Which forests could be protected by corporate zero deforestation commitments? A spatial assessment

Abstract

The production of palm oil, soy, beef and timber are key drivers of global forest loss. For this reason, over 470 companies involved in the production, processing or distribution of these commodities have issued commitments to eliminate or reduce deforestation from their supply chains. However, the effectiveness of these commitments is uncertain since there is considerable variation in ambition and scope and there are no globally agreed definitions of what constitutes a forest. Many commitments identify high conservation value forests (HCVFs), high carbon stock forests (HCSFs) and forests on tropical peatland as priority areas for conservation. This allows for mapping of the global extent of forest areas classified as such, to achieve an assessment of the area that may be at reduced risk of development if companies comply with their zero deforestation commitments. Depending on the criteria used, the results indicate that between 34% and 74% of global forests qualify as either HCVF, HCSF or forests on tropical peatland. However, we found that the total extent of these forest areas varies widely depending on the choice of forest map. Within forests which were not designated as HCVF, HCSF or forests on tropical peatland, there is substantial overlap with areas that are highly suitable for agricultural development. Since these areas are unlikely to be protected by zero-deforestation commitments, they may be subject to increased pressure resulting from leakage of areas designated as HCVF, HCSF and tropical peatland forests. Considerable uncertainties around future outcomes remain, since only a proportion of the global market is currently covered by corporate commitments. Further work is needed to map the synergies between corporate commitments and government policies on land use. In addition, standardized criteria for delineating forests covered by the commitments are recommended.

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Environ. Res. Lett. 15 (2020) 064021 https://doi.org/10.1088/1748-9326/ab8158
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LETTER
Which forests could be protected by corporate zero deforestation
commitments? A spatial assessment
Floris Leijten1,2, Sarah Sim1, Henry King1and Peter H Verburg2,3
1Safety and Environmental Assurance Centre, Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire, United Kingdom
2Environmental Geography Group, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam 1081HV, The
Netherlands
3Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
E-mail: floris.leijten@unilever.com
Keywords: zero deforestation commitments, high conservation value forests, high carbon stock forests, tropical peatlands, commodities,
corporate commitment
Supplementary material for this article is available online
Abstract
The production of palm oil, soy, beef and timber are key drivers of global forest loss. For this
reason, over 470 companies involved in the production, processing or distribution of these
commodities have issued commitments to eliminate or reduce deforestation from their supply
chains. However, the effectiveness of these commitments is uncertain since there is considerable
variation in ambition and scope and there are no globally agreed definitions of what constitutes a
forest. Many commitments identify high conservation value forests (HCVFs), high carbon stock
forests (HCSFs) and forests on tropical peatland as priority areas for conservation. This allows for
mapping of the global extent of forest areas classified as such, to achieve an assessment of the area
that may be at reduced risk of development if companies comply with their zero deforestation
commitments. Depending on the criteria used, the results indicate that between 34% and 74% of
global forests qualify as either HCVF, HCSF or forests on tropical peatland. However, we found
that the total extent of these forest areas varies widely depending on the choice of forest map.
Within forests which were not designated as HCVF, HCSF or forests on tropical peatland, there is
substantial overlap with areas that are highly suitable for agricultural development. Since these
areas are unlikely to be protected by zero-deforestation commitments, they may be subject to
increased pressure resulting from leakage of areas designated as HCVF, HCSF and tropical peatland
forests. Considerable uncertainties around future outcomes remain, since only a proportion of the
global market is currently covered by corporate commitments. Further work is needed to map the
synergies between corporate commitments and government policies on land use. In addition,
standardized criteria for delineating forests covered by the commitments are recommended.
1. Introduction
Commodity-driven deforestation is a major driver of
global forest loss accounting for approximately 27%
of global forest loss (Curtis et al 2018). Recogniz-
ing this, many multinationals sourcing deforestation-
risk commodities have adopted goals to eliminate
or reduce deforestation from their supply chains
(Lambin et al 2018). These zero-deforestation com-
mitments (ZDCs) typically focus on the four agri-
cultural commodities most strongly associated with
tropical deforestation: beef, palm oil, soy, paper and
pulp (Henders et al 2015, Newton and Benzeev 2018).
