Amazon Fund 10 Years Later: Lessons from the World’s Largest REDD+ Program

Abstract

Results-Based Funding (RBF) for Reducing Emissions from Deforestation and Forest Degradation (REDD+) has become an important instrument for channeling financial resources to forest conservation activities. At the same time, much literature on conservation funding is ambiguous about the effectiveness of existing RBF schemes. Many effectiveness evaluations follow a simplified version of the principal-agent model, but in practice, the relation between aid providers and funding recipients is much more complex. As a consequence, intermediary steps of conservation funding are often not accounted for in effectiveness studies. This research paper aims to provide a nuanced understanding of conservation funding by analyzing the allocation of financial resources for one of the largest RBF schemes for REDD+ in the world: the Brazilian Amazon Fund. As part of this analysis, this study has built a dataset of information, with unprecedented detail, on Amazon Fund projects, in order to accurately reconstruct the allocation of financial resources across different stakeholders (i.e., governments, NGOs, research institutions), geographies, and activities. The results show that that the distribution of resources of the Amazon Fund lack a clear strategy that could maximize the results of the fund in terms of deforestation reduction. First, there are evidences that in some cases governmental organizations lack financial additionality for their projects, which renders the growing share of funding to this type of stakeholder particularly worrisome. Second, the Amazon Fund allocations did also not systematically have privileged the municipalities that showed the recent highest deforestation rates. rom the 10 municipalities with the higher deforestation rates in 2017, only 2 are amongst the top 100 receiving per/Ha considering the 775 municipalities from Legal Amazon. Third, the allocation of the financial resources from the Amazon Fund reflects the support of different projects that adopt significantly diverging theories of change, many of which are not primarily concerned with attaining further deforestation reductions. These results reflect the current approach adopted by the Amazon Fund, that do not actively seek areas for intervention, but instead wait for project submissions from proponents. As a consequence, project owners exert much influence on to the type of activities that they support how deforestation reduction is expected to be attained. The article concludes that the Amazon Fund as well as other RBF programs, should evolve over time in order to develop a more targeted funding strategy to maximize the long-term impact in reducing emissions from deforestation.

Full text

Article
Amazon Fund 10 Years Later: Lessons from the
World’s Largest REDD+ Program
Juliano Correa 1, * , Richard van der Hoff 2,3 and Raoni Rajão3
1Sanford School of Public Policy, Duke University, Durham, NC 27708, USA
2Nijmegen School of Management, Radboud Universiteit, Nijmegen 6525 AJ, The Netherlands;
richard.vanderhoff@gmail.com
3Department of Production Engineering, Universidade Federal de Minas Gerais, Belo Horizonte,
MG 30161-010, Brazil; rajao@ufmg.br
*Correspondence: juliano.correa@duke.edu; Tel.: +1-919-904-6453
Received: 27 December 2018; Accepted: 26 February 2019; Published: 19 March 2019


Abstract:
Results-Based Funding (RBF) for Reducing Emissions from Deforestation and Forest
Degradation (REDD+) has become an important instrument for channeling financial resources to
forest conservation activities. At the same time, much literature on conservation funding is ambiguous
about the effectiveness of existing RBF schemes. Many effectiveness evaluations follow a simplified
version of the principal-agent model, but in practice, the relation between aid providers and funding
recipients is much more complex. As a consequence, intermediary steps of conservation funding
are often not accounted for in effectiveness studies. This research paper aims to provide a nuanced
understanding of conservation funding by analyzing the allocation of financial resources for one of
the largest RBF schemes for REDD+ in the world: the Brazilian Amazon Fund. As part of this analysis,
this study has built a dataset of information, with unprecedented detail, on Amazon Fund projects,
in order to accurately reconstruct the allocation of financial resources across different stakeholders
(i.e., governments, NGOs, research institutions), geographies, and activities. The results show that
that the distribution of resources of the Amazon Fund lack a clear strategy that could maximize
the results of the fund in terms of deforestation reduction. First, there are evidences that in some
cases governmental organizations lack financial additionality for their projects, which renders the
growing share of funding to this type of stakeholder particularly worrisome. Second, the Amazon
Fund allocations did also not systematically have privileged the municipalities that showed the
recent highest deforestation rates. rom the 10 municipalities with the higher deforestation rates in
2017, only 2 are amongst the top 100 receiving per/Ha considering the 775 municipalities from Legal
Amazon. Third, the allocation of the financial resources from the Amazon Fund reflects the support
of different projects that adopt significantly diverging theories of change, many of which are not
primarily concerned with attaining further deforestation reductions. These results reflect the current
approach adopted by the Amazon Fund, that do not actively seek areas for intervention, but instead
wait for project submissions from proponents. As a consequence, project owners exert much influence
on to the type of activities that they support how deforestation reduction is expected to be attained.
The article concludes that the Amazon Fund as well as other RBF programs, should evolve over time
in order to develop a more targeted funding strategy to maximize the long-term impact in reducing
emissions from deforestation.
Keywords:
REDD+; Amazon Fund; Results-Based Funding; benefit distribution; resource allocation;
climate change funding; effectiveness; forest conservation funding
Forests 2019,10, 272; doi:10.3390/f10030272 www.mdpi.com/journal/forests

Forests 2019,10, 272 2 of 20
1. Introduction
The international allocation of funds to activities intended to funding forest conservation—directly
or indirectly—is said to be a “highly cost-effective way of reducing greenhouse gas emissions on climate
change” [
1
]. Among many types of financial mechanisms for pursuing this approach, Results-Based
Funding (RBF) for Reducing Emissions from Deforestation and Forest Degradation (REDD, or REDD+
for a broader suite of activities) has become an important instrument for channeling financial resources
to forest conservation activities [
2
,
3
]. RBF can be defined as the “transfer of money or material goods
conditional upon taking a measurable action or achieving a predetermined performance target” [
4
–
7
].
The success of RBF instruments for REDD+ stems from political controversies related to initial REDD+
proposals that favored offset-based markets [
8
]. Brazilian government, in particular, has been known
to challenge the use of markets on the basis of sovereignty concerns [
6
,
9
]. Instead, Brazil created the
Amazon Fund in 2008 in order to receive results-based payments for achievements in deforestation
reductions [
10
], which plummeted between 2004 and 2012 [
11
–
13
]. Similar developments have
also occurred in international forest governance debates, as the Green Climate Fund became the
central financial instrument for REDD+ [
14
], testifing the growing prevalence of RBF approaches
in forest governance. Despite this dominance, the effectiveness of RBF has been challenged by
scholars [
5
,
7
,
15
–
18
], while others have showed that donor and receiving countries and stakeholders
often disagree on how to best evaluate these schemes and distribute the resources [19,20].
This research paper aims to enhance the understanding of intermediary stages of RBF for forest
conservation by reconstructing the allocation of financial resources from the Brazilian Amazon Fund
to individual projects and analyzing the underlying rationales behind this allocation. Between 2008
and 2017, the Amazon Fund has received more than USD 1.2 billion in donations, committed USD
667.3 million for the financial support of 96 approved projects, and thereby represents the largest and
most longstanding RBF initiatives in forest governance worldwide [
10
,
13
,
21
]. An analysis of financial
resource allocation could, therefore, provide important lessons on the intermediary stages of RBF
(
as Amazon Fund
) to REDD+ and other conservation purposes. Our analysis exposes the underlying
intervention logic (or ‘theory of change’) adopted for redistributing financial resources, which is useful
for identifying the main factors for successful or failing forest conservation funding. The remainder
of this paper proceeds as follows. Section 2reviews the literature on related resource allocations,
including the theories of change, criteria for resource allocation, benefit-sharing mechanisms, and
impacts. Section 3then outlines our approach and Section 4presents data about the distribution of
Amazon Fund resources. Section 5concludes with our main findings and their implications for impact
and policy making.
2. Aid Effectiveness and the Complex Relations between Service Providers and Service Users
Deforestation reduction [
17
,
22
] is a relatively recent trend in the broader context of development
aid that has usually targeted health, education, or biodiversity conservation [
16
,
23
]. Although using
the same model, the literature generally refers to aid as funding for REDD+ initiatives, since the former
seems to be charity while the last is close to the climate change concepts, where developed countries
should fund initiatives of forest conservation to offset their historical emissions [2].
Although this aid could come in many forms, RBF has become an increasingly appealing approach
due to its simplicity from both the donor and receiver sides. On the donor side, the payments are done
based on the measurement of a result already achieved, reducing substantially the transactional risk.
On the receiver side, RBF promises the transfer of resources with “no strings attached” as countries
are able to decide how best to invest the payments. Since receiving countries would want to receive
an increasing volume of resources, they would be incentivized to invest the RBF proceedings in a
way that reduces deforestation the most. A closer look, however, reveals that many of the issues that
have plagued REDD+ and development aid more in general are still present in RBF, namely: benefit
distribution, intervention design, and effectiveness.

