Download progress toward a consensus on carbon emissions from tropical

PROGRESS TOWARD A
CONSENSUS ON CARBON EMISSIONS
FROM TROPICAL DEFORESTATION
POLICY BRIEF
Winrock International Team
Dr. Nancy Harris, Senior Carbon and Land Use Specialist,
Ecosystem Services Unit, Winrock International
Dr. Sandra Brown, Director and Chief Scientist, Ecosystem
Services Unit, Winrock International
Dr. Stephen C. Hagen, Senior Research Scientist, Applied
Geosolutions, LLC
Woods Hole Research Center Team
Dr. Alessandro Baccini, Assistant Scientist, Woods Hole
Research Center
Dr. Richard Houghton, Senior Scientist, Woods Hole
Research Center
Woods Hole Research Center
Science, Education, and Policy for A Healthy Planet
Progress toward a Consensus on Carbon Emissions from Tropical Deforestation
A
Disclaimer: Any views expressed
in this policy brief are those of the
authors. They do not necessarily
represent the views of the authors’
institutions, Meridian Institute, or the
financial sponsors of this policy brief.
ISBN: 978-0-615-72677-9
Date of Publication November 2012
CONTENTS
Executive Summary........................................... 1
Acknowledgments............................................. 2
Acronyms .......................................................... 3
1. Introduction................................................... 4
2. Main Differences........................................... 4
Emissions from Deforestation vs. from
Land-Use Change.................................................. 4
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Different Spatial Scales ........................................ 5
Different Carbon Stock Maps............................... 5
Different Carbon Emission Models....................... 6
3. Toward Consensus ........................................ 6
4. Conclusions and Implications
for REDD+ Monitoring....................................... 8
5. Agreement on a Pantropical
Benchmark........................................................ 8
References ........................................................ 9
EXECUTIVE SUMMARY
During 2012, our research groups – Woods Hole Research Center
(WHRC) and Winrock International (Winrock) – published two
estimates of carbon emissions from tropical deforestation that,
upon first review, seemed to differ widely.1,2 We have closely
examined the differences in these estimates and conclude that
there is more agreement than we realized previously. The largest
differences relate primarily to definitional issues and to the
differing scopes of the two studies. Based on our examination and
analyses of the data and methodologies, both groups agree that,
when accounting for the same carbon pools and for the same
time frame of 2000–2005, emissions from gross deforestation in
tropical regions contributed 3.0 Gt CO2 yr-1 (0.8 Pg C yr-1) to the
atmosphere. This value does not fully account for emissions from
tropical forest land-use change because it excludes emissions from
mineral soils and peat as well as emissions from forest degradation
activities, which could account for another 2.3 Gt CO2 yr-1 (0.6 Pg
C yr-1). Uncertainty in deforestation emissions, as reported by the
Winrock team, is ± 1.1 Gt CO2 yr-1 (0.3 Pg C yr-1), but the uncertainty
in the other emission sources has not been estimated.
Although our consensus on deforestation emissions indicates
convergence despite differing methods and data, our estimates still
differ with respect to where these emissions occur. Both groups
agree that the best approach for accurately quantifying carbon
emissions from land-use change is to spatially align activity data
and emission factors. The consensus on deforestation emissions
at the pantropical scale provides an unbiased historic benchmark
against which current and future emissions can be compared.
Baccini et al. (2012).
Harris et al. (2012).
1
2
Progress toward a Consensus on Carbon Emissions from Tropical Deforestation
1
ACKNOWLEDGMENTS
We gratefully acknowledge Doug Boucher of Union of Concerned Scientists;
Matthew Hansen of the University of Maryland; Ronald McRoberts of the
U.S. Department of Agriculture Forest Service; Sassan Saatchi of the National
Aeronatics and Space Administration Jet Propulsion Laboratory and University
of California, Los Angeles Institute for Environment; Andreas Tveteraas of
Norway’s International Climate and Forest Initiative; and Daniel Zarin of the
Climate and Land Use Alliance for their useful inputs and suggestions for this
policy brief.
