Quantifying Benefits of the New York Declaration
on Forests
September 23, 2014
Michael Wolosin, Ph.D.
Managing Director, Research and Policy
[email protected]
Quantifying Benefits of the New York Declaration on Forests
This paper analyzes the main measurable outputs of the New York Declaration on
Forests, by quantifying the total emissions reduced or avoided, and total area of forest
conserved or restored, that would be achieved by the combined measures included in
the Declaration. It attempts to frame the analytical question appropriately given the form
of the Declaration; simplify the analysis to allow quantification of outputs without
sacrificing accuracy; gather available high-quality estimates; synthesize available
estimates into a consistent set of summary ranges for the outputs; and address the
quality of the outputs by assessing strengths and weaknesses of data sources and
analyzing sensitivity to key assumptions.
I. Defining the Question
a. Geographic Scope
In order to estimate the benefits of the Declaration, it is critical to determine the
geographic scope of the analysis: should it be broad at a global scale, narrow and
limited only to areas under direct control of the Declaration’s signers or somewhere in
between? There are several reasons to pursue the analysis at the global scale, including
both broad interpretive and technical considerations.
The Declaration is a commitment among signers to “do [their] part to achieve” a set of
outcomes “in partnership” that are global in scope. The signers include nations,
companies, company associations, non-governmental organizations, indigenous
peoples associations, and others, with “varying mandates, capabilities, and
circumstances.” This group of signers will collectively have influence over forest and
land use globally through direct governance, purchasing, foreign policy and foreign
assistance, consensus-driven international conventions, and many other levers; and
they are committing to specific goals and action items that will slow, halt, and reverse
global forest loss. As such, the ambitious Declaration goals will be pursued and will
impact global forests beyond those within the boundaries or direct control of
signatories.
From the technical standpoint, an analysis of the outputs at a more limited scope would
need to:
•
Address uncertainty in the list of signers. The Declaration is a living document,
with active recruitment of signatories intended to continue through the Paris
COP. It is not technically feasible to analyze all potential signers from the start,
2
nor is it feasible or helpful to adjust and update the analysis each time the
signatory group expands.
•
Explicitly assess the scope of influence of signers. Country signatories could be
judged to have control (or not) only within their borders, on deforestation
commodities purchased directly by the government, commodities that enter
their borders, etc. It is unclear how to assess scope if a signer provides finance
to non-signers. There are similar uncertainties in defining the scope of company
signers: whether to count only land areas under direct operational control of
companies, the land areas that produce all the goods purchased or traded by
signers, the area under control by all companies that are trading partners of
signers, etc. It is neither technically nor politically feasible to draw these
boundaries in a consistent way.
•
Explicitly determine how to treat goals that will be delivered by non-signers.
Some of the goals within the Declaration reflect existing agreements on
deforestation that countries not covered by this effort have signed on to. For
example, the Bonn Challenge of restoring 150 million hectares by 2020 is
restated in this Declaration. As a result, countries signed up to the Bonn
Challenge who are not signing the Declaration will help deliver some of this
restoration area.
•
Access data that is not publicly available – or not available at all. Completely
transparent and traceable supply chains are not yet a reality, and companies
treat their purchasing data as proprietary. Any attempt to limit the analysis of
commodity goals to only those companies signing the Declaration would require
estimates of how much they purchase of each relevant commodity, and from
where. It is not feasible to require detailed purchasing data from all company
signatories.
With these considerations, it seems clear that a global scope is both the right approach
in terms of intent, and the more feasible approach analytically.
b. Analytical Simplification
There are ten goals listed in the New York Declaration. These are:
1.
At least halve the rate of loss of natural forests globally by 2020 and strive to end natural forest
loss by 2030.
2.
Support and help meet the private-sector goal of eliminating deforestation from the production of
agricultural commodities such as palm oil, soy, paper and beef products by no later than 2020,
recognizing that many companies have even more ambitious targets.
3.
Significantly reduce deforestation derived from other economic sectors by 2020.
3
4.
Support alternatives to deforestation driven by basic needs (such as subsistence farming and
reliance on fuel wood for energy) in ways that alleviate poverty and promote sustainable and
equitable development.
5.
Restore 150 million hectares of degraded landscapes and forestlands by 2020 and significantly
increase the rate of global restoration thereafter, which would restore at least an additional 200
million hectares by 2030.
6.
Include ambitious, quantitative forest conservation and restoration targets for 2030 in the post2015 global development framework, as part of new international sustainable development goals.
7.
Agree in 2015 to reduce emissions from deforestation and forest degradation as part of a post2020 global climate agreement, in accordance with internationally agreed rules and consistent
with the goal of not exceeding 2°C warming.
8.