Forests 2019,10, 272 3 of 20
One of the key design choices around REDD+ programs concerns the definition of “who needs to
be involved, whose interests are at stake, and the expected co-benefits and required safeguards” [
19
].
Moreover, their discussion of approaches to reducing tropical forest degradation highlights the
importance of contextualizing local realities, responding to new knowledge and experience, and
incorporating the full complexity of forest loss and degradation, among others [
24
,
25
]. Many scholars
have highlighted the issues of equitable sharing of net benefits from REDD+ projects (e.g., [
26
,
27
]).
For instance, Luttrell, Loft, Fernanda Gebara, Kweka, Brockhaus, Angelsen, and Sunderlin [
27
]
distinguish a number of possible rationales for the distribution of REDD+ benefits. They have
emphasized: (1) actors with legal rights; (2) actors achieving reductions in emissions; (3) low-emitting
forest stewards; (4) actors incurring the costs of REDD+ implementation; (5) effective facilitators of
REDD+ implementation; and (6) the poorest actors. They note great variation in how implementing
countries apply these rationales, implying that this is a function of context, project design and the
beneficiaries (see also [
8
]). Some scholars find that “equity can have significant positive feedback on
program outcomes and legitimacy over the longer term” [
26
,
28
,
29
]. According to Vatn and Vedeld [
30
],
market-based approaches were found to be the most problematic among governance structures, since
they do not address equity. These observations suggest a theme of providing equal opportunities to
stakeholders. Yet rigorous analysis, and even comprehensive evaluations of net benefits and their
distribution, are scarce, in part because of the way decisions are made about distributions of resources
within and across REDD+ projects [19].
Another key aspect of RBF is the choice, by the receiving country, of the interventions that
will be supported by the program. [
27
,
31
]. Weatherley-Singh and Gupta [
32
], for example, find that
REDD+ activities must directly target the drivers of deforestation, such as forest fires and illegal
logging, as well as structural drivers, such as changes in land tenure and land-use planning. However,
they argue that not all drivers are considered, as most schemes do not address cattle ranching,
corruption, roadbuilding, and/or commodity demands, among others (see also [
9
,
33
]). As important
as the choice of the type of intervention is, the definition of the territories that will be prioritized
by REDD+. Wolosin, Breitfeller, and Schaap [
10
] show that the geographical distribution of REDD+
finance can be largely explained by priorities on tree cover, tree-cover loss, and carbon emissions at
national (70%–94%) and subnational (58%–72%) levels, though institutional capacity and political
commitments have also been influential. Other work highlights significant gaps for specific priority
areas. Some scholars point to areas in the Amazon region facing high deforestation pressure that are
important for emissions and biodiversity [
33
–
35
]. Other scholars argue for additional investments in
the network of protected areas, given their importance to date in curbing deforestation and the risks
from deforestation dynamics [
36
,
37
]. Still others argue that support should also consolidate pristine or
intact or stable forests to ensure long-term conservation (e.g., [
35
]). While the majority of available
literature strongly emphasizes improved protection of high-risk areas, at the least for prioritizing
additional impacts in the short run, various goals play parts within comprehensive approaches to
forest conservation.
Finally, different studies have pointed out that it is not clear that RBF leads to the efficient use
of resources, as initially assumed. The proponents of RBF expected that, since receiving countries
have a direct financial incentive to reduce deforestation, they would strive to support actions on
the ground that contribute directly to that aim. However, a closer look suggests that that empirical
evidence on the effectiveness of RBF schemes is either lacking or points to contradictory effects [
5
],
a problem already well known in relation to development aid [
38
]. On the one hand, authors such
as Restivo, Shandra, and Sommer [
17
] argue that more bilateral aid from the United States Agency
for International Development (USAID) has a lowering effect on forest loss. On the other hand,
studies such as Hermanrud and de Soysa [
22
] report that forest conservation funding from Norway’s
International Forest and Climate Initiative (NICFI), one of the largest aid initiatives in the world and
the main donor to the Amazon Fund, has had no effect in halting forest area loss. In a similar way,
Bare, Kauffman, and Miller [
18
], for example, argue that forest conservation funding in sub-Saharan

Forests 2019,10, 272 4 of 20
Africa “is not associated with reduced deforestation rates at the national scale” and even claim that
short-term impacts had negative effects. Both studies have strong limitations, since they do not
control for other drivers of deforestation, such as agricultural prices, and they assume that relatively
small-scale programs (as a percentage of the country’s Gross National Product), are going to show
effects at national level [
39
]. Nevertheless, these studies show that there is a growing concern with the
effectiveness of RBF, in general and NICFI in particular.
The problem with evaluating the effectiveness of RBF initiatives is that the relations between
service users (aid providers) and service providers (aid users) are much more complex than a
simplified reading of the principal-agent model found in the studies cited above. According to
Paul [
7
], the contracted agency relationship is often one between the donor organization and a recipient
organization or ministry, whereas results may come from other organizations that ultimately spend
the financial resources from these donations but have no direct relation with the donor organization
(i.e., non-contracted agency relation). In this respect, for example, the UN-REDD+ programme from the
United Nations Development Program (UNDP) supports 94 projects in Cambodia, Sri Lanka, Panama,
Paraguay, Democratic Republic of the Congo, and Nigeria. However, UNDP are directly related only
to the governmental focal point of each country, relating only indirectly with the local beneficiary [
40
].
According to Van der Hoff, Rajão and Leroy [
19
], the indirect relations between financial donations,
‘project performance’, and deforestation rates underlie discursive tensions between donor and
recipient countries. While formally all parties agree that RPF should be based solely on deforestation
reductions already achieved, donors are also increasingly concerned with the lack of evidence of
efficiency of funded projects in driving additional reductions, and in this way fueling a virtuous circle.
These tensions and conflicts suggest that the intermediary processes of forest conservation funding are
poorly understood, particularly with respect to how they affect aid effectiveness. Some authors have
suggested that addressing these conflicts requires new approaches to aid effectiveness evaluations
that account for the complex relations of RBF for REDD+, particularly the intermediary stages of
forest conservation funding. This could imply, for instance, that transfers should be conditional upon
desired results, as within well-implemented payments for ecosystem services (PES) approaches [
28
].
Such conditions could also require environmental additionality—that is, providing more ecosystem
services than they would provide in the non-existence of such payments [
41
,
42
]. In addition, REDD+
should be ‘financially additional’, beyond already planned funding [
43
]. While attractive, the idea of
adding specific demands of additionality to RBF goes against the simplicity and ‘hands off’ approach
that made RBF popular in the first place. Furthermore, this approach would entail a return of many
elements of the project-based model defined by Verified Carbon Standard among others, which have
also proven to be highly problematic [44]
The growing body of literature presented above presents valuable insights on how RBF should
be designed and presents some of its dilemmas and contradictory results. But while allot has been
said about how large RBF programs should look like, until recently we lacked a strong record of
largescale schemes to look back and draw lessons from concrete experiences. This study provides
the first comprehensive analysis of the first decade of the Amazon Fund, the world largest REDD+
RBF program [
45
,
46
]. Our study aims to reveal the design choices adopted by the Fund by analyzing
its resource distribution across beneficiaries, activities, and geographies. While this study does not
provide a quantitative impact analysis of the fund, it allows us to understand how the allocation
of financial resources corresponds with various REDD+ design choices, as reflected in the available
literature on REDD+, and the extent to which this may affect its long-term effectiveness. From this,
this study draws lessons that could be used to improve the Amazon Fund in Brazil and other large
RBF programs.
3. Research Approach and Methodology
This research paper conceptualizes the Amazon Fund as an intermediary organization that
links the forest conservation funding provided by donor organizations to the individual projects

