Locating REDD: A global survey and analysis of REDDreadiness and demonstration activities

Gillian A. Cerbu a, Brent M. Swallow b,*, Dara Y. Thompson b

a Forest Research Institute of Baden-Wuerttemberg (FVA), Wonnhaldestr. 4, 79100 Freiburg, GermanybDepartment of Rural Economy, Faculty of Agricultural, Life and Environmental Sciences, 515 General Services Building, University of Alberta,

Edmonton, Alberta, Canada, T6G 2H1

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0

a r t i c l e i n f o

Published on line 29 October 2010

Keywords:

REDD

REDD+

Avoided deforestation

Poisson

Count model

a b s t r a c t

Mechanisms that support reduced emissions from deforestation and forest degradation

(REDD/REDD+) have potential to counteract a large share of global greenhouse gas emissions

if implemented effectively across the tropics. In 2007 the conference of parties to the United

Nations Framework Convention on Climate Change called upon parties and international

organizations to promote REDD through investments in capacity building and demonstra-

tion activities. This prompted many new actors to become involved in REDD activities at a

variety of locations and scales. A global survey of REDD activities was undertaken in 2009 to

enable better understanding of the intensity and geographic distribution of these activities.

Existing compilations, literature review, web-based sources, face-to-face and telephone

interviews, and e-mail questionnaires were used to compile data for the inventory. Inter

alia, data were collected on the location of activities and official and unofficial factors

influencing location choices. Inventory data were combined with secondary data to esti-

mate a statistical count model (Poisson) of factors affecting the number of REDD activities

undertaken in the 64 developing countries that experienced significant emissions from

deforestation. The results show that there were at least 79 REDD readiness activities and 100

REDD demonstration activities as of October 2009. Of these, the largest shares of REDD

readiness and demonstration activities were implemented in Indonesia (7 and 15 respec-

tively) and Brazil (4 and 13 respectively), countries widely agreed to have the greatest

potential for reducing forest-based emissions. The statistical results found no national

characteristic to have a statistically-significant effect on the number of REDD readiness

activities, but five national characteristics to have significant effects on the number of REDD

demonstration projects. Baseline CO2 emissions, forest carbon stock, number of threatened

species, quality of governance, and region all had significant effects. The results reveal the

importance of biodiversity and good governance, and the relative unimportance of human

need and opportunity cost of land. The results also reveal a bias against Africa and toward

Latin America. Unless this pattern is countered, REDD and REDD+ may have geographic

biases that undermine its broad political support.

# 2010 Elsevier Ltd. All rights reserved.

avai lable at www.sc iencedi rec t .com

journal homepage: www.elsevier.com/locate/envsci

* Corresponding author. Tel.: +1 780 492 6656; fax: +1 780 492 0268.E-mail addresses: brent.swallow@ualberta.ca, B.Swallow@cgiar.org (B.M. Swallow).

1462-9011/$ – see front matter # 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsci.2010.09.007

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 169

1. Introduction

The Intergovernmental Panel on Climate Change (IPCC) has

identified deforestation in developing countries as a major

cause of greenhouse gas (GHG) emissions and afforestation as

one of the few viable options for sequestering carbon dioxide

(CO2) from the atmosphere (IPCC, 2007). Despite having both

technical and economic potential to reduce net emissions,

however, international climate agreements largely avoided

the forestry sector until 2005. The Clean Development

Mechanism (CDM) of the Kyoto Protocol contains very limited

provisions for afforestation and reforestation projects (A/R) in

developing countries to generate emission offsets that Annex

1 countries can use to meet their emission reduction

commitments. As the name of the mechanism implies, it

was hoped that CDM projects would also produce develop-

ment benefits in countries hosting CDM projects. Proponents

of the development potential for A/R CDM projects pointed to

the potential benefits to African countries, where land

degradation rates are high and wood is an important source

of energy (Desanker, 2005).

