ArticlePDF Available

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


Abstract and Figures

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.
This content is subject to copyright. Terms and conditions apply.
Environ. Res. Lett. 15 (2020) 064021
Environmental Research Letters
7 November 2019
19 March 2020
19 March 2020
27 May 2020
Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.
Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.
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
3Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
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
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
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
2.1. 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
of the supplementary material for an extended ver-
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
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),
Schulze et al 2019 and
Hansen et al 2019
Sensitivity analysis:
e and
Belward 2005
Bontemps et al 2016
Buchhorn et al 2019
Hansen et al 2019
(two maps with 10% and
30% canopy cover
Schaaf and Wang 2015
Sexton et al 2013
(two maps with 10% and
30% canopy cover
Shimada et al 2014,2019
High conserva-
tion value
HCV 1 Species
Biodiversity Hotspots Hoffman et al 2016
Key Biodiversity Areas BirdLife International 2018
Nationally Designated
Protected Areas
IUCN 2018
ecosystems and
mosaics and Intact
Forest Landscapes
Intact Forest Landscapes Potapov et al 2017,2008
and habitats
Areas of high forest
biodiversity significance
Hill et al 2019
Areas of high overlap of
nature’s contributions
and people’s needs
in terms of coastal risk
crop pollination,
erosion protection,
reduction of flood risk,
water quality and
water supply.
Chaplin-Kramer et al 2019,
Stehfest et al 2014 and
Presence of Indigenous
Garnett et al 2018
Heritage Sites
(part of Nationally
Designated Protected
High carbon
Above-ground biomass (t C ha1) 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
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
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 ha1. Although some potential HCSF may
still be released for development, all tropical forests
containing more than 75 t C ha1are 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
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 ha1
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
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.
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 ha114.89 (13.53–17.00) 80% (73%—91%)
75 t C ha112.75 (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
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 ha1is 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 ha1. 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
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
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
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 ha1if 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
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
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
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
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
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.
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
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
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.
Atmadja S and Verchot L 2012 A review of the state of research,
policies and strategies in addressing leakage from reducing
emissions from deforestation and forest degradation
(REDD+)Mitig. Adapt. Strateg. Glob. Chang. 17 311–36
Austin K G, Lee M E, Clark C, Forester B R, Urban D L, White L,
Kasibhatla P S and Poulsen J R 2017 An assessment of high
carbon stock and high conservation value approaches to
sustainable oil palm cultivation in Gabon Environ. Res. Lett.
12 014005
Avitabile V et al 2016 An integrated pan-tropical biomass map
using multiple reference datasets Glob. Change Biol. 22
Baccini A et al 2012 Estimated carbon dioxide emissions from
tropical deforestation improved by carbon-density maps
Nat. Clim. Change 2182–85
e E and Belward A S 2005 GLC2000: a new approach to
global land cover mapping from earth observation data Int.
J. Remote Sens. 26 1959–77
Bastos Lima M G, Persson U M and Meyfroidt P 2019 Leakage
and boosting effects in environmental governance: a
framework for analysis Environ. Res. Lett. 14 105006
BirdLife International 2018 Digital boundaries of important bird
and biodiversity areas from the world database of key
biodiversity areas February 2018 Version.
Bontemps S, Defourny P, Van Bogaert E, Arino O, Kalogirou V
and Perez J R 2016 ESA CCI land cover website
Brown E, Dudley N, Lindhe A, Muhtaman D R, Stewart C and
Synnott T 2013 Common guidance for the identification of
high conservation values
Brown S and Zarin D 2013 What does zero deforestation mean?
Science 342 805–7
Buchhorn M, Smets B, Bertels L, Lesiv M, Tsendbazar N-E,
Herold M and Fritz S 2019 Copernicus global land service:
land cover 100m: epoch 2015: globe. Dataset of the global
component of the Copernicus Land Monitoring Service
2019 (
Busch J and Ferretti-Gallon K 2017 What drives deforestation and
what stops it? A meta-analysis Rev. Environ. Econ. Policy 11
Carlson K M, Heilmayr R, Gibbs H K, Noojipady P, Burns D N,
Morton D C, Walker N F, Paoli G D and Kremen C 2018
Effect of oil palm sustainability certification on deforestation
and fire in Indonesia Proc. Natl Acad. Sci. 115 121–6
Chaplin-Kramer R et al 2019 Global modeling of nature’s
contributions to people Science 366 255–8
Chazdon R L, Brancalion P H S, Laestadius L, Bennett-Curry A,
Buckingham K, Kumar C, Moll-Rocek J, Vieira I C G and
Wilson S J 2016 When is a forest a forest? Forest concepts
and definitions in the era of forest and landscape restoration
Ambio 45 538–50
Cheyns E, Silva-Castañeda L and Aubert P-M 2019 Missing the
forest for the data? Conflicting valuations of the forest and
cultivable lands Land Use (in press)
CIESIN 2018 Gridded Population of the World, Version 4 (Gpwv4):
Population Density, Revision 10 (Palisades, NY: NASA
Socioeconomic Data and Applications Center)
Corley R H V 2009 How much palm oil do we need? Environ. Sci.
