Question:
You are given the full text of a scientific research paper.

    Summarize the study’s **policy implications**—how the research findings can inform policy or decision-making. Your response should:
    - Interpret what the results suggest for governments, regulators, or institutions
    - Highlight potential actions, strategies, or decisions informed by the study
    - Use policy-appropriate language without speculation beyond the study’s conclusions
    - Be clear, practical, and informative

    Write a short paragraph for the “Policy Implications” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41558-020-00956-w
1Nelson Institute for Environmental Studies, Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison,
WI, USA. 2Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI, USA. 3Department of Applied Economics,
Oregon State University, Corvallis, OR, USA. 4Forests Team, World Resources Institute, Washington, DC, USA. 5Department of Geographical Sciences,
University of Maryland, College Park, MD, USA. ✉e-mail: fanny.moffette@wisc.edu
C
ost-effective reduction of deforestation should be a corner-
stone in the suite of climate change mitigation strategies.
Land-use change accounts for 6–17% of global carbon emis-
sions1. At any reasonable carbon price, avoided deforestation pro-
vides 7.2–9.6 times the abatement potential of reforestation2. There
are a range of strategies for accomplishing this—protected areas3,
payments for ecosystem services4 and supply-chain initiatives5,
among others. However, all of these strategies require monitoring
of deforestation activities. Given that deforestation rates are often
highest in low- and middle-income countries with limited resources
to create effective monitoring systems6, the availability of low-cost
monitoring technologies may be an important support to policy
aimed at avoiding deforestation.
This Analysis estimates the impact of the Global Land Analysis
and Discovery (GLAD) alerts of tree cover loss7 on deforestation
trends across 22 tropical countries between 2011 and 2018. The sys-
tem that we examine is the first to offer loss alerts at high spatial
resolution (∼30 m) and frequency (up to every 8 days, depending on
cloud cover). The GLAD alerts use all available Earth observation
data from Landsat 7 and Landsat 8 satellites, which combined have
a revisit rate of 8 days. Each day, all new images are first masked for
clouds and shadow, and then a loss detection algorithm is applied to
all unobscured land, evaluating the reflectance of the current obser-
vation together with baseline Landsat metrics. Each 0.00025° pixel
(28 m at the Equator) flagged as loss becomes an alert. The alerts
map all forms of tree cover loss including logging, clear-cuts and
intensive fires in both natural forests and plantations. These alerts
and the date of first detection are provided without charge through
a simple interface called Global Forest Watch (GFW).
Available in Chinese, English, French, Indonesian, Portuguese
and Spanish, GFW also enables users to select a specific area for
which they can receive alerts via e-mail. The e-mails state the num-
ber of pixels with suspected deforestation within the subscribed
area, list the ten most recent alerts with geographic coordinates
and include a hyperlink to view the locations on a map, as well as
a link to download a file containing all alerts with coordinates and
detection dates (Supplementary Fig. B1). Access to and use of the
alerts require relatively low technical capacity. They can be down-
loaded on a cellphone during a temporary Internet connection and
a cellphone GPS without Internet can be used to navigate to the
site of probable deforestation. Before GLAD alerts, most ongo-
ing prevention of deforestation relied on voluntary reports and/
or ranger patrols. Because rangers and volunteers are not omni-
present, this undermined policymakers’ ability to locate emerging
deforestation hotspots.
Assuming that free deforestation alerts reduce the cost to poli-
cymakers of monitoring forests, thereby reducing the cost of imple-
menting deforestation policy, we examine two questions: whether
making these alerts available affected deforestation rates, and
whether areas that were actively monitored by subscribers using the
system saw a change in deforestation trends. Recent work8 in Brazil
has shown the substantial contributions of the government’s own
real-time monitoring system to the reduction in national deforesta-
tion trends. However, it is not known whether providing deforesta-
tion monitoring information freely via a platform unaffiliated with
any governmental institution and accessible from anywhere on the
globe could have an impact on land-use trends. Here we show that
under certain conditions, it can.
We use a random sample of 1 km2 grid cells from all countries
that began receiving alerts before 2018, and estimate the impact
of GLAD alerts on deforestation using two different strategies. We
first examine whether having access to GLAD alerts for a particu-
lar region decreases deforestation using the gradual rollout of the
alert system that began in 2016 (see timeline in Fig. 1a). Second,
we study whether using GLAD alerts via subscription results in
a decrease in deforestation rates. In the case of both access and
use, we compare trends in areas covered earlier with those cov-
ered later, holding constant factors known to affect deforestation,
such as location and biophysical characteristics. Figure 1b shows
the countries analysed for this study and the distribution of for-
est within them. Our main outcome is the annual probability of
forest loss9 extracted at the grid-cell level for 2011 through 2018
The impact of near-real-time deforestation alerts
across the tropics
Fanny Moffette   1,2 ✉, Jennifer Alix-Garcia3, Katherine Shea4 and Amy H. Pickens5
Reducing deforestation to mitigate climate change necessitates monitoring of deforestation activity. However, while freely
available deforestation alerts on forest loss are available, the effect of these alerts and the presence of subscribers in a particu-
lar area is unclear. Here, we show that subscriptions to alerts in 22 tropical countries decrease the probability of deforestation
in Africa by 18% relative to the average 2011–2016 levels. There is no effect on other continents, and the availability of alerts
does not significantly change deforestation outcomes. This decrease in Africa is higher in protected areas and concessions,
suggesting that alerts either increased capacity to enforce existing deforestation policy or induced the development of more
effective anti-deforestation policies. Calculated using the social cost of carbon for avoided deforestation in Africa, we estimate
the alert system’s value to be between US$149 million and US$696 million.
