GPG2003 IPCC 1996GL i) Land category based approach covering forest land, cropland, grassland, wetland, settlement and others

i) Approach based on four categories namely 5A to 5D (refer to Section 5.1) All land categories not included such as coffee, tea, coconut etc. Lack of clarity on agro-forestry

ii) These land categories are further sub divided into;

land remaining in the same use category

1. other land converted to this land category

ii) Forest and grassland categories defined in 5A and 5B

iii) Methods given for all carbon pools; AGB, BGB, dead organic matter and soil carbon and all non-CO2 gases

iii) Methods provided mainly for aboveground biomass and soil carbon.

2. Assumes as a default that changes in carbon stocks in dead organic matter pools are not significant and can be assumed to be zero, i.e. inputs balance losses.

3. Similarly, belowground biomass increment or changes are generally assumed to be zero

iv) Key source/sink category analysis provided for selecting significant

land categories sub-land categories C-pools CO2 and non-CO2 gases

iv) Key source/sink category analysis not provided

v) Three tier structure presented for choice of methods, Activity Data and Emission Factors

v) Three tier structure approach presented but its application to choice of methods, AD and EF not provided

vi) Biomass and soil carbon pools linked vi) Changes in stock of biomass and soil carbon in a given vegetation or forest type not linked

4.29

Key Activity Data Required for GPG2003 and IPCC 1996GL

GPG2003 IPCC 1996GL FOREST LAND i) Area of forest land remaining forest land

Disaggregation according to climatic region, vegetation type, species, management system, age etc.

ii) Area of other land category converted to forest land

Disaggregation as mentioned above iii) Forest area affected by disturbances iv) Forest area undergoing transition from state (i) to (j) v) Area of forest burnt vi) Total afforested land derived from cropland/grassland vii) Area of land converted to forest land through

natural regeneration establishment of plantations

Category 5A to 5D i) Area of plantation/forests ii) Area converted annually iii) Average area converted (10-year average) iv) Area abandoned and regenerating

20-years prior to year of inventory 20-100 years prior to the year of inventory

v) Area under different land use/management systems and soil type

during year-t (inventory year) 20-years prior to year-t

vi) Area under managed organic soils

CROPLAND, GRASSLAND, WETLAND ETC. Similar categorization as above

4.30

Key Emission Factors Required for GPG2003 and IPCC 1996GL

Average annual net increment in volume suitable for industrial processing

Annual biomass transfer into deadwood

Biomass expansion factor (BEF) for conversion of annual net increment (including bark) to above ground tree biomass increment

Annual biomass transfer out of deadwood

Root:shoot ratio appropriate to increment Litter stock under different management systems

Biomass expansion factor (BEF) for converting volumes of extracted roundwood to total aboveground biomass (including bark)

Soil organic carbon in different management systems

Mortality rate in naturally and artificially regenerated forest

Mass of biomass fuel present in area subjected to burning

Number of emission factors common to both Above-ground biomass growth rate, biomass density Above-ground biomass stock, soil carbon density Fraction of biomass left to decay

4.31

Rationale for Adopting GPG2003

Addresses most of the methodological limitations and inadequacies of IPCC 1996GL

Adopts key source/sink category analysis, which enables dedication of limited inventory resources to key source/sink categories, CO2 pools and non-CO2 gases

Enables estimation of carbon stock changes and non-CO2 emissions for all the relevant geographic area

Accounts for all the five carbon pools Ensures consistent representation of land for long-term periodic

inventories Reduces uncertainty in GHG estimates

4.32

Reporting of GHG Inventory in the LUCF Sector – IPCC 1996GL

LUCF categories CO2 emissions

CO2 removal/uptake

CH4 N2O CO NOx

5A. Changes in forest and other woody biomass stocks

5B. Forest and grassland conversion

5C. Abandonment of croplands, pastures, plantation forests, or other managed lands

5D. CO2 emissions and removals from soils

5E. Others TOTAL

4.33

Reporting of GHG Inventory in the LUCF Sector – GPG2003

Net CO2 emissions / removals (1) CH4 N2O NOx CO Greenhouse gas source and sink categories

IPCC guidelines

(Gg) 5. Total Land-Use Categories 5.A. Forest Land 5.A.1. Forest Land remaining Forest Land 5A 5.A.2. Land converted to Forest Land 5A, 5C, 5D 5.B. Cropland 5.B.1. Cropland remaining Cropland 5A, 5D 5.B.2. Land converted to Cropland 5B, 5D 5.C. Grassland 5.C.1. Grassland remaining Grassland 5A, 5D 5.C.2. Land converted to Grassland 5C, 5D 5.D. Wetlands (2) 5.D.1. Wetlands remaining Wetlands 5A, 5E 5.D.2. Land converted to Wetlands 5B, 5E 5.E. Settlements (2) 5.E.1. Settlements remaining Settlements 5A 5.E.2. Land converted to Settlements 5B, 5E 5.F. Other Land (2) 5.F.1. Other Land remaining Other Land 5A 5.F.2. Land converted to Other Land 5B, 5E 5.G. Other (please specify) (2) Harvested Wood Products (2)

4.34

Mapping/Linkage BetweenIPCC 1996GL and GPG2003

GPG2003 based on land-use category approach, provides a procedure to link inventory estimates of GPG2003 to IPCC 1996GL, based on Category 5A to 5D

However, the inventory estimates obtained using IPCC 1996GL could be different from the estimates obtained using GPG2003 due to the following reasons:

Inclusion of additional land categories, e.g. agro-forestry, coconut, coffee, tea

Inclusion of additional carbon pools; below-ground biomass, dead organic matter, etc.