In recent years, the number of companies adopting
ZDCs has grown rapidly to at least 484, representing
an unknown market share (Donofrio et al 2019).
However, the effectiveness of ZDCs is uncertain
since there is considerable variation in ambition and
scope (Jopke and Schoneveld 2018, Taylor and Streck
2018). In addition, there are no globally agreed defini-
tions of what constitutes a forest; variations arise from
consideration of tree density, tree height, ecological
properties etc (Chazdon et al 2016). The choice of
forest definition influences estimates of forest areas
globally and therefore deforestation estimates. As an
example, Romijn et al (2013) demonstrated the total
© 2020 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
area estimated to have been deforested between 2000
and 2009 in Indonesia increased by 27% when using
Indonesia’s national forest definition instead of the
Food Agricultural Organization (FAO) definition.
Many companies identify high conservation value
forests (HCVFs) (Brown et al 2013) and high car-
bon stock forests (HCSFs) (Rosoman et al 2017) as
priority areas for conservation within their ZDCs.
HCVFs are defined as forests of outstanding biolo-
gical, ecological, social or cultural significance and
divided into six categories: four focus on biod-
iversity, habitat and ecosystem conservation, and a
further two on community needs and cultural val-
ues (Brown et al 2013). HCSFs are defined by a
practical, field-tested methodology—the high car-
bon stock approach (HCSA)—that prioritizes forests
for conservation based on their above-ground bio-
mass (AGB) carbon, while respecting communities
rights’ to their lands and typically integrating the
findings of an HCV assessment (Rosoman et al 2017).
In addition, many companies have also committed to
the protection of forest on tropical peatlands (New-
ton and Benzeev 2018). The adoption of these so-
called ‘No Deforestation, No Peat and No Exploita-
tion’ (NDPE) commitments has been limited to the
oil palm sector in Southeast Asia where 74% of the
palm oil refining capacity is now covered by such
commitments (Steinweg et al 2017). Recently, the
Roundtable on Sustainable Palm Oil (RSPO) integ-
rated the HCSA into its Principles and Criteria (RSPO
2018). Discussions are ongoing as to whether the HCS
approach should also be included by other stand-
ard bodies, including the Roundtable on Responsible
Soy (RTRS), the Forest Stewardship Council (FSC)
and the United Nations Reducing Emissions from
Deforestation and forest Degradation (REDD+) Pro-
gramme (Cheyns et al 2019).
Although commitments to protect HCVF and
HCSFs have been recognized as potentially effect-
ive approaches for implementing a ZDC (Garrett
et al 2019), the spatial extent of HCVFs and HCSFs
is unknown (Pirker et al 2016, Carlson et al 2018).
Both approaches were developed for local, case-by-
case application requiring on-the-ground field visits
and stakeholder consultation. As a result, mapping
has been conducted mainly at the local scale, leav-
ing unclear what the global coverage of the ZDCs is.
In addition, concerns around deforestation extend to
the development of new production areas on forests
and other biomes which fall outside of the HCVF and
HCSF classifications—a phenomenon often referred
to as activity leakage (Meyfroidt et al 2018, Bastos
Lima et al 2019). Therefore, the primary objective of
this paper is to make an estimate of the global land
area that could be classified as HCVF, HCSF or forest
on tropical peatland, and hence at reduced risk of
development if companies comply with their ZDCs. A
secondary objective is to identify the remaining forest
areas that are at risk of conversion due to agricultural
development or forestry.
2. Methodology
A stepwise approach was adopted to estimate the
global forested land area that can be classified as
HCVF, HCSF or forest on tropical peatlands. First,
a forest reference map for the current situation was
created (section 2.1.1). Then, HCVFs (section 2.1.2),
HCSFs (section 2.1.3) and tropical peatland forests
(section 2.1.3) were identified separately by matching
a variety of data sources to the official definitions and
descriptions listed in the HCV guidelines and HCSA
toolkit.
The forested areas that were not classified as
HCVF, HCSF or tropical peatland forest were inter-
sected with several maps displaying agricultural suit-
ability for the four main deforestation-risk commod-
ities, market accessibility, future land use change
projections and areas where commodity-driven
deforestation and forestry are considered the main
driver of forest loss (sections 2.2.1–2.2.3).