Provide support for the development and implementation of strategies to reduce forest emissions.
9.
Reward countries and jurisdictions that, by taking action, reduce forest emissions—particularly
through public policies to scale-up payments for verified emission reductions and private-sector
sourcing of commodities.
10. Strengthen forest governance, transparency and the rule of law, while also empowering
communities and recognizing the rights of indigenous peoples, especially those pertaining to their
lands and resources.
The first and fifth goals dominate this analysis. These goals are hereafter referred to as
the forest loss goal (goal #1), and the restoration goal (goal #5). Other goals in the
Declaration can generally be considered as subsets or subordinate to these two goals
for the purposes of this analysis for the following reasons.
Goal #2, “Support and help meet the private-sector goal of eliminating deforestation
from … agricultural commodities … no later than 2020,” can be viewed primarily as a
subset of the forest loss goal (goal #1) with two caveats. First, this goal suggests
eliminating all deforestation for agricultural commodities rather than just loss of “natural
forests.” The conversion of existing plantation forests (as opposed to natural forests) to
non-forest agricultural production, which would be eliminated by 2020 under the
commodity goal (goal #2) but is not explicitly addressed in the forest loss goal (goal #1),
is a minor consideration.1 Ignoring this consideration may slightly underestimate the
quantitative benefits achieved by the Declaration. Second, the commodity goal (goal
#2) may suggest a more rapid decrease than that put forth by the forest loss goal (goal
#1). Two recent papers have estimated the proportion of commercial agriculture-driven
forest loss by area to be around 50 percent2 or as high as 71 percent.3 Another study
estimates net forest loss emissions from conversion to pasture or croplands, and
draining and burning of peatlands together at around 60 percent,4 which includes some
non-commercial agriculture and is influenced by the high emissions of peatland
1
The FAO FRA 2010 estimates that only 7% of global forest area is planted forests. Forest plantations are
rarely cleared for conversion to non-forest agriculture.
2
Hosonuma et al 2012.
3
Forest Trends 2014. 4
Houghton 2012.
4
burning. To be conservative (i.e., to err in the direction of lower estimates), for our core
analysis, we assume that the commodity objective does not imply reducing forest loss
beyond the 2020 goal of halving forest loss.
Goal #3, “Significantly reduce deforestation derived from other economic sectors by
2020” could be interpreted as seeking to address deforestation from activities such as
mining, infrastructure, and urban expansion, which together make up about 15 percent
of forest area clearing.5 It is debatable whether this goal is intended to include clearing
for local or subsistence agriculture, which is clearly economic and is about 30 percent
of forest loss, or the various economic drivers of forest degradation and clearing that
may not immediately lead to conversion of forests to other land uses (such as livestock
grazing in forests or fuel wood gathering), or those activities that involve forest clearing
followed by natural forest regeneration (such as timber and logging operations).
Considering the uncertainty around which economic activities are included in this goal,
and given that the goal is stated without a numeric target, it is difficult to quantify the
outcome of achieving this objective.
The “agricultural commodities” goal (goal #2) and the “other economic sectors” goal
(goal #3) together may suggest a more rapid decrease in deforestation by 2020 than
that proposed by the forest loss goal (goal #1). We address the potential for more rapid
reductions in Section III, which includes a sensitivity analysis that accelerates forest
protection to achieve a 75 percent reduction by 2020.
Goal #6, which provides a commitment to “Include ambitious, quantitative forest
conservation and restoration targets for 2030 in the post-2015 global development
framework…” should not restrain the analysis. The forest targets that emerge with
international consensus from the SDG process are unlikely to be more ambitious than
the forest loss goal and the restoration goal in the New York Declaration (goals #1 and
#5), which serves as a statement of ambition from global leaders on these issues.
All the other commitments (goals #4 and #7-10) in the Declaration are essentially
procedural or co-benefit targets, and will not in and of themselves achieve additional
quantitative benefits in terms of emissions or land area. We therefore collapse the
analysis to just the forest loss goal and the restoration goal (goals #1 and #5).
c. Conclusion: The Analytical Questions
Together, the above considerations lead to a well-defined, limited, and analytically
feasible set of estimates. These include six distinct numbers: the tons CO2 emissions
removed by restoring 150 million hectares of deforested and degraded lands by 2020;
5
Hosonuma et al 2012.
5
the tons CO2 emissions removed by restoring another 200 million hectares of
deforested and degraded lands by 2030; the tons CO2 emissions avoided and the
hectares of forest conserved by halving the rate of natural forest loss globally by 2020;
and the tons CO2 emissions avoided and the hectares of forest conserved by halting
forest loss globally by 2030.