Estimation of biomass increment and losses in each land category, sub-category

Linking of biomass and soil carbon for each land category Use of improved default values

4.35

Methodological Issues/Problems in GHG Inventory Using IPCC 1996GL

Compatibility of IPCC 1996GL land categories to national classification High uncertainty of inventory, AD and EF Lack of disaggregated data, particularly on vegetation types Lack of clarity for reporting estimates of emissions/removal in

managed natural forest Lack of consistency in estimating/reporting total biomass or only

above-ground biomass Lack of methods for below-ground biomass and for incorporating non-

forest areas, such as coffee, tea, coconut, cashew nut Difficulty in differentiating managed (anthropogenically impacted) and

natural forests Ambiguity in terminology, e.g. forest, afforestation, reforestation,

managed forest Complexity of the methodology

4.36

Methodological Issues

GPG2003 Approach and Suggested Improvements

4.37

Issue: Lack of compatibility of IPCC land/forest category/vegetation types/systems/formats and national circumstances or classification of forests

GPG2003 approach Improvement suggested - Land category-based approach covering six broad categories such as forest and cropland overcomes the problem of IPCC category vs. national classification - Parties to use nationally relevant sub-categories (For e.g., forest land could be further categorized; evergreen, deciduous, eucalyptus, teak etc.) - All land included for inventory; full and consistent representation of land

- Adopt GPG2003 approach for consistent and full representation of all land categories - Replace the IPCC categories given in IPCC software with nationally relevant classification (e.g., replace Acacia by other plantation species or natural forest types) - FAO provides default forest type classification for each country - Preferable to use national forest classification. If not available, use FAO classification - Initiate national remote sensing of forest areas

4.38

Issue: High uncertainty in inventory estimation

GPG2003 approach Improvement suggested - Adopt key source/sink category analysis to identify land categories, C-pools and non-CO2 gases to allocate inventory resources - Estimate all relevant C-pools and non-CO2 gases - Select appropriate tier/method for each AD/EF, based on key category analysis - Use nationally relevant land categorization and disaggregate appropriately - Use nationally derived EF/RF - Adopt methods provided in GPG2003 for uncertainty estimation and reduction - GPG2003 and EFDB provides additional default data - Adopt QA/QC procedures

- Adopt disaggregated classification of vegetation types/land use and use appropriate EF/RF - Initiate national forest inventory program and field studies to generate EF/RF, suited to national circumstances at disaggregated level such as species/climate/soil - Initiate studies on biomass extraction, consumption and losses - Adopt linking of biomass and soil carbon pools for each land parcel or forest stand - Parties should initiate activities to shift inventory methods from tier 1 to tier 3

4.39

Issue: Lack of consistency in estimating/reporting total biomass or only above-ground biomass

GPG2003 approach Improvement suggested - The equations and worksheets for each land sub-category covers all the C-pools - Selection of C-pools depends on the key category analysis - Biomass expansion factors are given for conversion from AGB to other pools

- National forest inventory studies should estimate changes in the stocks of all C-pools for the selected forest stands - Develop regional or national biomass expansion factors for different forest types

4.40

Issue: Methods for below-ground biomass not provided in default approach

Issue: Estimation (or differentiation) of managed (anthropogenically impacted) and natural forests

GPG2003 approach Improvement suggested - The equations and methods linking AGB and BGB are given - Default values for estimating BGB, using AGB estimates are given

- Estimate through field studies AGB:BGB ratios and biomass expansion factors for different forest/plantation types, age stands and management systems

GPG2003 approach Improvement suggested - Clarity provided on definition of managed and forest land - Consistent representation of lands (Chapter 2), covering all land categories and full geographic area of a country ensures inclusion of all land area and avoids double counting

- Initiate satellite or remote sensing based monitoring of managed and natural forest -Countries may use the satellite maps from institutions such as FAO, UNEP, NASA and other regional and international institutions -If satellite based monitoring is not feasible, adopt traditional surveys could be adopted

4.41

Issue: Lack of methods for savanna/grassland

Issue: Lack of methods for incorporating non-forest areas, such as coffee, tea, coconut, cashew nut, as well as ambiguity about agro-forestry

GPG2003 approach Improvement suggested - Land category based approach includes all land categories (Chapter 2) - Cropland category includes non-forest areas such as coffee, tea, coconut etc. (Chapter 3.3) and agro-forestry

- Monitor carbon stock changes in different C-pools in non-forest land categories such as coffee, tea, coconut, annual croplands etc.

GPG2003 approach Improvement suggested - Methods are included for savanna/grassland in Chapter 3.4 - Grassland - Methods, equations and worksheets provided for CO2 and non-CO2 emissions (from fires in savanna and grasslands)

- Initiate studies to monitor increments and losses of biomass and soil carbon stocks in savanna and grasslands

4.42

Issue: Absence of linkage between biomass and soil carbon

In IPCC 1996GL changes in stocks of biomass and soil carbon are estimated in different worksheets and are not linked

GPG2003 approach Improvement suggested - Clear linkage between biomass and soil carbon established - The land category based approach where for each land category or sub-category (even for a given forest stand), the stocks of all the C-pools, including biomass and soil carbon are estimated

- National forest inventory studies should monitor biomass as well soil carbon stock changes - Need to develop models linking biomass and soil carbon

4.43

Problems Relevant to Activity Data and Emission Factors

GPG2003 Approach and Suggested Improvements

4.44

Examples of Problems Relevant to AD and EF

Examples of Activity Data Examples of Emission Factors Lack of data on non-forest/fruit trees Inappropriate default values given in IPCC

