ArticleThe extent and distribution of joint conservation-
development funding in the tropicsGraphical AbstractHighlightsd We calculate funding extent for World Bank joint
conservation and development projects
d We show where funding is targeted within the tropics
d We find funding is not driven by conservation or
development need
d Governance and political-economic factors appear to drive
fundingReed et al., 2020, One Earth 3, 753–762
December 18, 2020 ª 2020 The Author(s). Published by Elsevier
https://doi.org/10.1016/j.oneear.2020.11.008Authors
James Reed, Johan Oldekop,
Jos Barlow, ..., Josh van Vianen,
Malaika Yanou, Terry Sunderland
Correspondence
j.reed@cgiar.org
In Brief
This study analyzes the extent and
distribution of World Bank and GEF
funding for joint conservation and
development in the tropics, whether it is
directed to areas of greatest
environmental and development need,
and finally what factors drive funding
allocation decisions. Total spending was
US$16.5 billion across 75 countries. We
find that neither biodiversity nor HDI
status are driving funding allocation, but
rather that governance and political-
economic factors are most likely more
influential.Inc.
ll
OPEN ACCESS
llArticle
The extent and distribution of joint conservation-
development funding in the tropics
James Reed,1,2,13,* Johan Oldekop,3 Jos Barlow,4,5 Rachel Carmenta,2,6 Jonas Geldmann,7,8 Amy Ickowitz,1
Sari Narulita,1 Syed Ajijur Rahman,1,9 Josh van Vianen,1,10 Malaika Yanou,1,11 and Terry Sunderland1,12
1Center for International Forestry Research, Bogor, Indonesia
2University of Cambridge Conservation Research Institute, Cambridge, UK
3Global Development Institute, The University of Manchester, Manchester M13 9PL, UK
4Lancaster Environment Centre, Lancaster University, Lancaster, UK
5Universidade Federal de Lavras, Lavras, Minas Gerais 37200-000, Brazil
6Tyndall Centre and the School of International Development, University of East Anglia, Norwich, UK
7Center forMacroecology, Evolution andClimate, Globe Institute, University of Copenhagen, Universitetsparken 15, Copenhagen EDK-2100,
Denmark
8Conservation Science Group, Department of Zoology, University of Cambridge, Downing St., Cambridge CB2 3EJ, UK
9Department of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing, China
10Forest Carbon, Jakarta, Indonesia
11University of Amsterdam, Amsterdam, the Netherlands
12University of British Columbia, Vancouver, Canada
13Lead Contact
*Correspondence: j.reed@cgiar.org
https://doi.org/10.1016/j.oneear.2020.11.008SCIENCE FORSOCIETY This study analyzes 381 projects of theWorld Bank and the Global Environment Fa-
cility (GEF) concluded between 1995 and 2013 to show how much money is spent on joint conservation and
development in the tropics, where the money is directed, whether it is directed to areas of greatest environ-
mental and development need, and finally what factors drive funding allocation decisions. The total extent of
funding was US$16.5 billion across 75 countries, representing approximately US$870million per year. Coun-
tries with high biodiversity and low human development receive nomore funding for integrated conservation
and development than other countries. Notably, countries with a low biodiversity status receive relatively
more funding than highly biodiverse countries and there was no association between development need
and funds received. Therefore, we find that neither biodiversity nor human development status explain fund-
ing allocation, but rather that governance and political-economic factors are most likely more influential.SUMMARYDespite ongoing debates about the viability of sustaining economic growth while maintaining environmental
integrity, international sustainability agendas increasingly propose reconciling socio-economic development
and global environmental goals. Achieving these goals is impeded by limited funding and a lack of informa-
tion onwhere financial flows to integrate environment and development are targeted.We analyzeWorld Bank
and Global Environment Facility data to investigate the extent and distribution of such funding across the tro-
pics. We find a misalignment between funding flows and need with highly biodiverse, low development
(HBLD) countries receiving no more funding than non-HBLD countries. Countries with low biodiversity
receive more funding than highly biodiverse countries and there was no statistical association between a
country’s development status and funds received. Rather than environment-development need, funding ap-
pears to be driven by governance and political-economic factors. Future research should investigate how
such factors and funding flows are associated with conservation and development outcomes.INTRODUCTION Several of the 20 internationally agreed Aichi biodiversity targetsContemporary international commitments recognize, more than
ever before, the importance of reconciling social and environ-
mental agendas to address global sustainability challenges.1–3One Earth 3, 753–762, Decem
This is an open access article undof the Convention on Biological Diversity incorporate a social or
economic component (e.g., Targets 1, 2, and 3) and Target 11
specifically calls for a more equitable approach to conservation.
In a similar vein, the Sustainable Development Goals (SDGs) areber 18, 2020 ª 2020 The Author(s). Published by Elsevier Inc. 753
er the CC BY license (http://creativecommons.org/licenses/by/4.0/).
ll
OPEN ACCESS Articleframed around the pledge to ‘‘leave no one behind’’—a recogni-
tion of the need to support those people furthest behind first—
and the commitment to inclusivity is now well established in
SDG rhetoric, with an understanding that goals need to be ad-
dressed in a holistic manner. This agenda has been widely
endorsed by national governments (officially adopted by 193
countries) and is largely considered to be a more equitable
approach to development relative to predecessors.
However, this integrated agenda is far from new. The Brundt-
land report (1987) linked environment and development within its
mandate on ‘‘Our Common Future’’ and was followed by the Rio
declaration of 1992 that explicitly recognized the inter-related
challenges faced by humanity and the environment when at-
tempting to enhance economic development while also halting
depletion of natural resources. The post-Rio soundbite declared
that ‘‘nothing less than a transformation of our attitudes and
behavior [is required] to bring about the necessary changes.’’
Governments recognized that fundamental policy changes
were needed to develop a ‘‘grand survival plan’’ for humanity
that ensured future economic decision making fully considered
environmental impacts.4
Since 1992, global GDP has continued to rise, funding for
biodiversity conservation has increased,5–8 the global network
of protected areas has grown,9 and global hunger has fallen.10
Yet since 1970 the number of birds, mammals, reptiles, and
amphibians has decreased by more than half11 while globally
aggregated statistics mask important geographic and temporal
heterogeneity, persisting inequalities, and sharp declines in
environmental health and biodiversity.12,13 In particular, the
global tropics, where many conservation and development
challenges intersect, continue to experience alarming losses
of biodiversity and areas of persistently low human welfare.