We also thank the additional authors of the two seminal papers that provided
the impetus for this collaboration:
Silvia Petrova, Winrock International Ecosystem Services Unit
William Salas, Applied Geosolutions, LLC
Peter V. Potapov, University of Maryland, Department of Geographical Sciences
Alexander Lotsch, The World Bank Group
Scott J. Goetz, Wayne S. Walker, Nadine T. Laporte, Mindy Sun, Joseph L.
Hackler, Pieter S.A. Beck, and Sudeep Samanta, Woods Hole Research Center
Damien Sulla-Menashe and Mark A. Friedl, Boston University, Department of
Geography and Environment
Ralph Dubayah, University of Maryland, Department of Geographical Sciences
The authors thank Mary Paden for her assistance with editing, Wenceslao
Almazan for his assistance with graphic design, and Gaëlle Callnin and CMO
& Client Strategy for their assistance with translation. The authors also thank
Michael Lesnick, Sarah Walen, Mallorie Bruns, Liz Duxbury and Selena Elmer of
Meridian Institute for organizing and facilitating the process that produced this
policy brief.
This policy brief was made possible with financial support from the
Government of Norway’s International Climate and Forest Initiative and the
Climate and Land Use Alliance.
2
ACRONYMS
CO2
Carbon dioxide
COP
Conference of Parties
FAO
Food and Agriculture Organization of the
United Nations
Gt CO2 yr-1
Gigatons of carbon dioxide per year
GOFC-GOLD Global Observation of Forest and Land Cover
Dynamics
IPCC
Intergovernmental Panel on Climate Change
LiDAR
Light Detecting and Ranging
Pg C yr-1
Petagrams of carbon per year
UNFCCC
United Nations Framework Convention on
Climate Change
REDD+
Reducing Emissions from Deforestation
and Forest Degradation, and the Role of
Conservation of Forest Carbon Stocks,
Sustainable Management of Forests and
Enhancement of Carbon Stocks
WHRC
Woods Hole Research Center
Progress toward a Consensus on Carbon Emissions from Tropical Deforestation
3
1. INTRODUCTION
Our recently published estimates of carbon emissions from
tropical deforestation appear to differ by a factor of three. A
study by Baccini et al., published by a team at Woods Hole
Research Center (WHRC), estimated gross emissions of carbon
from forest land use and land-use change as 2.2 Pg C yr-1 (8.1 Gt
CO2) over the period 2000 to 2010. Another study by Harris et
al., published by a team from Winrock International, reported
much lower gross carbon emissions from tropical deforestation
between 2000 and 2005 of 0.81 Pg C yr-1 (3.0 Gt CO2 yr-1). The
apparent disparity between the two estimates, 0.81 vs. 2.2 Pg C
yr-1, was both surprising and troublesome for our understanding
of the global carbon balance and for REDD+ policymakers.3
Here, we identify the major reasons for the differences in our
estimates. To make a more precise comparison, we then align
the scope of our analyses for the two methodologies employed.
Doing so shows that estimated emissions of carbon from
deforestation at the pantropical scale are remarkably similar,
despite the use of different underlying data sets and models.
However, this convergence does not extend to our estimates of
where these emissions have occurred.
2. MAIN DIFFERENCES
Emissions from Deforestation vs.
from Land-Use Change
The goal of the Harris et al.4 paper was to provide spatially
explicit estimates of committed emissions from gross loss
of forest cover (i.e., deforestation) across the tropics with
statistically derived estimates of uncertainty. The authors’
intent was to provide an unbiased historic benchmark of carbon
emissions from gross tropical deforestation, derived from
remote sensing data, against which future emissions could be
measured. Harris et al. used remote sensing observations of
gross forest cover loss to below 25% tree cover and assumed
that these observed changes constituted deforestation events.
No attempts were made to attribute these changes in land cover
to specific land uses. The advantages of this approach were the
consistency, transparency, and relatively fine spatial resolution
of the estimates.