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172

Analysis
NATURe ClimATe CHAnge
(this outcome does not distinguish between natural and anthro-
pogenic sources of forest loss). This measure is equal to one if
there is a positive amount of deforestation during a given year,
zero if there is none.
To evaluate the impact of alerts, we examine the data in two ways.
First, we use the country as the unit of analysis and apply country
fixed effects and year-by-continent fixed effects. Second, we use the
grid cell as the unit of analysis and parallel the country model. We
apply grid-cell and year-by-continent fixed effects, annual measures
of average temperature, cumulative precipitation and the same flex-
ible time trends, with covariates measured at the grid-cell level. Our
approach eliminates the effect of time-invariant characteristics that
influence deforestation (for example soil suitability, slope and so
on), general macroeconomic events affecting all countries or spe-
cific regions, impacts of weather and confounding variation result-
ing from changes in transport or production technologies over time.
More details can be found in the Methods.
Alert availability does not affect deforestation rates
To estimate the impact of alert availability, we examine only coun-
tries that had access to alerts by September 2018. In this case, the
counterfactual is countries that received alerts later. If it were the
case that forests with higher deforestation rates received alerts
sooner than forests with lower rates, and if those rates continued
to be the same over time, then even in the absence of an effect of
alerts, the estimated impact (approximately the deforestation rate
of early recipients minus that of late recipients) would show that
the availability of alerts had increased deforestation. In this case,
the comparison we make would understate the impact of alert
availability. The reverse is true if forests at low risk of deforestation
receive alerts first.
Tests of differences in deforestation rates between late- and
early-enrolled countries before 2016 suggest that the former provide
a plausible counterfactual for the latter, as there are no significant
differences in deforestation in this pre-alert period (Supplementary
Tables A3–A5). If we assume that time trends continue in a similar
way across comparison groups in the absence of the alerts, then our
results are not likely to be biased.
There is no robust evidence of any impact of alert availability
on average or in any particular region (Fig. 2 and Supplementary
Table A2). Results using the alternative outcomes of per cent forest
loss and winsorized per cent forest loss are qualitatively similar to
the binary outcome estimates (Supplementary Tables A6 and A7).
As it is plausible that countries may take time to adapt to the avail-
ability of alerts, we also examine a specification with a 1 yr lag of
alert availability (Supplementary Tables A8–A10). Although results
from this specification are not statistically different from zero, the
sign of the average effect switches to become negative.
Lower deforestation in African forests with subscriptions
To assess the impact of using GLAD alerts for monitoring, we
first identified users who have the ability to act on alert informa-
tion. These users, through their subscriptions to GFW, can select
either an existing jurisdiction, upload a shapefile or identify the
boundaries of a region about which they wish to receive alerts.
Leveraging answers to voluntary questions, we divide subscrip-
tions into two categories: those with intent to control deforestation
and those without. Those without intent include students, academ-
ics or staff from the World Resources Institute (WRI) or affiliates,
whereas those with intent to control include all other subscrip-
tions to GLAD attached to areas smaller than 100 Mha. The spatial
distribution and total number of subscriptions with and without
February 2014
GFW (2.0) was
launched but
initially provided
only annual data
March 2016
GLAD alerts ﬁrst launched
with Peru, Republic of the
Congo and Kalimantan
(Indonesia)
February 2017
Brunei, Malaysia, Indonesia, Papua New
Guinea, Timor-Leste, Burundi, Cameroon,
Central African Republic, Equatorial
Guinea, Gabon, Democratic Republic of
the Congo, Rwanda and Uganda
August 2016
Brazil
November 2017
Colombia,
Ecuador, French
Guyana, Guyana,
Suriname and
Venezuela
a
b
Fig. 1 | Timeline of GLAD rollout and study region. a, The rollout of GLAD alerts on GFW. b, The forest cover within our study region in 2010 (green
shading). Study countries are outlined in orange; we exclude the Amazon biome of Brazil in our estimations.
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Analysis
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intent are presented in Fig. 3. We provide summary statistics and
discussion of these different classifications in the Methods and
Supplementary Section B1.