The development benefits of the CDM have been very slow

to materialize, however, as has the forestry component of the

CDM project portfolio. Africa currently hosts 1.95% of all CDM

projects, most of which are located in the country of South

Africa. Afforestation/deforestation projects make up just

0.54% of the global portfolio of CDM projects (UNFCCC, 2010)

and the first afforestation/reforestation CDM project in Africa

was approved only in October 2009 (Carbon Positive, 2009). At

the same time, contributions to a climate change adaptation

fund have been meagre at best, with the Adaptation Fund only

becoming operational in 2009. Lack of additional finance for

climate change mitigation and adaptation has weakened

political support for international climate agreements among

African leaders (Fleshman, 2008).

Momentum toward a new agreement on forest carbon

began at the 11th Conference of the Parties (COP11) to the

United Nations Framework Convention on Climate Change

(UNFCCC) in Montreal in 2005 when the Governments of Papua

New Guinea and Costa Rica called for the inclusion of reducing

emissions from deforestation in developing countries (RED) in

the Convention (UNFCCC, 2005). After deliberations, the

parties agreed that: developing countries are encouraged to

undertake voluntary actions to reduce emissions from

deforestation; international organizations and other stake-

holders are encouraged to support capacity building, develop-

ment of appropriate methodologies, and demonstration

activities in developing countries; and the Subsidiary Body

for Scientific and Technological Advise (SBSTA) should

undertake a work program to resolve key issues of definition

and methodology (UNFCCC, 2005).

Considerable progress on RED was achieved between 2005

and 2009, some of which was captured in the Decision of

COP13 in Bali, Indonesia where an additional ‘‘D’’ for forest

degradation was added and RED became REDD. This change,

strongly promoted by the Central African Forest Commission

(COMIFAC) countries, resulted in parties being encouraged to

‘stimulate further action to reduce emissions from deforestation and

forest degradation in developing countries’ (UNFCCC, 2007). This

widening of REDD’s scope in turn opened negotiations about

the potential for going beyond REDD to include sustainable

forest management. Several Annex 1 countries and multi-

lateral agencies indicated support for REDD in their formal

communications at COP13, with a very large financial

commitment made by the Government of Norway. Three

United Nations agencies (Food and Agriculture Organization,

United Nations Development Program, United Nations Envi-

ronment Program) announced the formation of UN-REDD and

the World Bank announced the establishment of the Forest

Carbon Partnership Facility. The Centre for International

Forestry Research and the Collaborative Partnership on

Forests held the first ‘Forest Day’ as a major side event to

COP13 in Bali, Indonesia.

Discussions on a more inclusive REDD continued at

UNFCCC meetings in 2008 and 2009, with the wider scope

reflected in the acronym, ‘REDD+’. Further agreement on the

importance and nature of a REDD+ mechanism was achieved

at COP15 in Copenhagen in 2009. Two of the 13 decision

reached at COP15 address REDD+. Decision 4 (4/CP.15)

provides ‘‘Methodological guidance for activities relating to

reducing emissions from deforestation and forest degradation and

the role of conservation, sustainable management of forests and

enhancement of forest carbon stocks in developing countries’’

(UNFCCC, 2009a). Perhaps more importantly, Article 6 of the

Copenhagen Accord (Decision 2/CP.15) commits the global

community to substantive and immediate action on REDD+:

‘‘We recognize the crucial role of reducing emission from deforesta-

tion and forest degradation and the need to enhance removals of

greenhouse gas emission by forests and agree on the need to provide

positive incentives to such actions through the immediate establish-

ment of a mechanism.’’ (UNFCCC, 2009b). The shorthand of

REDD+ is now institutionalized in the international dialog.