2.1. Estimating the global extent of HCVF, HCSF
and forests on tropical peatland in 2017
2.1.1. Mapping forest areas.
To create a forest reference map for the current situ-
ation, the binary forest map from (Schulze et al 2019)
was used. This 1 km2resolution map is based on a
hybrid forest map created by (Schepaschenko et al
2015) and represents the year 2000, calibrated with
the most recent FAO statistics. We modified the forest
extent to represent the year 2017 using 1 km2raster
data on tree cover gain (2000–2012) and tree cover
loss (2000–2017) from (Hansen et al 2013,2019),
accessed through Google Earth Engine. Recognising
that tree cover loss data from Hansen et al do not dis-
tinguish between temporary loss and permanent con-
version (Curtis et al 2018) and that tree cover gain
data include plantation forests and herbaceous crops
(Tropek et al 2014), we tested the sensitivity of the
mapped forest area using both the original and our
updated Schulze map. In addition, we tested the sens-
itivity of mapped forest areas arising from the choice
of forest map by using nine alternative global forest
maps (table 1). Finally, all spatial data were conver-
ted to an equal-area Eckert IV projection as advocated
in (Šavriˇ
cet al 2015).
2.1.2. Mapping HCV forests.
We used the HCV guidelines (Brown et al 2013) to
identify and map HCVFs using 12 distinct indicat-
ors that together cover the full range of HCV cat-
egories, as shown in table 1(see also table 1 avail-
able online at stacks.iop.org/ERL/15/064021/mmedia
of the supplementary material for an extended ver-
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Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Table 1. List of indicators and data sources to identify HCSF, HCVF and forest on tropical peatlands.
Indicator Thresholds Data sources
Forest extent
2017
Hybrid forest map for the year 2000 that
integrates eight different forest products,
validated with crowdsourced data, consist-
ent with FAO statistics, updated to the year
2017 based on remotely sensed data of tree
cover loss and gain between 2000–2017. As
a sensitivity analysis, nine different forest
maps are considered. Percentage tree-cover
maps from (Hansen et al 2019) and (Sexton
et al 2013) were converted to binary forest
maps (forest/no forests) by applying a 10%
and 30% threshold, following the official
forest definitions of the (FAO 2012) and
the United Nations Framework Conven-
tion on Climate Change (UNFCCC 2006),
respectively.
Schulze et al 2019 and
Hansen et al 2019
Sensitivity analysis:
•Bartholom´
e and
Belward 2005
•Bontemps et al 2016
•Buchhorn et al 2019
•Hansen et al 2019
(two maps with 10% and
30% canopy cover
threshold)
•Schaaf and Wang 2015
•Sexton et al 2013
(two maps with 10% and
30% canopy cover
threshold)
•Shimada et al 2014,2019
High conserva-
tion value
HCV 1 Species
diversity
Biodiversity Hotspots Hoffman et al 2016
Key Biodiversity Areas BirdLife International 2018
Nationally Designated
Protected Areas
UNEP-WCMC and
IUCN 2018
HCV 2
Landscape-level
ecosystems and
mosaics and Intact
Forest Landscapes
Intact Forest Landscapes Potapov et al 2017,2008
HCV 3
Ecosystems
and habitats
Areas of high forest
biodiversity significance
Hill et al 2019
HCV 4
Ecosystem
services
Areas of high overlap of
nature’s contributions
and people’s needs
in terms of coastal risk
reduction,
crop pollination,
erosion protection,
reduction of flood risk,
water quality and
water supply.
Chaplin-Kramer et al 2019,
Stehfest et al 2014 and
CIESIN 2018
HCV 5
Community
needs
Presence of Indigenous
Community
Garnett et al 2018
HCV 6
Cultural
values
UNESCO World
Heritage Sites
(part of Nationally
Designated Protected
Areas)
UNEP-WCMC and IUCN
2018
High carbon
stock
Above-ground biomass (t C ha−1) within
tropical forest areas.
75 (Low Density Forest) Santoro and Cartus 2019
Sensitivity analysis:
•Avitabile et al 2016
•Baccini et al 2012
35 (Young Regenerating
Forest)
Pan-tropical
peatlands
Presence of peatland in the tropics. Pan-tropical soils having at
least 30 cm of decomposed
or semi decomposed organic
material with at least 50% of
organic matter.