We restrict the sample to the subgroup of forested grid cells from
areas that have GLAD subscriptions with intent to control by 2018
(the results of those without intent are in Supplementary Tables
B8–B11). In contrast to the previous estimation, the treatment vari-
able in this analysis varies within countries. Because the sample
is composed only of areas that ever had a GLAD subscription, we
eliminate one source of bias: the situation where forests at higher
risk of deforestation are more likely to be monitored than forests at
lower risk of deforestation. However, the approach does not elimi-
nate the possibility that forests at higher risk of deforestation may
be monitored sooner than forests at lower risk of deforestation. If
this is the case, using the same logic described above, our estimates
provide a lower bound of the effect of subscription to the GLAD
alerts on deforestation.
Summary statistics suggest that areas with early subscriptions
tend to have had slightly higher deforestation before alert availabil-
ity (Table 1 and Supplementary Section B2). Pre-alert trends across
the different subscription groups (Supplementary Tables B3–B5)
were not statistically significantly different using the binary defor-
estation and winsorized outcomes, but show significant differences
for the per cent deforestation outcome. This supports the validity of
comparisons for the first two outcomes but not for per cent defores-
tation. Additional robustness checks detailed in the Supplementary
Information confirm large, negative and statistically significant
effects on forest in Africa.
The average effect of subscriptions on deforestation is negative
(the opposite sign of the impact of alert availability), but statistically
insignificant and small compared with the average yearly 2011–2016
deforestation probability (0.18). Results using winsorized per cent
deforestation and per cent deforestation as outcomes tell the same
story (Supplementary Tables B6 and B7). The effect of subscrip-
tions on deforestation is probably a conservative estimate because
some areas are likely to receive more monitoring than others, which
should increase the standard errors and decrease the statistical sig-
nificance of the estimated coefficients.
Across regions, we estimate significant avoided deforestation
in the African subsample, but no robust effects in Asia and South
America. The coefficient for Africa implies an 18% decrease in
the probability of deforestation relative to the 2011–2016 lev-
els (0.04/0.22) (Fig. 4). As the average deforestation in these cells
is 1.86%, the estimated avoided deforestation per year is equal to
495.27 km2. If we apply a carbon density of 6,000 MtCO2 km−2
(the low end of the valuation for forests in this region10), and if
we value the amount of avoided carbon emissions resulting from
alert subscriptions at the social cost of carbon (US$50 t−1)11, we
calculate the alert system’s value at US$149 million. With a car-
bon density of 28,100 MtCO2 km−2 (the estimated value of the
dense forests in our sample1), we estimate a value of US$696 mil-
lion. Both numbers exceed the costs of developing and maintaining
the system. Furthermore, this number represents a lower bound,
as impacts of using GLAD are larger with a 1 yr lagged specifica-
tion (Supplementary Tables B14–B16), which suggests that when
subscribers have more experience with the alerts, their impact on
deforestation is greater. Although some cells had subscriptions
for multiple years, these total benefits are only calculated for the
last year (when all subscriptions were in place) to avoid overstating
the impacts.
Finally, by way of identifying a mechanism through which
deforestation might be controlled, we examine whether the effect
of subscriptions varies with local policy efforts. For this analysis,
we include interactions between subscriptions and indicators for
whether or not a grid cell is more than 50% contained by a conces-
sion or protected area. Information on concessions is not available
for all countries, so these estimates are for the subsample of coun-
tries that have both concession and protected areas data (Methods).
Marginal effects are presented in Fig. 5 and are extracted from
Supplementary Table B2. The individual interactions are negative
and statistically significant—subscriptions have a stronger deterrent
effect in both protected areas and in forest concessions.
−0.05
0
0.05
0.10
Marginal effect of GLAD availability
All continents
Africa
Asia
South America
Geographic coverage
Fig. 2 | Impact of GLAD availability on deforestation. The point estimates (shaded bars) and 95% confidence intervals are shown for the impact of GLAD
availability on the probability of deforestation on average for all continents are shown as well as for Africa, Asia and South America. Each bar shows the
impact from a different estimation. The first is from column 6 in panel A of Supplementary Table A2 and the other three are from column 6 in Panel B of
the same table, showing the total effect (that is, the linear combination of the baseline and interaction effects).
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Analysis
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These mechanisms are particularly relevant for the African sub-
sample—more of the subscribed forests in Africa are in protected
areas and concessions (57% versus less than 33% in the other two
regions). Separate estimates for these interactions by continent
show robust results only for African countries (Supplementary
Tables B19–B21). There is also a consistently statistically significant
Subscriptions with intent to control deforestation
Subscriptions without intent to control deforestation
0
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19 20
Fig. 3 | Spatial distribution and number of subscriptions. Subscriptions with (top) and without (bottom) intent to control deforestation are shown.