REDD+ is concerned with both reducing emissions and

enhancing carbon stocks through actions that address

deforestation, forest degradation, forest conservation and

sustainable forest management. This expansion of REDD to

REDD+ has in turn spurred a movement towards the inclusion

of net negative changes in carbon stocks across all lands and

land uses (i.e. including agriculture) expressed as reducing

emissions from all land uses (REALU) or through using the full

accounting scheme for agriculture, forestry and land use

(AFOLU) (van Noordwijk et al., 2009). In the remainder of this

paper, we will use the acronym REDD to refer to activities

undertaken as REDD (reduced deforestation and forest

degradation) and REDD+.

Despite this progress on REDD/REDD+ and consensus that a

more inclusive form of forest carbon accounting needs to be

reflected in a post-Kyoto agreement, the exact form that a

REDD agreement will take remains to be decided. Several

important issues still need to be resolved. First, payment/

compensation approaches need to be determined — should

REDD be fund-based, market-based or a mixture? Second,

baseline emission levels, also known as reference scenarios,

need to be set. Will historical baseline emissions be used as the

reference case or will forward-looking baselines be used to

adjust to the circumstances of countries that have experi-

enced low historical rates of deforestation and development?

Third, how will deforestation and forest degradation be

defined and measured (Myers Madeira, 2008)? Fourth, how

will sub-national level activities be integrated into national

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0170

plans and approaches (M. Herold, personal communication, 29

February 2009)?

It is widely recognized that it will take time, consultation,

research, experience, and investment to develop fully opera-

tional national REDD mechanisms. Several agencies have

offered to assist developing countries to prepare for large-

scale implementation of REDD. For example, the Forest Carbon

Partnership Facility (FCPF) has a Readiness Mechanism for

providing capacity building and technical assistance to

developing countries. Readiness activities supported by the

FCPF include estimation of forest carbon stocks, analysis of the

sources of forest emissions, construction of national reference

scenarios, evaluation of the opportunity costs of possible

REDD interventions, development of REDD strategies, and

monitoring, reporting and verification systems (FCPF, 2010). In

addition, a variety of organizations and agencies have offered

to assist developing countries to implement REDD demon-

stration activities. Guidance for both REDD readiness and

demonstration activities is provided by the COP decisions

summarized above. However, there is great concern that new

investments in REDD will once again be biased against certain

groups of countries. At its May 2009 meeting, the African

Ministers Conference on the Environment (AMCEN) declared

its resolve to (inter alia) (6) ‘‘call for the improvement of the Clean

Development Mechanism to ensure equitable geographic distribution

of projects contributing to sustainable development on the continent’’

and (28) agree that other mitigation measures being identified, such

as additional measures to complement the United Nations Collabo-

rative Programme on Reducing Emissions from Deforestation and

Forest Degradation in Developing Countries, including afforestation

and sustainable agriculture and land-use management, should be

vigorous, realistic and flexible to ensure the effective participation of

African countries, especially smallholder land users’’ (18) (AMCEN,

2009).

In 2009, a study was undertaken to develop a global

inventory of REDD readiness and demonstration activities.

The study was motivated by concerns about the possibility of

REDD investments being directed to a small number of

countries and the mistakes of the CDM being inadvertently

repeated with REDD. The study had three objectives: (1)

quantify the amounts and types of REDD investment across

the world; (2) identify apparent gaps in that investment; and

(3) identify factors affecting the geographic distribution of

activities. This paper summarizes the results of that study.

This paper also presents results from a cross-country

statistical analysis of factors affecting the number of REDD

readiness and demonstration activities. Conclusions are

drawn about the apparent mismatch between actual REDD

investments and the potential for REDD to achieve real

reductions in emissions.

Table 1 – Framing questions for interviews conducted with RE

1. Why did your ‘‘Organization X’ decide to implement a REDD project a

‘Project country’? (In terms of location choice?)