Gumbricht et al 2017
sion of this list, including the official definition of
each HCV category and the rationale for selecting
each indicator). To harmonize the different datasets,
all indicators were converted to a 1 km2resolution.
We used three different thresholds to classify HCVFs,
defined as forest areas containing at least one, two, or
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Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
three HCV categories. The different levels of cover-
age were used to represent the uncertainty in the final
HCVF classification and illustrate the sensitivity of
the mapped spatial extent to the indicator selection.
We assumed that areas with multiple overlapping cat-
egories are more likely to qualify as HCVF.
2.1.3. Mapping HCS forests.
According to the HCS Toolkit Version 2.0 (Roso-
man et al 2017), potential HCSF can be identified
based on an above-ground biomass (AGB) threshold
of 35 t C ha−1. Although some potential HCSF may
still be released for development, all tropical forests
containing more than 75 t C ha−1are generally desig-
nated as HCSF (Rosoman et al 2017). We used both
thresholds to indicate the range of uncertainty in
the classification of HCSF and its mapped spatial
extent. Above ground biomass data from (Santoro
and Cartus 2019), representing the year 2017, were
resampled from a resolution of 1 ha to a resolution
of 1 km2using the majority resampling approach.
For sensitivity analyses, two alternative AGB carbon
maps were considered (see table 1). Since the HCS
approach is not applicable for forests outside the trop-
ics (Rosoman et al 2017), these were not classified
as HCS.
2.1.4. Mapping forests on tropical peatland.
Tropical peatland forests were mapped using data on
the pan-tropical extent of peatlands in 2011 from
(Gumbricht et al 2017).
2.2. Evaluating forests at risk of agricultural
development
We evaluated the deforestation risk of forest areas
not designated as HCVF, HCSF or tropical peat-
land. Forests designated as HCV or HCS were for
this analysis defined as forests with at least two over-
lapping HCVF categories or at least 75 t C ha−1
if located in the tropics (section 2.1). The risk of
potential future conversion of forest was assessed
using three alternative approaches to account for the
uncertainty in future development: (1) by identify-
ing and overlaying suitable and accessible expansion
areas for the 4 main deforestation-risk commodit-
ies (section 2.2.1), (2) by using integrated assessment
model predictions (section 2.2.2), and (3) by mask-
ing areas where commodity-driven deforestation and
forestry are considered the main drivers of forest loss
(section 2.2.3).
2.2.1. Overlap with suitable and accessible expansion
areas for the 4 deforestation-risk commodities.
Data on agro-ecological suitability for oil palm, soy-
bean and pasture were sourced from the International
Institute for Applied Systems Analysis/Food and Agri-
culture Organization (2012) and Van Velthuizen et al
(2007), and resampled to a resolution of 1 km2using
the majority resampling approach (a list of all data
sources can be found in table 2 of the supplementary
material). These suitability maps include eight differ-
ent suitability classes for current agricultural or pas-
toral production areas as well as those which could be
developed for future production. To identify suitable
areas for forestry, a similar suitability map for poten-
tial production forests was made by classifying a con-
tinuous suitability map from (Schulze et al 2019) into
eight suitability classes. For each commodity, poten-
tial areas for expansion were identified by excluding
areas already under production. For oil palm, soy-
bean and pasture, only the estimated fraction of any
given grid cell currently under cultivation was known
(Ramankutty et al 2010, International Food Policy
Research Institute 2019). We therefore excluded grid
cells where cropland or pastureland already extend
over more than 50% of the area (a sensitivity ana-
lysis towards this assumption is provided in the sup-
plementary material). Grid cells comprising urban
land were also excluded, using data from (Schneider
et al 2003). Forest areas outside the HCV, HCS
and tropical peatland areas overlapping with suit-
able expansion areas were assumed to be at risk
of conversion.
As inaccessible lands may face lower risk of devel-
opment (Busch and Ferretti-Gallon 2017), we refined
the analysis by mapping the joint distribution of mar-
ket accessibility and agricultural suitability for forests
falling outside the HCV, HCS and tropical peat-
land areas. Data from (Weiss et al 2018) on travel
time to the closest port or the closest city with at
least 50 000 inhabitants—resampled to a resolution
of 1 km2using bilinear interpolation—were used as
a proxy for market accessibility. To obtain an over-
all measure of agricultural suitability for the 4 com-
modities, a raster layer was created indicating, for
each grid cell, the highest overall suitability class of
the 4 suitability layers (a separate map for each of
the 4 commodities is presented in the supplementary
material).