Policy 12 134–9
Curtis P G, Slay C M, Harris N L, Tyukavina A and Hansen M C
2018 Classifying drivers of global forest loss Science 361
Doelman J C et al 2018 Exploring SSP land-use dynamics using
the IMAGE model: regional and gridded scenarios of
land-use change and land-based climate change mitigation
Glob. Environ. Change 48 119–35
Donofrio S, Rothrock P, Associate S, Calderon C, Assistant R,
Hamrick K and Weatherer L 2019 Targeting zero
deforestation: company progress on commitments that
count, 2019 a collaborative analysis between forest trends
and Ceres based upon supply change data editors authors
contributors in partnership with (https://www.forest-trends.
FAO 2012 FRA 2015 Terms and Definitions (Rome)
FAO 2018 The State of the World’s Forests 2018 – Forest Pathways to
Sustainable Development (Rome: Food and Agriculture
Organization of the United Nations)
Fernandes L, Dias O, Dias D V and Magnusson W E 2015
Influence of environmental governance on deforestation in
municipalities of the Brazilian Amazon (
Garnett S T et al 2018 A spatial overview of the global importance
of Indigenous lands for conservation Nat. Sustain.
Garrett R D et al 2019 Criteria for effective zero-deforestation
commitments Glob. Environ. Change 54 135–47
Gumbricht T, Roman-Cuesta R M, Verchot L, Herold M,
Wittmann F, Householder E, Herold N and Murdiyarso D
2017 An expert system model for mapping tropical wetlands
and peatlands reveals South America as the largest
contributor Glob. Change Biol. 23 3581–99
Hansen M C et al 2013 High-resolution global maps of
21st-century forest cover change Science
Hansen M C et al 2019 Hansen Global Forest Change v1.6
(2000-2018) | Earth Engine Data Catalog | Google
Developers (
Henders S, Persson U M and Kastner T 2015 Trading forests:
land-use change and carbon emissions embodied in
production and exports of forest-risk commodities Environ.
Res. Lett. 10 125012
Hertel T W 2018 Economic perspectives on land use change and
leakage Environ. Res. Lett. 13 075012
High Carbon Stock Approach Steering Group 2019 Summary
progress and highlights – November 2016 to January 2019
Hill S L L, Arnell A, Maney C, Butchart S H M, Hilton-Taylor C,
Ciciarelli C, Davis C, Dinerstein E, Purvis A and Burgess N
D 2019 Measuring forest biodiversity status and changes
globally Frontiers For. Global Change 2
Hoffman M, Koenig K, Bunting G, Costanza J and Williams K J
2016 Biodiversity Hotspots (version 2016.1) [Data set]
International Food Policy Research Institute/Food and
Agriculture Organization 2012 Global Agro-ecological
Zones (GAEZ v3.0)
International Food Policy Research Institute 2019 Global
spatially-disaggregated crop production statistics data for
2010 Version 1.0 - IFPRI HarvestChoice Dataverse
Johnston C M T 2016 Global paper market forecasts to 2030
under future internet demand scenarios J. For. Econ.
25 14–28
Jopke P and Schoneveld G C 2018 Corporate commitments to
zero deforestation an evaluation of externality problems and
implementation gaps (
Environ. Res. Lett. 15 (2020) 064021 F Leijten et al
Lake S, Rosenbarger A and Winchester C 2016 Palm Risk
Assessment Methodology: Prioritizing Areas, Landscapes, and
Mills (
Lambin E F et al 2018 The role of supply-chain initiatives in
reducing deforestation Nat. Clim. Change 8109–116
Lambin E F and Meyfroidt P 2011 Global land use change,
economic globalization, and the looming land scarcity Proc.