The vast majority of the 900 million people living in poverty
today reside in sub-Saharan Africa (SSA) and South Asia,14
with the first increase in global hunger in over a century occur-
ring in 2016,15 and the largest increase in deforestation in the
Brazilian Amazon since records began occurring in 2019 (see
http://terrabrasilis.dpi.inpe.br). The continued combination of
economic, environmental, and political pressures on tropical
land means that poverty, food insecurity, and biodiversity loss
remain some of the most pressing concerns of the global envi-
ronment and development community. Furthermore, these
challenges are amplified by an increasingly unstable climate,
the impacts of which will be felt hardest by those living in
already vulnerable tropical geographies who have limited ca-
pacity to respond.16,17
In recognition of the need to reconcile global environmental
commitments with local economic and socio-cultural realities,
a variety of concepts—such as Integrated Conservation and
Development Projects, Payments for Environmental Services,
Ecosystem Approaches, and Integrated Landscape Ap-
proaches—have sought to deliver improved outcomes for both
society and environment at regional or landscape scales.18–21
Furthermore, international development agencies and the big in-
ternational conservation NGOs have increasingly adopted more
holistic strategies within their agendas whereby they aim to bet-
ter integrate nature and people.22,23 Despite this recent focus
and thewidespread appeal of such ‘‘win-win’’ strategies, applied
examples of effectiveness at scale have been elusive.24–27754 One Earth 3, 753–762, December 18, 2020Due to the complexity of integrating often conflicting conser-
vation and development agendas, a number of impediments to
their effective reconciliation have been identified28–30 and a num-
ber of critiques of the feasibility of such strategies docu-
mented.31–33 Apparent proponents of integrated approaches
assert that a major impediment to progress is a lack of funding,
with solutions orientating around calls for an increase in funding
from both the public and private sectors.34,35 Such views reso-
nate with the biodiversity conservation literature that has consis-
tently lamented insufficient funding for the protection of na-
ture.5,36–38 The issue of financial allocation is more contested
within the development literature with strong arguments in favor
of increased funding for developing countries contrasted with
claims that development aid leaves unsustainable legacies,
has no effect on growth, or even exacerbates poverty traps.39–43
Due to its considerable financial leverage and the normative
power of its development theories, the World Bank provides a
highly relevant case study to examine the allocation of joint
development and environmental funds.44 In response to pres-
sure from member governments and NGOs, the World Bank
increased its commitment to the environment within its project
portfolio post-Rio 1992,45 coincidingwith the creation of its sister
organization, the Global Environment Facility (GEF).6 Conse-
quently, the World Bank is now established as the largest inter-
national donor to biodiversity conservation,7,46 spending (com-
bined with the GEF) in excess of US$300 million annually.47
Having long been recognized for its considerable allocation of
funds for development aid, it is reasonable to contend that
post-Rio the World Bank has been one of—if not the—principal
funder(s) for initiatives that integrate environment and develop-
ment agendas.48 With recent commitments to more integrated
approaches to land management, funding for joint conservation
and development continues to increase, but as yet there is
limited analysis of the extent of funding to such initiatives. Our
analysis therefore complements previous efforts48,49 and has a
targeted focus on the largest funders and a biome of specific
concern for integrated conservation and development efforts.
Finally, our index of countries detailing their respective environ-
mental and development needs offers insight into where fi-
nances are being invested pre-emptively. As such, we provide
a resource for researchers and decision makers detailing previ-
ous spending and our analysis can help to inform future conser-
vation and development decision-making.
Here, we use publicly available data sources of theWorld Bank
and the GEF to investigate the extent and distribution of funding
for integrated conservation and development since the 1992 Rio
Earth Summit. We restrict our analysis to the tropics, which con-
tains the majority of the world’s biodiversity, has the highest pro-
portion of threatened species, and has relatively low develop-
ment status and response capacity that is far below the global
North.17 Our objectives are to determine how much funding
has been allocated toward joint conservation and development,
identify where funding is directed, assess whether this funding is
targeted toward areas of greatest environmental and develop-
ment need, and finally consider what factors are driving funding
allocation.
As integrated environment-development initiatives become
more prevalent, major investments are being made; however,
‘‘accurate estimates of the financial magnitude of these
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Article OPEN ACCESS
Figure 1. Extent of World Bank and GEF
funding
Absolute (A) and relative proportion (B) of funding for
World Bank, GEF, and co-funded (GEF & World
Bank) integrated environment and development
projects from 1995 to 2013 in tropical countries.
GFC, global financial crisis; MA, Millennium
Ecosystem Assessment; MDGs, Millennium Devel-
opment Goals.investments are not available.’’22 Given also the apparent lack of
evidence regarding the effectiveness of integrated ap-
proaches25,26 and a shortfall in available funding,35 it is useful
to consider the extent and distribution of previous financial flows.
To determine effectiveness, an important first step is to better
understand where and how funding for integrated conservation
and development has been applied.
RESULTS
Allocation of funding for integrated projects
Our study period (1995–2013) was determined by completed
projects following the first cycle of post-Rio funding, and data
availability (completed projects) thereafter. We specifically tar-
geted World Bank and GEF projects with both an environ-
mental and development component (see the Experimental
Procedures). From an initial 2,622 project reports we collated
relevant financial flow data for 381 World Bank, GEF, and co-
funded (i.e., World Bank and GEF) integrated environment
and development projects. Funding was distributed across 75
tropical countries, although both the volume and frequency of
funding per country varied greatly (Figures 1 and 2). The abso-Onlute volume of funding for the 19-year
period was in excess of US$16.5 billion,
representing an average investment of
almost US$872 million per year. Unless
otherwise stated, we refer here to the
aggregate spending of both the World
Bank and GEF toward integrated projects
over the study period.
There was a noticeable increase in
financing in the mid to late 1990s,
perhaps as a response to the 1992 Earth
Summit and pressure from NGOs and
member governments. The first half of
the last decade (2000–2005) saw a
decrease in financing; however—with
spikes in 2002 and 2004—it is worth
noting that the Millennium Development
Goals, which incorporated environmental
sustainability targets were established in
2000, and the Millennium Ecosystem
Assessment (MA), which explicitly recog-
nized human wellbeing and ecosystem
linkages, was launched in 2001. Funding
then rose again in the latter half of the
last decade, possibly in response to the
publication of the MA in 2005, with a
noticeable dip in 2009, potentially relatedto the 2008 economic crash. The large decrease shown for
the years 2012 and 2013 is likely not representative of the
actual financial commitment for these years; the data were
retrieved from completed World Bank/GEF projects and we
speculate that a proportion of the evaluation reports for pro-
jects concluded in 2012/2013 were yet to be made available
at the time of our screening. Regardless, we have shown that
there has been considerable investment (in excess of US$16
billion) from the World Bank and GEF toward integrated conser-
vation and development in the tropics.
Funding alignment with environment and
development need
We mapped and overlaid the human development index (HDI)
and species richness data to identify tropical areas with high
biodiversity and low human development scores, whichwe cate-
gorize as highly biodiverse, low development (HBLD) areas (see
the Experimental Procedures) and then overlaid this information
with the financial flows data obtained from our screening of
World Bank and GEF projects. This allowed us to illustrate how
funding is targeted to environment and development across
the tropics (Figure 2).e Earth 3, 753–762, December 18, 2020 755
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OPEN ACCESS Article
Figure 2. Overlay of World Bank and GEF
financial investment with areas of high to
low biodiversity and low to high development
status in the Tropics
HBLD, high biodiversity low development; HDI,
human development index; BIO, biodiversity.A visual assessment could suggest that the funding for envi-
ronment and development has been well targeted—areas with
greater investment appear to be well-distributed among HBLD
countries (Figure 2). For example, biodiversity rich and relatively
economically poor areas of Central America, West Africa, and
Southeast (SE) Asia have clearly received a significant share of
the absolute investment. However, a statistical assessment re-
veals a more nuanced funding landscape with some interesting
patterns related to conservation need, level of development,
biodiversity status, and geographic region.
Over the entire study period, there was a large variation in the
amount and frequency of funding different countries received
(Figure 3). For example, at the lowest end of the spectrum
Djibouti and Côte D’Ivoire received approximately US$2 million
for one project, while Mexico, Brazil, India, and Indonesia
received in excess of US$1 billion each for 81 projects collec-
tively (see Table 2).
Following Sachs et al.50 we hypothesized that areas of high
biodiversity would broadly overlap with areas of low develop-
ment. We further hypothesized that funding for joint environment
anddevelopmentwould correlatewell with these identifiedHBLD
countries. However, the evidence did not support this relation-
ship; while HBLD countries are broadly financially supported
(35 of the 39 received funding), we found no statistically signifi-
cant difference in funding per capita or funding per area received
between HBLD and non-HBLD countries. There was also no sta-
tistical association between a country’s development status and
funds received measured either in per capita or per area terms.