2.2.2. Overlap with land use projections.
An alternative estimate of the conversion risk placed
on areas falling beyond the HCVF, HCSF and trop-
ical peatland forests classifications was derived using
spatially explicit land use projections of cropland
and pastureland expansion at 5 arc minutes resolu-
tion from the Integrated Model to Assess the Global
Environment (IMAGE) 3.0 model (Doelman et al
2018). These projections were made for the period
2020–2030 and based on the second Shared Socioeco-
nomic Pathways (SSP2) scenario (a ‘middle-of-the-
road’ scenario for future climate mitigation action)
(O’Neill et al 2014). Forest areas that were not clas-
sified as HCVF, HCSF or tropical peatland forest and
were found to overlap areas of projected cropland or
pastureland expansion were considered to be at addi-
tional risk of development.
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Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Table 2. Estimated total extent of high conservation value forests, high carbon stock forests and tropical peatland forests. High carbon
stock estimates are based on (Santoro and Cartus 2019), while the parentheses behind the high carbon Stock estimates indicate the lower
and upper range using two alternative above-ground biomass maps (i.e. Avitabile et al (2016) and Baccini et al (2012).
Geographic scope Type of forest Million km2% total forest area
Global High conservation value
≥1 category 25.67 65%
≥2 categories 10.61 27%
≥3 categories 2.56 7%
Tropical High conservation value
≥1 category 13.59 73%
≥2 categories 7.09 38%
≥3 categories 2.17 12%
High carbon stock
≥35 t C ha−114.89 (13.53–17.00) 80% (73%—91%)
≥75 t C ha−112.75 (12.30–15.26) 68% (66%—82%)
Peatland forests 0.62 3%
2.2.3. Overlap with areas where commodity-driven
deforestation and forestry are dominant drivers of
forest loss.
Finally, we assessed the overlap between forests not
classified as HCS, HCV or tropical peatland and areas
where forestry and commodity-driven deforestation
are classed as the main drivers of forest loss. Data
on the drivers of forest loss at 10 km2resolution
were sourced from Curtis et al (2018). This provides
an indication of the forest areas that are at addi-
tional risk of development, assuming these forests will
indeed be subject to forestry or commodity-driven
deforestation.
3. Results
3.1. The estimated extent of HCVF, HCSF and
forests on tropical peatland in 2017
Based on the updated Schulze et al map for the year
2017, the global forest area amounts to 39.4 million
km2(this compares with an area of 40.3 million km2
if the Schulze et al map is not updated). Figure 1
shows the variation in the spatial extent of HCVF
and HCSF, depending on the stringency of the cri-
teria. The total extent of HCVF and HCSF combined
comprises between 34% and 74% of global forests, of
which between 28% and 34% has already been des-
ignated as protected area (UNEP-WCMC 2018). The
global extent of HCVF alone encompasses between
7% and 65% of global forests (table 2), with indigen-
ous lands accounting for the largest part of potential
HCVF (i.e. 43% of all potential HCVF, see figure 1 of
the supplementary material).
Since HCSFs are by definition limited to the trop-
ical zone, the total extent is much smaller than the
extent of HCVF and varies in the range of 31%–
43% of global forests, which equates to 66%–91%
of all tropical forests, depending on the choice of
AGB map and whether an AGB threshold of 35 or
75 t C ha−1is applied. Within the tropical zone,
there is an overlap between HCVF and HCSF, with
the percentage of total tropical forest for which the
two classifications converge—measured by the Jac-
card Similarity Index (Intersection over Union)—
varying between 14% and 67%, depending on both
the AGB threshold and minimum number of overlap-
ping HCV categories. Tropical peatland forests com-
prise 3% of all tropical forests, of which between 82%
and 98% overlap with HCVFs and HCSFs.
At a regional or country level, the large sensitiv-
ity of the extent of HCVF and HCSF to the choice of
criteria can lead to dramatic differences (see figures 2
and 3 of the supplementary material). For example,
the total extent of HCVF, HCSF and tropical peat-
land forest in Sub-Saharan Africa varies in the range
of 51%–84% of all forests.