1996GL Lack of time-series data for 5B, 5C and 5D

Default data not suitable for national circumstances

Lack of availability of disaggregated data

Lack of EF at disaggregated level

Lack of data on biomass / fuelwood / charcoal consumption

Lack of growth rate data of non-forest or fruit trees

Lack of data on managed land abandoned

Low reliability and high uncertainty of data

Land use, forest cover, forest conversion data etc., out of date, leading to extrapolations based on past data

Lack of EF for; Biomass density growth rate, soil

carbon at species/forest type/climatic region level

Distinction of fractions of biomass burnt on-site and off-site and left to decay

Default data normally provides upper value, leading to over estimation

Low reliability and high uncertainty of data

4.45

GPG2003 Approach

To minimize the uncertainty involved in inventory estimation originating from activity data and emission factors, the GPG2003 has provided multiple approaches. Key source/sink category analysis enables focusing of

inventory efforts on the identified key source/sink categories, incorporating AD and EF

Three-tier approach for choice of AD and EF Additional default values for emission and removal

factors Provision of improved sources of data, including EFDB

4.46

Improvements for the Future

Non annex-I Parties may have to: Initiate dedicated inventory programs

Provide infrastructural and technical support for sustained inventory

process

This may involve: Organizing periodic forest inventories

Satellite or remote-sensing-based land-use maps

Development of nationally relevant emission/removal factors

Likely that many countries lack resources needed to initiate satellite-

based monitoring

Obtain satellite maps from institutions such as FAO, UNEP and

NASA and undertake ground truthing

4.47

Changes in Forest and Other Woody Biomass Stocks

Worksheet 5.1

4.48

Steps

Step 1: Estimate total biomass carbon uptake by using area under different plantations/forests (AD) and annual biomass growth rate (removal factor)

Step 2: Estimate total biomass consumption by adding commercial harvest, fuelwood consumption and other wood use

Step 3: Estimate the net carbon uptake or release by deducting the consumption or loss from total biomass carbon uptake

4.49

Methodological Issues or Problems, Relevant to 5A Category

Lack of compatibility of IPCC land/forest category/vegetation types/systems/formats and national circumstances or classification of forests

Lack of clarity for reporting estimates of emissions/removals in managed natural forest

Lack of consistency in estimating/reporting total biomass or only above-ground biomass

Methods for below-ground biomass not provided in default approach Estimation (or differentiation) of managed (anthropogenically impacted) and

natural forests Lack of methods for incorporating non-forest areas, such as coffee, tea,

coconut, cashew nut Carbon pools – There are five carbon pools. The default method of IPCC

1996GL Estimates only the living biomass (above-ground biomass) because below-ground

biomass stock is assumed to remain stable Assumes dead biomass stock to remain unchanged

4.50

Issues Relating to AD and EF, Relevant to 5A Category

Lack of availability of disaggregated data Lack of data on non-forest/fruit trees Lack of data on biomass/fuelwood/charcoal

consumption data Lack of data on biomass growth rate for different

vegetation types

4.51

Approach to Addressing Issues Relating to Activity Data for LUCF Category 5A

Activity data Tier 1 Tier 2 Tier 3 Area of forest/ plantations

- Data from national sources such as the Ministry of Environment/Forests/Natural Resources - If national source unavailable, use international data sources such as FAO and TBFRA - Data is normally at national aggregated level for major plantation/forest categories - Verify, validate and update national and international data sources

- Data largely from national sources such as the Ministry of Environment etc. - The data on area should be disaggregated according to different plantation/forest types at an appropriate scale

- Data from national remote sensing or satellite assessment sources - Data available at fine grid scales for different plantation/forest types - Geo-referenced forest area data to be used

Harvest categories or types of wood (e.g., saw logs & veneer logs, pulpwood, & other industrial roundwood)

- Data not likely to be available - Data not likely to be available - If available, national aggregate biomass harvest data to be used

- Quantities of biomass harvested from different plantation / forest categories to be obtained and used

4.52

Approach to Addressing Issues Relating to Activity Data…

Activity data Tier 1 Tier 2 Tier 3 Commercial harvest (quantity of different harvest categories mentioned above)

- FAO provides data in the form of roundwood - The roundwood data to be converted to aboveground (whole tree) biomass using biomass expansion ratio - Verify, validate and update the data source

- National level aggregate commercial harvest statistics to be used

- Country-specific commercial harvest data from different forest categories at resolution corresponding to Tier 3 forest/plantation categories to be used

Traditional fuelwood use

- FAO provides data on fuelwood and charcoal use - Verify, validate and update the data source

- National level fuelwood consumption data from national sources, at aggregate level to be used

- Country-specific fuelwood extraction data for Tier 3 forest / plantation categories to be used

Other wood use Same approach as adopted for commercial harvest or traditional fuelwood use

4.53

Combining Tiers

Inventory experts could adopt different tiers for different activity data Party could use Tier 2 for activity data on area of

forest/plantations, while using Tier 1 for commercial harvest and traditional fuelwood with data from FAO Yearbook of Forest Products

Inventory experts could use different tiers for activity data and emission factors Tier 2 for area of forest/plantations (AD) and

Tier 1 for annual growth rate of above-ground biomass (EF)

4.54

Emission/Removal Factors

The key emission/removal factors include: annual biomass growth rate, carbon fraction of dry matter,

biomass expansion ratio

Biomass Expansion Ratios (BERs) as given in IPCC 1996GL are required to convert commercial roundwood harvested biomass (in m3) to total above-ground biomass (in tonnes)

Similarly, AGB:BGB ratio is required to estimate BGB using data on AGB and the conversion ratio, according to GPG2003.