Because land-cover changes were sampled in individual pixels
down to 30 m resolution across the tropics, the approach of
Harris et al. allowed improved co-location of estimated carbon
stocks with the forest areas actually lost. Emission estimates, as
Zarin (2012).
Data available at: http://appliedgeosolutions.com/science-paper.html.
3
4
4
well as related uncertainties, were published by Harris et al. at
a spatial resolution of 18.5 km. It is likely that the Harris et al.
analysis captured most forest land-use changes that result in
a large change in canopy cover, such as when forest is cleared
for cropland or pasture. Their analysis also likely captured
some large logging operations (for industrial wood harvest),
some shifting cultivation, and some natural disturbances, such
as fire. However, the proportion of total emissions among the
land-use changes captured in the estimates cannot be known
with certainty.
The goal of Baccini et al.5 was to update the record of net
emissions from tropical forest land use and land-use change.
To do this, the authors used satellite-derived products on
forest carbon stocks and data reported to the Food and
Agriculture Organization of the United Nations (FAO) through
2010. Baccini et al. used a bookkeeping model6 that provided
annual estimates of carbon emissions and removals from
the terrestrial biota resulting from changes in land use. The
bookkeeping model tracks annual per-hectare changes in
carbon stocks when (1) forest area is cleared for cropland,
pasture, or shifting cultivation; (2) forests are harvested; (3)
plantations are established; and (4) agricultural lands are
abandoned and returned to forest. The sum of all changes in
carbon for all areas under management defines the annual net
flux of carbon from all changes in forest land use, reflecting
both carbon emissions to, and removals from, the atmosphere.
This net flux takes into account the fact that the fallow phases
of shifting cultivation and post-harvest regrowth of forests
can sequester substantial amounts of carbon, partly or wholly
offsetting the previous emissions (Figure 1).
Gross emissions from all forest land use and land-use change
reported by Baccini et al., reflecting only emissions and
disregarding removals of carbon, were estimated as the sum
of emissions resulting from clearing for cropland, clearing
for pasture, clearing as part of shifting cultivation cycles,
and harvesting of fuelwood and industrial timber (Figure 1).
Data on the extent of land use for recent decades are those
reported by individual countries to FAO, including estimates of
net changes in natural forest area and in planted forests (from
the FAO Global Forest Resource Assessments, released every
five years), and these data lack estimates of their uncertainty.
In the bookkeeping model, the area-change data were
distributed among cropland and grazing land using information
reported in FAOStat. Data on the extent of shifting cultivation
are not available across the pantropics, so instead Baccini et al.
made the assumption that if FAO statistics reported larger
losses of natural forests than the sum of increases in cropland
and grazing land (as reported in the FAOStat database) in
Data available at Woods Hole Research Center: http://whrc.org/mapping/
pantropical/carbondataset_form.htm.
6
Houghton et al. (1983).
5
a given year, then these additional losses in forest area
represented lands entering shifting cultivation cycles. Data
on rates of fuelwood harvest and industrial wood harvest
were also obtained from FAOStat. Baccini et al. did not
define deforestation explicitly and reported emissions by
carbon pool rather than by land-use process (Figure 1). This
caused Harris et al. to conclude that the sum of the individual
components of gross emissions (2.2 Pg C yr-1 or 8.1 Gt CO2e yr-1)
represented the most appropriate value to compare against
their estimate of gross emissions from deforestation
(0.8 Pg C yr-1 or 3.0 Gt CO2 yr-1).
(e.g., different agencies may be responsible for reporting
different statistics, thus values do not align); reported data
are of variable quality and unknown accuracy; the estimation
of land-use change is not a straightforward process due to
the integration of many different datasets; and the areachange data cannot be co-located with forest carbon-stock
data. Baccini et al. compensated for this last disadvantage by
weighting the mean stock data for each region by those areas
experiencing deforestation.