Darker colours indicate greater numbers of subscriptions.
Table 1 | Summary statistics
Early
Late
Never
Normalized difference
Mean
s.d.
Mean
s.d.
Mean
s.d.
(early versus late)
Outcomes
 Deforestation over 2011 to 2016 (0/1)
0.23
0.42
0.18
0.38
0.23
0.42
0.09
 Per cent deforestation over 2011 to 2016
0.78
4.52
0.42
2.79
0.55
2.77
0.07
 Winsorized per cent deforestation over 2011
to 2016
0.30
0.81
0.21
0.67
0.30
0.81
0.08
Controls
 Average precipitation per day (mm)
0.22
0.20
0.20
0.08
0.18
0.08
0.09
 Average temperature
25.85
2.71
26.35
3.05
26.01
2.57
−0.12
 Protected areas (%)
12.27
32.45
21.88
41.04
13.49
33.80
−0.18
 Distance to nearest port within country (km)
289.64
357.25
371.06
280.97
616.58
598.17
−0.18
 Distance to nearest road (km)
7.34
11.31
20.89
25.49
10.35
15.80
−0.49
 Distance to nearest urban centre with
>100,000 inhabitants (km)
141.52
97.05
234.90
170.47
139.54
95.04
−0.48
Number of grid cells
205,457
194,203
262,844
399,660
Early and late subscription groups are based on whether the grid cell had at least one subscription before or by April of 2017 (that is, when 50% of the sample was covered by a subscription; Supplementary
Fig. B2). Normalized differences are a scale-free measure of the difference in distributions between samples, which has advantage that it is directly interpretable in terms of how much average standard
deviation is the mean from one sample to the mean of the other sample17.
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Analysis
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negative impact of concessions with subscriptions in the Asian sub-
sample, although not in protected areas (Supplementary Fig. B3).
This effect is not visible in the average impact in Asia because con-
cessions cover a small part of the estimation sample (14%). As our
strategy does not account for the endogenous placement of protected
areas and concessions, we present these results as intriguing correla-
tions. However, these correlations confirm the anecdotal evidence
on alerts: that they are used for enforcing protected area policy and
for controlling illegal deforestation within forest concessions.
Benefits and limitations of use
Our Analysis presents strong evidence that freely available
forest-change detection data can support decreased deforestation.
The use of GLAD alerts through online subscriptions with intent
−0.10
−0.05
0
0.05
Marginal effect of GLAD subscriptions
All continents
Africa
Asia
South America
Geographic coverage
Fig. 4 | Impact of GLAD subscriptions on deforestation. The point estimates (shaded bars) and 95% confidence intervals are shown for the impact of
GLAD subscriptions with intent to control deforestation on the probability of deforestation on average for all continents as well as for Africa, Asia and
South America. Each bar shows the impact from a different estimation: the first is from column 6 in panel A of Supplementary Table B1 and the other
three are from column 6 in panel B of the same table, showing the total effect (that is, the linear combination of the baseline and interaction effects).
−0.10
−0.05
0
0.05
Marginal effect of subscriptions
In PA
In concession
Outside PA/concession
Location
Fig. 5 | Estimates of GLAD subscriptions in concessions and protected areas on deforestation. The point estimates (shaded bars) and 95% confidence
intervals are shown for the impact of GLAD subscriptions with intent to control deforestation on the probability of deforestation. Each bar shows a
marginal effect: the first is from subscriptions in areas without protected areas (PA) or concessions, the second is in protected areas and the third in
concessions. The estimates come from column 6 of Supplementary Table B2.
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Analysis
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to control deforestation led to an 18% decrease in the probability
of forest loss in the African countries included in our sample. The
estimated carbon benefits of the avoided deforestation from GLAD
subscriptions—from US$149 million US$696 million—is a low-end
estimate. It is likely that real benefits are much higher, given that
impacts seem to be increasing over time, and that there are a num-
ber of substantial co-benefits alongside avoided carbon emissions,
including biodiversity and watershed protection, that are not valued
in the calculation.
Our empirical approach supports a causal interpretation. Using
the timing of availability or subscription to the alert system helps
alleviate an important source of bias, as all observed units are even-
tually affected by the intervention. We control for confounding land
characteristics, temporal shocks and regional trends. Although we
cannot definitively lay out the causal chain that drives this result,
there are a number of reasons why the effects of the alert system
were large and statistically significant for only the African coun-
tries in our sample. First, countries in Africa had minimal or no
broad-scale deforestation monitoring technology before GLAD.
Because of this, the introduction of freely available alerts con-
stituted a new source of information to support policy interven-
tions. Indeed, there is evidence that Cameroon’s government relies
on GLAD for deforestation monitoring12, and that efforts to stem
deforestation in Africa have recently been boosted through regional
agreements (https://afr100.org/content/home).