The remainder of this brief presents an analysis of these numbers. The results of the
analysis will give estimates of the Declaration’s outputs in the form of: “If the goals of
the Declaration were met, they would together remove or avoid CO2 emissions of [X-Y]
gigatons and conserve or restore [X-Y] million hectares of forest area.”
II. Quantitative Analysis
a. Forest Loss Goal (goal #1)
The forest loss goal to “At least halve the rate of loss of natural forests globally by 2020
and strive to end natural forest loss by 2030,” embeds the forest component of the 5th
Aichi biodiversity target for 20206 and extends and strengthens the target to eliminate
forest loss by 2030. Estimation of both the forest area conserved and the tons CO2
emissions avoided are required.
Key factors in this analysis include:
•
How the term “natural forest” is interpreted vis-à-vis existing data sources on
forest area and forest loss.
•
Inclusion or exclusion of soil and peat carbon in addition to above-ground
biomass in estimates of CO2 emissions from clearing.
•
The approach to a baseline or business-as-usual scenario, and which period is
used if an historical baseline is preferred.
•
The assumed timing of deforestation reductions across the time period.
•
Tons of CO2 emissions avoided per hectare per year for various land use
transitions and forest types.
•
Geographic scope of available estimates (global versus tropical).
•
The treatment of forest clearing followed by natural regeneration.
•
The treatment of forest carbon loss in standing forests (degradation).
•
The assumption that forests will maintain their capacity to grow and sequester
carbon at historical rates through 2030 even in a changing climate.
6
“By 2020, the rate of loss of all natural habitats, including forests, is at least halved and where feasible
brought close to zero, and degradation and fragmentation is significantly reduced.”
6
There is an extensive and rapidly evolving literature available with estimates of historical
forest area loss and of emissions from such loss. We limit the scope of this analysis to
compiling estimates of historical forest area and forest carbon loss that most closely
match the forest loss goal’s scope. There is a preference for studies with more recent
baselines, global geographic scope and globally consistent methodologies, estimates
of both area and emissions, and inclusion of soil and peat emissions. When using
studies that take a land use perspective, we omit forest degradation, which from a landuse perspective is not generally considered to be a “loss of forest”. However, we do
include in the analysis data that take the land cover approach rather than the land use
approach, in which case forest degradation and deforestation are more difficult to
distinguish. We omit studies that include only net forest area loss or emissions, as they
mask the amount of forest loss by cancelling it out with regrowth elsewhere.7
We briefly explain how well known sources differ, and why they may be appropriate or
inappropriate estimators for our purposes. For the primary analyses, we use an
historical baseline – assuming that annual forest loss area and emissions through 2030
would match recent historical losses.8 We also assume that forest loss reduction is
achieved at a constant rate over the analysis period – i.e., that if 10 million hectares of
forest loss per year is the historical rate, and we halve that rate to 5 million hectares by
2020, the area of forest loss decreases linearly over the period 2011 to 2020.
For our first analysis, we use low and high estimates for both global gross forest area
loss and global average carbon biomass per hectare from the FAO from the most
recently available global analyses and time periods. At the lower end, we use the gross
forest area loss from the survey method estimated at 13 million hectare per year
average from 2000-2009 (FAO 2010), while at the higher end we use the estimate of
13.5 million hectare per year average from 2000-2004 from the remote sensing survey
(FAO & JRC 2012). For the lower end estimate of carbon, we apply the global average
of carbon stock in biomass in 2009 (71.6 tons C/ha), while at the higher end we add in
another 17.8 tons C/ha of deadwood carbon. We leave out soil carbon for this analysis,
which averages another 72.3 tons C/ha. While some soil carbon is lost when forests on
mineral soils are cleared – about 25 percent by some estimates – the losses are less
7
We do not include IPCC estimates in the core analysis, as the IPCC focused its historical analysis on net
land use and land use change emissions and did “… not assess individual gross fluxes that sum up to
make the net land use change CO emission” (WGI Chapter 6.3.2.2 and Table 6.2). However, we do
compare our estimates of forest area change and emissions through 2030 to the IPCC scenarios (see
below in Section III).
8
The IPCC WGIII report admits a large range of uncertainty in both the baseline AFOLU emissions and in
the base case projections. This is because dramatically different forest and land use patterns are possible,
both to meet particular transformation pathways and even in the baseline scenarios (11.9.2). For example,
there are tradeoffs between using land for bioenergy to displace fossil fuels, versus maintaining and/or
expanding forests as carbon sinks. However, the ensemble average for the baseline models (Section
6.3.1.5) does suggest a decline in AFOLU emissions of about 25% from 2010 to 2030 (Figure 6.5), so we
include in the Section III a sensitivity analysis with a similar baseline applied to deforestation emissions.