The advantage of the Baccini et al. approach is that all forest
land use and land-use changes, including those considered
as forest degradation and carbon stock enhancements in
the REDD+ negotiations, are included and are broken down
by driver of land-use change (e.g., clearing for cropland,
timber harvesting). The disadvantages of this approach are
that country estimates are often internally inconsistent
In the Harris et al. analysis, estimates of forest area loss,
carbon emissions, and associated uncertainties were calculated
at the 18.5 km block scale, where each block represented
approximately 350 km2 of land area. Block-scale estimates
were then aggregated to the larger scales of interest (e.g.,
country, continental, pantropical). With respect to the spatial
scale, Harris et al. took a “bottom up” approach to estimating
emissions (and associated errors) by aggregating individual
blocks. The Harris et al. group introduced a procedure for
detailed accounting of uncertainty at the 18.5 km block scale
and set forth a model of how this can be done in the future
with higher resolution datasets.
Figure 1. Comparison of Gross Emissions and Gross
Removals of Carbon from Land-Use Processes.
8.5
2
0.45
1.5
1
6.5
0.64
4.5
0.15
0.5
0.81
0
0.81
-0.45
-0.5
-0.15
-0.56
-1
-1.5
0.23
Harris et al.
Baccini et al.
2.5
0.5
-1.5
-3.5
Gross C emissions (+) or removals (-)
(Gt CO2 yr-1)
Gross C emissions (+) or removals (-)
(Pg C yr-1)
2.5
-5.5
Industrial Logging
Fuelwood Harvest
Shi
ing Culvaon
Soils
Deforestaon
Different Spatial Scales
In contrast, Baccini et al. took a “top down” approach with
respect to spatial scale. Emission estimates were generated
for eight large regions across the tropics (e.g., sub-Saharan
Africa, South America) rather than for individual pixels or
blocks. Although the bookkeeping model can be applied
at any spatial resolution, Baccini et al. took a conservative
approach and identified the regional and continental scales as
minimum reporting units. The uncertainties associated with
the carbon emissions reported by Baccini et al. were indicated
by evaluating emission estimates based on model simulations
of different scenarios using alternative data.
Different Carbon Stock Maps
Harris et al. used a map of aboveground biomass (1-km
resolution) derived by Saatchi et al. (2011)7 using methods
based on satellite observations and calibrated with ground
studies.8 For consistency with the forest-loss product used in
their analysis,9 Harris et al. considered pixels in the biomass
map as candidate pixels for potential deforestation in their
model only if tree cover in the pixel was higher than 25% in
the year 2000. Belowground biomass was estimated from
aboveground biomass using an empirically derived allometric
equation.10 Harris et al.’s emission estimates included losses
of biomass carbon only; other carbon pools (including carbon
Saatchi et al. (2011).
Ibid.
9
Hansen et al. (2010).
10
Mokany et al. (2006).
7
Note: Emissions and removals for Baccini et al. are reported in this figure
by land-use process rather than by carbon pool, as reported in the original
paper. Numeric values displayed in the bars are in units of Pg C yr-1 .
8
Progress toward a Consensus on Carbon Emissions from Tropical Deforestation
5
in mineral soils, peat, and dead wood) were not included due
to a lack of reliable information regarding their spatial extent,
distribution, and magnitude.
Carbon stocks in aboveground biomass were estimated by
Baccini et al. using a combination of field measurements colocated with Light Detecting and Ranging (LiDAR) observations
and satellite imagery. The map, produced at 500-m spatial
resolution (vs. 1-km as used by Harris et al.), estimates carbon
stocks for the year 2007–2008 rather than the year 2000,
and no forest cover mask was used to exclude areas that
had been deforested prior to the year 2008 from the forest
carbon stock estimates. Instead of using pixel-based biomass
estimates directly (as Harris et al. did) to estimate emissions,
Baccini et al. “weighted” their biomass values per region of
the bookkeeping model to represent the average biomass
of deforested lands using the same 2000–2005 forest-loss
product used by Harris et al. to identify areas of deforestation.7
Belowground biomass was assumed to be 20% of aboveground
biomass. Soil carbon emissions were also included in the
Baccini et al. analysis, and like Harris et al., they did not include
emissions from peat or from the dead-wood pool.