2. Why do you think investors (i.e. Investor X related to the project) wan

‘Project Region’? Within the ‘Project country’? (In terms of location ch

3. Do you know of any additional REDD projects being implemented in t

4. Does your organization have any intentions to implement further RED

2. Methods

2.1. Inventory of REDD preparedness and demonstrationactivities

Due to the rapidly-evolving nature of this field, this paper can

only offer a snapshot of the state of REDD demonstration and

national readiness activities until October 2009. The REDD

demonstration and readiness activities included in this

analysis were in the planning or implementation stages

within non-Annex I countries (as per UNFCCC definitions) at

that time. Given the consensus understanding of REDD as of

October 2009, the inventory has an emphasis on avoided

deforestation and forest degradation. Reforestation and

afforestation activities for carbon management were not

included if they did not include an avoided deforestation and/

or reduced forest degradation component.

REDD demonstration activities are here defined as activi-

ties implemented in a particular sub-national region or unit,

i.e. national park, with the intention of reducing deforestation

or forest degradation in that particular area. REDD readiness

activities generally have national capacity building, policy

development or land-cover change monitoring as their main

objective, although they may concentrate activity in particular

sub-national locations.

Existing compilations, online databases of forest carbon

projects (including Planet Action, 2009; Johns and Johnson,

2008), project design documents (PDDs), e-mail communica-

tion and interviews with representatives of institutions

known to be engaged in REDD were used to generate an

initial list of REDD activities. A Microsoft Access database of

the projects was created on the basis of this literature review,

internet sources, face-to-face and phone interviews, and an

email questionnaire. Four open-ended questions were used to

frame the interviews (Table 1).

Particular attention was given to the rationale for the

location of REDD activities. Both official (explicitly stated) and

unofficial (implied) factors were identified and categorized

into a list of 21 different factors. Descriptive statistics were

used to analyze the choice of demonstration sites by various

actors.

2.2. Count model analysis of factors affectingcountry-level REDD activities

A statistical model was estimated to further evaluate factors

affecting the location of REDD readiness and demonstration

activities. Two separate models were developed, one for the

number of REDD readiness activities (REDDpr) and one for the

number of REDD demonstration activities (REDDred). Both

DD+ stakeholders.

t the ‘Project site’? Within the ‘Project Region’? Within the

ted to invest in a REDD project within the ‘Project Area’? Within the

oice?)

his region? Country?

D projects in the region?

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 171

models were derived using the same process. The dependent

variable (REDDpr or REDDred) is a discrete non-negative

integer variable (ranging from 0 to 6 for REDDpr and from 0 to

15 for REDDred). Econometric models that use such dependent

variables are known as count models (Greene, 2008).

The econometrics literature identifies the progression of

count data modeling, which starts with the Poisson regression

model (Greene, 2008). In the Poisson specification, the number

of REDD projects (REDD) would be determined by:

ProbðREDDiÞ ¼expð�miÞmREDD

i

REDDi!(1)

where mi is the conditional mean and variance of the Poisson

distribution (Faria et al., 2003).

Thus, mi will be dependent on the set of explanatory

variables, designated by the vector xi. Assuming the function is

logistic, the value for mi would therefore be defined as:

ln mi ¼ xib (2)

mi is both the mean and variance of this distribution, which

implies that:

E½REDDijxi� ¼ V½REDDijxi� (3)

A limitation of the Poisson model is the assumption in

Eq. (3) that the mean is equal to the variance, otherwise known

as the equi-dispersion condition (Greene, 2008). However,

observed data is typically over-dispersed, meaning that the

variance is greater than the mean. Over-dispersion suggests

that observations may be clumped around some value (which

would be approximately equal to the mean), with notable

outliers that lead to a large variance.