Combining tiers – Inventory experts could adopt different tiers for different emission factors

4.55

Approach to Addressing Issues Relating to Emission/Removal Factors

Emission/removal factor

Tier 1 Tier 2 Tier 3

Annual biomass growth rate

- Default values of average annual biomass growth rate to be used for each forest / plantation category from global databases - Verify, validate and update international data sources

- Use country-specific data available for as many forest/plantation categories - Use default data if country-specific data is not available for a given forest/plantation category

- Use annual increment data from detailed periodic forest inventory/monitoring system - Species-specific allometric biomass functions could also be used

Carbon fraction of dry matter

- Use default data - Use default data, if forest species-specific data are not available

- Use forest species-specific carbon fraction data obtained from laboratory estimations

Biomass expansion ratio (BER)

- Use default BER to convert commercial harvest data to total aboveground biomass removed in commercial harvest - BER requires conversion from m3 to tons and expansion ratio to convert commercial harvest data to total biomass removed

- Inventory experts encouraged to develop country-specific BERs for different plantation / forest categories - Default values to be used in the absence of national data

- Estimate BER values at species level - BERs for biomass increment, growing stock and harvest differ for a given species or a stand, requiring separate estimation

4.56

Sources of AD

Activity data Tier 1 Tier 2 Tier 3 Area of plantation/forests

- National sources such as the Ministry of Environment / Forests/ Natural Resources - International data sources such as FAO and TBFRA

- National sources such as the Ministry of Environment /Forests/Natural Resources

- National remote sensing/satellite assessment sources

Harvest categories (e.g., sawn wood, industrial wood and fuelwood)

- - National sources - National sources according to forest/plantation categories

Commercial harvest (e.g. industrial roundwood)

- FAO Yearbook of Forest Products Website: www.fao.org

- National sources - FAO Yearbook of Forest Products

- Country-specific data according to forest/plantation categories - National production /consumption data

Traditional fuelwood use

- FAO Yearbook of Forest Products Website: www.fao.org

National data sources

- FAO Yearbook of Forest Products

- Country-specific data - National production /consumption data

Other wood use Same as for commercial harvest/fuelwood use

4.57

Sources of EF/RF

EF/RF Tier 1 Tier 2 Tier 3 Annual biomass growth rate

- Default values from IPCC 1996GL and GPG2003 - EFDB

- Default data; 1996GL, GPG2003 - Country-specific data - EFDB

- National forest inventory or monitoring system - Allometric equations

Carbon fraction of dry matter

- Default data of 0.5 - Default data of 0.5 - Species-specific data from laboratory estimations

Biomass expansion ratio (BER)

- Default values of 1.8 - Default data of 1.8 - National data for key forest types

- Species-specific data from measurements

4.58

Assessment of Emission Factors and Strategy for Improvement

To reduce uncertainty, it is desirable to use nationally derived AD and EF at as disaggregated level as possible

Example: Annual growth rate (AGR) of biomass is mean annual above-ground biomass growth rate expressed in t/ha/year. AGR varies with

Forest or vegetation or plantation types (e.g. evergreen/ deciduous/eucalyptus)

Climatic region based on latitude and rainfall (e.g. humid, sub-humid, semi-arid, arid)

Age of the forest or plantation stand Management system or silvicultural practice (e.g. thinning,

fertilizer application, fire management)

4.59

Default Values Currently Available for AGR – IPCC 1996GL

AGR for natural regeneration Tropical and temperate By continent: Africa, Asia and America Forest type: Moist, seasonal and dry Age: 0-20 and 20 to 100 years

AGR for plantations Tropical: Acacia, Eucalytpus, Tectona, Pinus, mixed hardwoods, mixed

softwoods Temperate: fir and pine

Assessment Very few categories; only 5 plantation types Single value for natural regeneration (e.g. 11 t/ha/year for moist forests) Single value for plantations (e.g. eucalyptus 14.5 t/ha/year for tropical region) The coarse categories and global AGR not likely to match national or sub-

national circumstances High uncertainty likely

4.60

Default Values Currently Available for AGR – GPG2003

AGR for natural regeneration Latitude: tropical, temperate, boreal Continents: Africa, Asia, America Age class: < 20 years and > 20 years Rainfall range (mm/yr): >2000, 100-2000, < 1000

AGR for plantations Continents: Africa, Asia, America Species: eucalyptus, pines and other for Africa, two categories for Asia and four

categories for America Rainfall range class: as above (four categories) Range and mean given

Assessment AGR values are within a short range Multiple values are available only for eucalyptus and pine Very limited or absence of values for natural but managed forests, secondary forests,

different forest types Values for eucalyptus range from 10 to 60 m3/ha/year Generally default AGR values are all in the higher range

4.61

Short-term Strategy for Improving AGR Values

Disaggregate the land use, forest or vegetation types occurring in the country at as fine a level as possible along the following lines or using other more nationally relevant stratification:

Different forest types / vegetation types / plantations Latitude: tropical, temperate, boreal Rainfall zone (mm/yr): humid (>2000), semiarid (500-1000), arid

(<500) Age of the stand: 0-5 yr, 5 to 10 yr, 10-20 yr, > 20yr Management system: naturally regenerated or planted Other category

Allocate area of different forest types/plantations in the country, using forest map, rainfall zone map, soil map and other statistical information

4.62

Short-term Strategy…

Check IPCC 1996GL, GPG2003, EFDB and other global sources and select the closest default values

Check if any national forest Inventory studies are available (many NAI Parties have them) and collect the growth rate data

Review the national and international literature (web sites of FAO, CGIAR centers, universities, books and reports

Compile all the default values available from national and international sources for the disaggregated or stratified forest/plantation types

Select the most appropriate AGR for each stratum of the forest/plantation types

4.63

Long-term Strategy for Improving AGR Values

Initiate national forest inventory studies Disaggregate/stratify the forest/plantation types Adopt sampling technique as explained in

GPG2003 (Chapter 3 and 4) Adopt permanent plots with proper boundaries

marked for periodic revisits Refer to any text book on ‘forest mensuration’ or

web sites such as www.winrock.org, www.cifor.org, etc., for methods of measurement and estimation

Estimate the standard deviation or variance

4.64

IPCC Inventory Software Provides a Microsoft Excel based approach where AD and EF/RF

data can be input to obtain net annual carbon uptake/release  The key features or limitations in using the software are:

The names or type of forest/plantation category in a country may be different from the categories defined in the IPCC software

The IPCC software can be changed to nationally relevant categories (e.g. Acacia spp. can be changed to other spp.)