Moreover, we found that countries with low biodiversity received
more funding per capita relative to countries with high biodiver-
sity after controlling for land area, governance, and inequality
(Figure 4), implying that national biodiversity status is not an influ-
ential determinant of donor’s funding decisions. We also tested
the relationship between threatened species as opposed to
high biodiversity (using IUCN red list data) and funding allocation,
but again found no statistically significant association (Figure 4).
Given that SSA countries are the World Bank’s stated top pri-
ority (see http://www.worldbank.org/en/news/press-release/
2016/07/12) it is interesting—and of concern—to note the mini-
mal overlap between HBLD countries and investment in this re-
gion. Of the 47 SSA tropical countries, 24 were characterized as
HBLD. Of these 24, 12 were in the bottom 2 quintiles of funding
(i.e., below US$150 million across the study period), with 7 in the756 One Earth 3, 753–762, December 18, 2020lowest quintile (below US$55 million) and 4
received no funding. Malawi received the
most funding in SSA both in absolute vol-
ume and by unit area (US$301 million and
US$2,421 million/km2, respectively) and
yet failed tomake the top 10most highly in-
vested tropical countries by either metric.
Indeed, when ranked by funding per unitarea, only 6 of the top 20 countries with the highest rates of in-
vestment were HBLD countries, with Bangladesh ranked highest
in ninth, and only 4 were in SSA (Malawi, Burundi, Gambia, and
Rwanda). In terms of absolute volume of investment, SSA is the
region with the second highest amount of investment (of four)
(Table 1). However, the total investment of US$3.4 billion is
dwarfed by the US$8.5 billion committed to Latin America and
Caribbean (LAC) (more than half of the total commitment). Both
regions had a similar number of projects funded (142 and 148,
respectively); however, funding was spread across 39 SSA
countries and only 22 LAC countries, indicating a lower average
project funding commitment in SSA.
Of the top 5 most heavily invested countries, three (Mexico,
Brazil, andMadagascar) are not HBLD countries and yet account
for US$7.4 billion (44% of the total investment—see Table 2).
Meanwhile, four HBLD countries (Angola, Equatorial Guinea, Re-
public of Congo, and South Sudan) received zero funding across
the study period (Figure 2) and of the five least invested coun-
tries, two (Côte D’Ivoire and Zimbabwe) are HBLD (Table 2).
Governance and political-economic factors
The influence of governance on both the targeting and effec-
tiveness of aid spending has been a contested issue.51–55
Some authors have shown that better governance is positively
correlated with aid delivery,7 while others have shown that do-
nors continue to fund corrupt countries with relative funding to
such countries increasing after the Cold War.56 Focusing on
protected areas, Hickey and Pimm47 showed that World
Bank investment decisions are not influenced by a country’s
governance status. However, our findings show that a signifi-
cantly greater proportion of finance for joint conservation and
development is invested in countries with better governance
ratings.
Our results corroborate those of Hickey and Pimm47 in that
‘‘there is no evidence to suggest that countries with lower-
cost structures receive more investments.’’ For example, the
countries with the lowest average cost structure were
Zimbabwe, Djibouti, and the Seychelles, each of which
featured in the lowest quintile of absolute funding; these fig-
ures, however, are not proportional, and it should also be
noted that each of these countries only had a minimal number
of projects (<3) across the study period. Finally, consistent
with Miller48 we find that national population size is positively
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Article OPEN ACCESS
Figure 3. All countries in receipt of funding, with funding received
plotted against average HDI
Colors indicate relative biodiversity rank based on combined national-level
species richness range data (see Experimental Procedures).correlated with financing for joint environment and develop-
ment initiatives.
Of course, there are a multitude of further factors that may in-
fluence how the World Bank (and other funding agencies) deter-
mineswhere to invest. For example, it has beenwidely discussed
how World Bank rhetoric on sustainable development has not
been matched in reality while the Bank’s internal structures
incentivize the disbursement of money, yet fail to sufficiently pro-
mote or reward environmental impact assessments.44,57–60
Further factors, not fully considered here, include historical polit-
ical or military alliances between donor and recipient countries,
colonial legacies, securing access to natural resources, and level
of socio-political stability within recipient countries (see Hicks
et al.,49 chapter four, for more detailed examples). We found
that countries with a higher governance rating received more
funds per area (Figure 4D), supporting the idea that stabilitymight
be a consideration. Furthermore, recent funding allocation may
also be partially driven by other conservation targets, such as
climate change mitigation potential. For example, the enormous
carbon storage and carbon sequestration values of the humid
tropical forests in South America, Central and West Africa, and
SE Asia likely explains part of their appeal to funders, while the
absence of strong climate change mitigation benefits may
explain the lack of funding in some of the dry regions. Finally,
several SSA countries that received limited or no funding ex-
perienced extended periods of conflict during the study period.
Nevertheless, we hope that this study provides a useful prelimi-
nary investigation of the amount and distribution of funding for
initiatives with integrated environment and development objec-
tives in the tropics, upon which future research, discussion, and
decision-making can build.DISCUSSION
The HBLD areas presented here are intended to stimulate a
broader discussion around the targeting, allocation, and appro-
priate use of funds for both—and particularly joint—environment
and development approaches in the tropics. We are not aware of
a previous attempt at developing global priority areas for inte-
grated conservation and development. However, Sachs et al.50
overlaid poverty (using infant mortality as a proxy) and threatened
species data that showbroadly similar patterns to themaps in this
study,withareasof highpoverty andspecies threatmostly located
in SSA, South Asia and East Asia, and the Pacific. Meanwhile,
conservation planning as a field has a history of identifying
priority areas based on, for example, vulnerability,61 ecosystem
services,62 or more recently ‘‘key biodiversity areas’’ (see
http://www.keybiodiversityareas.org/site/mapsearch). Achieving
consensus for such priority areas has not been without its chal-
lenges63,64—not least because both biodiversity and the threats
to its conservation are unequally distributed, while reactive and
proactive approaches to conservation planning will produce
varying priority outcomes.65 Furthermore, data availability for
biodiversity at the national scale is poor. Despite the Convention
on Biological Diversity (CBD) requirement of countries to provide
such data, we were able to find just two sources of data each rep-
resenting a single year within our study period. Future effort is
needed to develop national and sub-national biodiversity indices;
such datawould enable longitudinal analysis of biodiversity status
and how it relates to other factors.
It is difficult to assess how our findings compare with other
studies due to a lack of systematization in reporting and
analyzing data across studies and donors,56 and the limited
previous analysis of joint conservation and development
financing.25 However, World Bank and GEF funding for biodiver-
sity aid has previously been estimated at almost US$11 billion for
the period 1980–2008, or equivalent to approximately US$393
million annually,7 with 90% of this allocation targeted toward
biodiversity projects linked with development objectives.48
Meanwhile, Hickey and Pimm47 suggest that World Bank and
GEF spending on biodiversity projects is in the region of
US$309 million per annum. Hicks et al.49 provide the highest es-
timate we were able to find, reporting that the World Bank spent
in excess of US$13 and US$25 billion in the 1980s and 1990s,
respectively; these figures, however, are based on environ-
mental aid without the condition to be linked with a development
component. Variation in funding numbers can also be attributed
to factors, including the extent of in-country co-financing, incon-
sistency in how projects are classified by researchers, and
indeed by the World Bank itself,46 and differences in the US$
standard rate applied. Additional research is needed to deter-
mine the total investment toward integrated conservation and
development from across the spectrum of funding sources to
better evaluate funding distribution and accurately calculate
the shortfall between current spending and global commitments
to development and the environment.35
Our main finding shows that funding decisions for integrated
environment and development projects are neither driven by
biodiversity nor HDI status, and SSA countries, in particular, do
not receive an amount commensurate with their HBLD status.