Forests 2019,10, 272 5 of 20
(see Figure 1). Created in 2008, the Amazon Fund was the first large scale RBF program to be
implemented. As such, the fund played an important role in shaping the discussions around REDD+
at the United Nations Framework Convention on Climate Change (UNFCCC). For this reason, the
UNFCCC’s Warsaw Framework for REDD+ adopted, to a large degree, the modus operandi pioneered
by Brazil. Financial donations to the Amazon Fund mainly come from Norway’s International
Climate and Forest Initiative (NICFI) and the German Development Bank (KfW). The Amazon Fund
consists of a steering committee (COFA), which is responsible for establishing allocation guidelines,
and a technical committee (CTFA), which is responsible for approving results in terms of reducing
emissions from deforestation. The managing organization of the Amazon Fund is the Brazilian
Development Bank (BNDES) and is responsible for the approval (or rejection) of submitted project
proposals according to predefined guidelines, as well as for the receipt and allocation of financial
resources. Since 2015, BNDES has also become eligible to receive financial resources from the Green
Climate Fund (decree 8.576/15), whereas other organizations like the government-owned bank Caixa
Econômica Federal (CEF) and the Brazilian Biodiversity Fund (FUNBIO) may also become recipients.
Financial resources are allocated to a wide variety of organizations. Federal government organizations
include the Brazilian Agricultural Research Corporation (EMBRAPA), the Brazilian Institute for
Space Research (INPE), the Brazilian Institute for the Environment and Renewable Natural Resources
(IBAMA) and the National Police Force (FNSP). Non-governmental organizations also abound and
include the Sustainable Amazon Foundation (FAS), the Amazon Institute for Human and Environment
(IMAZON), Amazon Environmental Research Institute (IPAM), and The Nature Conservancy (TNC),
among others. State government organizations are mostly represented by the environmental or
agricultural secretariats of the nine Brazilian states in the Legal Amazon, while some state secretariats
outside this region were also recipients. Finally, municipal government secretariats and federal
universities were also supported financially by the Amazon Fund.
Understanding how forest conservation funding to the Amazon Fund contributes to the effective
reduction of emissions from deforestation and forest degradation involves connecting the project
activities (each with a specific shared benefit), geographies, and supported activities, to the overall
objective of emissions reduction. The Amazon Fund already provides an annual report that divides
the funding distribution according to four broad categories: (1) monitoring and control, (2) land
tenure regularization, (3) sustainable production, and (4) scientific and technological development [
13
].
However, to understand the allocation of financial resources in light of the design outlined above, it is
necessary to further refine the available information from the Amazon Fund. For this purpose, we have
built a project database with detailed information on the beneficiaries, activities and geographies that
received financial resources from the Amazon Fund (see Figure S8 in Supplementary Materials).
Our primary data source is the Amazon Fund
´
s website, as well as its annual activity reports
(see Figures S3 and S6 in Supplementary Materials). We collected all data available on all of the
96 projects that received support between 2008 and 2017. This data includes project objectives,
beneficiaries, implementing organization, territorial scope, committed and disbursed amounts, and
activities conducted, among other information. Websites of project owners provided additional
information. To refine the data for providing geographical information, we used the municipality as
the entity. In Brazil, municipalities reflect the smallest geographical unit for monitoring deforestation,
applying public policies, allocating government resources, and evaluating (see Tables S1 and S2 in
Supplementary Materials).
One of the main challenges of generating data at the municipal level is the variation of project
target areas, which may involve biomes, river basins, protected areas, or indigenous territories.
Based on the available literature, we designed rules to determine the municipalities encompassed
by each project (see Figure S5 and Tables S3 and S4 in Supplementary Materials). When project
disbursements covered multiple municipalities, we used a weight factor in order to determine
the share of financial support that each municipality received (see Figure S7 in Supplementary
Materials). After the geographical allocation of financial resources, we further categorized the dataset

Forests 2019,10, 272 6 of 20
by main-component, which reflects the Amazon Fund
´
s theory of change. As projects may contribute
to multiple main-components, we conducted one interview by email with a BNDES manager, the
managing organization of the Amazon Fund, who replied with a spreadsheet including data dividing
the investments of each Amazon Fund project by main-component. Finally, we further categorized
the dataset by activity (also called specific-components). As a main-component can be composed
by multiple activities, if more than one activity by main-component was verified, then the amounts
were equally divided across them. The assumptions in response at divergences or limitations of
data collected are presented at Figure S6 in Supplementary Materials. The final database contains
10,493 lines of information structured by project, location, main-component, and specific-component
(see Figures S1, S4 and S8 in Supplementary Materials). The procedures for collecting and interpreting
data, and constructing the database, are detailed in the supplements outcomes (see Figure S2 in
Supplementary Materials). The Amazon Fund accountability is in Brazilian Reais currency. All financial
data were converted from Brazilian reais to US dollars by using the rate for the day they were received,
which corresponds with the methodology used for the English publications of the Amazon Fund.
To evaluate the additionality of the Brazilian governmental agencies budgets (accountable in Brazilian
reais) with the Amazon Fund disbursements, we used an average exchange rate between 2009 and
2017, in order to reduce the effects of exchange rate fluctuation.
Forests 2018, 9, x FOR PEER REVIEW 6 of 21
multiple activities, if more than one activity by main-component was verified, then the amounts were
equally divided across them. The assumptions in response at divergences or limitations of data
collected are presented at figure S6 in supplementary Materials. The final database contains 10,493
lines of information structured by project, location, main-component, and specific-component (see
figures S1, S4 and S8 in Supplementary Materials). The procedures for collecting and interpreting
data, and constructing the database, are detailed in the supplements outcomes (see figure S2 in
Supplementary Materials). The Amazon Fund accountability is in Brazilian Reais currency. All
financial data were converted from Brazilian reais to US dollars by using the rate for the day they
were received, which corresponds with the methodology used for the English publications of the
Amazon Fund. To evaluate the additionality of the Brazilian governmental agencies budgets
(accountable in Brazilian reais) with the Amazon Fund disbursements, we used an average exchange
rate between 2009 and 2017, in order to reduce the effects of exchange rate fluctuation.
Figure 1. The Flows of Amazon Fund. Redd, Reducing Emissions from Deforestation and Forest
Degradation; CEF, Caixa Econômica Federal; FUNBIO, Brazilian Biodiversity Fund; IBAMA,
Brazilian Institute for the Environment and Renewable Natural Resources; EMBRAPA, Brazilian
Agricultural Research Corporation; FNSP, National Police Force; INPE, Brazilian Institute for Space
Research; CENSIPAM, Center for the Management of the Amazon Protection System.
Figure 1.
The Flows of Amazon Fund. Redd, Reducing Emissions from Deforestation and Forest
Degradation; CEF, Caixa Econômica Federal; FUNBIO, Brazilian Biodiversity Fund; IBAMA, Brazilian
Institute for the Environment and Renewable Natural Resources; EMBRAPA, Brazilian Agricultural
Research Corporation; FNSP, National Police Force; INPE, Brazilian Institute for Space Research;
CENSIPAM, Center for the Management of the Amazon Protection System.