Names of categories, used in the column, are not included in the calculation procedure of the worksheets, and thus can be easily changed

Forest/plantation categories: Option exists for 18 categories, which is a limitation if a country has more than 18 categories

If the number of forest/plantation categories is more than provided Option 1: Insert additional rows only if the inventory expert has

capacity to modify the ‘macros’ Option 2: Merge smaller or homogeneous categories such that the

total number of rows (or categories) is not >18.

4.65

Illustration of Inventory Software – IPCC 1996GL

Total carbon uptake (in tons) = (Area of forest/plantation category in kha) * (Annual growth rate in t/ha/year) * (C-fraction of dry matter)

From IPCC Software – Sheet 5-1s1

4.66

Worksheet for Estimating Total Carbon Uptake – GPG2003

T o ta l c a rb o n u p ta k e in liv in g b io m a s s u n d e r fo re s t la n d re m a in in g fo re s t la n d ( in to n s ) = (A re a o f fo re s t la n d re m a in in g fo re s t la n d , a c c o rd in g to e a c h fo re s t ty p e in h a ) * (A v e ra g e a n n u a l in c re m e n t in ra te o f to ta l b io m a s s , a c c o rd in g to e a c h fo re s t ty p e s in t /h a /y e a r ) * (C -f ra c tio n o f d ry m a tte r)

S o ftw a re fo r G P G 2 0 0 3 : S in c e th e a p p ro a c h a d o p te d b y G P G 2 0 0 3 is b a s e d o n la n d c a te g o r ie s , a n d a d d it io n a l C -p o o ls a re in c lu d e d , th e s o f tw a re d e v e lo p e d fo r IP C C 1 9 9 6 G L is n o t a p p lic a b le . H o w e v e r , U N F C C C h a s in it ia te d th e p ro c e s s to d e v e lo p th e In v e n to ry S o ftw a re fo r G P G 2 0 0 3 .

M o d u le F o re s t L a n d S u b -m o d u le F o re s t L a n d R e m a in in g F o re s t L a n d W o rk sh e e t F L -1 a : A n n u a l c h a n g e in c a rb o n s to c k s in l iv in g b io m a ss ( in c lu d e s a b o v e a n d b e lo w g ro u n d b io m a ss ) 1

S h e e t 1 o f 4 L a n d -u se C a te g o ry 2

In itia l la n d u se

L a n d u se d u rin g

re p o r tin g y e a r

S u b -c a te g o r ie s

fo r re p o r tin g

y e a r 3

A re a o f fo re s t la n d

re m a in in g fo re s t la n d (h a )

A

A v e ra g e a n n u a l n e t in c re m e n t in v o lu m e

su ita b le fo r

in d u s tr ia l p ro c e ss in g

(m 3 h a -1

y r -1 )

B

B a s ic w o o d

d e n s ity ( to n n e s d .m p e r

m -3 fre sh

v o lu m e )

C

B io m a ss e x p a n s io n fa c to r fo r

c o n v e rs io n o f a n n u a l n e t in c re m e n t ( in c lu d in g

b a rk ) to a b o v e g ro u n d tre e

b io m a ss in c re m e n t

(d im e n s io n le s s )

D

A v e ra g e a n n u a l

a b o v e g ro u n d b io m a ss

in c re m e n t ( to n e s

d .m .h a -1 y r -

1 ) E = B C D

E

R o o t-sh o o t ra tio n

a p p ro p r ia te to in c re m e n ts

(d im e n s io n le s s )

F

A v e ra g e a n n u a l

b io m a ss in c re m e n t a b o v e a n d

b e lo w g ro u n d ( to n n e s

d .m . h a -1

y r -1 ) G = E (1 + F )

G

C a rb o n fra c tio n o f d ry m a tte r

(d e fa u lt is 0 .5 )

( to n n e s C

to n n e d .m . -1 )

H

A n n u a l in c re a se

in c a rb o n d u e to

b io m a ss in c re m e n t ( to n n e s C

y r -1 ) I= A G H

I

F L F L (a ) (b ) A IV D B E F 1 G W R G T O T A L C f Δ C F F G

(c ) S u b to ta l T o ta l

1 C a lc u la tio n s a re b a se d o n d e fa u lt m e th o d 2 F L s ta n d s fo r fo re s t la n d . 3 L a n d u se sh o u ld b e fu r th e r d iv id e d a c c o rd in g to fo re s t ty p e a n d c lim a tic z o n e s in th e c o u n try

4.67

Forest and Grassland Conversion (5B)

Worksheet 5.2

4.68

Steps for 5B Step 1: Estimate annual loss of biomass due to

conversion Step 2: Estimate quantity of carbon released from

fraction of biomass burnt on-site Step 3: Estimate quantity of carbon released from

fraction of biomass burnt off-site Step 4: Estimate carbon released from decay of

above-ground biomass Step 5: Estimate total annual CO2 release from

burning and decay of biomass, resulting from forest and grassland conversion

4.69

Issues in Estimating CO2 Emissions from

Biomass – Forest and Grassland Conversion

Lack of compatibility between IPCC 1996GL vegetation types and national circumstances or classification