2
7
rapid and less well studied; we assume that the underestimation error in omitting soil
carbon is somewhat offset by the overestimation error in assuming that all biomass and
deadwood carbon is emitted rapidly.
In 2012, two research groups published estimates of emissions from tropical
deforestation that seemed to differ substantially, based on very different methodologies
and data sources. Surprisingly, a careful assessment later in the year9 found a
“consensus within the scientific community that emissions from tropical deforestation
between 2000 and 2005 were 3.0 ± 1.1 Gt CO2 yr-1.” This summary estimate and
uncertainty bounds exclude emissions from mineral soils after forest loss and from peat
draining and fire, which are estimated at an additional 0.3 Gt and 1.0 Gt CO2 yr-1,
respectively. Forest degradation – the net change in carbon stocks due to shifting
cultivation and fuelwood harvest – is estimated by Baccini et al. to be another gigaton
or so, but we omit degradation as discussed above. While these studies are incomplete
geographically – including only the tropics – most gross forest loss over the period
studied took place in the tropics. Other strengths also make these estimates a useful
input to our analysis. Both source papers also estimate average forest area losses over
the period 2000—2005 at about 8.5 million hectares (Winrock) and 9.7 million hectares
(WHRC). For this second analysis, we use as a lower bound the Winrock area estimate,
along with a low-end emissions estimate of 3.2 Gt CO2 yr-1, which is equal to the
lower-bound emissions from forests (3.0 – 1.1 Gt) plus the estimates of peat and soil
emissions (0.3 + 1.0 Gt). At the higher end, we use the WHRC area and the higher
bound estimate of emissions including all three sources, 5.4 Gt CO2 yr-1.
As the third source, we use estimates from the most comprehensive, consistent, and
high-resolution global analysis of forest cover available to date. Published in 2013, the
Hansen et al. dataset has quickly become the go-to source for deforestation data. For
the purposes of this paper, there is a critical difference between this source and others:
it takes a land cover perspective rather than a land use perspective. In other words, the
Hansen et al. data include as “forest loss” any clearing of forests – including harvesting
of forest plantations, insect infestations, fires, etc. – even if the area will be left to
regrow. In other words, the data include forest “churn” on both the deforestation and
reforestation side, so should be interpreted as maxima. This source also, by itself, does
not include emissions values. Regardless, we include two global estimates from this
source. At the low end, we use the estimate of global gross forest loss from 2000-2012
minus the area of land that experiences both loss and subsequent regrowth by 2012; at
the higher end, we use global gross forest loss from 2000-2012 without adjusting for
regrowth. For emissions we multiply both area estimates by the biomass per hectare
estimate from FRA (2010).
9
Winrock (Harris et al 2012) and Woods Hole (Baccini et al 2012) were synthesized in Harris et al 2012b.
8
Our last source is an unpublished analysis (N. Harris and K. Brown, pers. com.) that
combines national forest area loss estimates from Hansen et al. (2013) with national
mean forest carbon density estimates from Saatchi et al. (2011),10 for all significant
tropical forest countries (n=75). Because of limitations in the Hansen et al. data, and the
global importance of Brazil and Indonesia as the top two forest emitters, alternative
estimates of forest loss for these two countries replace the Hansen et al. estimates.11 At
the low end, we use the most recent 3-year period, 2010-2012; at the higher end, we
use the 2001-2005 average. The estimates yielded by applying these various baselines
are compiled in Table 1 and Figure 1, while strengths and weaknesses of these various
data sources are compiled in Table 2.
Table 1: Avoided CO2 emissions and avoided forest area loss by 2030
FAO
Low
High
Winrock &
WHRC
Low
High
Hansen
Low
High
Harris & Brown
Low
High
Full Range
Low
High
Gt/yr avg
1.7
2.2
1.6
2.7
2.1
2.3
1.5
1.6
1.5
2.7
Gt/yr 2030*
3.4
4.4
3.2
5.4
4.2
4.6
2.9
3.2
2.9
5.4
34.1
44.3
32.0
54.0
42.4
46.4
29.3
32.4
29.3
54.0
6.5
6.8
4.3
4.9
8.1
8.8
3.9
4.1
3.9
8.8
13.0
13.5
8.5
9.7
16.2
17.7
7.7
8.3
7.7
17.7
130.0
135.0
85.5
97.2
161.5
176.9
77.2
82.9
77.2
176.9
Gt total
MHa/yr avg
MHa/yr 2030*
MHa total
*Note: These rows are equal to the baseline estimates of annual emissions and forest loss for each source.