The negative binomial (NB) model is an alternative to the

Poisson model that allows for over-dispersion, where the

variance of the dependent variable exceeds its mean, and

under-dispersion, where the variance of the dependent

variable is less than the mean. The NB model for the number

of REDD projects in a country i will be:

E½REDDijxi; i� ¼ expðaþ xibþ eiÞ ¼ imi (4)

where the b parameters are unknown. The value of vi is equal

to exp(ei), which is gamma distributed with mean 1 and vari-

ance 1/u (Greene, 2008). The gamma distribution has a scale

parameter u (where u > 0). mi is the Poisson distribution expec-

tation for the conditional mean, which can vary by the unob-

served heterogeneity vi. Thus, the expected value of REDD is

represented by mi* where:

m�i ¼ E½REDDijxi; i� ¼ mi i (5)

This additional component of the variation, the variation in

vi implies that:

V½REDDijxi� ¼ þmi 1þ 1u

� �mi

� �; where

1u¼ Var ½i� (6)

An initial investigation of the data reveals that there is a

large number of observations that have zero values for the

dependent variable; in other words, about half of the non-

Annex 1 countries that had emissions from deforestation had

no REDD activities as of October 2009. The lack of REDD

activity may indicate that countries had not decided to

support REDD, that funding agencies have consciously

decided not to invest in a particular region, or that there is

no funding available due to a lack of agencies focused on

reduced deforestation-type projects for that country. In other

words, there may be two processes involved in determining

the number of REDD activities (Erdman et al., 2008). In such

circumstances, the zero inflated negative binomial (ZINB)

model could be a more appropriate specification (Faria et al.,

2003). However in the interest of brevity, we will estimate only

the Poisson and negative binomial specifications in this

analysis.

The inventory data were used to create variables to

represent the number of REDD readiness and demonstration

activities undertaken in non-Annex 1 countries that are

candidates for REDD. Those data were then combined with

quantitative indicators of factors identified through the

survey as motivating the location of activities. Data for the

quantitative indicators were collected from a variety of

sources, as shown in Table 3, especially Venter et al. (2009).

Following Venter et al. (2009), we only included the 64

countries that had positive rates of deforestation over the

previous 10 years and complete data for all indicators

included in the analysis.

Perhaps the most obvious explanatory variable to include

in a model of the intensity of REDD activity is baseline

emissions from the forest sector – the higher the baseline

emission of a country, the more projects we would expect to

be located in the country. Here we use data on baseline

forest emissions calculated by Venter et al. (2009), who in

turn use data from the FAO Global Forest Resource

Assessment (FAO, 2006) to calculate historic deforestation

rates and remote sensing data to estimate forest area. In

addition to baseline emissions, we include forest carbon

stock in the analysis, as more carbon stock implies more

standing forest and thus greater incentive to implement

projects. This measure is a derived product of total land area

(FAO, 2006), percentage of the total land that is forested

(ESA, 2008) and the average carbon density of each nation

(IPCC, 2003).

Venter et al. (2009) and others argue that the priorities for

REDD investments should also reflect biodiversity conserva-

tion objectives. Our interviews confirmed that REDD investors

indeed do pay attention to biodiversity. There are several

measures of biodiversity and threats to biodiversity; in this

analysis, we use the number of IUCN Red List threatened

species in 2009 as a measure of threat to biodiversity (IUCN,

2009). Following the survey results, the model also includes a

variable that measures the quality of governance – the

Government Effectiveness Index (Kaufmann et al., 2009).

Government effectiveness, which is the general quality of

the national government, is assumed to be a plausible proxy

for good governance – more stability would imply greater

likelihood for REDD projects, particularly from international

funders. Survey results suggest that community benefits play

a role in project location selection. To reflect this, we included

the Human Development Index as an explanatory variable

(UNDP, 2009). Additionally, the average opportunity cost of

agriculture was included (Venter et al., 2009). A higher

opportunity cost may imply a heightened likelihood of

converting to agriculture, and thus possibly less local support

for REDD.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0172

3. Results and discussion

3.1. Overview of REDD readiness and demonstrationactivities

It is evident that there was something of a rush to create new

projects and readiness activities that might be eligible for

funding or entry into an official pipeline under a climate

agreement that was anticipated to come into effect after

COP15 in Copenhagen in December 2009. In the lead up to

COP15, a variety of actors from around the world mobilized to

begin REDD readiness and demonstration activities in a

variety of developing countries. This section describes three

key aspects of that investment: the location of REDD readiness

and demonstration activities, the investors involved in REDD

activities in these areas, and the reasons that project

developers give for choosing the location of activities.