We suggest this warrants further investigation and considerationOne Earth 3, 753–762, December 18, 2020 757
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OPEN ACCESS Article
A B C
HDI [High] HDI [High] HDI [High]
HDI [Low]
HDI [Low]
HDI [Low]
Biodiversity [High]
Biodiversity [High]
WGI
Biodiversity [Low]
Biodiversity [Low]
WGI
Population
WGI
Population
Gini coefficient (average)
Population
Gini coefficient (average)
Gini coefficient (average) Red List (average)Red List (average)
−100 −50 0 50 −100 −50 0 50 −100 −50 0 50
Estimate Estimate Estimate
D E F
HDI [High] HDI [High] HDI [High]
HDI [Low]
HDI [Low]
HDI [Low]
Biodiversity [High]
Biodiversity [High]
WGI
Biodiversity [Low]
Biodiversity [Low]
WGI
Total area
WGI
Total area
Gini coefficient (average)
Total area
Gini coefficient (average)
Gini coefficient (average) Red List (average)Red List (average)
−80 −40 0 40 −80 −40 0 40 −50 0 50
Estimate Estimate Estimate
Figure 4. Predictors of joint conservation-development funding
Coefficients of regression models for: (A) funding per area as a function of high and low HDI (baseline is medium), high and low biodiversity (baseline is medium),
WGI, population size, and Gini coefficient; (B) funding per area as a function of high and low HDI (baseline is medium), high and low biodiversity (baseline is
medium), WGI, population size, Gini coefficient, and red list scores; (C) funding per area as a function of high and low HDI (baseline is medium), WGI, population
size, Gini coefficient, and red list scores; (D) funding per capita as a function of high and low HDI (baseline is medium), high and low biodiversity (baseline is
medium), WGI, population size, and Gini coefficient; (E) funding per capita as a function of high and low HDI (baseline is medium), high and low biodiversity
(baseline is medium), WGI, population size, Gini coefficient, and red list scores; and (F) funding per capita as a function of high and low HDI (baseline is medium),
WGI, population size, Gini coefficient, and red list scores. Thick lines of bars represent standard errors and thin lines represent 95% confidence intervals.
Variables Variables
Variables Variables
Variables Variablesfor future integrated environment and development funding allo-
cation.While we acknowledge that increasing fundingmight only
be part of the solution to effectively reconciling environment and
development agendas, it is important to recognize that for biodi-
versity conservation at least, the effect of funding has been
shown to be significant.8
Concluding comments
Our objective for this article was to identify where funding for in-
tegrated environment and development has been targeted. Con-
trary to our hypotheses this study shows that HBLD areas across
the tropics receive no more funding for integrated conservation
and development than non-HBLD countries. In a similar inverse
outcome to our expectation, countries with a low biodiversity rat-
ing receive relatively more funding than highly biodiverse coun-
tries. Furthermore, we find no statistical association between758 One Earth 3, 753–762, December 18, 2020development status and funds received. Therefore, neither biodi-
versity nor HDI status are driving funding allocation, and our anal-
ysis shows that governance and political-economic factors as
proxied by inequality in our models are likely more influential.
Fulfilling the objectives of the SDGs and other internationally
agreed commitments toward climate, conservation, and devel-
opment will require transformational shifts in thinking and the
way in which we define and measure progress. It has long
been recognized—and is increasingly accepted—that GDP per
capita is an inadequate metric for development, and particularly
human welfare.66–68 We must therefore incorporate other vari-
ables that cumulatively contribute toward a country’s economic,
social, and environmental health. It is also widely accepted that
financial resources are scarce and ‘‘must be used where they
can have the largest effect’’ (see https://www.worldbank.org/).
Progress toward international goals with limited resources
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Article OPEN ACCESS
Table 1. Regional distribution of total funding for conservation
and development
Region No. of projects Investment (US$)
East Asia and Pacific 66 2,714,183,508
South Asia 25 1,858,139,739
Latin America and 148 8,583,362,104
Caribbean
Sub-Saharan Africa 142 3,411,943,139
Total 381 16,567,628,490
Regions are based on World Bank classification.therefore demands a concerted financial strategy that prioritizes
key areas where multiple benefits can be achieved, including
conservation of biodiversity and ecosystems and mitigating the
effects of climate change while ‘‘leaving no one behind.’’
Future research to assess the performance of integrated envi-
ronment-development initiatives is urgently required to enhance
our understanding of the appropriateness and effectiveness of
global financial flows for such endeavors. Furthermore, future
research could disaggregate country-level patterns to achieve
a better understanding of in-country flows and the role of partic-
ular sites or site dynamics. Our study provides guidance for
future investment decisions related to integrated conservation
and development across the tropics and generates discussion
around how—and why—finances are targeted and ultimately
to what effect. Our results suggest that a specific consideration
is warranted for those countries recognized as HBLD areas that
will be among the most negatively impacted as a result of inac-
tion to the threats of global environmental change.EXPERIMENTAL PROCEDURES
Resource availability
Lead contact
Further information and requests for data should be directed to the Lead Con-
tact, James Reed (j.reed@cgiar.org).
Materials availability
This study did not generate new unique materials.
Data and code availability
The full dataset for this study is openly available in the CIFOR repository at
https://doi.org/10.17528/CIFOR/DATA.00251.Method summary
This article is based on an analysis of the publicly available datasets of the
World Bank and the GEF (all project IDs are provided in the publicly available
CIFOR data repository listed above).
In addition to the World Bank and GEF we identified a broad range of insti-
tutions that provide funding for initiatives that integrate environment and devel-
opment objectives; however, a lack of available and transparent data—basic
principles for aid organizations (see, for example, Paris declaration on aid
effectiveness, 2005)—precluded the majority of sources from being used for
this study. Nevertheless, as probably the largest funders of joint environment
and development globally, this review of theWorld Bank and the GEF provides
a relevant case study upon which further research can build. Moreover, the
World Bank and the GEF should be commended for systematically making
project data freely available; the implementation completion and results and
terminal evaluation reports provide an excellent—and often underutilized—
resource for research examining aid allocation patterns for economic develop-
ment and environmental conservation,69 albeit that data can be hidden in
voluminous reporting frameworks.We were interested in identifying where, geographically, the World Bank
and GEF invested in projects that incorporated linked environment and
development agendas on land in the tropics. Our study period was
1995–2013; the start date was considered to be a realistic point at which
joint environment and development projects would be concluded in
response to the 1992 Rio Earth Summit. The end date was determined
by the most recently concluded project data availability. To overcome is-
sues associated with data inconsistency, this review only focuses on
concluded projects.
Search strategy
The World Bank is recognized as a primary funder for international develop-
ment and so therefore we focused our initial search strategy on the World
Bank’s environmental topic and corresponding 33 subthemes (ranging from
‘‘adaptation to climate change’’ to ‘‘wildlife resources’’; see Table S1 for full
list) anticipating this would provide a suite of projects that contained both envi-
ronment and development components.
The GEF was established on the eve of the Rio Earth Summit to address the
planet’s most pressing environmental problems (see www.thegef.org) and its
project database is categorized into eight main focal areas. For this review we
applied project type and status filters to capture full size and closed projects
from the following GEF focal areas: Biodiversity, Climate Change, Land
Degradation, and Multi Focal Area. To further expand our search, we also
used Integrated Natural Resource Management filter as our own search
term (Table S2).