Different Carbon Emission Models
Harris et al. multiplied the observed quantity of forest
area lost between 2000 and 2005 in each 18.5 km block
by the estimated forest carbon stock in aboveground and
belowground biomass in the block in the year 2000. Because
the exact locations of forest loss within a given block were
unknown, Harris et al. repeated a randomization procedure in
which forested 1-km pixels within a given 18.5 km block were
selected randomly until the forest-loss quota for the block was
met. The total carbon values of selected 1-km pixels were then
summed across the block to derive an emissions estimate.
This procedure was repeated 1,000 times per block to derive
a “best estimate” as well as an uncertainty range for carbon
emissions occurring within each block. Harris et al. estimated
average committed emissions over a single five-year time
interval (2000–2005) for which remote sensing observations
on gross forest cover loss were available. Because Harris et al.
focused on gross emissions, they also simplified the accounting
by assuming that all biomass carbon in the deforested
landscape was converted immediately to atmospheric
carbon dioxide, that is, their estimates reflected “committed”
emissions to the atmosphere that will occur regardless of
future land-use or land-management decisions.
In contrast, Baccini et al. reported annual emissions averaged
over a 10-year time interval (2000–2010). They used a carbon
bookkeeping model based on a series of carbon response
curves, one curve for each type of land-use change. Each
curve, defined by up to 22 parameters, simulates the annual
fluxes of carbon to and from the atmosphere after a given
6
land-use change occurs. Parameters for carbon response
curves are defined individually for each ecosystem type within
a given geographic region. The bookkeeping approach does not
use the concept of committed emissions but instead includes
model assumptions about the proportion of biomass and soil
carbon that is converted to atmospheric carbon dioxide each
year. Each year’s emission estimate therefore includes some
emissions from past land use changes (i.e., “legacy” emissions)
as well as a fraction of the emissions from the current
year’s land-use change events. The remaining emissions
are delayed to subsequent years to account for future
decomposition processes.
3. TOWARD CONSENSUS
For this paper, we aligned the scopes of the two analyses to
facilitate a direct comparison between emissions estimates.
We found that despite the use of different assumptions and
independent data sets for estimating forest area change
and forest biomass carbon stocks, estimates of gross
carbon emissions from gross deforestation for the period
2000–2005 are identical at the pantropical scale: 0.8 Pg C yr-1
(2.9 Gt CO2 yr-1) for both studies, excluding emissions from
mineral soils and peat.
We arrived at this conclusion using the following common
framework:
1. The Harris et al. deforestation emissions estimate was
assumed to be most comparable to the sum of the
Baccini et al. clearing for cropland, clearing for grazing
land, and (first-time) clearing for shifting cultivation
emission estimates (i.e., these were considered to be
“deforestation” whereas other land-uses of wood harvest
and the rotational cycles of shifting cultivation were
considered to be “forest degradation”);
2. Both groups tested the impacts of including and excluding
soil carbon in the estimates;
3. Both groups estimated emissions for the same time period
(2000–2005);11
4. Both groups estimated only gross (not net) emissions.
We recognize that forest degradation processes contribute
a significant and highly uncertain quantity of carbon to the
atmosphere on an annual basis in addition to the emissions
from deforestation alone. We have not included these
Baccini et al. used a time period of 10 years, but for this paper, WHRC redid
its analysis to include only the years 2000-2005 to match the period used
by Harris et al. The Winrock group did not change anything in its analysis
published originally as Harris et al.
11
Table 1. WHRC and Winrock Teams’ Estimates of Forest Carbon Stocks in Areas of Deforestation, Rates of Forest Loss, and
Carbon Emissions, by Region.