Table 2: Strengths and weaknesses of forest loss data sources
Study
FAO FRA2010
FAO FRA2010
RSS
Winrock &
WHRC
Hansen 2013
Harris & Brown
Strengths
- Global scope
- Data through 2009
- Very detailed area estimates
- Land use change methodology
- Global scope
- Globally consistent
- Land use change methodology
- Globally consistent
- Land use change methodology
- Error estimates included
- Global scope
- Globally consistent
- Data through 2012
- High resolution
- Best available area and
emissions data carefully
integrated
Weaknesses
- Country data collection methods vary
- Emissions not a focus
- Peat loss omitted
- Emissions not a focus
- Pantropical only
- Area not a focus
- Data through 2005
- Tree cover rather than land use methodology
- Emissions not a focus
- Peat loss omitted
- Heavily harvested forests before reestablishment
of tree cover are construed as forest loss
- Pantropical only
- Same weaknesses as in Hansen 2013 for all
countries except Brazil and Indonesia
10
The analysis uses the mean carbon density values for a 25% forest cover threshold, from Table S3.
http://www.pnas.org/content/suppl/2011/05/24/1019576108.DCSupplemental/ pnas.201019576SI.pdf
11
Margono et al. 2014 for Indonesia, and SEEG 2014 for Brazil.
9
b. Restoration Goal (goal #5)
The restoration goal embeds the Bonn Challenge target to “restore 150 million hectares
of deforested and degraded lands by 2020,” and increases the target in the following
decade, as it will “strive to restore at least another 200 million hectares by 2030.” For a
sense of scale, 15 to 20 million hectares of restoration per year would seek to double or
more the historical rate of afforestation and natural forest expansion, estimated to be 78 million hectares per year globally.12 With these targets stated in hectares, the analysis
need only assess tons CO2 removed for a given area of global restoration.
Key factors in such an analysis include:
• Distribution of restoration actions across geographic regions, current land
statuses, and fully restored land statuses.
• Tons of additional CO2 removed from the atmosphere per hectare per year by a
restored landscape compared to the unrestored state.
• Timing of restoration actions over the goal period.
Additional factors that could influence the outcomes include:
• Failure rate of restoration actions.
• Use or removals of post-restoration lands, for example if degraded lands are
restored to short-rotation plantation forests.
The most complex of these factors are the first two – as both the carbon storage
potential of degraded lands and that of restored lands vary substantially. For example,
the average carbon stock of the mostly dry forests of Mozambique in East Africa is 43
tons of carbon (C) per hectare, while that of the mostly wet forests in Cote d’Ivoire is
four times higher – 177 tons C per hectare.13 If rewetting of peat lands is included as a
target type of restoration, the difference in carbon stock between degraded and
restored lands can jump by an order of magnitude.
Forest restoration failure rates can be very high – especially if pursued on inappropriate
land or with the wrong mix of species.14 For this analysis, we assume that restoration
targets are met with successful projects – that appropriate areas and species are
selected, that restoration attempts are inflated by reasonable failure rates, and/or that
failed plantings are replaced within the time frame, leading to 350 million hectares of
successfully restored area by 2030.
12
FAO (2010) estimates gross forest loss of 13m ha/yr for 2000-2010 and 5.2m ha/yr of net forest area
loss, suggesting 7.8m ha/yr average forest gain. FAO & JRC (2012) estimates 7.3m ha/yr of forest gain.
13
FAO (2010). Global Tables, Table 11.
14
For example, one study of efforts to reduce desertification in China through afforestation cites an 85%
failure rate (Cao et al. 2011), while a study of efforts in Brazil (Wuethrich, 2007) found that non-diverse tree
plantings in publicly funded reforested areas in Brazil yielded only 2% successful establishment.
10
A very rough first cut analysis makes assumptions about the timing and averages the
first and second factors above by applying a simple global average CO2 uptake of
restored land per hectare per year. A reasonable range from the literature is 6-9 tons
CO2 per hectare per year.15 If the distribution of restoration action is even from 20112020 and 2021 through 2030,16 then sequestration reaches 2.1-3.2 gigatons (Gt) CO2
per year in 2030 when the full target is reached, averaging 1-1.5 Gt CO2 sequestered
per year over the whole period, for a total over the period of 21-31 Gt CO2 (see Table 3).
Two additional sources provide more sophisticated assessments of the greenhouse gas
sequestrations that would likely be achieved by restoration targets. The first (Verdone et
al., in review) estimates sequestration achieved by meeting the Bonn Challenge
objectives, accounting for likely distribution of restoration across biome (temperate,
humid tropics, and dry tropics) and across restoration type (planted forests, naturally
regenerated forests, and agroforestry). The results suggest that restoring 150 million
hectares through 2020 would sequester about 53 Gt CO2eq over 50 years, or about 1
Gt annually.