3.1.1. Distribution of REDD readiness and REDDdemonstration activitiesA total of 100 REDD demonstration activities and 79 national

REDD readiness activities were captured in the inventory.

Several survey respondents indicated additional projects at

the planning or design stages; however, detailed information

on these projects was not readily available at the time the

study was conducted.

REDD readiness activities were found to be relatively

evenly distributed across Africa, Asia and Latin America.

The Amazon region of South America leads with 21 of the 79

readiness activities recorded. Countries in the East Asia and

Pacific region are engaged in a total of 19 national REDD

readiness activities, while the countries of Central America

and the Caribbean have a total of 13 readiness activities. A

Fig. 1 – REDD demonstration and readiness activities by region

total of 22 readiness activities were found in Africa, 11 in East

Africa, 8 in Central Africa, and 3 in West Africa. There were

only 2 readiness activities being implemented in South Asia

(Fig. 1).

In contrast, most REDD demonstration activities have been

concentrated in the East Asia, Pacific and Amazon regions.

There are 40 demonstration projects in the East Asia and

Pacific region and 31 in the Amazon region. Africa hosts 18

demonstration activities and South Asia 2 demonstration

activities (Fig. 1).

Within the regions, countries have attracted varied

amounts of REDD investment. Indonesia, located in the East

Asia and Pacific Region, has the most number of REDD

readiness activities (6). Also in the East Asia and Pacific region,

Vietnam and Papua New Guinea are both implementing 4

readiness Lao PDR is implementing 3, Vanuatu 2, and

Cambodia and Thailand 1 each. Paraguay and Guyana, in

the Amazon region, are both implementing 5 readiness

activities; Brazil is engaged in 4 readiness activities, while

Colombia and Peru are engaged in 2 each. Madagascar, in East

Africa, is also host to 5 readiness activities, while Tanzania is

host to 3, and Ethiopia, Kenya and Uganda, 1 each. Central

Africa, Democratic Republic of Congo and Cameroon are

engaged in 4 national readiness activities each, Republic of

Congo 2, and Central African Republic and Gabon 1 each. Costa

Rica and Panama are engaged in 4 national readiness

activities, Mexico 2, and Belize, Guatemala and Nicaragua 1

activity each. Meanwhile, Liberia (West Africa) is involved in 2

readiness activities and Ghana, 1, while Nepal (South Asia) is

involved in 2 national readiness activities.

Within the East Asia and Pacific region, Indonesia emerges

as the most popular site for REDD demonstration activities

with 15, followed by Papua New Guinea and the Philippines

.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 173

with 1 project each. Brazil leads the tally in the Amazon Region

of South America (13), followed by 7 in Ecuador, 5 in Peru, 4 in

Colombia, 3 in Bolivia, and 1 each in Argentina, Guyana, and

Venezuela. In Central America and the Caribbean, Mexico has

4 demonstration activities, followed by Costa Rica with 2, and

Belize, El Salvador, Guatemala, Honduras, Nicaragua and

Panama with 1 each. In East Africa, Madagascar hosts 3

demonstration activities, and Ethiopia, Kenya and Uganda 1

each. In Central Africa, the Democratic Republic of Congo is

host to 3 demonstration activities, and Cameroon, 2. Ghana,

Ivory Coast, South Africa and Mozambique each host 1

demonstration activity. Reasons for this geographic distribu-

tion of REDD readiness and demonstration activities are

suggested by the organizations involved in the activities

themselves, and from the results of the count model presented

below.