Absence of forest and grassland conversion data for the inventory year as well as the 10-year average

Lack of methods for savanna/grassland burning Lack of disaggregated activity data on biomass stock before

and after conversion Lack of clarity on fraction of biomass burnt on-site, off-site

and left to decay Biomass burnt for energy is reported in the energy sector

4.70

Approach for Addressing Issues Relating to Activity Data

Activity data

Tier 1 Tier 2 Tier 3

Area converted annually Average area converted (10-year average)

- Gross area converted at the national level can be obtained from national sources such as the Ministry of Environment / Forests/Natural Resources - If national source unavailable, international data sources on deforestation such as the FAO and TBFRA - Normally, average annual rates of conversion are extrapolated to the inventory year

- Forest/grassland area converted according to different types, available at the national level from government /ministry sources to be used - The data on area should be disaggregated according to different forest / grassland types at an appropriate scale - If direct annual estimates not available, use average annual rates of conversion

- Disaggregated according to forest/grassland types and geo-referenced data from periodic satellite/remote sensing assessments could be used - Countries can use direct estimates of spatially disaggregated areas converted annually

4.71

Approach for Addressing Issues Relating to Emission Factors

Emission factor Tier 1 Tier 2 Tier 3 Aboveground biomass before and after conversion

- Use default coefficients to estimate carbon stock change in biomass, resulting from land use conversions - Default assumption is that all biomass is cleared during conversion, leading to zero biomass after conversion

- Country-specific estimates of biomass stocks before and after conversion could be generated nationally

- Biomass data from national forest inventory studies in different forest/grassland categories subjected to conversion - Biomass could be estimated using species-specific allometric equations - Geo-referenced biomass change data at finer spatial scales

4.72

Approach to Emission Factors…

Fraction of biomass burnt on-site and off-site

- Country-specific fraction of biomass burnt on-site and off-site to be generated nationally - Apportion fraction of biomass carbon loss due to on-site and off-site burning from field measurements

- Field measurements of biomass fraction burnt on-site and off-site in different forest/grassland categories subjected to conversion

Fraction of biomass oxidised

- Use default data, if no measurements are available

Carbon fraction of biomass

- Laboratory estimation of carbon fraction for different species

Fraction of biomass left to decay

- Use default values

- Use default data - Field measurements of biomass left to decay in different forest/grassland categories subjected to conversion

4.73

Sources of AD

AD factors Tier 1 Tier 2 Tier 3 Area converted annually Average area converted (10-year average)

- FAO: Tropical Forest Assessment Report

- National data on area conversion at disaggregated level - If no national data, use data from FAO: Tropical Forest Assessment Report

- Ministry of Land Resources - Satellite or remote sensing data

4.74

Sources of EFEF Tier 1 Tier 2 Tier 3

Aboveground biomass before and after conversion

- Data from national forest inventory at finer scales according to forest/grassland categories - Ecological / silvicultural studies in different categories

Fraction of biomass burnt on-site and off-site

- IPCC 1996GL - GPG2003 - EFDB

- National/regional scientific literature - EFDB - GPG2003 - National forest inventory

data - Biomass consumption data according to forest/grassland categories

Fraction of biomass oxidised

- Default value of 0.9

- Default value of 0.9

- National forest inventories - Field measurements

Carbon fraction of biomass

- Default value of 0.5

- Default value of 0.5

- Published data at species level

Fraction of biomass left to decay

- Default value of 10 t/ha

- Default value of 10 t/ha

- National forest inventory

4.75

Abandonment of Managed Lands

Worksheet 5C

4.76

Estimation Procedure

Step 1: Estimate the annual carbon uptake in above-ground biomass, using the area abandoned (during the previous 20 years) and annual biomass growth

Step 2: Estimate the total carbon uptake from area abandoned (during 20–100 years) and annual growth rate

Step 3: Estimate the total C-uptake from abandoned land (Step 1 + Step 2)

4.77

Issues in Estimating CO2 Uptake from Abandonment of Managed Lands

Lack of compatibility between vegetation types given in IPCC 1996GL and national classification for abandoned land

Lack of methods to identify managed land abandoned and regenerating according to different vegetation types for the past 20 years and 20–100 years

Absence of annual data for above-ground biomass growth for abandoned land according to different vegetation types for the past 20 years and 20–100 years

4.78

Approach to Addressing Issues Relating to Activity Data and Sources of Data

Activity data Tier 1 Tier 2 Tier 3 Managed area abandoned during previous 20-years

- National land use statistics of managed cropland, pastures etc. abandoned and regenerating from historical land use statistics or records - Aggregated data from national sources

- National land use statistics on managed area abandoned and regenerating at disaggregated level

Managed area abandoned during previous 20-100 years

- Data on area abandoned during 20-100 years prior to inventory need to be obtained at the national level - It is likely that land abandoned for over 20 years may have regenerated into a forest

- Very few countries likely to have data for this period

- National historical land use statistics, based on remote sensing providing data at finer scales according to different climatic, soil and management systems for

- 20-years and - 20-100 years

4.79

Approach to Addressing Issues Relating to Removal Factor and Source of Data

Removal factor

Tier 1 Tier 2 Tier 3

Annual growth rate (upto 20 years) Annual growth rate (20-100 years)

- Default data from IPCC 1996GL, GPG2003, EFDB sources

Default data disaggregated according to soil, climatic and management systems from national sources

- Annual growth rate from national forest inventory studies at finer scales under different soil, climatic and management systems at2 periods