To apply the Verdone et al. estimates to a different time period (20 years instead of 50
years), and to an expanded target (150 million hectares by 2020 plus another 200
million hectares by 2030), we assume that the 200 million hectares of additional
restoration through 2030 would be achieved with the same distribution across biomes
and restoration methods as the first 150 million hectares, and take two different
approaches to recalculation. First, we account for an equally distributed amount of
restored area over the period (e.g., assuming that 15 million hectares is restored per
year from 2010-2020 in Verdone’s model) but treat the amount of sequestered carbon
per hectare per year as constant over the full period modeled for any given hectare
restored. This analysis yields estimates of 1.3 Gt CO2 sequestered per year on average,
reaching 2.8 Gt per year in 2030, and totaling 25.8 Gt over the period 2011-2030. This
is likely an overestimate as carbon sequestration rates are not constant from the time of
restoration, but rather increase over a period of time and then flatten out, in an
approximately logistic shape.
15
Estimates in this range were used, for example, in Houghton (2012) and Houghton (2013) (RA Houghton,
2014, pers. com.). Watson et al (2000) cite ranges for afforestation and reforestation that could indicate
higher values (1.5-4.4 tCO /ha/yr for the boreal zone, 5.5-16.5 for temperate, and 15-29 for tropical). An
average of these estimates weighted by forest biome area is 12.66 tCO /ha, still over the 6-9 tCO /ha/yr
range we use. However, these estimates are multi-decade averages rather than averages for the first
decade or two after restoration. If adjusted to account for a 10-year linear ramp-up in sequestration per
hectare, a hectare restored in the first year of a 20-year period will average 9.8 tCO /yr, and one restored in
the tenth year will average 7.5 tCO /yr. These averages would need to be further adjusted downward, as
they exclude lower-carbon restoration such as agroforestry, which is modeled by Verdone et al as 1/3 of
the expected total.
16
15m ha restored per year from 2011 through 2020 (for a total of 150m ha), and 20m ha per year through 2030 (another 200m ha). 2
2
2
2
2
11
To model this more realistic scenario, we take the same equal distribution of restoration
actions over each decade as above, but on top of that we apply a linear increase in
sequestration for each unit of restored area the first 10 years followed by a constant
level of sequestration for the following 40 years. This approach yields estimates of 1.0
Gt CO2 sequestered per on year average, reaching 2.5 Gt per year in 2030 (and
continuing to rise to 3.4 Gt per year by 2040 before leveling off), and totaling 19.2 Gt
over the period 2011-2030.
The second analysis of the greenhouse gas implications of restoration targets is based
on analysis for the Land Use chapter of the recently published New Climate Economy
(NCE) report (WRI 2014). While the Verdone et al. paper was used as one source for this
analysis, it was not the sole source – and the NCE methodology differed significantly
(pers. com.). For the higher-end estimate, we apply similar adjustments to the simpler
approach above (from a constant 15 million hectares/year restored from 2011-2030 to
20 million hectares/year in 2021-2030, and assuming constant carbon sequestration
over time from each hectare of restored area) to the higher NCE estimate. For the
lower-end estimate, we use the lower range value and apply the slower 10-year rampup in sequestration per hectare. These estimates are compiled in Table 3 and Figure 1.
Table 3: CO2 sequestration with restoration goal by 2030
Global Average
Low
Verdone et al.
High
Low
Full Range
NCE
High
Low
High
Low
High
Gt/yr avg
1.0
1.5
1.0
1.3
0.6
1.7
0.6
1.7
Gt/yr 2030
2.1
3.2
2.5
2.8
1.6
3.4
1.6
3.4
20.6
30.8
19.2
25.8
11.8
33.5
11.8
33.5
Gt total
c. Summary of Forest Declaration Outcomes
Table 4: Summary Estimates
1.
4.
2.
2030 Forest loss goal5.
6.
7.
11. 2030 Restoration goal12.
13.
14.
18. 2030 Total
19.
20.
21.
CO removed or avoided
1.5 – 2.7 Gt CO / year average
29.3 – 54.0 Gt CO total
2.9 – 5.4 Gt CO / year in 2030
0.6 – 1.7 Gt CO / year average
11.8 – 33.5 Gt CO total
1.6 – 3.4 Gt CO / year in 2030
2.1 – 4.4 Gt CO / year average
41.2 – 87.5 Gt CO total
4.5 – 8.8 Gt CO / year in 2030
2
2
2
2
2
2
2
2
2
2
3.
8.
9.
10.
15.
16.
17.
22.
23.
24.
Forest area conserved or restored
3.9 – 8.8 m ha / year average
77.2 – 176.9 m ha total
7.7 – 17.7 m ha / year in 2030
17.5 m ha / year average
350 m ha total
20 m ha / year in 2030
21.4 – 26.3 m ha / year average
427 – 527 m ha total
27.7 – 37.7 m ha / year in 2030
12
d. Example Summary Statements
•
“Achieving the outcomes of the New York Declaration on Forests would
conserve or restore more than 425 million hectares of forest total by 2030.”