Supplementary searches
As our screening of the World Bank and GEF projects proceeded and our un-
derstanding of the functioning of the respective databases increased, it
became apparent that there were limitations to our initial search strategy. As
such, we designed and conducted a second search strategy of both theWorld
Bank and GEF databases. For the second World Bank search we selected 10
topic filters and 17 sub-areas (Table S3). For the second GEF search we used
the same strategy but included medium size projects.
In total, our searches yielded 2,622 project reports: 1,244 projects from the
World Bank and 1,378 projects from the GEF. As the World Bank and the GEF
often co-fund projects, we removed duplicates from the dataset and then pro-
ceeded with project report screening, data extraction, and analysis.
Project screening
Five reviewers independently screened all captured projects with an imple-
mentation completion report (World Bank) or evaluation report (GEF),
applying the following inclusion criteria for a project to be included in the final
suite of studies: (1) located within the tropics—in part within the Tropics of
Cancer and Capricorn (countries listed in Table S4), (2) had a terrestrial
land-use focus, and (3) contained both an environmental and developmental
objective. A total of 9 months (five reviewers, two full-time and three part-
time from June 2017 to April 2018) was required for designing the strategy
and extracting project bibliographic data and relevant information for total
project cost, duration, environmental and developmental objectives, and
outcomes and risk assessment. After removal of duplicates and screening
for relevance, from a total of 2,622 reports the final suite of studies for anal-
ysis totaled 381 projects (World Bank, GEF, and co-funded projects
combined).
Biodiversity and development data
We used proxy indicators for national-level measures of ‘‘biodiversity’’ and
‘‘development’’ to determine highly biodiverse, low development (HBLD)
areas. These data consist of the HDI (UNDP—average value across the study
period) and spatial overlays of species richness range maps for birds, mam-
mals, and amphibians using data from biodiversitymapping.org that combines
data from BirdLife International and the International Union for Conservation of
Nature.70,71 Both the conservation and development measures were trans-
formed into three-level ordinal measures (using tercile values—birds, mam-
mals, and amphibians were ranked individually; a national scale ranking of
low, medium, or high was then calculated for both biodiversity and develop-
ment) and combined into a binary measure of HBLD countries or non-HBLD
countries: countries with medium to high biodiversity ranges and medium toOne Earth 3, 753–762, December 18, 2020 759
ll
OPEN ACCESS Article
Table 2. The 10 countries receiving the Most (n = 5), and the least (n = 5) environment and development funding by volume
Investment FPU No. of Biodiversity HBLD
Country (US$, Millions) (US$/km2) projects WGI HDI status country
Most Mexico 3,520 1,498 (16) 24 
0.11 (M) 0.72 (H) 6 (M) no
Brazil 3,200 358 (38) 32 0.03 (M) 0.7 (H) 9 (H) no
India 1,340 401 (37) 14 
0.25 (M) 0.53 (L) 7 (M) yes
Indonesia 1,040 548 (31) 11 
0.63 (L) 0.63 (M) 7 (M) yes
Madagascar 708 1,062 (21) 14 
0.42 (L) 0.49 (L) 5 (L) no
Least Djibouti 1.8 79 (63) 1 
0.72 (L) 0.4 (L) 4 (L) no
Côte D’Ivoire 2.34 7.15 (73) 1 
1.06 (L) 0.41 (L) 7 (M) yes
Comoros 3.43 1,961 (14) 1 ND 0.47 (M) ND no
Seychelles 3.79 7,602 (5) 2 0.21 (H) 0.73 (H) 3 (L) no
Zimbabwe 4.26 9.75 (71) 3 
1.35 (L) 0.44 (L) 8 (H) yes
FPU, funding per unit area, numbers in brackets indicate rank out of all recipient countries in dataset, n = 75. When ranked by FPU, none of the top five
recipient countries (Maldives, Samoa, Antigua and Barbuda, Kiribati, Seychelles) are HBLD countries, and Côte D’Ivoire, Central Africa Republic, and
Zimbabwe of the bottom five (Sudan, Chad, Côte D’Ivoire, Central Africa Republic, and Zimbabwe) are HBLD countries. WGI, world governance index;
HDI, human development index. Biodiversity status reflects our own ordinal ranking for country-level biodiversity: high (H), medium (M), and low (L).low development ranges (i.e., countries exhibiting relatively high biodiversity
and relatively low development) were categorized as HBLD countries (see Fig-
ure 2). We use the HDI (http://hdr.undp.org/en/data) as our measure of devel-
opment because poverty and human wellbeing are increasingly recognized as
multi-dimensional and thought of as encompassing more than income and
consumption, which have been typically used as measures of develop-
ment66—although we also conduct a robustness test using GDP per capita.
We acknowledge that there are varying frameworks of how funding for con-
servation and/or development should be prioritized and distributed. For
example, the HBLD areas we analyze are areas where medium to high biodi-
versity status overlaps with low development status. However, an alternative
way to prioritize funding would be to target those areas where biodiversity
has already been impacted. To test this relationship, we collected threatened
species data from the IUCN red list and again took the average country values
across the study period and included this as an additional variable in our
models (see below). It should also be noted that, despite our inclusion of the
Gini coefficient in our analysis, country-level GDP and HDI metrics obscure
a high level of regional inequality, especially in the larger countries. For
example, the vast majority of the Brazilian Amazon and Caatinga regions
have medium to low HDI scores, which is not reflected in the country-level
scores; indeed, within the single state of Pará there are 142 municipalities
covering the spectrum of very low to high HDI. Finer grained spatial assess-
ments of projects would enable a richer understanding of environment-devel-
opment funding priorities.
We also collected other publicly available national-level development data,
including world governance index (WGI) measures (World Bank Group, 2018),
GDP per capita (World Bank Data), Gini index measures of inequality (World
Bank Data), population (World Bank Data), and environmental performance in-
dex measures (Yale University). We screened projects and collected data be-
tween June 2017 and April 2018 and transformed all financial data to 2010 US$
values.
Statistical analysis
We use the various datasets extracted from World Bank and GEF reports
and combine these with those extracted from additional data sources
(detailed above) to run a series of linear regression models to understand
what predicts total funding received (expressed as both US$ per capita
and US$ per km2). All measures were taken as averages across the study
period 1995–2013. We ran three models for each outcome: the first
outcome was funding per area, regressed as a function of (1) HDI, biodiver-
sity, WGI, population size, and Gini coefficient; (2) included threatened spe-
cies (red list) to the variables listed in (1); (3) removed biodiversity and re-
tained threatened species. The second outcome was funding per capita—
the same set of variables (described above for outcome one) were included760 One Earth 3, 753–762, December 18, 2020across three models with the exception of population, which was
substituted for total area. All models were run in Stata 15 using Hubert-
White robust standard errors. We also ran the models with regional
dummies, but in the end did not include these since they were highly corre-
lated with the other independent variables and since some regions had
very few observations (South Asia only had three observations). All models
were run using robust standard errors. Descriptive statistics and regression
results are provided in Tables S5 and S6, respectively.SUPPLEMENTAL INFORMATION
Supplemental Information can be found online at https://doi.org/10.1016/j.
oneear.2020.11.008.
ACKNOWLEDGMENTS
This study is part of the CGIAR Research Program on Forests, Trees and
Agroforestry (CRP-FTA). This collaborative program aims to enhance the
management and use of forests, agroforestry, and tree genetic resources
across the landscape from forests to farms. Funding for this study was pro-
vided by the International Climate Initiative (IKI) of the Federal Ministry for the
Environment, Nature Conservation, Building and Nuclear Safety (BMUB)
grant 18_IV_084, the United States Agency for International Development
(USAID) Forest and Biodiversity Office, and the CGIAR FTA program. R.C.
was supported by the Frank Jackson Foundation. We are very grateful for
the comments of the anonymous reviewers that helped to improve the
manuscript.