Forests 2019,10, 272 7 of 20
4. Results: Resource Allocations by the Amazon Fund
Currently, the approval of projects and disbursements are made on the basis of criteria and
guidelines updated biannually by COFA. The 2017–2018 document lists 14 minimum requirements
that potential projects must meet, some (i.e., items B4, B5, B6, B7, and B14) determining conceptual
boundaries of project activities. Projects also must demonstrate coherence with environmental and
forest policies, most notably the national Action Plan for the Prevention and Control of Deforestation
in the Legal Amazon (PPCDAm), including its manifestations in state governments (PPCDs), and the
national policy for Regenerating Native Vegetation (ProVeg) [
13
]. Projects are also evaluated with
respect to coherence with Brazil’s National REDD+ Strategy (ENREDD+), which in turn incorporates
implementation of PPCDAm and compliance with the Brazilian Forest Code. Finally, projects are
expected to be financially additional, i.e., to go beyond existing public environmental budgets and
other forms of finance.
The Amazon Fund maintains an open channel for submissions indicating that 80% for the
resources should be invested in the Amazon biome (an area that encompasses 40% of the country).
In addition to that, the fund also has made public calls aiming at fostering specific activities, such
as sustainable production, inclusive value chains and the management of indigenous lands. These
calls account for 8.4% of the resources committed by the fund by December 2017. A recent call for
forest restauration from 2017 added a spatial priority criteria that provides up to 12 points (from a total
of 100) if the project is located in a high priority water basin within a municipality blacklisted as a top
priority for deforestation control by the Ministry of Environment [
47
]. In both the calls and the open
submission channel, however, the Amazon Fund adopts largely a passive approach, waiting for project
owners to send proposals, rather than actively identifying areas under high risk of deforestation where
the impact of the resources would be maximized in terms of deforestation reduction.
4.1. Benefit Distribution Across Stakeholders
The distribution of financial commitments across stakeholders shows some variation across years
(Figure 2, left panel). In 2017, over 95% of a total of USD 667.3 million went to state governments
(USD 256.6 million) or NGOs (USD 241.1 million) or federal governments (USD 140.6 million), with
their shares varying considerably per year. Of a total of USD 140.4 million in 2013, about 70% (or USD
102.9 million) went to projects of state governments that received almost no such commitments either
two years earlier or two years later. This peak took place as a consequence of a change in the rule of
the Amazon Fund that allowed the approval of larger “structural projects”, as the implementation of
the Rural Environmental Register (CAR). By contrast, commitments to NGOs projects were relatively
stable over time, averaging USD 22 million until 2016, though rising to USD 44.5 million in 2017
(implying variation in the NGOs’ share). Commitments to federal government projects were also
uneven, with slight peaks in 2012 and 2017 (USD 31.7 million, 41.2 million).
Forests 2018, 9, x FOR PEER REVIEW 7 of 21
4. Results: Resource Allocations by the Amazon Fund
Currently, the approval of projects and disbursements are made on the basis of criteria and
guidelines updated biannually by COFA. The 2017–2018 document lists 14 minimum requirements
that potential projects must meet, some (i.e., items B4, B5, B6, B7, and B14) determining conceptual
boundaries of project activities. Projects also must demonstrate coherence with environmental and
forest policies, most notably the national Action Plan for the Prevention and Control of Deforestation
in the Legal Amazon (PPCDAm), including its manifestations in state governments (PPCDs), and the
national policy for Regenerating Native Vegetation (ProVeg) [13]. Projects are also evaluated with
respect to coherence with Brazil’s National REDD+ Strategy (ENREDD+), which in turn incorporates
implementation of PPCDAm and compliance with the Brazilian Forest Code. Finally, projects are
expected to be financially additional, i.e., to go beyond existing public environmental budgets and
other forms of finance.
The Amazon Fund maintains an open channel for submissions indicating that 80% for the resources
should be invested in the Amazon biome (an area that encompasses 40% of the country). In addition to
that, the fund also has made public calls aiming at fostering specific activities, such as sustainable
production, inclusive value chains and the management of indigenous lands. These calls account for 8.4%
of the resources committed by the fund by December 2017. A recent call for forest restauration from 2017
added a spatial priority criteria that provides up to 12 points (from a total of 100) if the project is located
in a high priority water basin within a municipality blacklisted as a top priority for deforestation control
by the Ministry of Environment [47]. In both the calls and the open submission channel, however, the
Amazon Fund adopts largely a passive approach, waiting for project owners to send proposals, rather
than actively identifying areas under high risk of deforestation where the impact of the resources would
be maximized in terms of deforestation reduction.
4.1. Benefit Distribution Across Stakeholders
The distribution of financial commitments across stakeholders shows some variation across
years (Figure 2, left panel). In 2017, over 95% of a total of USD 667.3 million went to state governments
(USD 256.6 million) or NGOs (USD 241.1 million) or federal governments (USD 140.6 million), with
their shares varying considerably per year. Of a total of USD 140.4 million in 2013, about 70% (or USD
102.9 million) went to projects of state governments that received almost no such commitments either
two years earlier or two years later. This peak took place as a consequence of a change in the rule of
the Amazon Fund that allowed the approval of larger “structural projects”, as the implementation of
the Rural Environmental Register (CAR). By contrast, commitments to NGOs projects were relatively
stable over time, averaging USD 22 million until 2016, though rising to USD 44.5 million in 2017
(implying variation in the NGOs’ share). Commitments to federal government projects were also
uneven, with slight peaks in 2012 and 2017 (USD 31.7 million, 41.2 million).
Figure 2. Annual committed (L) and disbursed (R) amounts per stakeholder (in million USD).
Figure 2. Annual committed (L) and disbursed (R) amounts per stakeholder (in million USD).

Forests 2019,10, 272 8 of 20
However, the ability of different stakeholders to approve projects with the Amazon Fund did not
match their implementation capabilities. In the last decade, only USD 405.3 of 667.3 million (i.e., 60.7%)
has been transferred to project owners. Average annual disbursements to state governments have
hovered between USD 16 and 21 million in most years, with a sudden peak of USD 47.6 million in 2014
and then a sharp drop to USD 4.8 million in 2015. Disbursements to federal government increased
exponentially from a small base of only USD 2.4 million even in 2014 to USD 37.7 million in 2017.
Finally, disbursements to NGOs steadily increased from USD 6.4 million in 2010 to USD 30.7 million in
2017. From these three groups of beneficiaries, the Federal Government has been demonstrated the
largest implementation gap, starting with a very low implementation rate and reaching the execution
of only 47% of the committed values by 2017. This was followed by the State Governments, whose
spending rates stayed below 50%. Municipalities, Universities, and NGOs, in contrast, presented a
better implementation capacity, being able to invest most of the resources obtained from the Fund.
To understand these variations in disbursements, we must also consider the characteristics of
the projects supported by the Fund. Federal government projects, for instance, were concentrated
within eight projects involving six recipient agencies. Of the total amounts in this category, USD
64.3 million (i.e., 47.2%) went to organizations that develop satellite-based monitoring systems and
provide information on deforestation trends, namely INPE and CENSIPAM. Another USD 35.9 million
(i.e., 26.7%) went to organizations responsible for enforcing environmental laws and policies, namely
IBAMA and FNSP. The remaining USD 40.5 million (i.e., 25.9%) went to EMBRAPA units to disseminate
knowledge about sustainable production and the recovery of degraded areas throughout Brazil, and
to the Brazilian Forest Service (SFB) for the collection of information aiming to increase the forest data
available (see Section 4.3). While the IBAMA manage to invest 17.5% of the funds received, by 2017,
INPE and CENSIPAM used only 58.6%, implying that the development of a radar-based monitoring
system is lagging behind schedule.
The committed and disbursed peaks for state government projects in 2013 and 2014 (Figure 3)
correspond with contextual factors as well, including a surge in state government projects toward
development and implementation of the Rural Environmental Register (CAR). CAR is a federal
policy instrument introduced in 2012, with the adoption of the new Forest Code (law 12.651/2012),
to enhance law enforcement capacity. However, despite the federal law and a centralized national
system, the registers must be executed at state or municipal level (art 29, §1). CAR implementation
has, therefore, become a major concern for state governments, especially after the system went live
in 2014 [
48
]. This can be seen in both spending and appeals to the Amazon Fund [
13
]. Within the
13 states that have approved projects, 85% of disbursements went to seven of the nine inside the
Amazon Biome.
The linear increase in disbursements to NGOs reflects yet another set of contextual factors, in this
case related to Amazon Fund process adjustments over time. Disbursements to projects were slow,
to start, due to rigid assessment procedures intended to show professionalism; in the eyes of donor
organizations and BNDES management, that slowness also reflected some lack of understanding
of project owners [
13
,
19
]. Minutes of COFA meetings indicate that, in response to these challenges,
the Amazon Fund adopted a number of measures in order to facilitate and accelerate the disbursement
process, including public calls for submitting project proposals. While the consequences of these
responses are reflected in the linear increase in approved projects and disbursements to NGOs, 80% of
the financial resources were concentrated in half of the NGOs that received support from the Amazon
Fund. While the Amazon Fund does include distributional equity amongst its performance criteria,
this concentration reveals that usually only high-capacity and professional civil society organizations,
such as FAS, IMAZON, and TNC, are able to access the fund (see Figure S9 in Supplementary Materials).
In addition to exposing the implementation capability of different governmental agencies,
a comparison between the disbursement of the Amazon Fund with the yearly government budget
also reveals the ability of the Fund to foster additional actions. One of the key principles of the first
donation contract signed in 2008 between Norway and Brazil was the warrant that the Amazon Fund