Natl Acad. Sci. 108 3465–72
Lyons-White J and Knight A T 2018 Palm oil supply chain
complexity impedes implementation of corporate
no-deforestation commitments Glob. Environ. Change
50 303–13
Masuda T and Goldsmith P D 2009 World soybean production :
area harvested, yield, and long-term projections
12 143–62
Mermoz S, Bouvet A, Le Toan T and Herold M 2018 Impacts of
the forest definitions adopted by African countries on
carbon conservation Environ. Res. Lett. 13 104014
Meyfroidt P et al 2018 Middle-range theories of land system
change Glob. Environ. Change 53 52–67
Miranda M et al 2003 Mining and Critical Ecosystems: Mapping the
Risks (Washington, DC: World Resources Institute)
Newton P and Benzeev R 2018 The role of zero-deforestation
commitments in protecting and enhancing rural livelihoods
Curr. Opin. Environ. Sustain. 32 126–33
O’Neill B C et al 2014 A new scenario framework for climate
change research: the concept of shared socioeconomic
pathways Clim. Change 122 387–400
Pirker J, Mosnier A, Kraxner F, Havlík P and Obersteiner M 2016
What are the limits to oil palm expansion? Glob. Environ.
Change 40 73–81
Potapov P et al 2008 Mapping the world’s intact forest landscapes
by remote sensing Ecol. Soc. 13 51
Potapov P et al 2017 Intact forest landscapes 2016
Ramankutty N, Evan A T, Monfreda C and Foley J A 2010 Global
Agricultural Lands: Pastures, 2000 (Palisades, NY: NASA
Socioeconomic Data and Application Center (SEDAC))
Romijn E, Ainembabazi J H, Wijaya A, Herold M, Angelsen A,
Verchot L and Murdiyarso D 2013 Exploring different forest
definitions and their impact on developing REDD+
reference emission levels: a case study for Indonesia Environ.
Sci. Policy 33 246–59
Rosoman G, Sheun S S, Opal C, Anderson P and Trapshah R 2017
The HCS approach—putting no deforestation into practice
(Singapore) (
RSPO 2018 RSPO P&C for the production of sustainable palm oil
( Statutes.pdf)
Santoro M and Cartus O 2019 Dataset record: ESA biomass
climate change initiative (biomass_cci): global datasets of
forest above-ground biomass for the year 2017, v1 Cent.
Environ. Data Anal. (
c B, Jenny B, White D and Strebe D R 2015 Cartography and
geographic information science user preferences for world
map projections user preferences for world map projections
Schaaf C and Wang Z 2015 MCD43A4 V006 | LP DAAC : NASA
Land Data Products and Services (
Schepaschenko D et al 2015 Development of a global hybrid forest
mask through the synergy of remote sensing, crowdsourcing
and FAO statistics Remote Sens. Environ. 162 208–20
Schneider A, Friedl M A and Woodcock C E 2003 Mapping urban
areas by fusing multiple sources of coarse resolution
remotely sensed data IGARSS 2003. 2003 IEEE Int.
Geoscience and Remote Sensing Symp. Proc. (IEEE Cat. No.
03CH37477) (Toulouse, France, 21–25 July 2003)
(Piscataway, NJ: IEEE) pp 2623–5
Schulze K, Malek Ž and Verburg P H 2019 Towards better
mapping of forest management patterns: a global allocation
approach For. Ecol. Manage 432 776–85
Senior M J M, Brown E, Villalpando P and Hill J K 2015 Increasing
the scientific evidence base in the ‘high conservation value’
(HCV) approach for biodiversity conservation in managed
tropical landscapes Conserv. Lett. 8361–7
Sexton J O et al 2016 Conservation policy and the measurement
of forests Nat. Clim. Change 6192–96
Sexton J O et al 2013 Global, 30-m resolution continuous fields of
tree cover: landsat-based rescaling of MODIS vegetation
continuous fields with lidar-based estimates of error Int. J.
