Transcript Document

Alternative approaches to manage carbon sinks in small farm forestry:
Mexico’s experience and methods
Ben H.J. De Jong; El Colegio de la Frontera Sur (ECOSUR), Tabasco, México
E-mail: [email protected]
WORKSHOP ON FORESTRY AND CLIMATE CHANGE
ASSESSING MITIGATION POTENTIAL: LESSONS LEARNED
India International Center
New Delhi; sept 23 & 24, 2002
Presentation:
Brief outline of how project functions
Project area
Present status of the Scolel Té project
C-sequestration potential of proposal without baseline
Baseline:
Afforestation
Avoiding deforestation
Monitoring:
Pools
Frequency
Leakage:
How is leakage treated
Assessing level of error in leakage assumptions
Project area
Chiapas
N
0
20
40
Kilometers
Scolel Té communities
Baseline area
2´800,000 ha
Present Status:
Sales of voluntary carbon offsets of the Scolel Té project
Year
Sales
(tonnes carbon)
Purchaser
1997
5,500
FIA (Formula One)
1998
5,500
FIA (Formula One)
1999
5,500
FIA (Formula One)
2000
6,573
FIA (Formula One), Future Forests
2001
9,297
FIA (Formula One and World Rally), Future Forests
2002
13,297
FIA Foundation (Formula One and World Rally), Future Forests
Total
45,667
Example of Plan Vivo
Number of participants, area committed, and tC purchased
in two eco-regions of Chiapas, Mexico
Tropics
Coffee with shade trees
Hectares
Producers1
Potential
(tC ha-1)
Purchased2
(tC)
101
101
73
2,801
6
6
54
609
153
149
146
8,357
Green manuring
56
112
45
5,406
Improved fallow
89
81
146
3,635
3,000
3
100
3,000
47
13
137
3,588
214
192
102
9,492
3,665
657
Living fences
Taungya
Forest conservation
Sub-tropics
Forest restauration
Improved fallow
Total
1
2
36,888
Producers are either individual farmers or whole communities
Difference between potential per ha and purchased due to baseline, part of carbon purchased
and risk buffer
Taungya with Cedrela odorata
Calculation of C-sequestration potential:
CO2FIX model approach
Parameters used to estimate carbon fluxes
in agroforestry systems in Chiapas, Mexico (from De Jong et al 1997).
Parameters
Tropics
Sub-tropics
6 x 25
5 x 30
Initial humus content of the soil (MgC ha-1)
75
75
Basic Wood Density (kg m-3)
500
450
Carbon content (% of dry weight)
50%
50%
Rotations (years)
Dry weight increment relative to stem
years after planting years after planting
increment during one rotation:
0-10 10-20 20-25
0-10 10-20 20-30
Foliage
0.7
0.4
0.4
0.8
0.6
0.2
Branches
0.6
0.4
0.4
0.8
0.5
0.2
Roots
0.7
0.4
0.4
0.9
0.6
0.3
Turnover rates:
Foliage
0.5
0.3
Branches
0.05
0.05
Roots
0.07
0.07
0.1
0.05
1
3
100
200
Humification factor
Litter residence time (years)
Stable humus residence time (years)
Estimated yearly increment (CAI) of the tree components
M 3/ Yr
30
TAUNGYA AND ENRICHED
20
FALLOW
COFFEE WITH SHADE
10
LIVE FENCE
0
7
13
19
YEAR
25
tC/ha
Flux outcome of Coffee with shade trees, according to production level I to III
140
120
Level III
Level II
100
Level I
Av-Level III
80
Av-Level II
Av-Level I
60
40
20
0
0
25
50
75
100
125
150
Baselines - The carbon offset potential of any activity must be calculated relative to a baseline. The
baseline describes the current status of carbon stocks or emissions and expected changes in the absence
of the project - the so called 'business as usual' scenario. Reductions in emissions relative to the baseline
may be claimed as carbon offsets. The construction of a baseline must be clearly described giving the
sources of all information and a justification of any assumptions used.
Sequestration – carbon stocks in
existing vegetation and expected
changes in land use. Current carbon
stocks should be estimated through
biomass surveys or using data in
the literature. The expected change
in land use should take account of
prevailing socio-economic pressures
in the region.
Conservation – the expected rate
of deforestation and the resulting
carbon emissions. Setting baselines
for forest conservation will require
data on the carbon density of
existing forest vegetation and an
analysis of regional land-use trends.
Approach used in the afforestation projects: Project = potential – initial carbon
Some of the required data of the initial stage
Plot Size :
(ha)
Orientation: N
S
E
O
Current use:
Corn
Shrub
Fallow
pasture
20 - 50cm
50 cm -1 m
coffee
Other
Soil data: color:
DEPTH
CLASS
0 – 20 cm
>1m
Current vegetation:
NOTHING FEW (<25%)
HERBS
SHRUBS
SMALL TREES (< 5M)
MEDIUM TREES (5-10M)
LARGE TREES (10-20M)
VERY LARGE TREES (>20M)
MODERATE (25-50%)
ABUNDANT (> 50%)
Baseline reductions applied to an afforestation proposal in the tropics
with varying amount of initial biomass, such as coffee with shade trees.
(based on amount of C present at initial stage in the various pools)
Note: C-density of each pool according to measured densities in forest and non-forest plots
Deforestation baseline: Multi-project approach with construction of
spatially explicit carbon risk matrices
Objective:
to develop a simple approach to spatially explain deforestation and associated carbon fluxes
with various predisposing and driving factors.