In contrast to Africa, in Asia there is some evidence that the
private sector has already made large investments in monitoring
of deforestation and fires13,14. In this situation the free alert system
may have provided additional information for only some areas of
forest, as alerts were already available in part of the region. As our
identification of effects relies on a break in the deforestation trend
when alerts became available or subscriptions activated, there
would be no detectable effect where monitoring was already taking
place via existing systems. We do observe a correlation between
subscriptions in concessions and decreases in deforestation in
Asia, but these forests do not cover a large enough area to drive a
region-wide impact.
In Latin America, the effects may be muddied both by existing
monitoring systems and by political unrest. Regarding existing sys-
tems, in 2017, the Peruvian government stopped using GLAD and
created their own early warning system15. This means that GLAD
subscriptions within Peru are not necessarily representative of the
areas where the government is taking more action. Colombia and
Venezuela, which constitute a substantial portion of the South
American sample, were facing political challenges when subscrip-
tions became available in November 2017. Finally, South American
countries in our sample, except Peru, have also had access to GLAD
for a shorter time period than Africa and Asia. It is likely to take
time for the implementation of deforestation policy to effectively
use the alert system, as evidenced by our analysis of lag effects.
The alerts that we analyse here are the highest resolution, most
accessible and highest frequency system ever created. They are not,
however, the first. During the early 2000s, the first near-real-time
deforestation monitoring system used coarse-resolution data
(500 × 500 m)16. These systems were accessible to only a few coun-
tries and required specific technical skills to manipulate the data.
Until recently, data-driven forest monitoring depended, at best, on
yearly forest cover maps6. Yearly wall-to-wall maps were relatively
scarce until the release of the first worldwide annual deforestation
maps9, which greatly decreased the burden of data access. Still,
annual maps are insufficient for stopping deforestation in action,
and those available require GIS skills to use effectively. Although
GFW has hosted a few lower-resolution and less-accessible alert
systems, including FORMA (Forest Monitoring For Action) and
Terra-I, the GLAD system represents a substantial improvement in
scale, coverage and accessibility (Supplement C).
The alert system has an important limitation: a loss event can only
be detected if there are no clouds above it when satellites pass over.
If an area of forest is cleared and there are clouds overhead when the
satellite images the area, it will not be detected. As images can only
be obtained every 8 days, a month or more may pass before an image
without clouds blocking the clearing can be obtained, particularly
during rainy seasons or in regions with persistent cloud cover. This
means that there can be a time lag between the clearing event and the
actual alert publication, so interventions to stop further deforestation
may also be delayed. This affects their utility, but it does not affect
our impact estimates as we do not directly include the alerts in our
estimation. The fact that we detect an impact suggests that even this
imperfect measure provides additional important information to con-
trol deforestation. Furthermore, we do not think that variation in alert
lags across continents drives the variation in our results—forests in
Africa, Asia and South America have similar cloud-free image avail-
ability (an average of 76% months per year) during our study period.
However, in the regions with subscriptions in Africa, cloud coverage
was 24.7% lower than in South America and 15.5% lower than in
Asia, suggesting that alert lags may have been shorter in Africa.
This Analysis contributes to the ongoing discussion about how
to address climate change through forest conservation policies in
developing countries and via zero-deforestation supply-chain ini-
tiatives. It is particularly relevant because a substantial number of
developing countries do not have the resources to produce their
own deforestation alert system. The globally consistent platform
analysed here can also facilitate worldwide investments in climate
change interventions.
Important unanswered questions remain about how the benefits
of GLAD alerts will evolve in the long term, the most effective ways
to encourage use and the technical limits of such large-scale moni-
toring. Nonetheless, we conclude that the GLAD system of freely
available near-real-time forest monitoring provides an immensely
valuable scaffold for the implementation of national forest policies,
and subsequently to the world that benefits from the global public
goods provided by standing forests.
Online content
Any methods, additional references, Nature Research report-
ing summaries, source data, extended data, supplementary infor-
mation, acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41558-020-00956-w.
Received: 16 March 2020; Accepted: 26 October 2020;
Published online: 4 January 2021
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Analysis
NATURe ClimATe CHAnge
Methods
Sample. We use a stratified random sample of 1 km2 grid cells drawn from across
countries with proportional allocation within states. To ensure a representative
sample, countries are divided into two groups: those with more than 75,000
forested grid cells and those with fewer than 75,000 forested grid cells. For the first
group, we randomly selected 10% of all grid cells. For the second group, we first
determined the minimum random sample percentage according to Lohr18 and then
proportionally sample this percentage of grid cells across states within countries.
The sample was limited to grid cells that had a minimum of 50% forest cover in 2010
and at least 25 ha of forest with canopy cover ≥60%. Brazilian forests in the Amazon
biome are excluded from the analyses due to the existing monitoring system there.
We study all countries that had access to the alerts before 2018 (Fig. 1a).