Digit. Earth 6427–48
Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa
R and Lucas R 2014 New global forest/non-forest maps from
ALOS PALSAR data (2007-2010) Remote Sens. Environ. 155
Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa
R and Lucas R 2019 New global forest/non-forest maps from
ALOS PALSAR data (2007–2010) (
Stehfest E et al 2014 Integrated assessment of global
environmental change with IMAGE 3.0 - Chapter 7.6 (The
Hague: Environmental Assessment Agency)
Steinweg T, Drennen Z, Advisers C and Rijk G 2017 Unsustainable
Palm Oil Faces Increasing Market Access Risks |
Unsustainable Palm Oil Faces Increasing Market Access
Risks: NDPE Sourcing Policies Cover 74 Percent of
Southeast Asia’s Refining Capacity (Updated Version) Figure
1: Indonesian and Malaysian refining capacity covered by
NDPE Sourcing Policies (www.chainreactionresearch
Taylor R and Streck C 2018 The elusive impact of the
deforestation-free supply chain movement ending tropical
deforestation: a stock-take of progress and challenges
Thornton P K 2010 Livestock production: recent trends, future
prospects Phil. Trans. R. Soc. B365 2853–67
Tropek R, Sedl´
cek O, Beck J, Keil P, Musilov´
a Z, Šímov´
a I and
Storch D 2014 Comment on “high-resolution global maps
of 21st-century forest cover change” Science 344 981
UNEP-WCMC and IUCN 2018 Protected Planet: The World
Database on Protected Areas (WDPA)/The Global Database
on Protected Areas Management Effectiveness (GD-PAME)]
(Accessed: 23 November 2018)
UNFCCC 2006 Definitional issues related to reducing emissions
from deforestation in developing countries (Accessed 26
February 2020) (
Van Velthuizen H et al 2007 Mapping biophysical factors that
influence agricultural production and rural vulnerability
Weber A K and Partzsch L 2018 Barking up the right tree? NGOs
and corporate power for deforestation-free supply chains
Sustain 10 3869
Weiss D J et al 2018 A global map of travel time to cities to assess
inequalities in accessibility in 2015 Nature 553 333–36
... Expansion of tree crops, most notably oil palm, have caused a loss in natural areas in recent years (Hoang and Kanemoto 2021;Fagan et al. 2022). As a result, researchers, international organizations and national policies responded in order to prevent further deterioration of especially the biodiversity rich tropical forests in the region, in particular in the insular parts of Southeast Asia (Carlson et al. 2018;Leijten et al. 2020). Our findings show that, especially for oil palm, the locations of the most suitable areas are not expected to change much. ...
Full-text available
Cultivation of tree crops such as coconut, oil palm and rubber are an important source of income in Southeast Asia, both for the national economies and for the local population. Climate change has the potential to drastically affect the suitability for growing these crops, but until now the impacts thereof on existing production areas have not been considered. This study combines climate change projections with data on crop cultivation to analyze how suitability for coconut, oil palm and rubber will change under different scenarios in Southeast Asia. We find that projected increases in total precipitation and longer dry periods in the insular part of Southeast Asia will result in 127,000 ha of current coconut and 1.17 Mha of current oil palm area will no longer be highly suitable under the most severe climate scenario. Conversely, increasing temperature in the mainland part of the region will cause 97,000 ha of current rubber cultivation area to become highly suitable. Increasing temperatures will also allow for potential expansion of rubber and coconut cultivation in the northern mainland part of the region, while the potential highly suitable area for oil palm cultivation will decrease. These changes in crop suitability may result in impacts on local farmers, including fall in yields and displacement of cultivation areas. This, in turn, may add pressure to biodiversity conservation in the region since areas that become highly suitable are disproportionally located within Key Biodiversity Areas.
... Many companies have, therefore, made voluntary 'zero-deforestation commitments' (ZDCs) for tropical commodity supply chains 4,5 . ZDC-compliant commodities cannot be cultivated on recently forested land, and ZDCs could effectively protect tropical rainforest from encroachment 6 if uptake of the commitments is widespread 7 . However, ZDCs could then displace agricultural expansion to other biomes, primarily tropical grassy biomes (grasslands, savannas and shrublands 8 ) and dry forests (closed-canopy forests with highly seasonal rainfall 9 ) 10,11 . ...
Full-text available
Many companies have made zero-deforestation commitments (ZDCs) to reduce carbon emissions and biodiversity losses linked to tropical commodities. However, ZDCs conserve areas primarily based on tree cover and aboveground carbon, potentially leading to the unintended consequence that agricultural expansion could be encouraged in biomes outside tropical rainforest, which also support important biodiversity. We examine locations suitable for zero-deforestation expansion of commercial oil palm, which is increasingly expanding outside the tropical rainforest biome, by generating empirical models of global suitability for rainfed and irrigated oil palm. We find that tropical grassy and dry forest biomes contain >50% of the total area of land climatically suitable for rainfed oil palm expansion in compliance with ZDCs (following the High Carbon Stock Approach; in locations outside urban areas and cropland), and that irrigation could double the area suitable for expansion in these biomes. Within these biomes, ZDCs fail to protect areas of high vertebrate richness from oil palm expansion. To prevent unintended consequences of ZDCs and minimize the environmental impacts of oil palm expansion, policies and governance for sustainable development and conservation must expand focus from rainforests to all tropical biomes.
... Large-scale commercial agriculture, primarily for cattle ranching, soybean and palm oil, is responsible for an estimated 40 percent of deforestation (FAO and UNEP, 2020). Therefore, tackling deforestation through ensuring deforestationfree commodity chains is a key strategy for combating deforestation (FAO and UNEP, 2020;Hoang and Kanemoto, 2021;Leijten et al., 2020). ...