Forests 2019,10, 272 9 of 20
would not replace but supplement tax payer funds [
2
,
20
,
22
,
49
]. However, it is possible to observe
that the increases in disbursements to federal agencies coincided with their decreasing governmental
budgets, particularly after 2014 (Figure 4). This suggests the occurrence of a partial substitution for
the agency expenditure of taxpayer-funded budgets using the Amazon Fund. For instance, IBAMA’s
committed budgets to reduce deforestation, combat fires, and conduct environmental inspections have
been reducing since 2012, with a strong reduction from USD 50.64 million in 2014 to USD 29.07 million
in 2017. These reductions have been partially offset by Amazon Fund disbursements starting in 2015.
Similarly, INPE
´
s budget fell from USD 84.5 million in 2010 to USD 43.63 million in 2017, 2017, and
CENSIPAM has also lost more than 70% of its governmental funding from 2009 and 2017. In those
three cases, the Amazon Fund played an important role offsetting those budgetary losses from 2015
onwards, in the case of CENSIPAM even outmatching governmental funds. Those trends include
rising implementation rates for turning federal commitments into disbursements, which increased
from 3.7% in 2014 to 26.8% in 2017.
Forests 2018, 9, x FOR PEER REVIEW 8 of 21
However, the ability of different stakeholders to approve projects with the Amazon Fund did
not match their implementation capabilities. In the last decade, only USD 405.3 of 667.3 million (i.e.,
60.7%) has been transferred to project owners. Average annual disbursements to state governments
have hovered between USD 16 and 21 million in most years, with a sudden peak of USD 47.6 million
in 2014 and then a sharp drop to USD 4.8 million in 2015. Disbursements to federal government
increased exponentially from a small base of only USD 2.4 million even in 2014 to USD 37.7 million
in 2017. Finally, disbursements to NGOs steadily increased from USD 6.4 million in 2010 to USD 30.7
million in 2017. From these three groups of beneficiaries, the Federal Government has been
demonstrated the largest implementation gap, starting with a very low implementation rate and
reaching the execution of only 47% of the committed values by 2017. This was followed by the State
Governments, whose spending rates stayed below 50%. Municipalities, Universities, and NGOs, in
contrast, presented a better implementation capacity, being able to invest most of the resources
obtained from the Fund.
Figure 3. Implementation rates as disbursed, divided by committed (consolidated amounts), by
Stakeholder.
To understand these variations in disbursements, we must also consider the characteristics of
the projects supported by the Fund. Federal government projects, for instance, were concentrated
within eight projects involving six recipient agencies. Of the total amounts in this category, USD 64.3
million (i.e., 47.2%) went to organizations that develop satellite-based monitoring systems and
provide information on deforestation trends, namely INPE and CENSIPAM. Another USD 35.9
million (i.e., 26.7%) went to organizations responsible for enforcing environmental laws and policies,
namely IBAMA and FNSP. The remaining USD 40.5 million (i.e., 25.9%) went to EMBRAPA units to
Figure 3.
Implementation rates as disbursed, divided by committed (consolidated amounts), by
Stakeholder.
These observations cannot, by themselves, confirm a direct causal relationship between the
increasing financial disbursements from the Amazon Fund and the decreasing budgets of the recipient
federal agencies. Furthermore, it should be highlighted that the period following 2015 witnessed one of
the worst political, economic, and fiscal crises in Brazil’s history. At the same time, however, contextual
factors seem to correspond with an interpretation that the forest conservation funding provided
through the Amazon Fund lacks in some instances financial additionality, particularly considering
the unfavorable political climate for environmental protection [
50
], the greater flexibility within forest

Forests 2019,10, 272 10 of 20
legislation since 2012 [
51
], multiple bills for reducing environmental protection during election year
2018, and, as a consequence of all these factors, rising deforestation rates since 2012 [52].
Forests 2018, 9, x FOR PEER REVIEW 10 of 21
Figure 4. Comparison of Federal Committed Budgets with the Amazon Fund disbursements for INPE,
IBAMA and CENSIPAM (used average 2009–2017 exchange rate: 2.434). Committed amounts
represent the Portuguese term, ‘Empenhado’, an act that guarantees that there is an amount necessary
to pay for an assumed commitment and creates a payment obligation for the government.
These observations cannot, by themselves, confirm a direct causal relationship between the
increasing financial disbursements from the Amazon Fund and the decreasing budgets of the
recipient federal agencies. Furthermore, it should be highlighted that the period following 2015
witnessed one of the worst political, economic, and fiscal crises in Brazil’s history. At the same time,
however, contextual factors seem to correspond with an interpretation that the forest conservation
funding provided through the Amazon Fund lacks in some instances financial additionality,
particularly considering the unfavorable political climate for environmental protection [50], the
greater flexibility within forest legislation since 2012 [51], multiple bills for reducing environmental
protection during election year 2018, and, as a consequence of all these factors, rising deforestation
rates since 2012 [52].
4.2. Geographical Distribution
Spatially, Amazon Fund allocations display a large concentration (Figure 5a) in 64 municipalities
along the (Figure 5a) region stretching from the southeast of Pará towards the western regions in the
Mato Grosso, Rondônia and Acre states, municipalities that contain, since 2000, the highest
consolidated deforestation rates in Brazil. NGO and state projects explain much of this concentration
(Figure 5b and 5c), whereas federal projects had no significant contribution, mainly due to their
nationwide focus (Figure 5c and 5d). Federal government projects are the most evenly distributed
across the landscape, averaging below 26 USD/ha, which could be due to the all-encompassing nature
of the geographic information systems (GIS—Geographic Information Systems ) and remote sensing
activities that these projects tend to promote. At the same time, disbursements to larger federal
agencies, such as EMBRAPA, tend to concentrate in eight cities in the Legal Amazon, including Rio
Branco, Manaus, Boa Vista and Macapá, where these agencies are located (Fig. 5d). Finally, while
municipalities benefit indirectly from various types of support, direct support only went to 6 of the
772 municipalities in the Legal Amazon and amounted to only USD 7.8 million. Most of these
resources (65.2%) went to the municipal government of Alta Floresta, in northern Mato Grosso. In
addition, the Amazon Fund had also financed research of the state universities of Pará (in Belem) and
Amazonas (in Manaus) as well as to the development of satellite-based monitoring systems by INPE
in Manaus (Figure 5g).
State government projects are mostly responsible for monitoring and control (Figure 5c),
particularly through activities, as the structuring of environmental secretariats, CAR implementation,
and training of firefighters (see section 4.3 for details). State governments that more actively sought
0
20
40
60
2009 2011 2013 2015 2017
IBAMA
IBAMA Committed
AF Disbursed
0
20
40
60
80
2009
2010
2011
2012
2013
2014
2015
2016
2017
INPE
INPE Committed
AF Disbursed
0
10
20
30
40
2009
2010
2011
2012
2013
2014
2015
2016
2017
CENSIPAM
CENSIPAM Commited
AF Disbursed
Figure 4.
Comparison of Federal Committed Budgets with the Amazon Fund disbursements for INPE,
IBAMA and CENSIPAM (used average 2009–2017 exchange rate: 2.434). Committed amounts represent
the Portuguese term, ‘Empenhado’, an act that guarantees that there is an amount necessary to pay for
an assumed commitment and creates a payment obligation for the government.
4.2. Geographical Distribution
Spatially, Amazon Fund allocations display a large concentration (Figure 5a) in 64 municipalities
along the (Figure 5a) region stretching from the southeast of Parátowards the western regions in
the Mato Grosso, Rondônia and Acre states, municipalities that contain, since 2000, the highest
consolidated deforestation rates in Brazil. NGO and state projects explain much of this concentration
(Figure 5b,c), whereas federal projects had no significant contribution, mainly due to their nationwide
focus (Figure 5c,d). Federal government projects are the most evenly distributed across the landscape,
averaging below 26 USD/ha, which could be due to the all-encompassing nature of the geographic
information systems (GIS—Geographic Information Systems) and remote sensing activities that
these projects tend to promote. At the same time, disbursements to larger federal agencies, such as
EMBRAPA, tend to concentrate in eight cities in the Legal Amazon, including Rio Branco, Manaus,
Boa Vista and Macapá, where these agencies are located (Figure 5d). Finally, while municipalities
benefit indirectly from various types of support, direct support only went to 6 of the 772 municipalities
in the Legal Amazon and amounted to only USD 7.8 million. Most of these resources (65.2%) went to
the municipal government of Alta Floresta, in northern Mato Grosso. In addition, the Amazon Fund
had also financed research of the state universities of Pará(in Belem) and Amazonas (in Manaus) as
well as to the development of satellite-based monitoring systems by INPE in Manaus (Figure 5g).
State government projects are mostly responsible for monitoring and control (Figure 5c),
particularly through activities, as the structuring of environmental secretariats, CAR implementation,
and training of firefighters (see Section 4.3 for details). State governments that more actively sought the
support of Amazon Fund for monitoring and control were Acre, Maranhão, Tocantins, and Rondônia.
Particularly, Acre has a strong presence in investments in sustainable production, spread throughout
its territory (Figure 5e,f). The distribution of resources also portrays low intensity towards Land
Tenure Regularization activities, independent of the region or stakeholder (Figure 5h), However, the
Amazon Fund allocations did not systematically privilege the municipalities that showed the recent
highest deforestation rates. For instance, from the 10 municipalities with the highest deforestation
rates in 2017, only two were amongst the top 100 receiving per/Ha, considering the 775 municipalities
from Legal Amazon. Furthermore, the support from the Amazon Fund tend to arrive in a context in
which clearings have already been reduced substantially due to other factors or the depletion of forests
(see Table S5 and Figure S10 in Supplementary Materials). This spatial pattern of project distribution