The countries with fewer than 75,000 forested grid cells are: Burundi (70),
Rwanda (638), Timor-Leste (3,862), Brunei (4,830), Uganda (8,813), Equatorial
Guinea (25,352) and French Guiana (74,855). Using the same formula to sample
the grid cells in the countries with more than 75,000 forested grid cells would lead
to sampling less than 10%. To ensure representation within countries, sampling
for these locations is stratified at the second administrative level. Supplementary
Table A11 presents the population of forested grid cells, sample sizes and
corresponding sampling percentages. Sampling weights used in empirical
estimations correspond to the inverse of the probability that the observation is
included due to sampling design.
To control for the lack of precision from GLAD alerts in wetland areas due
to changes in water levels, we exclude wetland areas. Specifically, we used the
landform map for Indonesia from the University of Maryland19, the published
map of wetlands for the Congo Basin20 and the Pantanal biome from Brazil. For
the countries without a regional map, we used the international CIFOR dataset21.
The final sample includes 662,504 grid cells.
Subscriptions. Subscriptions are classified as with and without intent to control
deforestation. We exclude subscriptions for areas greater than 100 Mha (this area
corresponds to a little more than the size of a large state (such as Mato Grosso,
Brazil) or a small country (Portugal for example) as they are considered too large to
be used for monitoring and are most probably associated with users exploring the
platform. To create the with-intent group, we exclude all subscriptions associated
with the academic sector as well as those subscriptions created by staff from WRI
or affiliates (together these constitute the without-intent subscriptions). The end
result is a total of 558 subscriptions with intent and 734 without. The number of
grid cells ever covered by a subscription with intent is 399,660, whereas those that
have ever been covered by a subscription without intent total 298,574.
Of the 558 subscriptions with intent, 302 declared their primary job
responsibility using a drop down menu within the alerts system. Supplementary
Table B22 details these responses. The majority of these were GIS specialists, with a
relatively large number of programme managers, technical staff, land-use planning
specialist, reporters, and forest/park managers. Of those that did not answer this
question but did declare their location, the largest number was from Indonesia.
The subscriptions layer has time and spatial variation. Each polygon of the
layer corresponds to a subscribed area with a date of initiation of the subscription.
Although it is the case that some subscribers terminate their subscriptions, to
circumvent the potential endogeneity problems with using this variation, we
assume that once a subscription has been made, the user remained interested in
the subscribed area until the end of our study period. Grid cells are marked as
with subscription from the date of the first subscription.
Annual deforestation and forest cover. Our main dependent variable is annual
deforestation9 extracted at the grid-cell level for 2011 through 2018. We use this
annual measure of deforestation (rather than the sum of alerts for that year)
because the alerts are reliant on single observations (making them more susceptible
to error) and are designed to be conservative, whereas the annual product maps
loss are based on the entire year and can capture the area of loss more accurately9.
We prefer a binary measure of deforestation that is equal to one if there is a positive
amount of deforestation during a given year. The reason for this is that the two
continuous outcomes (per cent deforested and a winsorized version of per cent
deforested) have substantial outliers, even in the case of the winsorized measure.
For example, the per cent deforestation outcome ranges from zero to 100. However,
the measure at the 75th percentile is zero, and at the 95th, 3.57. For the winsorized
version of this variable, the 75th percentile value is still zero, and the 95th is 3.29.
This type of skewness, particularly when combined with measurement error, can
result in considerable bias in regressions.
Other covariates. We also extract a number of covariates to use as control
variables. As weather can influence both the choice to deforest and the
measurement of forest cover, data on temperature are extracted from NASA Land
surface temperature22 and precipitation from Climate Hazards Group InfraRed
Precipitation with Station data (CHIRPS; ref. 23). To control for transportation costs
(and because deforestation frontiers are known to be more intensely concentrated
near urban centres), we calculate three measures of distance. First, the distance
to the nearest urban centre with more than 100,000 inhabitants, which can be
downloaded from the Joint Research Centre’s Global Human Settlement Layer24.
Second, the distance to the nearest port according to the World Port Index from
the National Geospatial-Intelligence Agency25. Third, the distance to the nearest
road was calculated on the basis of the original road data of OpenStreetMap
(available from Geofabrik26). Identifiers for the second administrative level are
assigned to each grid cell using version 3.6 of GADM (ref. 27).
We determine whether a grid cell is contained within a protected area using
lists of worldwide protected areas provided by the IUCN and UNEP-WCMC
(ref. 28), and available on the GFW website. Data identifying the location of forest
concessions come from various country-specific sources and are available on
the GFW website. About half of the countries in our sample provided data on
concessions in at least one of the following categories: logging, oil palm and wood
fibre. Countries are Brunei, Cameroon, Central African Republic, Democratic
Republic of the Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, Papua New
Guinea and the Republic of Congo. Finally, for each grid cell, the per cent observed
per year is the average per cent per month of the area with forest cover in 2010 with
at least one clear observation (excludes areas obscured by cloud or shadow).