The outcome of the analysis could then to be used as a simple tool to estimate deforestation
dynamics and associated carbon fluxes with readily available data sources, such as estimated
population density and dynamics, development of infrastructure and other public services,
among others, which in turn can be integrated in a multi-project baseline scenario that could
offer readily available without-project carbon emission estimations in the future of any
project within the study area.
Land-use in the 1970s
Land-use in the 1990s
LU/LC Change between 1970s and 1990s
Deforestation closed forest
Deforestation disturbed forest
Degradation closed forest
Degradation disturbed forest
Without change
Slight increase in biomass
Restored sec. vegetation
Restored forest
Spatial correlation between deforestation and various
“predisposing” and “driving” forces.
Spearman’s correlation coefficients between deforestation and causal factors of change
Causal factor
Slope gradient
Distance to roads
Distance to agriculture
Population density
Scarcity index
** Significant at 0.01 level
* Significant at 0.05 level
Study area
0.117
1.000**
0.997**
0.984**
0.886 *
Highlands
0.900**
1.000**
0.988**
1.000**
0.829 *
Cañadas
0.817**
1.000**
0.996**
1.000**
-0.900 *
Selva
-0.650
1.000**
0.999**
0.927**
0.771
(Castillo-Santiago et al 2002)
Relation between the predisposing factor “distance to agricultural fields” and
driving factor “density of farmers”, and carbon emissions (in % of standing stock),
including 95% confidence intervals
Carbon flux in % of Stock
1.0%
0.9%
Distance to
agriculture
0.8%
0 - 500 m
> 500 m
0.7%
0.6%
0.5%
0-2
2-5
5 - 10
10 - 20
Density of farmers (km-2)
> 20
Carbon “risk” map, based on predisposing factor “distance to agriculture” and
driving factor “ density of farmers”
Risk categories
0.65%
0.70%
0.80%
0.90%
Vegetation map of 1996
N
0
5
10
Kilometers
Vegetation classes
Closed forest
Open-disturbed forest
Secondary vegetation
Open areas
Overlay of carbon risk map and 1996 vegetation map
Sum of Vulnerable C in each risk-cell
Example baseline emission estimation
(Applying lower limit of 95% Conf. Int.)
Distance to agric.
> 500 m 0 - 500 m
farmers
0- 2
2- 5
5 -10
tC
21,923
33,255
1,554
30,386
23,906
844
Accumulated baseline emission estimations for
Juznajab la Laguna, Chiapas, Mexico
20000
15000
10000
5000
0
0
5
10
15
20
Year
Monitoring indicators
The technical specification details how the production of carbon offsets will be monitored.
Monitoring indicators are based on easy to measure variables relating to the management
requirements given in the technical specification and the associated changes in carbon
stocks/emissions. For example:
•Sequestration - Measurements of planting density (only once), survival rates (% of total)
to define replanting necesities, and tree diameter and height after reaching minimum size
(measurements based on sampling).
•Conservation - Monitoring of forest conservation will include periodic measurements of
forest cover, and monitoring indicators relating to sustainable forest management
(establishment of fire breaks, establishment of institutions to regulate forest use,
implementation of activities designed to reduce pressure on forest resources etc.) may also
be used.
A monitoring template is included in each technical specification to indicate which data to record.
Monitoring guidelines for coffee with shade trees (Cedrela odorata)
Decision tree to select C pools (Based on Sathaye and Ravindranath 1997)
Emission
Capture
Direction of change
Large
Small
Size of pool
Fast
Slow
Fast
Rate of Change
Include pool
Slow
Rate of Change
To be
considered
Possible criteria:
•Cost
•Development of measuring methods
•Modelling with verified data
Do not
include pool
Types and causes of leakage that could occur in the Scolel Té project
(Typology according to Aukland et al 2002)
Types of leakage
Causes of leakage
Activity shifting
Activities that cause emissions are not permanently avoided but displaced to another
area.
Outsourcing
Farmers purchase or contract out of the commodities and services previously provided
on-site, thus shifting the responsibility for the activity to another party. (eg. Selling or
renting the land where the trees are planted)
Super-acceptance
The carbon-emitting activity in the non-project area of the participating party changes
faster (negative leakage) or slower (positive leakage) than predicted by the baseline,
such as when participating and non-participating farmers start forestation activities on
non-project land without carbon subsidies.
C-sequestration systems and alternative livelihood activities
C-sequestration activity
Alternative livelihood activity to avoid leakage
Coffee with shade trees
No change in productivity expected, no need for alternative
livelihood activity
Living fences
No change in productivity expected, no need for alternative
livelihood activity
Taungya
Permanent corn production through combination of corn with green
manure in project and non-project area, to avoid deforestation in
remaining non-project area
Improved fallow
Permanent corn production through combination of corn with green
manure in project and non-project area, to avoid deforestation in
remaining non-project area
Natural regeneration in
abandoned pasture
Improved pasture with high quality fodder species in non-project
area, to avoid a shift in grazing to non-project community forests
Risk Buffer
The risk buffer is a reserve of unsold carbon credits.
The aim is to allow the Carbon Fund to cover any unexpected shortfall in carbon
credits supplied to purchasers. The project operational manual justifies the size
of the risk buffer and state how it is maintained.
The size of the risk buffer is determined by the risks associated with carbon credits sold via
the Carbon Fund, for example:
•Loss of sequestered carbon due to fire in forestry projects
•Producers failing to maintain offset activities for specified timeframes
•Inaccuracies in carbon modelling or baseline assumptions
The Scolel Té project maintains a risk buffer of 10% of carbon sold via the project.
Thank you
More info: www.planvivo.org
E-mail: [email protected]