To test how sensitive the results are to the choice of
forest map, figure 2shows the variation in the extent
of HCVF, HCSF and tropical peatland forest when
different forest maps are considered. Forests desig-
nated as HCVF or HCSF are here defined as forests
containing at least two overlapping HCVF categories
or exceeding the AGB threshold of 75 t C ha−1. To
account for the uncertainty in the extent of HCSF and
HCVF, the error bars in figure 2denote the upper and
lower range of the extent of HCSF, HCVF and tropical
peatland forest using the full range of criteria shown
in figure 1(see table 3 of the supplementary material
for a detailed comparison of the 10 forest maps based
on the Jaccard Similarity index). The total extent of
forests designated as HCVF, HCSF or tropical peat-
land depends to a large extent on the choice of forest
map and varies in the range of 11–40 million km2,
notably because large areas considered forest by some
maps are classified as closed shrublands or woody
savannahs by others.
3.2. Forest at risk of agricultural development
Figure 3shows the extent to which potential suitable
expansion areas for the 4 deforestation-risk commod-
ities overlap with forest areas and forest areas des-
ignated as HCVF, HCSF or tropical peatland, based
on different land suitability thresholds. Depending
on the suitability classes included, 39%–92% of the
5

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Figure 1. Spatial overview of forests potentially at reduced risk of development due to the corporate zero-deforestation
commitments, based on a range of criteria to delineate high conservation value forest (HCVF) and high carbon stock forest
(HCSF).
areas suitable for forestry expansion and 57%–80%
of the areas suitable for the expansion of oil palm
plantations overlap with forests designated as HCVF,
HCSF or tropical peatland. The total forest area out-
side HCVFs, HCSFs and tropical peatland forests that
is suitable for forestry ranges between 0.57 to 17.81
million km2, while the area suitable for oil palm plant-
ations ranges between 0.30 and 6.82 million km2.
Potential suitable expansion areas for pasturelands
and soybean fields are much more abundant resulting
in a lower percentage overlap with forests (36%–52%
and 6%–36%, respectively). Still, given their over-
all larger extent, the total forest area not covered by
HCVFs, HCSFs and peatland forests is as high as
19.73 million km2for pastureland and 10.61 million
km2for soybean fields.
To assess where pressures from forestry and agri-
cultural expansion are especially high, figure 4dis-
plays the joint distribution of market accessibility—
classified into eight octiles—and agricultural suitab-
ility, based on the highest land suitability class for
the four commodities (see figure 5 of the supple-
mentary material for four separate maps per com-
modity). Forests not designated as HCVF, HCSF or
tropical peatland forest with high market accessib-
ility and agricultural suitability tend to be clustered
in the Eastern United States, Central Europe, East
China, the Gran Chaco in Latin America and near
6

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Figure 2. Total extent of forests and forests designated as high conservation value forest (HCVF), high carbon stock forest (HCSF)
or located on tropical peatland, based on 10 different forest maps. Forests designated as HCV or HCS are here defined as forests
with at least two overlapping HCVF categories or at least 75 t C ha−1if located in the tropics. Error bars denote the upper and
lower range of the total extent of HCVF, HCSF and tropical peatland forest using the other criteria to delineate HCVF and HCSF
shown in figure 1. CCT denotes canopy cover threshold.
Figure 3. Overlap of agro-ecological suitability for four main deforestation-risk commodities with forests designated as high
conservation value forest (HCVF), high carbon stock forest (HCSF) and tropical peatland forest. The error bars denote the
uncertainty in the total extent of HCVF, HCSF and tropical peatland forests. Suitable areas outside forests do not include urban
areas or areas already under cultivation or used for production.
the Swahili Coast in Sub-Saharan Africa (see fig-
ures 6(a)–(c) of the supplementary material for three
zoom maps of Latin America, Sub-Saharan Africa
and Southeast Asia; the three main global deforest-
ation regions (FAO 2018)). Around 36% of these
forest areas with the highest market accessibility and
agricultural suitability are estimated to be already
used as production forests, based on data from
(Schulze et al 2019).