AUTHOR CONTRIBUTIONS
J.R. conceived the study. J.R. and J.v.V. designed the study. M.Y., S.N., and
J.R. collected the data. J.O., A.I., and J.R. analyzed the data. J.R. wrote the
original draft. All authors contributed to developing, writing, reviewing, and
finalizing the paper.
DECLARATION OF INTERESTS
The authors declare no competing interests.
Received: January 8, 2020
Revised: September 21, 2020
Accepted: November 20, 2020
Published: December 18, 2020
ll
Article OPEN ACCESSREFERENCES
1. Cutter A., Osborn D., Romano J., Ullah F. (2015). Sustainable
Development Goals and Integration: Achieving a better balance between
the economic, social and environmental dimensions. In: Stakeholder
Forum, A Study Commissioned by the German Council for Sustainable
Development.
2. Costanza, R., Fioramonti, L., and Kubiszewski, I. (2016). The UN
Sustainable Development Goals and the dynamics of well- being. Front.
Ecol. Environ. 14, 59.
3. Griggs, D., Stafford-Smith, M., Gaffney, O., Rockström, J., Öhman, M.C.,
Shyamsundar, P., Steffen, W., Glaser, G., Kanie, N., and Noble, I. (2013).
Sustainable development goals for people and planet. Nature 495,
305–307.
4. Roaf, S., Crichton, D., and Nicol, F. (2009). Adapting Buildings and Cities
for Climate Change: A 21st Century Survival Guide (Routledge).
5. Balmford, A., Gaston, K.J., Blyth, S., James, A., and Kapos, V. (2003).
Global variation in terrestrial conservation costs, conservation benefits,
and unmet conservation needs. Proc. Natl. Acad. Sci. U S A 100,
1046–1050.
6. Tallis, H., Kareiva, P., Marvier, M., and Chang, A. (2008). An ecosystem
services framework to support both practical conservation and economic
development. Proc. Natl. Acad. Sci. U S A 105, 9457–9464.
7. Miller, D.C., Agrawal, A., and Roberts, J.T. (2013). Biodiversity, gover-
nance, and the allocation of international aid for conservation. Conserv.
Lett. 6, 12–20.
8. Waldron, A., Miller, D.C., Redding, D., Mooers, A., Kuhn, T.S., Nibbelink,
N., Roberts, J.T., Tobias, J.A., and Gittleman, J.L. (2017). Reductions in
global biodiversity loss predicted from conservation spending. Nature
551, 364–367.
9. UNEP-WCMC. (2018). Protected Planet Report (UNEP-WCMC, IUCN
and NGS).
10. von Grebmer, K., Bernstein, J., Hammond, L., Patterson, F., Sonntag, A.,
Klaus, L., Fahlbusch, J., Towey, O., Foley, C., Gitter, S., and Ekstrom, K.
(2018). 2018 Global Hunger Index: Forced Migration and Hunger
(Welthungerhilfe and Concern Worldwide).
11. IPBES (2019). Global Assessment Report on Biodiversity and Ecosystem
Services of the Intergovernmental Science Policy Platform on Biodiversity
and Ecosystem Services (IPBES Secretariat).
12. WWF (2018). Living Planet Report: Aiming Higher (WWF).
13. Dı́az, S., Settele, J., Brondı́zio, E.S., Ngo, H.T., Agard, J., Arneth, A., et al.
(2019). Pervasive human-driven decline of life on Earth points to the need
for transformative change. Science 366, eaax3100.
14. World Bank; InternationalMonetary Fund (2016). GlobalMonitoring Report
2015/2016: Development Goals in an Era of Demographic Change.
Overview Booklet (World Bank).
15. von Grebmer, K., Bernstein, J., Hossain, N., Brown, T., Prasai, N.,
Yohannes, Y., Patterson, F., Sonntag, A., Zimmerman, S.M., Towey, O.,
and Foley, C. (2017). 2017 Global Hunger Index: the inequalities of hunger.
Intl. Food Policy Res. Inst.
16. Ericksen, P., Thornton, P., Notenbaert, A., Cramer, L., Jones, P., and
Herrero, M. (2011). Mapping Hotspots of Climate Change and Food
Insecurity in the Global Tropics (CGIAR Research Program on Climate
Change, Agriculture and Food Security (CCAFS)).
17. Barlow, J., França, F., Gardner, T.A., Hicks, C.C., Lennox, G.D.,
Berenguer, E., Castello, L., Economo, E.P., Ferreira, J., Guénard, B.,
and Leal, C.G. (2018). The future of hyperdiverse tropical ecosystems.
Nature 559, 517–526.
18. Adams, W.M., Aveling, R., Brockington, D., Dickson, B., Elliott, J., Hutton,
J., Roe, D., Vira, B., and Wolmer, W. (2004). Biodiversity conservation and
the eradication of poverty. Science 306, 1146–1149.
19. Oldekop, J.A., Bebbington, A.J., Brockington, D., and Preziosi, R.F.
(2010). Understanding the lessons and limitations of conservation and
development. Conserv. Biol. 24, 461–469.20. Reed, J., Van Vianen, J., Deakin, E.L., Barlow, J., and Sunderland, T.
(2016). Integrated landscape approaches to managing social and environ-
mental issues in the tropics: learning from the past to guide the future.
Glob. Change Biol. 22, 2540–2554.
21. Wunder, S., Brouwer, R., Engel, S., Ezzine-de-Blas, D., Muradian, R.,
Pascual, U., and Pinto, R. (2018). From principles to practice in paying
for nature’s services. Nat. Sustain. 1, 145–150.
22. Kareiva, P., Groves, C., and Marvier, M. (2014). The evolving linkage be-
tween conservation science and practice at the Nature Conservancy.
J. Appl. Ecol. 51, 1137–1147.
23. McKinnon, M.C., Cheng, S.H., Dupre, S., Edmond, J., Garside, R., Glew,
L., Holland, M.B., Levine, E., Masuda, Y.J., Miller, D.C., and Oliveira, I.
(2016). What are the effects of nature conservation on human well-being?
A systematic map of empirical evidence from developing countries.
Environ. Evid. 5, 1–25.
24. Muradian, R., Arsel, M., Pellegrini, L., Adaman, F., Aguilar, B., Agarwal, B.,
Corbera, E., Ezzine de Blas, D., Farley, J., Froger, G., and Garcia-Frapolli,
E. (2013). Payments for ecosystem services and the fatal attraction of
win-win solutions. Conserv. Lett. 6, 274–279.
25. Sayer, J.A., Margules, C., Boedhihartono, A.K., Sunderland, T., Langston,
J.D., Reed, J., Riggs, R., Buck, L.E., Campbell, B.M., Kusters, K., and
Elliott, C. (2017). Measuring the effectiveness of landscape approaches
to conservation and development. Sustain. Sci. 12, 465–476.
26. Reed, J., van Vianen, J., Barlow, J., and Sunderland, T. (2017). Have inte-
grated landscape approaches reconciled societal and environmental is-
sues in the tropics? Land Use Pol. 63, 481–492.
27. Howe, C., Corbera, E., Vira, B., Brockington, D., and Adams, W.M. (2018).
Distinct positions underpin ecosystem services for poverty alleviation.
Oryx 54, 1–8, https://doi.org/10.1017/S0030605318000261.