Forests 2019,10, 272 11 of 20
confirms the apparent lack of strategy of the Amazon Fund, as a consequence of a largely passive
approach that waits for proposals rather than actively seeking opportunities for fostering projects in
areas with high deforestation risk.
Forests 2018, 9, x FOR PEER REVIEW 12 of 21
a b
c d
e f
g h
Figure 5. Spatial distribution of Amazon Fund investments per municipality by Stakeholder and by
main-component.
Figure 5.
Spatial distribution of Amazon Fund investments per municipality by Stakeholder and
by main-component.

Forests 2019,10, 272 12 of 20
4.3. Distribution Across Activities
Almost half of the total commitments (USD 667.3 million) has gone to monitoring and control (USD
326.7 million), while one third (USD 201.9 million) has gone to sustainable production (see Figure 6
and Table 1). The latter category has been relatively steady over time, as have the small land tenure
commitments. By contrast, the large investment monitoring and control have been uneven over time:
starting slow with an average of USD 20.3 million in the first four years, peaking in 2013 at USD
94.0 million, and then settling at an average of USD 30.6 million from 2015 on (Figure 6, left panel).
Finally, nearly all commitments for scientific and technological development occurred in 2012 (USD
40.7 million).
Forests 2018, 9, x FOR PEER REVIEW 13 of 21
4.3. Distribution Across Activities
Almost half of the total commitments (USD 667.3 million) has gone to monitoring and control
(USD 326.7 million), while one third (USD 201.9 million) has gone to sustainable production (see
Figure 6 and Table 1). The latter category has been relatively steady over time, as have the small land
tenure commitments. By contrast, the large investment monitoring and control have been uneven
over time: starting slow with an average of USD 20.3 million in the first four years, peaking in 2013
at USD 94.0 million, and then settling at an average of USD 30.6 million from 2015 on (Figure 6, left
panel). Finally, nearly all commitments for scientific and technological development occurred in 2012
(USD 40.7 million).
A B
Figure 6. Annual committed (A) and disbursed (B) amounts per main-component (in millions USD).
Although slightly slower than noted above, actual disbursements to individual projects have
corresponded to commitments, with most disbursements going to monitoring and control (49.6%)
and sustainable production (31.9%). Monitoring and Control was responsible for most of the
variation (see right graph of Figure 6), peaking in 2014 (USD 43.1 million) and 2017 (USD 53.5
million). Notably, disbursements for scientific and technological development have never gotten
much traction, slightly peaking only in 2013 and 2014, and also presents the lowest implementation
rate up to 2017 (Figure 7).
Figure 6. Annual committed (A) and disbursed (B) amounts per main-component (in millions USD).
Although slightly slower than noted above, actual disbursements to individual projects have
corresponded to commitments, with most disbursements going to monitoring and control (49.6%) and
sustainable production (31.9%). Monitoring and Control was responsible for most of the variation
(see right graph of Figure 6), peaking in 2014 (USD 43.1 million) and 2017 (USD 53.5 million). Notably,
disbursements for scientific and technological development have never gotten much traction, slightly
peaking only in 2013 and 2014, and also presents the lowest implementation rate up to 2017 (Figure 7).
Monitoring and control efforts involved mostly state and federal government projects (USD 187.1
million and USD 100.1 million, respectively). It was the only category, though, that included the
unique international project supported by the Amazon Fund, aiming to help develop the capacity
to monitor deforestation in eight neighboring countries that also contain the Amazon biome (USD
11.8 million). However, most of the monitoring and control investments (USD 113.0 million) was
allocated to CAR implementation. A large share of the funds provided for this activity (USD 102.5
million) was used by state governments to acquire equipment (GPS, computers, software) and provide
training for effective processing of CAR proposals. Another share (USD 52 million) was invested in
the capacity-building of environmental secretariats for CAR implementation and other environmental
policies, including the creation of municipal secretariats, the acquisition of cars and buildings, the
hiring of employees and training in-monitoring deforestation, landscape analysis, sustainable supply
chains, and measurement. In addition, some resources were used to promote CAR among landowners
and to provide georeferencing services for landowners. A small amount went to development of a
state system for granting environmental licensing to new businesses and companies. Therefore, in
total, 18% of the resources committed by the fund have been invested in the implementation of CAR.