These results merely indicate risk based on agro-
ecological suitability and accessibility and do not
account for projected changes in land use linked to
anticipated growth in demand, population density
and market accessibility. Projections from the IMAGE
7

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Figure 4. Joint distribution of market accessibility and agricultural suitability across forests not designated as high conservation
value forest (HCVF), high carbon stock forest (HCSF) or tropical peatland forest. Market accessibility is based on travel time to
the nearest port or city with at least 50 000 inhabitants and classified into eight octiles. Agricultural suitability is determined by
taking the highest suitability class for each grid cell after overlaying four suitability layers for forestry, oil palm cultivation,
soybean cultivation and pastureland—each comprising eight suitability classes. A separate map for each commodity is presented
in figure 5 of the supplementary material.
3.0 model for crop and pasture expansion between
2020 and 2030, reflect drivers of land use change.
These projections indicate that 38% of the total forest
area not designated as HCVF, HCSF or tropical
peatland forest, may be subject to land use change
for agricultural production. Assuming these predic-
tions provide a reasonable indication of the location
of future production for the four deforestation-risk
commodities, the total forest area at risk of becoming
converted to oil palm plantations, soybean fields or
pastureland becomes much smaller—especially in the
temperate zone—and decreases on average by 59%
(see figure 7 of the supplementary material). It is
important to note though that these estimates do not
account for potential leakage effects from protecting
HCVFs, HCSFs and tropical peatland forests.
Alternatively, focussing only on areas where
commodity-driven deforestation or forestry is con-
sidered the main driver of forest loss, the total forest
area at risk amounts to 56% of the total forest area not
classified as HCVF, HCSF or tropical peatland forest,
of which 17% overlaps with areas where commodity-
driven deforestation is considered the main driver of
forest loss.
4. Discussion and conclusions
This study provides a first approximation of the global
forest area that may be covered by corporate zero
deforestation commitments (ZDCs), defined on the
basis of three commonly used criteria: protection of
high conservation value forests (HCVF), high carbon
stock forests (HCSF) and tropical peatland forests.
The results show that between 34% and 74% of
all forests may classify as HCVF, HCSF or peatland
forest. This large range indicates the level of uncer-
tainty in the extent of forest that could be at reduced
risk of development if these commitments were fully
adopted. However, given that market coverage of the
corporate commitments is less than 100%, the protec-
ted area will be much smaller in reality and deforest-
ation can move to supply chains falling beyond the
scope of the corporate commitments (Garrett et al
2019). Moreover, even in case of full uptake, legally
protected forest accounts for only 28%–34% of the
forest area that we identified as meeting our ZDC
criteria.
In comparison with individual site-based local
assessments conducted by companies, our global
assessment of the spatial coverage of HCVF and HCSF
is likely to identify some different areas for conserva-
tion. First and foremost, this is because the methods
used to identify HCVFs and HCSFs were developed
for local assessments requiring extensive field work
and free, prior and informed consent from local com-
munities, meaning they are not easily applied at lar-
ger scales (i.e. the 1 km2resolution we use) (Lake
et al 2016, Pirker et al 2016). In addition, many of
the criteria documented in the HCV guidelines con-
tain ambiguous and subjective terms that depend
on individual assessors’ discretion. This has led to
an inconsistency between various local HCV assess-
ments (Senior et al 2015), meaning that there is no
consensus on what potential HCV indicators are most
appropriate. There are also no spatial data sets avail-
able on areas already designated as HCVF and HCSF
(Carlson et al 2018, HCSA Steering Group 2019)
to enable validation. Finally, all indicators used to
8

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
approximate the spatial extent of HCVF and HCSF
had to be resampled to a resolution of 1 km2, which
inevitably leads to some loss of spatial detail (Zhu et al
2017). To reduce uncertainty in the spatial extent of
forest protected under the corporate commitments,
standardized criteria for delineating forests and defin-
ing areas of HCVF and HCSF at the global scale and
across tropical and temperate forests are recommen-
ded. In addition, advances in remote sensing and
biodiversity mapping should be exploited to produce
more accurate and up-to-date indicators of HCVFs
and HCSFs.