28. Brown, K. (2003). Integrating conservation and development: a case of
institutional misfit. Front. Ecol. Environ. 1, 479–487.
29. Carrasco, L.R., Papworth, S.K., Reed, J., Symes, W.S., Ickowitz, A.,
Clements, T., Peh, K.H., and Sunderland, T. (2016). Five challenges to
reconcile agricultural land use and forest ecosystem services in
Southeast Asia. Conserv. Biol. 30, 962–971.
30. Reed, J., Barlow, J., Carmenta, R., van Vianen, J., and Sunderland, T.
(2019). Engaging multiple stakeholders to reconcile climate, conservation
and development objectives in tropical landscapes. Biol. Conserv. 238,
108229.
31. Redford, K.H., Padoch, C., and Sunderland, T. (2013). Fads, funding, and
forgetting in three decades of conservation. Conserv. Biol. 27, 437–438.
32. McShane, T.O., Hirsch, P.D., Trung, T.C., Songorwa, A.N., Kinzig, A.,
Monteferri, B., Mutekanga, D., Van Thang, H., Dammert, J.L., Pulgar-
Vidal, M., and Welch-Devine, M. (2011). Hard choices: making trade-offs
between biodiversity conservation and human well-being. Biol. Conserv.
144, 966–972.
33. Wells, M.P., and McShane, T.O. (2004). Integrating protected area man-
agement with local needs and aspirations. Ambio 33, 513–519.
34. Parker, C., Cranford, M., Oakes, N., and Leggett, M. (2012). The Little
Biodiversity Finance Book: A Guide to Proactive Investment in Natural
Capital (Global Canopy Programme).
35. Clark, R., Reed, J., and Sunderland, T. (2018). Bridging funding gaps for
climate and sustainable development: pitfalls, progress and potential of
private finance. Land Use Pol. 71, 335–346.
36. McCarthy, D.P., Donald, P.F., Scharlemann, J.P., Buchanan, G.M.,
Balmford, A., Green, J.M., Bennun, L.A., Burgess, N.D., Fishpool, L.D.,
Garnett, S.T., and Leonard, D.L. (2012). Financial costs of meeting global
biodiversity conservation targets: current spending and unmet needs.
Science 338, 946–949.
37. Waldron, A., Mooers, A.O., Miller, D.C., Nibbelink, N., Redding, D., Kuhn,
T.S., Roberts, J.T., and Gittleman, J.L. (2013). Targeting global conserva-
tion funding to limit immediate biodiversity declines. Proc. Natl. Acad. Sci.
U S A 110, 12144–12148.One Earth 3, 753–762, December 18, 2020 761
ll
OPEN ACCESS Article38. Coad, L., Watson, J.E., Geldmann, J., Burgess, N.D., Leverington, F.,
Hockings, M., Knights, K., and Di Marco, M. (2019). Widespread shortfalls
in protected area resourcing undermine efforts to conserve biodiversity.
Front. Ecol. Environ. 17, 259–264.
39. Sachs, J., McArthur, J.W., Schmidt-Traub, G., Kruk, M., Bahadur, C.,
Faye, M., and McCord, G. (2004). Ending Africa’s poverty trap.
Brookings Pap. Econ. Act. 2004, 117–240.
40. Easterly, W. (2006). The white man’s burden: why the West’s efforts to aid
the rest have done so much ill and so little good (Oxford University Press).
41. Clemens, M.A., Radelet, S., and Bhavnani, R.R. (2004). Counting chickens
when they hatch: the short-term effect of aid on growth (Center for Global
Development working paper).
42. Burnside, C., and Dollar, D. (2000). Aid, policies, and growth. Am. Econ.
Rev. 90, 847–868.
43. Savedoff, W.D., Levine, R., and Birdsall, N. (2005). When will we ever
learn? Recommendations to improve social development through
enhanced impact evaluation (Center for Global Development), p. 15.
44. Weaver, C. (2008). Hypocrisy trap: the World Bank and the poverty of re-
form (Princeton University Press).
45. Nielson, D.L., and Tierney, M.J. (2003). Delegation to International
Organizations: Agency Theory and World Bank Environmental Reform
(International organization), pp. 241–276.
46. Powers, R.M., Roberts, J.T., & Tierney, M.J. (2009). Greenwashing or
Mainstreaming? New Measures of Environmental Rhetoric and Funding
at the World Bank. In Unpublished paper presented at the PLAID Data
Vetting Workshop, Washington DC.
47. Hickey, V., and Pimm, S.L. (2011). How the World Bank funds protected
areas. Conserv. Lett. 4, 269–277, 276.
48. Miller, D.C. (2014). Explaining global patterns of international aid for linked
biodiversity conservation and development. World Dev. 59, 341–359.
49. Hicks, R.L., Parks, B.C., Roberts, J.T., and Tierney, M.J. (2010). Greening
aid? Understanding the environmental impact of development assistance
(Oxford University Press).
50. Sachs, J.D., Baillie, J.E., Sutherland, W.J., Armsworth, P.R., Ash, N.,
Beddington, J., Blackburn, T.M., Collen, B., Gardiner, B., Gaston, K.J.,
and Godfray, H.C.J. (2009). Biodiversity conservation and the
Millennium Development Goals. Science 325, 1502–1503.
51. Easterly, W., Levine, R., and Roodman, D. (2004). Aid, policies, and
growth: comment. Am. Econ. Rev. 94, 774–780.
52. Burnside, B.C., and Dollar, D. (2004). Aid, policies, and growth: reply. Am.
Econ. Rev. 94, 781–784.
53. Hansen, H., and Tarp, F. (2001). Aid and growth regressions. J. Dev. Econ.
64, 547–570.
54. Sachs, J.D. (2006). The end of poverty: economic possibilities for our time
(Penguin Books).
55. Tierney, M.J., Nielson, D.L., Hawkins, D.G., Roberts, J.T., Findley, M.G.,
Powers, R.M., Parks, B., Wilson, S.E., and Hicks, R.L. (2011). More dollars762 One Earth 3, 753–762, December 18, 2020than sense: refining our knowledge of development finance using AidData.
World Dev. 39, 1891–1906.
56. Easterly, W., and Williamson, C.R. (2011). Rhetoric versus reality: the best
and worst of aid agency practices. World Dev. 39, 1930–1949.
57. Wade, R. (1997). Greening the Bank: the struggle over the environment,
1970–1995. The World Bank: Its First Half Century, vol. 2 (Brookings
Institution Press), pp. 611–734.
58. Goldman, M. (2001). The birth of a discipline: producing authoritative
green knowledge, World Bank-style. Ethnography 2, 191–217.
59. World Bank (2001). World Development Report 2000/01: Attacking
Poverty (Oxford University Press).
60. Bosshard, P. (2004). TheWorld Bank’s Safeguard Policies under Pressure:
A Critique of the World Bank’s New Middle Income Country Strategy
(International Rivers Network).
61. Myers, N., Mittermeier, R.A., Mittermeier, C.G., Da Fonseca, G.A., and
Kent, J. (2000). Biodiversity hotspots for conservation priorities. Nature
403, 853–858.
62. Naidoo, R., Balmford, A., Costanza, R., Fisher, B., Green, R.E., Lehner, B.,
Malcolm, T.R., and Ricketts, T.H. (2008). Global mapping of ecosystem
services and conservation priorities. Proc. Natl. Acad. Sci. U S A 105,
9495–9500.