Estimation strategy. The basic estimation strategy exploits variation in either
access to alerts (to estimate the impact of availability) or use of subscriptions.
The estimation equation is:
di;c;k;y ¼ βGLADi;c;y þ ψiðcÞ þ αk;y þ μ0Xi;c;k;y þ ϵi;c;k;y
where di,c,k,y is a binary variable indicating deforestation in grid cell i in country c
of continent k in year y. GLADi,c,y is equal to the share of months that the country
had access to GLAD alerts in year y or, to assess the impact of subscriptions, an
indicator equal to one once over 50% of a grid cell fell within a subscribed area for
the first time. In the first case, because the treatment variable only changed across
countries and time, we first collapse the data to the country-year level (eliminating
the subscript i in the equation). In the second, the treatment variable varies within
countries and across time, so we run the estimation at the grid-cell level. β is the
estimated treatment effect. The samples differ across estimates as well. To estimate
the impact of availability, we use the sample of cells that had access to GLAD alerts
by 2018 and to assess the impact of subscriptions, those with a subscription at any
time before 2018.
We also include country (estimation of availability) or grid-cell (estimation
of use) fixed effects, ψi(c), which control for time-invariant characteristics of the
country or grid-cell affecting deforestation (such as soil suitability and slope).
In both specifications, we also include continent-year fixed effects, αk,y, which
control for general macroeconomic events occurring for all countries within the
same continent and influencing overall deforestation trends (global food demand,
world agricultural prices and so on); and a vector of controls Xi,c,k,y that includes:
average temperature, cumulative precipitation, interactions of year indicators with
the per cent area in protected areas at the baseline and the distances to the nearest
port, nearest road and nearest city. The distance/time interactions are included
to help eliminate confounding variation resulting from changes in transport or
production technologies that would affect the time trends of deforestation in more
remote areas differentially. μ is the vector of estimated parameters. Owing to the
small number of clusters in the country-level estimation, we calculated confidence
intervals using wild bootstrap clustering (a method that relies on random sampling
with replacement to calculate the confidence intervals). Residuals (ϵi,c,k,y) were
clustered at the second administrative level for the subscription estimations.
We apply sampling weights in each regression so that point estimates can be
considered to represent the population of forest.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
Datasets analysed for this study are available from the corresponding author upon
reasonable request.
Code availability
The codes used to generate the figures and tables are available via Zenodo29.
References
	18.	Lohr, S. Sampling: Design and Analysis 2nd edn (Cengage Learning, 2010).
	19.	Margono, B. A., Potapov, P. V., Turubanova, S., Stolle, F. & Hansen, M. C.
Primary forest cover loss in Indonesia over 2000–2012. Nat. Clim. Change 4,
730–735 (2014).
	20.	Bwangoy, J.-R. B., Hansen, M. C., Roy, D. P., Grandi, G. D. & Justice, C. O.
Remote sensing of environment wetland mapping in the Congo Basin using
optical and radar remotely sensed data and derived topographical indices.
Remote Sens. Environ. 114, 73–86 (2010).
	21.	Global Wetlands Map (Center for International Forestry Research, 2017);
https://doi.org/10.17528/cifor/006412
	22.	Land Surface Temperature [DAY] (1 MONTH - TERRA/MODIS) (NEO, 2020);
https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_M
Nature Climate Change | www.nature.com/natureclimatechange

Analysis
NATURe ClimATe CHAnge
	23.	CHIRPS: Rainfall Estimates from Rain Gauge and Satellite Observations
(CHIRPS, 2019); https://www.chc.ucsb.edu/data/chirps
	24.	GHSL - Global Human Settlement Layer (European Commission, 2019);
https://ghsl.jrc.ec.europa.eu
	25.	NGA’s World Port Index (NGA, 2017); https://services2.arcgis.com/
jUpNdisbWqRpMo35/arcgis/rest/services/WPI_Ports2017/FeatureServer
	26.	GEOFABRIK (Geofabrik, 2019); https://www.geofabrik.de/data/download.html
	27.	GADM data (GADM, 2018); https://gadm.org/data.html
	28.	World Database on Protected Areas (WDPA) (IUCN and UNEP-WCMC, 2016);
https://www.iucn.org/theme/protected-areas/our-work/quality-and-effectiveness/
world-database-protected-areas-wdpa
	29.	Moffette, F., Alix-Garcia, J., Shea, K. & H. Pickens, A. The Impact of
Near-real-time Deforestation Alerts Across the Tropics (Zenodo, 2020);
https://doi.org/10.5281/zenodo.4054099
Acknowledgements
We thank S. Jamilla, E. Goldman and I. Collins for creating the database. We thank
K. Chomitz, T. Coger, J. Engelmann, N. Harris, H. Nembhard, F. Stolle and N. Ullery
and participants at the Environmental and Resources Seminar of the University of
Wisconsin-Madison and at the Applied Economics Seminar at Oregon State University
for comments. We acknowledge funding from the World Resources Institute; the
organization had no input into the study design nor impact on the presentation of
the results.