Forests 2019,10, 272 13 of 20
Table 1. Distribution of project approvals to Amazon Fund projects (USD). CAR, Rural Environmental Register.
Activities State
Government
Federal
Government
Municipal
Government International NGOs Univ. Total
Scientific and Technological Development 4.457.301 40.461.961 13.990.780 9.383.341 68.293.383
Field collection and data inventory (Forest, Socioeconomic, Biodiversity, Maps)
1.771.039 31.709.135 366.095 33.846.268
Disseminate Environmental Education (Museum) 5.818.209 5.818.209
Development of New Forest Products 732.695 732.695
Develop environmental diagnoses and shared management tools, edit bulletins
and publications 1.693.133 4.736.591 6.429.724
Investment in research infrastructure (Laboratories, equipment,
facilities, universities) 1.771.039 1.263.966 3.914.055 6.949.059
Research on the production of native seedlings and techniques for reforestation
of degraded areas, development of Demonstration Units (pilots) to
disseminate knowledge * 915.224 8.752.827 4.849.377 14.517.427
Sustainable Production Activities 41.186.376 5.984.174 154.736.705 201.907.255
Economic Activities for Sustainable Forest Use and Recovery of Degraded Areas
41.186.376 5.984.174 154.736.705 201.907.255
Monitoring and Control 187.105.638 100.146.294 1.788.272 11.791.988 25.845.426 326.677.619
Structuring and strengthening of State and Municipal Environment Secretariats
(Acquire infrastructure, training in Monitoring deforestation, Landscape
Analysis, Sustainable Chain and Recovery Measure techniques) 52.018.486 1.376.210 14.254.668 58.656.955
Inspections, Enforcement and Environmental Police 29.571.660 29.571.660
Combat Forest Fires (States–Firefighters/Federal–GIS and Satellites) 32.543.336 6.282.451 38.825.788
Regularize the environmental situation or/and implement CAR 102.543.816 412.062 11.590.759 113.007.430
Improve Deforestation Monitoring System (GIS and Satellites) ** 64.292.183 11.791.988 76.084.171
Land tenure regularization 23.829.953 62.995 46.552.443 70.445.392
Land Regularization of Small and Middle size properties (Tenure, Deeds) 1.141.031 3.219.703 4.360.735
Territorial and Ecological Zoning, strengthening and empowerment of PA and
IT Management 22.688.922 62.995 43.332.740 66.084.657
Total 256.579.269 140.608.255 7.835.441 11.791.988 241.125.355 9.383.341 667.323.649
** e.g., improving software; improving the services for receiving, distributing and using satellite images produced; map and make available tools to shape changes in the use of land;
improving methods to estimate biomass and emissions; make available a solution for storing and processing a large volume of geospatial data, called “Brazil Data Cube”, between others.

Forests 2019,10, 272 14 of 20
Forests 2018, 9, x FOR PEER REVIEW 14 of 21
Figure 7. Implementation rates as disbursed divided by committed (consolidated amounts), by main-
component.
Monitoring and control efforts involved mostly state and federal government projects (USD
187.1 million and USD 100.1 million, respectively). It was the only category, though, that included
the unique international project supported by the Amazon Fund, aiming to help develop the capacity
to monitor deforestation in eight neighboring countries that also contain the Amazon biome (USD
11.8 million). However, most of the monitoring and control investments (USD 113.0 million) was
allocated to CAR implementation. A large share of the funds provided for this activity (USD 102.5
million) was used by state governments to acquire equipment (GPS, computers, software) and
provide training for effective processing of CAR proposals. Another share (USD 52 million) was
invested in the capacity-building of environmental secretariats for CAR implementation and other
environmental policies, including the creation of municipal secretariats, the acquisition of cars and
buildings, the hiring of employees and training in-monitoring deforestation, landscape analysis,
sustainable supply chains, and measurement. In addition, some resources were used to promote CAR
among landowners and to provide georeferencing services for landowners. A small amount went to
development of a state system for granting environmental licensing to new businesses and
companies. Therefore, in total, 18% of the resources committed by the fund have been invested in the
implementation of CAR.
Monitoring activities that were exclusively promoted by federal government organizations
involved the improvement of satellite-based monitoring systems for fighting deforestation
(PRODES—Annual Deforestation System and DETER—Real Time Deforestation System, USD 76.1
million) and forest fires (PREVFOGO-Fire Prevention System, USD 6.3 million). State governments
also invested in forest fire combat (USD 32.5 million), but emphasized control activities (e.g., the
creation of firefighter units), rather than monitoring activities. Other investments by federal
government organizations targeted the strengthening of law enforcement (USD 29.6 million) in two
projects by IBAMA and FNSP; this funding was mostly spent on the acquisition of vehicles,
helicopters, equipment, and buildings. While NGOs received much financial support from the
Figure 7.
Implementation rates as disbursed divided by committed (consolidated amounts),
by main-component.
Monitoring activities that were exclusively promoted by federal government organizations
involved the improvement of satellite-based monitoring systems for fighting deforestation (PRODES—
Annual Deforestation System and DETER—Real Time Deforestation System, USD 76.1 million) and
forest fires (PREVFOGO-Fire Prevention System, USD 6.3 million). State governments also invested in
forest fire combat (USD 32.5 million), but emphasized control activities (e.g., the creation of firefighter
units), rather than monitoring activities. Other investments by federal government organizations
targeted the strengthening of law enforcement (USD 29.6 million) in two projects by IBAMA and FNSP;
this funding was mostly spent on the acquisition of vehicles, helicopters, equipment,
and buildings
.
While NGOs received much financial support from the Amazon Fund (USD 241.1 million), their
support for monitoring and control activities was relatively small (USD 11.6 million) and only involved
CAR implementation.
In the category of sustainable production, resources mostly went to NGOs (USD 154.7 million)
and state government organizations (USD 42.1 million) (see Table 1). Nearly all state government
investments went to the promotion of sustainable forest activities, the acquisition of equipment
(tanks, driers, processing units’ machines, warehouses), and the provision of professional training and
technical assistance (in pisciculture and aquaculture, nut and Açaíextraction, pasture management,
as well as forestry and agroforestry systems). This result suggests that the social benefits from the
Amazon Fund in terms of rural poverty reduction and sustainable farming were carried out mostly by
NGOs and state governments.
Investments in regularizing land tenure, notably spending on territorial zoning and protected-area
management and indigenous lands, came almost exclusively from state governments (USD 23.8 million)
and NGOs (USD 46.6 million). This investment provides indirect benefits for indigenous peoples,
quilombos (descendants from fugitive slaves), riverine people, smallholders, and settlements. No such
investments were federal. Federal governments did invest substantially in scientific and technological

Forests 2019,10, 272 15 of 20
development, which involved field data collection by the Brazilian Forest Service (SFB) for building
the National Forest Inventory (USD 31.7 million).
Universities, by contrast, invested the most financial resources in scientific research (USD 4.7
million) and development of the research infrastructure (USD 3.9 million). For instance, one project
from the Federal University of Paráconducted research for the development of new products from
bioactive compounds of plants typical of the Amazon Biome (USD 0.7 million), and invested in the
development of new forest products, such as herbal medicines, cosmetics, and food products, among
others. Natura, a private cosmetics company from Brazil, announced in 2016 an investment of more
than USD 70 million in biodiversity inputs as part of its Amazon Program that aims to develop a new
line of products with origins in Amazon Biodiversity.
5. Amazon Fund Design Choices and Effectiveness
The findings of our analysis of the recipient projects in the Brazilian Amazon Fund reflect a broad
variety of stakeholders and activities. Following the categorization of Luttrell, Loft, Fernanda Gebara,
Kweka, Brockhaus, Angelsen, and Sunderlin [
27
], the recipient projects of the financial resources
from the Amazon Fund often involve the largely indirect contributions of effective facilitators, legal
rights holders, cost-incurring groups, forest stewards, or poor communities. Moreover, the Amazon
Fund’s financial resources were channeled towards the direct and structural drivers of deforestation,
but this distribution was not proportional to the importance of addressing these drivers, as argued
by some scholars (e.g., [
32
]). Investment patterns tend to reflect specific relations between specific
stakeholder groups and project activities. Although activities also vary considerably, there are some
general patterns. Federal government organizations tend to invest in development of monitoring
systems (45.7%) and inventory data (22.6%), which reflects a main concern with gaining control over
deforestation dynamics. State government organizations tend to invest mostly in CAR implementation
(40.1%) and capacity-building for state and municipal organizations (20.3%), thereby incurring many
of the costs of federal policies. Finally, investments by NGOs have mainly benefited local communities
who aim to adopt sustainable production activities (64.2%), but NGOs have also supported (more than
federal or state government organizations) land tenure regularization projects (19.3%).
The geographical distribution of financial resources seemed to follow a more focused rationale.
We found that many project organizations were located in municipalities with the highest consolidated
deforestation rater of Brazil. For instance, NGO projects for territorial and ecological zoning,
strengthening of PA and IT management, as well sustainable production, represent 30% of the total
disbursements from the Amazon Fund and were largely located in this region. Disbursements from the
Amazon Fund to the three main recipient categories have generally benefited municipalities located
in areas where deforestation threats are highest [
53
]. This observation only partially corresponds
with the findings by Wolosin, Breitfeller, and Schaap [
10
], as we found no evidence of substantial
contributions to areas with high tree cover, which are more commonly found in remote areas of the
Amazon biome [35].
Within the pre-established main-components of the Amazon Fund, we also found variation
in the activities that compose these categories. For instance, while most financial resources were
channeled to the strengthening of monitoring and control activities by federal and state governments
(USD 287.2 million), their investments have focused on monitoring activities like satellite imaging
(USD 70.6 million) and CAR implementation (USD 102.5 million). This result contrasts with the
substantially smaller investments in control activities like combating forest fires (USD 32.5 million) or
law enforcement (USD 29.6 million). This trend is representative of the broader resource allocation
within the monitoring and control category. Similarly, investments in land regularization were mainly
directed at indigenous territories and protected areas (USD 66.0 million), whereas smallholders
(USD 4.3 million) received much less support.
Based on our findings on the variations in financial resource distribution, we argue that the project
owners impose a substantial influence on the nature of activities that forest conservation funding