63. Mace, G.M., Balmford, A., Boitani, L., Cowlishaw, G., Dobson, A.P., Faith,
D.P., Gaston, K.J., Humphries, C.J., Vane-Wright, R.I., Williams, P.H., and
Lawton, J.H. (2000). It’s time to work together and stop duplicating con-
servation efforts.. Nature 405, 393.
64. Smith, R.J., Bennun, L., Brooks, T.M., Butchart, S.H., Cuttelod, A., Di
Marco, M., Ferrier, S., Fishpool, L.D., Joppa, L., Juffe-Bignoli, D., and
Knight, A.T. (2019). Synergies between the key biodiversity area and sys-
tematic conservation planning approaches. Conserv. Lett. 12, 12625.
65. Brooks, T.M., Mittermeier, R.A., da Fonseca, G.A., Gerlach, J., Hoffmann,
M., Lamoreux, J.F., Mittermeier, C.G., Pilgrim, J.D., and Rodrigues, A.S.
(2006). Global biodiversity conservation priorities. Science 313, 58–61.
66. Stiglitz, J. (1998). Towards a new paradigm for development (United
Nations Conference on Trade and Development).
67. Naidoo, R., and Fisher, B. (2020). Sustainable development goals:
pandemic reset. Nature 583, 198–201.
68. Hickel, J. (2020). Less is more: how degrowth will save the world (William
Heinemann).
69. Kareiva, P., Chang, A., and Marvier, M. (2008). Development and conser-
vation goals in World Bank projects. Science 321, 1638–1639.
70. Jenkins, C.N., Pimm, S.L., and Joppa, L.N. (2013). Global patterns of
terrestrial vertebrate diversity and conservation. Proc. Natl. Acad. Sci. U
S A 110, E2602–E2610.
71. Pimm, S.L., Jenkins, C.N., Abell, R., Brooks, T.M., Gittleman, J.L., Joppa,
L.N., Raven, P.H., Roberts, C.M., and Sexton, J.O. (2014). The biodiversity
of species and their rates of extinction, distribution, and protection.
Science 344, 1246752.
One Earth, Volume 3Supplemental InformationThe extent and distribution of joint conservation-
development funding in the tropics
James Reed, Johan Oldekop, Jos Barlow, Rachel Carmenta, Jonas Geldmann, Amy
Ickowitz, Sari Narulita, Syed Ajijur Rahman, Josh van Vianen, Malaika
Yanou, and Terry Sunderland
Table S1: World Bank database search strategy 
Topics  Subareas 
Environment Adaptation to climate change Environmental management 
Air Quality and clean air Environmental Protection 
Biodiversity Environmental strategy 
Brown Issue and Health Environmentally Protected Areas 
Carbon Policy and Trading Forests and Forestry 
Climate Change and 
Environment Global Environmental Facility 
Climate change and impacts Green Issues 
Climate Change Mitigation and 
Green House Gases Marine Environment 
Coastal and Marine 
Environment Montreal Protocol 
Dryland and Desertification Natural Disasters 
Ecosystems and Natural 
Habitats Natural Resources Management 
Environment and Energy 
Efficiency Persistent Organic pollutants 
Environmental disasters and 
degradation Pollution Management and control 
Environmental economics and 
policies Sustainable Land Management 
Environmental Engineering Tourism and Ecotourism 
Environmental Governance Water Resources Management 
Environmental Information 
Systems Wildlife Resources 
1 
 Table S2: GEF database search strategy 
Search Terms 
Focal Area Biodiversity 
Climate Change 
Land Degradation 
Multi Focal Area 
INRM 
Project Type Full Size 
Status Completed 
Table S3: Supplementary search strategy for the World Bank database. 
Topics Subareas 
Agriculture Climate Change & Agriculture 
Forestry Management 
Culture & Development Culture in Sustainable Development 
Energy Energy & Environment 
Energy & natural resources 
Energy resources development 
Gender Gender & Development 
Gender & Energy 
Health, Nutrition & 
Population Environment & health 
International Economics & 
Trade Trade & Environment 
Poverty Reduction Poverty, Environment & Development 
Rural Development Forestry management 
Natural Resources Management 
Sustainable Land& Crop Management 
Transport Transport & Environment 
2 
Water Resources Coastal & Marine Resources 
Water Conservation 
Table S4: List of tropical countries included in this study 
Antigua And Barbuda Eritrea Nicaragua 
Bahamas Ethiopia Niger 
Bangladesh Gabon Nigeria 
Belize Gambia Panama 
Benin Ghana Papua New Guinea 
Bolivia Guatemala Paraguay 
Botswana Guinea Peru 
Brazil Guinea-Bissau Philippines 
Burkina Faso Guyana   Rwanda 
Burundi Haiti   Samoa 
Cabo Verde Honduras Senegal 
Cambodia India Seychelles 
Cameroon Indonesia Sierra Leone 
Central African Republic Kenya Sri Lanka 
Kiribati 
Chad Sudan 
Lao PDR  
Colombia Suriname 
Liberia 
Comoros Tanzania Madagascar  
Congo DR Thailand Malawi 
Costa Rica Malaysia Togo 
Cote d'Ivoire Maldives Uganda 
Cuba Mali Venezuela 
Djibouti Mauritius Vietnam 
Dominican Republic Mexico Zambia 
Ecuador Mozambique Zimbabwe 
El Salvador Namibia 
Table S5: descriptive statistics for Fig. 4 
Standard 
Variable Name Mean 
deviation 
Funding_per_area 
2 24.19 91.651 (km ) 
Funding_per_capita 24.37 60.446 
High_HDI 0.35 0.479 
Low_HDI 0.35 0.479 
Medium HDI 0.31 0.464 
High Biodiversity 0.27 0.445 
Low Biodiversity 0.43 0.498 
3 
Medium Biodiversity 0.29 0.459 
WGI -0.46 0.581 
Population 3.91e+07 1.34e+08 
Total area 6.17e+07 1.19e+08 
Gini coefficient 45.11 6.976 
Red List 57.85 66.858 
Table S6: regression results for Fig. 4 
Funding Per Funding Per 
Area Capita 
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 
Variable Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE Estimate SE 
HDI [High] -29.78 39.46 -22.36 38.13 -22.41 36.99 -15.68 24.41 -10.86 27.36 -6.498 24.81 
HDI [Low] -46.84 41.71 -49.90 42.82 -48.59 41.62 -30.72 22.29 -35.23 22.16 -35.28 23.15 
Biodiversity -8.029 10.78 -4.752 11.05 22.40 24.20 -22.80 24.46 
[High] 
Biodiversity 20.59 22.58 17.97 22.05 23.30** 10.33 19.69** 9.326 
[Low] 
WGI 38.74* 19.83 34.89* 18.69 40.21* 22.24 9.294 18.10 7.299 16.92 10.00 18.32 
Population -8.79e- 5.67e- -5.82e- 4.77e- -6.07e- 5.39e- 0.00 0.00 0.00 0.00 
08 08 08 08 08 08 
Total area 0.00 0.00 0.00 0.00 -2.70e- 2.64e- 3.40e- 2.85e- 1.17e- 2.65e-08 
08 08 09 08 08 
Gini -3.098 1.924 -3.062 1.928 -3.387 2.131 -0.401 1.060 -0.551 1.155 -0.581 1.154 
coefficient 
(average) 
Red List (average) -0.141 0.0970 -0.191 0.116 -0.117 0.111 -0.141 0.122 
Constant  207.2 124.2 209.5 126.3 234.8 147.5 50.24 54.55 62.33 62.22 78.34 63.44 
Observations     69 69 69 69 69 69 
R-Squared 0.144 0.149 0.140 0.092 0.100 0.076 
*** p<0.01, ** p<0.05, * p<0.1 
4