WHRC team
Winrock team
Mean forest carbon stocks in deforested blocks (Mg C ha-1)
Sub-Saharan Africa
40
61
Latin America
88
90
South and Southeast Asia
56
144
Pantropics
69
95
Sub-Saharan Africa
3,610
1,889
Latin America
4,882
4,873
South and Southeast Asia
1,230
1,785
Pantropics
9,722
8,547
Rates of gross forest loss (103 ha yr-1)
Gross carbon emissions (Pg C yr-1) *
Sub-Saharan Africa
0.23
0.11
Latin America
0.47
0.44
South and Southeast Asia
0.11
0.26
Pantropics
0.81
0.81
Note: Estimates were made by the WHRC and Winrock groups using the common framework referenced at the beginning of Section 3.
* Carbon emission estimates reflect emissions from biomass only.
processes in this comparison because the Winrock team12
did not address them in its original analysis due to lack of
adequate quality data at the pantropical scale.
Our consensus on deforestation emissions was achieved
by considering only forest biomass carbon stocks; emission
estimates differ by ~10% when soil carbon emissions are
incorporated into the analyses—0.87 vs. 0.96 Pg C yr-1 (3.2 vs.
3.5 Gt CO2 yr-1) for the Winrock group and the WHRC group,
respectively. The Winrock team incorporated emissions from
soil carbon using spatial estimates of soil carbon stocks to a
depth of 30 cm from the World Harmonized Soil Database and
Intergovernmental Panel on Climate Change (IPCC) factors
related to the fraction of total soil carbon that is emitted upon
clearing forest for a new land use under given management
conditions (e.g., conversion to agriculture with full tillage and
medium nutrient inputs).13 They also developed assumptions
about the proportion of the total forest area lost per 18.5 km
block where soil carbon was likely to be impacted. The WHRC
team estimated ecosystem-level soil carbon stocks to a depth
Harris et al. and Baccini et al. refer only to information presented in the
original published papers. The research group names Winrock and WHRC
are included in this section to distinguish the work done as part of this
comparison effort from the original analyses.
13
Intergovernmental Panel on Climate Change (2006).
12
of one meter14 and modeled a loss of 25%15 when natural soils
were converted to cropland. These methodological differences
caused the Winrock team’s emission estimates to increase less
than the WHRC’s team upon incorporation of soil.
Although we have reached consensus in our emissions
estimates from gross deforestation at the pantropical scale
(excluding soil carbon emissions), significant differences
remain for sub-Saharan Africa and South and Southeast Asia
(Table 1). Estimates are similar between the two analyses
for Latin America, likely due to the large proportion of South
American emissions originating in Brazil and to the fact that
Brazil reports up-to-date forest area loss data to FAO derived
from remote sensing imagery similar to that used in the
Winrock analysis. In other words, about half of our consensus
is likely due to the use of similar data sets on forest area
change in Brazil (Table 1). The other half of our consensus
is more coincidental, as it results from differences in subSaharan Africa and South and Southeast Asia between the
two analyses that cancel each other out. The Winrock team
estimates higher carbon stocks and rates of forest loss, and
consequently higher emissions from South and Southeast
Post et al. (1982).
Schumacher and Freibauer (2011).
14
15
Progress toward a Consensus on Carbon Emissions from Tropical Deforestation
7
Asia, whereas the WHRC team estimates emissions from
sub-Saharan Africa that are more than twice as high as the
estimates of the Winrock team, due largely to higher estimates
of the rate of forest loss in this region, even though their
estimate of regional carbon stocks is lower.
4. CONCLUSIONS AND
IMPLICATIONS FOR REDD+
MONITORING
A systematic approach of spatially matching carbon stocks
with the forest areas that are cleared and reporting resulting
emissions and uncertainty at high spatial resolution, as done
by the Winrock team, is best suited for estimating carbon
emissions from gross deforestation. However, the WHRC
approach of including additional carbon fluxes associated with
land use and land-use change in tropical forests is critical for
developing a full accounting framework.
Harris et al. highlight the need for reporting emissions
estimates at the subnational scale and demonstrates a
valuable approach to rigorously account for uncertainty in
all components of an emissions analysis. Remote sensing
technology and methods are more than adequate to monitor
gross deforestation,16 which, for many developing countries,
is the most significant component of carbon emissions from
forest land use. Many governments are advancing in their
access to data and in their technical capabilities to improve
emissions estimates at national and subnational levels. With
new deforestation estimates based on higher resolution
remote-sensing data, estimates of carbon emissions from
deforestation will improve enabling countries to meet more
rigorous reporting requirements with reasonable certainty.