- 0-20 years and - 20-100 years

4.80

CO2 Emissions and Removals from Soils

5D and Worksheet 5-5

4.81

Steps for 5D

Step 1: Changes in soil carbon for mineral soils Step 2: Carbon emissions from intensively

managed organic soils Step 3: Carbon emissions from liming of

agricultural soils

4.82

Issues in Estimating CO2 Emissions/Removals

from Abandonment of Managed Lands

Absence of linkage between biomass carbon and soil carbon for different land categories or vegetation types

Ambiguity in classification of land-use and management systems, and soil types

Absence of activity data on land area under different conditions:

land-use/management systems soil type for periods t (inventory year), and t-20 intensively managed organic soils

Absence of emission factors such as soil carbon in mineral soils and annual loss rate of carbon in managed organic soils

4.83

Approach to Addressing Issues Relating to Activity Data

Activity data Tier 1 Tier 2 Tier 3 Area under different land use/management systems and soil type during year-t (inventory year)

- Define land use management systems used in the country - If no country-specific classification exists, use FAO classification - Land use statistics at national level in aggregated form or from FAO sources

- Define or identify country-specific land use / management systems at disaggregated level, according to soil type - If no national soil map exists, use FAO soil map

- National disaggregated land use/management systems at finer scales - Overlaying of national land use and soil survey maps - Geo-referencing of land use and soil type for inventory year

Area under different land use/management systems and soil type 20-years prior to year-t

- Land use statistics for the year t-20, for the identified land use categories from past historical statistics - If no national land use statistics are available for year t-20, use FAO database

- Area under different land use systems from national sources such as land use statistics from historical data source

- National, disaggregated land use / management systems at finer scales to be obtained from land use survey maps for the year t-20 and overlaid on soil maps

4.84

Approach to Addressing …

Area under managed organic soils

- If no national level data on area under managed organic soils subjected to intensive use (crop production / forestry) exists, use FAO/ global databases

- Country-specific area under intensively managed organic soils to be obtained from national land use and soil maps

- Detailed, disaggregated data on area subjected to intensive agricultural use or forestry to be obtained from national land use data or maps

Quantity of annual lime applied

If no national data exists on lime application, assume no emissions from lime

- Use country-specific data on the aggregate quantity of lime applied

- Use country-specific data on the quantity of different types of lime applied

4.85

Approach to Addressing Issues Relating to Emission/Removal Factors

EF/RF Tier 1 Tier 2 Tier 3 Soil carbon in different land use systems and soil types

- Soil carbon density data for broad land use / management systems from global soil carbon databases at aggregate level

- Soil carbon density data from country-specific sources, according to land use and soil type - If no, disaggregated data exists, use national level aggregates

- Generate soil carbon data for each land use / management system and the soil type, based on measurements - Soil carbon data could be at disaggregated level where the land use map and the soil map is overlaid to obtain sampling points

Annual rate of loss of carbon from managed organic soils

- Default values from global database at aggregate level

- Country-specific carbon loss rate according to major organic soil types and management systems - If no country=specific data exists, use global default values

- Obtain data for annual rate of loss of carbon from organic soils, using literature according to different land use - Generate carbon loss rate data based on measurements in different land use systems

4.86

Approach to Addressing…

Emissions/removal factor

Tier 1 Tier 2 Tier 3

Carbon conversion factor from lime to carbon

- Formula from IPCC 1996GL = 0.12 for limestone and 0.122 for dolomite

- Same as for Tier 1

- Same as for Tier 1

Base factor Tillage factor Input factor

Default values from IPCC 1996GL

Default values from IPCC 1996GL

Field measurement of soil organic carbon density of agriculturally impacted soils, using the method described for soil carbon in different land use/management systems

4.87

Sources of Activity DataActivity data Tier 1 Tier 2 Tier 3

Area under different land use/management systems and soil type during year-t (inventory year)

- FAO database (http:apps.fao.org)

- National land use survey data

- National land use maps overlaid on soil survey maps - Ministry of agriculture, forests and natural resources

Area under different land use/management systems and soil type 20-years prior to year-t

- FAO database (http:apps.fao.org)

- National land use survey data, historical

- National land use maps overlaid on soil survey maps - Ministry of agriculture, forests and natural resources

Area under managed organic soils

- Global databases

- National database on organic soils

- National database

Quantity of annual lime applied

- National statistics

- National statistics

- National statistics

4.88

Sources of Emission/Removal Factors

Activity data and emissions/removal factor

Tier 1 Tier 2 Tier 3

Soil carbon in different land use / management systems and soil types

- FAO soil survey database at aggregated land use / management system level - IPCC 1996GL

- National soil survey sources for different land use systems

- National forest inventory studies - Experimental studies in different land use systems

Annual rate of loss of carbon from managed organic soils

- Global database - IPCC 1996GL

- National sources - IPCC 1996GL

- National forest inventory in organic soils - Field studies on organic soil carbon

Carbon conversion factor from lime to carbon

- IPCC 1996GL

- IPCC 1996GL

- IPCC 1996GL

Base factor Tillage factor Input factor

- IPCC 1996GL - EFDB

- IPCC 1996GL - EFDB

- IPCC 1996GL - EFDB

4.89

Other Categories

Harvested wood products (HWP), wetlands and other sources/sinks

Default assumption of IPCC 1996GL is that: carbon removed in wood and other biomass from

forests is oxidized in the year of harvest Countries may report on HWP pools, if they can

document that existing stocks of forest products are in fact increasing

GPG2003-Appendix provides guidance on methodological issues for accounting emissions and removals from HWP

4.90

Uncertainty Estimation and Reduction

The good practice approach requires that estimates of GHG inventories be accurate They should neither be over- nor underestimated

as far as can be judged Causes of uncertainty could include:

unidentified sources and sinks lack of data quality of data lack of transparency

4.91

Uncertainty Analysis

Uncertainty analysis involves: Identifying types of uncertainties

measurement error, lack of data, sampling error, missing data, model limitations, etc.