Full-text available
Forests harbour a large proportion of the Earth’s terrestrial biodiversity, which continues to be lost at an alarming rate. Deforestation is the single most important driver of forest biodiversity loss with 10 million ha of forest converted every year to other land uses, primarily for agriculture. Up to 30 percent of tree species are now threatened with extinction. As a consequence of overexploitation, wildlife populations have also been depleted across vast areas of forest, threatening the survival of many species. Protected areas, which are considered the cornerstone of biodiversity conservation, cover 18 percent of the world’s forests while a much larger 30 percent are designated primarily for the production of timber and non-wood forest products. These and other forests managed for various productive benefits play a critical role in biodiversity conservation and also provide essential ecosystem services, such as securing water supplies, providing recreational space, underpinning human well-being, ameliorating local climate and mitigating climate change. Therefore, the sustainable management of all forests is crucial for biodiversity conservation, and nations have committed to biodiversity mainstreaming under the Convention on Biological Diversity (CBD). Mainstreaming biodiversity in forestry requires prioritizing forest policies, plans, programmes, projects and investments that have a positive impact on biodiversity at the ecosystem, species and genetic levels. In practical terms, this involves the integration of biodiversity concerns into everyday forest management practice, as well as in long-term forest management plans, at various scales. It is a search for optimal outcomes across social, economic and environmental dimensions of sustainable development. This study is a collaboration between FAO and the Center for International Forestry Research (CIFOR), lead centre of the CGIAR research programme on Forests, Trees and Agroforestry (FTA). Illustrated by eight country case-studies, the report reviews progress and outlines the technical and policy tools available for countries and stakeholders, as well as the steps needed, to effectively mainstream biodiversity in forestry.
... Ecosystems are at risk due to climate change, but developing practical restoration, protection, and management of ecosystems, is key to assist climate change mitigation and adaptation strategies (Malhi et al., 2020). For example, reforestation, afforestation, or prevented deforestation create a large carbon sink in its early decades and, in the longer term, store large amounts of carbon (Bonan and Doney, 2018;Leijten et al., 2020;Morecroft et al., 2019). Restored and natural environment can promote water retention and counter flooding as well as adjust rainfall. ...
Full-text available
Las plantas son organismos vivos sensibles a las condiciones de su entorno. A lo largo de la evolución, han desarrollado diferentes mecanismos de respuesta y adaptación a las situaciones ambientales adversas que las rodean (también llamadas estreses). Así consiguen sobrevivir y reproducirse. Pero el cambio climático supone un nuevo escenario. Situaciones como temperaturas extremas y periodos prolongados de sequías o inundaciones son algunos de los factores ambientales acrecentados por este fenómeno que limitan en gran medida su crecimiento, desarrollo, productividad y supervivencia.
... Ecosystems are at risk due to climate change, but developing practical restoration, protection, and management of ecosystems, is key to assist climate change mitigation and adaptation strategies (Malhi et al., 2020). For example, reforestation, afforestation, or prevented deforestation create a large carbon sink in its early decades and, in the longer term, store large amounts of carbon (Bonan and Doney, 2018;Leijten et al., 2020;Morecroft et al., 2019). Restored and natural environment can promote water retention and counter flooding as well as adjust rainfall. ...
Full-text available
Humans negatively influence Earth ecosystems and biodiversity causing global warming, climate change as well as man-made pollution. Recently, the number of different stress factors have increased, and when impacting simultaneously, the multiple stress conditions cause dramatic declines in plant and ecosystem health. Although much is known about how plants and ecosystems are affected by each individual stress, recent research efforts have diverted into how these biological systems respond to several of these stress conditions applied together. Studies of such “multifactorial stress combination” concept have reported a severe decrease in plant survival and microbiome biodiversity along the increasing number of factors in a consistent directional trend. In addition, these results are in concert with studies about how ecosystems and microbiota are affected by natural conditions imposed by climate change. Therefore, all this evidence should serve as an important warning in order to decrease pollutants, create strategies to deal with global warming, and increase the tolerance of plants to multiple stressful factors in combination. Here we review recent studies focused on the impact of abiotic stresses on plants, agrosystems and different ecosystems including forests and microecosystems. In addition, different strategies to mitigate the impact of climate change in ecosystems are discussed.