Despite these uncertainties, our results are relat-
ively consistent with previous attempts to map HCV
and HCS areas at larger scales. For example, Mir-
anda et al (2003) estimated that 37% of all areas des-
ignated as forest according to our reference map are
potentially HCVF, which compares with our estim-
ate of 27% (for two overlapping HCVF categories),
and Austin et al (2017) estimated the total extent of
HCSF in Gabon to be between 80% and 87% of
Gabon’s land, which compares with our estimate of
83% (regardless of the choice of carbon threshold).
Our analysis has also shown that the extent of
HCVF, HCSF and tropical peatland forests is contin-
gent on the choice of forest map, resulting in a range
from 11 to 40 million km2according to the criteria
specified in figure 2. This finding adds to a growing
body of literature showing that the definition of forest
significantly impacts estimates of forest cover and
forest cover change (Chazdon et al 2016, Sexton et al
2016, Mermoz et al 2018). The lack of a well-agreed
forest definition led nine environmental and social
NGOs to launch the Accountability Framework initi-
ative in 2016; a framework that has been developed to
provide companies with detailed guidance to imple-
ment their commitments and standardize definitions
of forest, deforestation, and related terms (Weber and
Partzsch 2018). Greater consensus on forest classific-
ation is needed to reduce the uncertainty in the area
covered by the corporate commitments and facilitate
more effective monitoring (Lyons-White and Knight
2018).
Even if the ZDCs are fully implemented across
all commodity markets, some of the environmental
and social benefits associated with the protection of
HCVF, HCSF and tropical peatland forest may be
undermined if agricultural expansion is displaced to
forests that are not covered, hence resulting in activ-
ity leakage (Meyfroidt et al 2018, Bastos Lima et al
2019). Using data on land suitability for the four main
deforestation-risk commodities, we have shown here
that many forest areas not designated as HCVF, HCSF
or tropical peatland forest are highly suitable for
the production of these commodities, indicating an
increased potential risk of development due to ZDCs.
Hence, with a growing world demand for all four
commodities (Corley 2009, Masuda and Goldsmith
2009, Thornton 2010, Johnston 2016), pressures on
potential expansion areas will likely increase and
could possibly come at the expense of forests or
other biomes, including savannahs and grasslands,
not designated as HCVF, HCSF or tropical peatland
forest, in particular when market accessibility is high
(Atmadja and Verchot 2012). In addition, pressures
may be higher at local scales due to imperfect sub-
stitution between commodities originating from dif-
ferent sources (Hertel 2018). However, the total area
at risk of development may be much smaller when
accounting for future land use change scenarios or
historic trends in commodity-driven deforestation or
forestry.
It should be noted, though, that there are many
factors that we have not been able to consider in our
assessment of the areas at risk of future agricultural
development. For example, there are a range of inter-
acting socio-economic factors—including mobility
of capital and labour, easy access to credit and dif-
ferences in price and terms (Atmadja and Verchot
2012)—policy and governance factors (Fernandes
et al 2015) as well as crop-to-crop cascade effects
(Lambin and Meyfroidt 2011), which are all likely
to affect future land use outcomes. Further work to
map synergies between corporate commitments and
government policies influencing land use outcomes
is recommended. This will help to refine estimates of
the potential effectivenes of national and supply chain
governance levers for halting deforestation and for
the identification of complementary strategies which
may accelerate efforts towards zero deforestation.
Acknowledgments
We thank Katharina Schulze for sharing data on cur-
rent production forests and suitability estimates for
forestry, Stephen T. Garnett for sharing data on the
presence of indigenous communities, Nynke Schulp
for sharing data on the supply of ecosystem services,
and Samantha Hill and Andy Arnell for sharing data
on forest biodiversity significance. In addition, we
would like to thank four anonymous reviewers for
their suggestions and comments. This paper con-
tributes to the objectives of the Global Land Project
(http://glp.earth/).
Funding
This work was funded by the Marie Skłodowska-
Curie actions (MSCA) grant agreement No. 765408
from the European Commission: COUPLED ‘Oper-
ationalising Telecouplings for Solving Sustainability
Challenges for Land Use’.
Data availability
Data on current production forests and forestry suit-
ability estimates can be obtained from Katharina
9

Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Schulze. Data on indigenous communities and forest
biodiversity significance are available from Stephen
T Garnett and Samantha Hill. The other data that
support the findings of this study are available upon
request from the corresponding author.
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