Methods for reducing uncertainties improving representativeness, using precise measurement

methods, correct statistical sampling, etc. Quantifying uncertainties

sources of data and information, techniques for quantifying uncertainty

Methods to combine uncertainties (simple propagation of errors and Monte Carlo analysis)

Estimates of C-stock changes, emissions and removals arising from LUCF activities have uncertainties associated with:

Area related and other activity data, biomass growth rates, expansion factors, biomass loss or consumption, soil carbon density, etc.

4.92

Methods of Estimating and Combining Uncertainties – GPG2003

Two methods: Simple propagation of errors (Tier 1) Monte Carlo analysis (Tier 2) Use of either Tier 1 or Tier 2 provides insight into how individual categories

and GHGs contribute to uncertainty in total emissions in a given year Tier 1 and Tier 2 methods of assessment of uncertainty are different from

methods or Tiers (1 to 3) of inventory estimation.

Tier 1 methods: Uncertainty associated is high as suitability of available

default parameters to a country’s circumstances is not known Application of default data in a country or region that has

different characteristics from those of the source of data leads to large systematic errors

4.93

Methods of Estimating… (Tier 2)

Country-specific data are used Data often only broadly defined

with very little stratification according to climate/management/soil/land use

Possible to assess uncertainties involved due to the national circumstances, based on a few national-level studies or direct measurements

Uncertainty is moderate compared to Tier 1 Statistical packages are readily available for

adopting Monte Carlo algorithm

4.94

Quality Assurance and Quality Control

Quality Control or QC is a system of routine technical activities to measure and control the quality of inventory as it is being developed

It is designed to: Provide routine and consistent checks to ensure data

integrity, correctness and completeness Identify and address errors and omissions Document and archive inventory material and record all QC

activities Quality Assurance or QA is a planned system of review

procedures conducted by personnel not directly involved in the inventory compilation/development process

4.95

QC – Tier 1

Tier 1 general methods focus on processing, handling, documenting, archiving and reporting procedures

QC procedure involves the following: Check integrity of database files Confirm appropriate data processing steps are correctly

represented in the database Confirm data relationships are correctly represented in the

database Ensure that data fields are properly labeled and have correct

design specifications Ensure adequate documentation of database and model

structure

4.96

QC – Tier 2

Tier 2 procedures are directed at specific types of data used in the methods and require knowledge of:

source/sink category type of data available parameters associated with emissions/removals

Tier 2 QC procedure focuses on the following checks: Check that land areas are properly classified and that no

double counting or omissions have occurred Ensure completeness of source/sink categories Check consistency of time series activity data Check sampling and extrapolation protocols adopted

4.97

QA – Tier 1 and Tier 2 Requires an expert review to assess the quality of inventory and

identify areas where improvements are necessary Tier 1:

Involves basic expert peer review by inventory agencies Apply review process to all source/sink categories, particularly

the key categories Tier 2:

Involves expert peer review, which includes review of calculations or assumptions identification if major models used have undergone peer

review assessment of documentation of models, input data and other

assumptions

4.98

Emission Factor Database

EFDB is an online database It is continuously updated with data that are reviewed by a panel

of experts It is menu driven and user friendly It requires use of Internet Explorer version 5.0 or Netscape

Navigator version 6.0 or higher coupled with Microsoft Office 97 for generating outputs in MS Word or Excel

It has multiple options, such as: Step-by-step search using IPCC source/sink category and gas Full text search using key words Find emission factor using unique ID

Results are displayed along with the following details: EF ID, gas, description, technologies/practices,

parameters/conditions, region/regional conditions, abatement/control technologies, other properties, value, unit, data provider, source of data

4.99

Steps Involved in Using EFDB

Step 1: Selection of the sector, e.g. LUCF (5)

Step 2: Selection of gases, e.g. CO2, CH4

Step 3: Display of the results

Step 4: Set the filter giving the conditions, such as gas, parameter/condition, region, etc.

4.100

LUCF Status – EFDB (2004 Aug.)

IPCC 1996GL category Emission factor records

Changes in Forest and Other Woody Biomass Stocks (5A)

34

Forest and Grassland Conversion (5B) 589 Abandonment of Managed Lands (5C) 0 CO2 Emissions and Removals from Soil (5D) 78 Other (please specify) (5E) 15 Total 716

The EFDB is an emerging database, initiated in 2002

EFDB expects all experts to contribute to the database

Currently, limited information for LUCF sector emission factors.

In future, with contribution from experts around the world, EFDB is likely to become a reliable source of data for emission/removal factors for GHG inventory

4.101

Conclusions and Strategy for the Future

NAI experts and compilation and synthesis reports by UNFCCC have identified a number of issues and problems in using IPCC 1996GL, including: Lack of clarity in the methods and inadequacies of

the methods Lack of AD and EF Low quality or reliability of AD and EF High uncertainty of AD and EF, leading to

uncertainty in inventory estimates Non-suitability

4.102

GPG2003 Approach

GPG2003 meant to overcome some of the methodological issues/problems identified in using IPCC 1996GL

Suggests methods to reduce uncertainty Suggests an improved land category and full carbon

(and non-CO2 gases) estimation based approach and methods

Adoption of GPG2003 approach will lead to: full and consistent representation, consideration and reporting

of all land categories full carbon (all 5 C-pools) estimation reduced uncertainty efficient use of limited inventory resources