•
“If achieved, the commitments in the New York Declaration on Forests together
are estimated to remove or avoid at least 40 gigatons of CO2 emissions by
2030.”
•
“Together, the outcomes of the New York Declaration on Forests if achieved are
estimated to reduce CO2 in the atmosphere by 4.5 to 8.8 billion tons per year by
2030.”
•
“If the goals of the New York Declaration on Forests were achieved, they could
together remove or avoid CO2 emissions of 2.1 to 4.4 gigatons per year and
conserve or restore 21 to 26 million hectares of forest area per year, on average
through 2030.”
Figure 1: Emissions impact of the New York Declaration on Forests through 2030
Total*
5"
100"
4"
80"
3"
60"
2"
40"
1"
20"
0"
0"
Gt/yr"average"
(leQ"axis)"
Gt/yr"2030"
(leQ"axis)"
Total"
120"
Full""
Range"
6"
NCE"
140"
Verdone""
et"al."
7"
Global""
Average"
160"
Full""
Range"
8"
Harris"&""
Brown"
180"
Hansen"
9"
Winrock"&"
"WHRC"
200"
Gigatons"CO2"(total)"
Restora(on*Goal*
10"
FAO"
Gigatons"CO2"(per"year)"
Forest*Loss*Goal*
Gt"total"
(right"axis)"
13
III. Quality Assessment
Impact estimates using different methodologies and different data sources converge on
similar ranges, but the ranges are wide. Ranges or minima should be used, as global
estimates of forest loss and emissions from forest loss have significant error bounds.
The estimates also require making assumptions that can drive additional variance
across sources, as discussed above. However, none of these sources of variance
prevent estimation of reliable ranges and minima, and the analysis above is in line with
other recent syntheses of the emissions potential from the forest and land use sectors.
a. Comparison With Other Syntheses
For example, a recent paper in Carbon Management (Houghton 2013) estimated annual
CO2 sequestration potential from the forest sector alone at 11 – 18 Gt CO2/year. This
estimate included 500 million hectares of reforestation to achieve 3.7 Gt/yr; halting both
deforestation and forest degradation to achieve another 5 Gt/yr; and allowing
secondary forests to fully regrow to achieve another 3.7 to 10 Gt/yr. The first two added
together are similar to our high-end total (8.7 vs. 8.8 Gt), although our area of
restoration is lower by 40 percent (about 1 Gt difference), excludes forest degradation
(another 1 Gt difference) and includes peat emissions, which Houghton excludes from
this estimate (another 1 Gt, in the opposite direction).
Our estimates also lie within the expected mitigation ranges in the IPCC AR5 WGIII
report. For example, the 2010-2030 cumulative global land-related emissions change in
the ensemble of transformation pathways, versus the base scenario, ranged from -20 to
281 Gt CO2 for the 550ppm pathways, and from -20 to 287 Gt CO2 for the 450ppm
pathways (WGIII, Table 11.10). While these totals include mitigation from bioenergy and
agriculture as well as forests, our estimated range of cumulative mitigation from forests
over the same period (41 to 87.5 Gt CO2) lies comfortably within the IPCC range, with
our high-end estimate from forests representing just 30 percent of the IPCC maximum
from all AFOLU. Forests are currently closer to 50 percent of AFOLU emissions.
Another source of comparison between our estimates and the IPCC report comes from
the land cover change estimates for the transformation pathways. The three core
models used by the IPCC vary wildly in their forecast of land cover change from 20052030 (WGIII, Figure 11.19). One of the models (GCAM) suggests that, for a 450ppm
scenario, there would be 800-850 million hectares of additional forest protection and
expansion compared to the baseline. This is well over our high-end estimate of 527
million hectares.
The two other models allow much greater forest loss than the goals of New York
Declaration would allow, suggesting only slight forest gains (IMAGE), or even significant
14
forest losses (REMIND-MAgPIE). Finally, there are estimates of economically viable
abatement potential from the forest sector, including reduced deforestation, forest
management, afforestation, and agro‐forestry (WGIII Table 11.8). They are estimated to
contribute 0.11 – 9.5 Gt CO2/yr of abatement in 2030 at carbon prices up to 50
USD/tCO2eq, and 0.2 – 13.8 Gt CO2/yr at prices up to 100 USD/t CO2eq. Our emissions
abatement estimates for 2030 from the New York Declaration goals range from 4.5 –