Since the early 2000s, many private companies, public-private coalitions, and governments have committed to remove deforestation from commodity supply chains. Despite these zero-deforestation commitments (ZDCs), high rates of deforestation persist and may even be increasing. On the upside, a few region- and commodity-specific ZDCs have contributed to reductions by up to hundreds of thousands of hectares of deforestation, with mixed evidence on associated leakage. ZDCs have also spurred progress in monitoring, traceability, and awareness of deforestation. On the downside, as currently implemented, supply chain initiatives only cover a small share of tropical deforestation. Government- and company-led ZDCs are just two components of broader policy mixes aimed at reducing deforestation. To be more impactful, ZDCs needs to cover entire biomes, supply bases of companies, and export and domestic markets, with special attention not to exclude marginal producers. Expected final online publication date for the Annual Review of Environment and Resources, Volume 48 is October 2023. Please see for revised estimates.
Full-text available
The global trade of agricultural commodities has profound social-ecological impacts, from potentially increasing food availability and agricultural efficiency, to displacing local communities, and to incentivizing environmental destruction. Supply chain stickiness, understood as the stability in trading relationships between supply chain actors, moderates the impacts of agricultural commodity production and the possibilities for supply-chain interventions. However, what factors determine stickiness, that is, how and why farmers, traders, food processors, and consumer countries, develop and maintain trading relationships with specific producing regions, remains unclear. Here, we use data on the Brazilian soy supply chain, a mixed methods approach based on extensive actor-based fieldwork, and an explanatory regression model, to identify and explore the factors that influence stickiness between places of production and supply chain actors. We find four groups of factors to be important: economic incentives, institutional enablers and constraints, social and power dimensions, and biophysical and technological conditions. Among the factors we explore, surplus capacity in soy processing infrastructure, (i.e., crushing and storage facilities) is important in increasing stickiness, as is export-oriented production. Conversely, volatility in market demand expressed by farm-gate soy prices and lower land-tenure security are key factors reducing stickiness. Importantly, we uncover heterogeneity and context-specificity in the factors determining stickiness, suggesting tailored supply-chain interventions are beneficial. Understanding supply chain stickiness does not, in itself, provide silver-bullet solutions to stopping deforestation, but it is a crucial prerequisite to understanding the relationships between supply chain actors and producing regions, identifying entry points for supply chain sustainability interventions, assessing the effectiveness of such interventions, forecasting the restructuring of trade flows, and considering sourcing patterns of supply chain actors in territorial planning.
More than 500 companies engaged in supply chains of forest-risk commodity have adopted zero-deforestation commitments (ZDCs). We use corporate social responsibility strategy and policy implementation theories to analyse the processes and conditions for ZDC implementation. We base our study on 35 semi-structured interviews with company representatives and sector actors, publicly available ZDC data, and company reports. The objective is to understand the opportunities and challenges of ZDC implementation at the level of companies. While past research addressed ZDC coverage and effectiveness, knowledge is still lacking on companies' perspectives on the implementation of their commitments. This study provides a unique perspective by integrating the direct experience and knowledge of private actors on an environmental governance regime. We find that companies see implementation of ZDCs as a journey and often rely on voluntary sustainability standards, aligning their strategies and key performance indicators (KPIs) to these. They engage directly in the supply chain, conducting projects “on-the-ground.” Implementing ZDCs requires the involvement of procurement departments and upper management, and collaborations within and between companies. Companies rely on service providers for in-depth knowledge and field implementation. They use monitoring tools, e.g. remote sensing, and see supply chain traceability as a prerequisite for implementation. Companies face numerous implementation challenges. Internally, companies often lack leadership on their ZDC, struggle to align commitments with the organization's operations and to manage suppliers, especially smallholders, and allocate insufficient resources. Externally, they lack common standards and stakeholder support, and face challenging regulatory conditions and missing market incentives. An uneven playing field creates leakage markets. Companies identify better leadership, technology and pre-competitive collaboration as potential solutions. Zero-deforestation commitments are unlikely to greatly contribute to reducing deforestation until better implementation processes, mechanisms, and conditions are in place.
New digital tools for monitoring forest‐ and land‐cover change have made it easier for civil society actors to call firms to account for deforestation. In response, companies in deforestation‐linked global value chains (GVCs) have turned to these technologies themselves. In contrast to many case analyses of technology in GVCs, which focus on how technology changes production processes, forcing governance to adapt, forest‐monitoring technologies change governance directly. Synthesising work on transaction characteristics and power relations in GVCs to address this novel situation, we argue that monitoring technologies’ effects on GVCs will likely depend on their accessibility. Proprietary technologies favour large‐scale operations and already established lead firms, while open technologies could support democratization. Treating forest‐ and value‐chain information as a public good could support more inclusive, equitable and sustainable value chains.