Further improvements will occur as countries develop their
own forest carbon stock estimates to use with the improved
maps of deforestation.
The WHRC group highlights shifting cultivation and fuelwood
extraction as causes of forest degradation. Given the
significance of these net emissions (difference between
the losses and gains by regrowth), more attention to
monitoring these activities is needed. Although newer
methods have emerged for monitoring forest degradation
from timber-extraction operations using both LiDAR and
medium- resolution imagery,17,18,19 these methods, and the
Global Observation of Forest and Land Cover Dynamics (2011).
Asner (2009).
18
Asner et al. (2005).
19
Souza and Roberts (2005).
16
17
8
corresponding capacity to implement them, need to be made
more available to countries. Robust and cost-effective methods
also need to be developed for monitoring shifting cultivation
– not only its extent but also the changes in practice and the
associated impacts on carbon dynamics (e.g., lengthening or
shortening rotation of the forest-fallow cycle).
The focus of comparing the Winrock and WHRC studies has
been on emissions from deforestation, but, as mentioned
above, other emission sources are also important for REDD+.
Emissions due to peat drainage and peat fire for an overlapping
time period have been estimated as 1.0 Gt CO2 yr-1 (0.27 Pg
C yr-1).20,21 We have shown in section 3 that emissions from
mineral soils after deforestation account for approximately
0.3 Gt CO2 yr-1 (0.10 Pg C yr-1). The IPCC advises that emissions
from forests remaining as forests (i.e., forest degradation) can
be estimated as the difference between the losses of carbon
due to degrading activities and the gains of carbon from forest
regrowth. As reported in Baccini et al., the net change in
carbon stocks due to shifting cultivation and fuelwood harvest
is about 1.0 Gt CO2 yr-1 (0.27 Pg C yr-1). Thus the total of the
other emission sources important to REDD+ is approximately
2.3 Gt CO2 yr-1 (0.64 Pg C yr-1), though this estimate is highly
uncertain due to the lack of reliable data.
5. AGREEMENT ON A
PANTROPICAL BENCHMARK
REDD+ negotiators within the United Nations Framework
Convention on Climate Change (UNFCCC) have previously
discussed, but thus far avoided, setting an explicit target for
emission reductions. Other groups, including the European
Commission, the Informal Working Group on Interim Finance
for REDD+, and the UK government’s Eliasch Review, have
converged around a target of cutting tropical deforestation
by 50% by 2020, but the benchmark against which this target
should be evaluated has been unclear. By achieving consensus
within the scientific community that emissions from tropical
deforestation between 2000 and 2005 were 3.0 ± 1.1 Gt CO2
yr-1 (0.8 ± 0.3 Pg C yr-1), researchers have given policymakers
an unbiased, historic benchmark to use in discussing
agreement on a target of reducing emissions from gross
tropical deforestation to below 1.5 Gt CO2 yr-1 (0.4 Pg C yr-1)
by 2020. If policymakers prefer to use a more inclusive target
by incorporating other emission sources important to REDD+
in addition to deforestation, then the 50% target increases to
about 2.6 Gt CO2 yr-1 (0.7 Pg C yr-1) by 2020.
Van der Werf et al. (2008).
Hooijer et al. (2010).
20
21
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et al. 2005. Selective logging in the Brazilian Amazon.
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Asner, G.P. 2009. Tropical forest carbon assessment:
integrating satellite and airborne mapping
approaches. Environmental Research Letters 4: (1-11).
doi:10.1088/1748-9326/4/3/034009.
Baccini, A. et al. 2012. Estimated carbon dioxide emissions
from tropical deforestation improved by carbondensity maps. Nature Climate Change 2: (182-185).
doi:10.1038/nclimate1354.
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