8.8 Gt CO2/yr, which fall well within the economic potential mitigation.
b. Forest Loss Sensitivity Analyses
To ensure that our estimates are not overly impacted by several key assumptions, we
undertake a number of sensitivity analyses. These analyses start with the same suite of
historical estimates as in Table 1, but apply them to the New York Declaration in
different ways as follows:
•
Increasing 2020 forest loss target to 75 percent: If we assume that forest loss
from commercial agriculture is around 60 percent, and loss from other
commercial activities such as mining, infrastructure, and urban expansion is
another 15 percent, then the goals in the Forest Declaration may suggest a more
rapid decline in forest loss by 2020 than the 50 percent suggested in the forest
goal. We estimate emissions abatement and forest area impacts if forest loss is
cut by 75 percent by 2020 rather than by 50 percent, with all other things being
equal (Table 5 and Figure 2, SA1).
•
Assuming a declining forest loss baseline: The set of base case models in IPCC
WG3 Chapter 6 suggest that net forest emissions may fall through 2030 by
about 25 percent with no intervention. We thus assess the impact on our
estimates of adjusting from a strictly historical baseline, to a baseline that
declines by 12.5 percent by 2020 and by 25 percent by 2030 (Table 5 and
Figure 2, SA2).
•
Achieving less than targeted in 2030: We assess the impacts of assuming only a
90 percent rather than a 100 percent cut in deforestation by 2030 to
acknowledge that the language of the goal includes some wiggle room with the
word “strive” (Table 5 and Figure 2, SA3).
•
Earlier and later start dates: We also assess the impacts of adjusting the time
period for achieving the 2020 goal in two ways. First, we change the first year of
action from 2011 to 2006, recognizing that Brazil’s achievement reducing
deforestation from 2004 to 2010 is not captured by some of the baseline
estimates (Table 5 and Figure 2, SA4). Second, and lastly, we delay the first year
of deforestation reduction to 2016 rather than 2011 (Table 5 and Figure 2, SA5).
15
Table 5: Forest Loss Sensitivity Analyses
SA1: 75% cut
in 2020
SA2: Declining
baseline
SA3: 90% cut
in 2030
SA4: 2006 start
SA5: 2016 start
Low
High
Low
High
Low
High
Low
High
Low
High
Gt/yr avg
1.8
3.4
1.1
2.0
1.4
2.6
1.3
2.4
1.7
3.2
Gt/yr 2030*
2.9
5.4
2.2
4.1
2.6
4.9
2.9
5.4
2.9
5.4
36.7
67.5
22.0
40.5
27.9
51.3
33.0
60.8
25.7
47.3
4.8
11.1
2.9
6.6
3.7
8.4
3.5
8.0
4.5
10.3
7.7
17.7
5.8
13.3
6.9
15.9
7.7
17.7
7.7
17.7
96.5
221.2
57.9
132.7
73.3
168.1
86.8
199.0
67.5
154.8
Gt total
MHa/yr avg
MHa/yr
2030*
MHa total
4"
80"
3"
60"
2"
40"
1"
20"
0"
0"
Gt/yr"average"
Gigatons"CO2"(total)"
100"
Baseline%
5"
SA5:%2016%%
start%
120"
SA4:%2006%%
start%
6"
SA3:%90%%%
cut%in%2030%
140"
SA2:%Declining%
baseline%
7"
SA1:%75%%%
cut%in%2020%
Gigatons"CO2"(per"year)"
Figure 2: Forest Loss Sensitivity Analyses
Gt/yr"2030"
The full range of estimates for the sensitivity analyses are compared to the core
analysis in Table 6, along with estimates of the combined restoration and forest loss
goals that substitute the wider ranges from the sensitivity analyses:
Table 6: Summary of Core and Sensitivity Analysis
Forest Loss Goal: Core Analysis Forest Loss Goal: Sensitivity Analyses Restoration Goal Summary Estimates: Sensitivity Analyses Low High Low High Low High Low High Gt/yr avg 1.5 2.7 1.1 3.4 0.6 1.7 1.7 5.0 Gt/yr 2030 2.9 5.4 2.2 5.4 1.6 3.4 3.8 8.8 Gt total 29.3 54.0 22.0 67.5 11.8 33.5 33.8 101.0 MHa/yr avg 3.9 8.8 2.9 11.1 17.5 20.4 28.6 MHa/yr 2030 7.7 17.7 5.8 17.7 20 25.8 37.7 MHa total 77.2 176.9 57.9 221.2 350 407.9 571.2 16
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Acknowledgements
The author thanks the following individuals for their many contributions to this analysis:
Doug Boucher, Christopher Delgado, Craig Hanson, Nancy Harris, Richard Houghton,
and several additional expert reviewers, along with Maria Belenky, Andreas DahlJoergensen, and Claire Langley of Climate Advisers. All errors remain the author's.
17