Full-text available
The world's forests are crucially important for both biodiversity conservation and climate mitigation. New forest status and forest change spatial layers using remotely sensed data have revolutionised forest monitoring globally, and provide fine-scale deforestation alerts that can be actioned in near-real time. However, existing products are restricted to representing tree cover and do not reflect the considerable spatial variation in the biological importance of forests. Here we link modelled biodiversity values to remotely sensed data on tree cover to develop global maps of forest biodiversity significance (based on the rarity-weighted richness of forest mammal, bird, amphibian and conifer species) and forest biodiversity intactness (based on the modelled relationship between anthropogenic pressures and community intactness). The strengths and weaknesses of these products for policy and local decision-making are reviewed and we map out future improvements and developments that are needed to enhance their usefulness.
Full-text available
In an increasingly interconnected world, leakage - broadly understood as unintended displacement of impacts caused by an environmental policy intervention - has become a major governance concern. Yet, leakage remains both loosely conceptualized and poorly understood as a phenomenon in policy making. To fill this gap and broaden the leakage research agenda, we conduct a state-of-the-art review of scientific assessments on leakage (particularly on land use) and combine it with conceptual and analytical frameworks from the environmental governance literature. We then propose a rigorous definition of leakage, discuss frequently overlooked political dimensions, and develop a typology of leakage pathways. Our analysis of leakage through a governance lens yields five core insights: (1) Leakage is not simply a mechanistic phenomenon, but a complex governance issue involving questions of institutional fit, interactions, and political agency. (2) Although the land use literature traditionally focuses on leakage through markets or activity displacement, a governance lens shows that it also occurs through information, motivation, or institutional channels. (3) As policy-makers may act strategically, the unintentionally of leakage should not be assumed but rather become an object of research. (4) A phenomenon not initially regarded as leakage can come to be framed as such through the action of 'problem brokers' and changes in policy fields. (5) Policy-makers and researchers should broaden their focus from only avoiding leakage to seeking positive spillovers and institutional synergies. These insights are illustrated with examples from two cases relating to land use policy in Brazil and Southeast Asia.
Full-text available
Zero-deforestation commitments are a type of voluntary sustainability initiative that companies adopt to signal their intention to reduce or eliminate deforestation associated with commodities that they produce, trade, and/or sell. Because each company defines its own zero-deforestation commitment goals and implementation mechanisms, commitment content varies widely. This creates challenges for the assessment of commitment implementation or effectiveness. Here, we develop criteria to assess the potential effectiveness of zero-deforestation commitments at reducing deforestation within a company supply chain, regionally, and globally. We apply these criteria to evaluate 52 zero-deforestation commitments made by companies identified by Forest 500 as having high deforestation risk. While our assessment indicates that existing commitments converge with several criteria for effectiveness, they fall short in a few key ways. First, they cover just a small share of the global market for deforestation-risk commodities, which means that their global impact is likely to be small. Second, biome-wide implementation is only achieved in the Brazilian Amazon. Outside this region, implementation occurs mainly through certification programs, which are not adopted by all producers and lack third-party near-real time deforestation monitoring. Additionally, around half of all commitments include zero-net deforestation targets and future implementation deadlines, both of which are design elements that may reduce effectiveness. Zero-net targets allow promises of future reforestation to compensate for current forest loss, while future implementation deadlines allow for preemptive clearing. To increase the likelihood that commitments will lead to reduced deforestation across all scales, more companies should adopt zero-gross deforestation targets with immediate implementation deadlines and clear sanction-based implementation mechanisms in biomes with high risk of forest to commodity conversion.
Full-text available
Supply chain sustainability has become a key issue for multinational corporations (MNCs). Hundreds of MNCs in agri-commodity sectors have recently committed to eliminate deforestation from their supply chains. In this article, we examine the power of non-governmental organizations (NGOs) participating in two initiatives that support the implementation of such commitments: the Accountability Framework initiative (AFi) and Transparency for Sustainable Economies (Trase). Drawing on document and literature research, participant observation as well as semi-structured interviews, we find that these NGOs exercise power with MNCs, in particular in terms of raising awareness and changing corporate self-perceptions. At the same time, though, there is a bias towards representing the positions and interests of materially strong actors in global supply chains. In doing so, NGOs risk reinforcing MNCs’ power over more marginalized actors. In this light, we argue that initiatives such as AFi and Trase can only be a first step towards a new economic system that respects ecological limits and delivers social justice. In order to shape transformative change, NGOs need to more actively push discussions about equitable distribution, emancipation and justice in natural resource governance.