Author contributions
F.M. and J.A.-G designed research, performed econometric analyses and led the writing.
K.S. managed the data compilation and contributed to the writing. A.H.P contributed to
the database.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41558-020-00956-w.
Correspondence and requests for materials should be addressed to F.M.
Peer review information Nature Climate Change thanks Juliano Assunção,
Johannes Reiche and Juan Robalino for their contribution to the peer review
of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Nature Climate Change | www.nature.com/natureclimatechange

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Corresponding author(s):
Fanny Moffette
Last updated by author(s): Oct 2, 2020
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Data collection
All data except the subscriptions and the percent observed (i.e. percent of the grid-cell that is not obscured by cloud or shadow) were
downloaded from publicly available websites. They were spatially compiled at the grid-cell level by ArcMap version 10.6 and the package
ArcPy. Different projections were used when creating the grid-cells: South America Albers Equal Area Conic, Africa Albers Equal Area
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country level with the standard "collapse" command in Stata-SE version 15.1.
Data analysis
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The codes used to generate the figures and tables are available in the Zenodo data repository (https://doi.org/10.5281/zenodo.4054099). All data necessary for
replication of the results in this paper are available from the corresponding author on reasonable request. Country and state GADM codes correspond are available
for download from GADM version 3.6 at https://gadm.org/data.html. The original Tree Cover 2010 and Deforestation data can be downloaded from https://
earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html. We excluded grid-cells in wetland areas using the wetland (landform) map for
Indonesia from Margono et al. (2014) (https://glad.umd.edu/dataset/primary-forest-cover-loss-indonesia-2000-2012), the map for the Republic of the Congo and
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dx.doi.org/10.3334/ORNLDAAC/1284), and the Pantanal biome from Brazil. For the countries without a regional map, we used the international CIFOR dataset
(https://www.cifor.org/globaladditional -wetlands/). The original precipitation data from CHIRPS can be downloaded from https://www.chc.ucsb.edu/data/chirps.
The original temperature data from NASA Land surface temperature can be downloaded from https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD_LSTD_M. The
original protected areas and concession areas (logging, oil palm, and forest) can be downloaded from www.globalforestwatch.org. The original major urban center,
defined as cities above 100,000 people, can be downloaded from JRC’s Global Human Settlement (https://ghsl.jrc.ec.europa.eu ). The original port data can be
downloaded from World Port Index from the National Geospatial-Intelligence Agency (https://www.arcgis.com/home/item.html?
id=b04b76b94059436e93757c301c10026c). The original road data (OpenStreetMap) can be downloaded from Geofabrik (https://www.geofabrik.de/data/
download.html). The original subscription layer with information on subscribers is available from World Resources Institute but restrictions apply to the availability
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subscription layer with information on subscriptions can be obtained upon reasonable request and with permission of World Resources Institute. The percent
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Study description
Quantitative methods were used to analyze the effect of the availability and the use of real-time forest monitoring on deforestation.
Research sample
We use a sample of 1 x 1 km grid-cells randomly drawn from forested grid-cells across all countries that began receiving alerts prior to
2018. These countries are: Peru, Republic of the Congo, Brazil (outside of the Amazon biome), Brunei, Malaysia, Indonesia, Papua New
Guinea, Timor Leste, Burundi, Cameroon, Central African Republic, Equatorial Guinea, Gabon, Democratic Republic of the Congo,
Rwanda, Uganda, Colombia, Ecuador, French Guyana, Guyana, Suriname, and Venezuela.
Sampling strategy
For the countries with less than 75,000 forested grid-cells, we follow Lohr (2000). For the countries with more than 75,000 forested grid-
cells, we randomly draw 10% of all grid-cells. To ensure representation within country, the random sampling is stratified at the second-
administrative level.
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Study covers the 2011-2018 period.
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Brazilian forests in the Amazon biome are excluded from the analyses due to the existing monitoring system there since the mid-2000s.
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    Policy Implications:

Answer:
• Monitoring land-use change is an essential component of any policy that is implemented to reduce deforestation.
• The availability of near-real-time alert systems is not enough to reduce deforestation; they must be used for enforcement and intervention.
• The use of near-real-time satellite-derived measures of deforestation on accessible and easy-to-use geospatial platforms can be an important tool in addressing land-use change.
• The value of satellite-based deforestation monitoring systems in terms of avoided carbon emissions is likely to exceed the costs of design and maintenance.
• Free, high-resolution and high-frequency deforestation alerts can reduce deforestation in settings where they provide new information and policymakers are able to integrate them into enforcement policy.