Forests 2019,10, 272 16 of 20
ultimately supports. The current approach adopted by the fund incentivize project submissions in
activities and geographies where they may be most successful in reducing deforestation had a limited
effect. Corresponding with the study by Weatherley-Singh and Gupta [32], for example, the Amazon
Fund restricts financial resource allocation to the four main-components of its theory of change,
while not addressing alternative factors, such as the impacts of cattle ranching, road construction,
international demand for agricultural products, or corruption. However, any project proposal that
adheres to the project quality criteria and guidelines of the Amazon Fund [
13
] may become eligible for
financial support. In other words, the Amazon Fund takes a more passive stance towards resource
allocation after the criteria and guidelines are in place. This view accounts for the great variety of
stakeholders, activities, and geographies, as described above, since each stakeholder category seems to
prefer a different investment strategy. Such behavior may ultimately undermine the effectiveness of
conservation funding provided by Norwegian and German donor organizations, at least in terms of
emissions reductions.
As already argued in Section 2, the Amazon Fund
´
s theory of change is generally geared
towards deforestation reduction, but the design choices of individual projects are primarily directed at
contributing to one or more main components. The evaluation of a completed project in northern Mato
Grosso [
46
], for instance, indicates that the project geared its intervention logic upon its contribution
to the main-components “sustainable development” and “monitoring and control”, and stated that
the main contribution to emissions reductions came from “the restoration of native vegetation and
pastures and the planting of native species in permanent protection areas”. The extent to which such
projects achieved emissions reductions was not stated in the report and would admittedly be a complex
methodological endeavor. The leeway that projects have in contributing to these main-components,
although important for attracting project proposals, accounts (at least partially) for the imbalanced
allocation of financial resources discussed above and may, to some extent, undermine the Amazon
Fund’s contribution to deforestation reduction.
It is important to note that this undermining of the Amazon Fund
´
s overall contribution is
by no means intentional. At the same time, there are also indications that some projects require a
more in-depth evaluation and a longitudinal approach in order to observe their outcomes come to
fruition. Particularly but not exclusively, projects from governmental organizations are under greater
pressure from critical considerations of their contribution to emissions reductions. One may argue
that investments in CAR implementation, for example, support more structural improvements of a
nation-wide instrument to enhance monitoring capacity, but some studies point out that it is still unclear
whether and to what extent this instrument, indeed, contributes to reducing deforestation [
48
,
54
].
In addition
, our analysis indicates that federal government organizations (i.e., CENSIPAM, INPE and
IBAMA) tend to lack financial additionality. Particularly, the substitutive nature of the Amazon Fund
financial resources of IBAMA projects is worrying, because these investments often involve more
direct contributions to reducing deforestation, most notably the enhancement of (the capacity for)
environmental inspections and fire combat. While the lack of funding for law enforcement may have
led to an even higher spike on deforestation rates, a country with a mature enough environmental
governance should be able to grant a stable source of public funding by giving priority to this agenda.
6. Conclusions
Our analysis also helps to understand why empirical studies seem ambiguous about the
effectiveness of forest conservation funding. As explained in Section 3, BNDES’ approach to
distributing financial resources from the Amazon Fund to individual projects occurs based on the
evaluation of project proposals based on the funds widely encompassing guidelines rather than a
strategic selection of projects based on the need to reduce deforestation in areas under threat. As a
consequence, our findings show that disbursements by the Amazon Fund to individual projects adhere
to very diverging theories of change within a broader REDD+ and RBF strategy. The contribution of
each individual project for deforestation reduction are complex to be measured and require additional

Forests 2019,10, 272 17 of 20
studies [
17
,
18
,
22
]. Nevertheless, our results suggest that the lack of strategic focus of disbursements
may compromise the ability of the fund to obtain further deforestation reductions on the short term.
It is particularly concerning the observation that the resources provided by the Amazon Fund
have offset budgetary losses from the Brazilian government in some areas, putting into question the
financial additionality of the fund. At the same time, deforestation rates have been on the rise since
2012, the same period during which the fund has started to take place more steadily [
19
].
It should
be emphasized that the fund is not expected to influence deforestation rates for the whole biome,
and the lack of additionality in some years can be explained by the economic and fiscal crisis in
Brazil. However, these trends taken jointly may weaken the credibility of financial support from the
Amazon Fund and other RBF programs on the long term. The sustainable development activities in
NGO projects seem to incite less critique, but these projects require much closer scrutiny in order to
understand the extent to which they indeed reduce deforestation. Our analysis confirms the argument
by Van der Hoff, Rajão, and Leroy [
19
] that the “demands for demonstrating the results of the Amazon
Fund in a scientifically rigorous manner are likely to become an important topic for donor countries”.
Alternatively, the Amazon Fund could adopt a more active approach to the allocation of financial
resources, for example, by prioritizing a smaller set of activities, with a strong geographical focus.
Most importantly, the Amazon Fund should actively identify potential locations and project owners
and assist them in constructing high impact proposals. Likewise, the fund should also improve its
impact monitoring capabilities and provide incentives to projects that deliver deforestation reductions
within the timeframe of the project. This is especially important, as the political climate in Brazil,
United States and other countries has become more hostile to environmental interests [52,53,55].
Supplementary Materials:
The following are available online at http://www.mdpi.com/1999-4907/10/3/272/s1,
Figure S1: Model for Database Structuration, Figure S2: Steps to collect the variables, Figure S3: Individual Project
Page on Amazon Fund website, Figure S4: Database structured at Level I—Projects, Figure S5: Diagram of rules to
determine municipalities encompassed by projects, Figure S6: Project Tree, Figure S7: Municipalities weighted by
project, Figure S8: Final Database Structure, Figure S9: Pareto graft for NGO’s and State projects (USD left bar and
% of committed amounts right side, Figure S10: Deforestation in Legal Amazon, PRODES-INPE (2017), Table S1:
Municipalities geospatial information sources, Table S2: Municipalities Data Source, Table S3: Variables included
in the main-component level, Table S4: Weight calculations per main-component, Table S5: 10 Municipalities with
the higher deforestation rates between 2016 to 2017. PRODES-INPE (2017), Table S6: Research assumptions in
response at divergences/limitations of data collection.
Author Contributions:
Conceptualization, J.C., R.v.d.H. and R.R.; Data curation, J.C.; Formal analysis, R.v.d.H.
and R.R.; Investigation, J.C. and R.v.d.H.; Methodology: J.C. and R.R.; Visualization, R.R.; Writing—Review &
Editing, J.C., R.v.d.H. and R.R.
Funding:
This research was supported by the Universidade Federal de Minas Gerais, through the Pro-Rectory of
Research-PRPq/UFMG. Also was financed by CAPES–Brazilian Federal Agency for Support and Evaluation of
Graduate Education within the Ministry of Education of Brazil.
Acknowledgments:
We thank Alexander Pfaff from Duke University for the inspiration and academic support,
and Centro de Inteligência Territorial (CIT), PRPQ/UFMG and CAPES for providing funding for this research.
Conflicts of Interest: The authors declare no conflict of interest.
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