Full-text available
Forests provide numerous ecosystem services, such as timber yields, biodiversity protection and climate change mitigation. The type of management has an effect on the provision of these services. Often the demands for these services can lead to conflict – wood harvest can negatively impact biodiversity and climate change mitigation capacity. Although forest management differences are important, spatially explicit data is lacking, in particular on a global scale. We present here a first systematic approach which integrates existing data to map forest management globally through downscaling national and subnational forest data. In our forest management classification, we distinguished between two levels of forest management, with three categories each. Level 1 comprised primary, naturally regrown and planted forests. Level 2 distinguished between different forest uses. We gathered documented locations, where these forest categories were observed, from the literature and a database on ecological diversity. We then performed multinomial logit regression and estimated the effect of 21 socio-economic and bio-physical predictor variables on the occurrence of a forest category. Model results on significance and effect direction of predictor variables were in line with findings of previous studies. Soil and environmental properties, forest conditions and accessibility are important determinants of the occurrence of forest management types. Based on the model results, likelihood maps were calculated and used to spatially allocate national extents of level 1 and level 2 forest categories. When compared to previous studies, our maps showed higher agreement than random samples. Deviations between observed and predicted plantation locations were mostly below 10 km. Our map provides an estimation of global forest management patterns, enhancing previous methodologies and making the best use of data available. Next to having multiple applications, for example within global conservation planning or climate change mitigation analyses, it visualizes the currently available data on forest management on a global level.
Full-text available
In this paper, we aim to assess the impacts of the forest definitions adopted by each African country involved in the global climate change programmes of the United Nations on national carbon emission estimations. To do so, we estimate the proportion of national carbon stocks and tree cover loss that are found in areas considered to be non-forest areas. These non-forest areas are defined with respect to a threshold on the percentage of tree cover adopted by each country. Using percent tree cover and aboveground biomass maps derived from remote sensing data, we quantitatively show that in many countries, a large proportion of carbon stocks are found in non-forest areas, where a large amount of tree cover loss can also occur. We further found that under the REDD+ framework (reduced deforestation, reduced degradation, enhancement and conservation of forest carbon stocks, sustainable management of forests), some partner countries have proposed activities related to only reducing deforestation, even when a large proportion of their carbon stocks are stored outside forests. This situation may represent a limitation of the efficiency of the REDD+ mechanism, and could be avoided if these countries choose a lower tree cover threshold for their definition of forests and/or if they were are engaged in other activities.
The future of nature's contributions A recent Global Assessment by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services has emphasized the urgent need to determine where and how nature's contribution matters most to people. Chaplin-Kramer et al. have developed a globalscale modeling of ecosystem services, focusing on water quality regulation, coastal protection, and crop pollination (see the Perspective by Balvanera). By 2050, up to 5 billion people may be at risk from diminishing ecosystem services, particularly in Africa and South Asia. Science , this issue p. 255 ; see also p. 184
In reaction to Greenpeace campaigns denouncing the impact of oil palm plantations in Southeast Asia, Golden Agri-Resources (GAR) – a major actor in the palm oil sector – adopted a zero-deforestation policy. The implementation of this policy raised a simple, albeit tricky, question: what is a forest? In response, Greenpeace, GAR and a consultancy firm developed a methodology for forest classification called the High Carbon Stock (HCS) Approach. Employing a vegetation classification based primarily on a threshold of carbon sequestration, the method identifies which forested zones to protect from conversion to agriculture. While currently gaining resonance in the realm of sustainability standards, its implementation in Indonesia and Liberia encountered resistance and criticism by rural dwellers and social NGOs. How did HCS advocates integrate local peoples’ concerns, interests and claims to compose commonality? By analysing the HCS methodology's content, implementation and progressive adaptation, this article shows how HCS advocates favoured a specific mode of composition: one that fits the liberal grammar and that has specific implications on the valuation of forest and cultivable lands. The HCS approach is thus more than a data collection tool; it encapsulates and reinforces a particular vision of the environment and how people should relate to it.
Conference Paper
Hundreds of companies with exposure to deforestation driven by palm oil, beef, soy, or wood production have committed to addressing deforestation. Many of these commitments have been made in the context of the Consumer Goods Forum Zero Net Deforestation, Commitment, the Tropical Forest Alliance 2020 (TFA 2020), and the New York Declaration on Forests (NYDF), and stipulate 2020 as a target year for eliminating deforestation from supply chains of agricultural commodities. As the 2020 deadline approaches, it is timely to review the status of forest-related supply chain commitments and identify implementation barriers and systemic challenges to the effectiveness of company action. The paper summarizes progress made, identifies challenges and evidence gaps, and recommends additional actions for reducing commodity-driven deforestation.