3.1.2. Institutions engaged in national and project REDDactivitiesThe inventory identified a total of 291 actors involved in REDD

activities. Of these, 282 were involved in REDD demonstration

activities; while 96 actors were involved in REDD readiness

activities. Several actors are engaged in both types of

activities. The 291 actors were divided into the following 8

categories for ease of comparison: bilateral/multilateral

development organization, government, local/indigenous

community, NGO/non-profit/charity, private company, United

Nations agencies (with the exception of UNREDD and

UNFCCC-related activities), university/research institution

and other (Table 2).

A wider array of actors are engaged in REDD demonstration

projects than in REDD readiness schemes (Table 2). All 8

categories were involved in REDD demonstration activities;

while only 5 categories were involved in national REDD

readiness schemes. Local/indigenous communities, United

Nations agencies (other than UNREDD and UNFCCC), and

other were not involved in REDD readiness activities.

The level of engagement in demonstration and readiness

activities varies across the institutional types. Governments

(local, regional and national from both Annex 1 and non-

Annex 1 countries) make up the largest group of actors

involved in REDD readiness activities, with a 47% share of

these activities, while governments comprise only 18% of

actors involved in demonstration activities. NGOs/non-profit/

charity actors had a 36% involvement in REDD demonstration

Table 2 – Types of actors involved in REDD demonstration and

Institution category TinR

NGO/non-profit/charity

Private company

Government

University/research institution

Bilateral/multilateral development organization

United Nations (non-UNREDD/UNFCCC related)

Local/indigenous community

Other

and a 26% involvement in REDD readiness activities. Private

sector actors made up 27% of agencies involved in demon-

stration activities and 7% of agencies involved in readiness

activities. University/research institutions comprised 9% of

actors involved in readiness activities and 8% of actors

involved in demonstration activities. Bilateral and multilateral

development organizations have a greater share of involve-

ment in national readiness activities (12%) than demonstra-

tion activities (5%). The data indicate that local/indigenous

communities are involved in only one demonstration activity

and no readiness activities (Table 2).

Some institutions stand out with regard to their level of

involvement in REDD activities. Of 291 institutions listed in the

database, the majority were involved in only 1 activity, while

10 were involved in at least 4 activities. The organizations with

the greatest involvement were World Wildlife Fund, which

was involved in 17 REDD activities, Conservation International

which was involved in 14 activities and Fauna and Flora

International which was involved in 8 activities (Fig. 2).

Following, the German Technical Cooperation (GTZ) was

involved in 6 REDD activities, while the Nature Conservancy,

the World Land Trust and the Government of Norway were

each involved in 5 activities (Fig. 2).

3.1.3. Official and unofficial REDD location selection criteriaIn order to understand some of the reasons for the uneven

geographical distribution of REDD activities, the survey of

REDD activities collected information on criteria that influ-

enced project site selection. Based on information drawn from

project websites, online articles, phone and face-to-face

interviews and e-mail correspondence, 86 location selection

criteria were identified for the 179 REDD activities included in

this survey. Those 86 criteria were then aggregated into 10

groups for ease of comparison: biodiversity benefits, business

value, climate benefits, community benefits, cultural value,

demonstration of user need, environmental values, medical

benefits, threat of deforestation and water conservation value.

Community benefits here denote development benefits

accruing to local communities through the implementation

of a project in terms of creation of livelihood alternatives,

improved infrastructure, etc.

The location selection criteria can be divided into official

and unofficial criteria (Cerbu et al., 2009). Official selection

criteria are those that are publicly stated in project design

documents, investor websites, and other official publications.

REDD readiness activities.

otal share of actorvolvement in

EDD projects (%)

Total share ofactor involvement innational REDDreadiness activities (%)

35.8 26.0

27.3 7.3

18.4 46.9

8.8 8.3

4.6 11.5

3.5 0

1.0 0

0.4 0

Fig. 2 – Agencies involved in REDD demonstration and readiness activities.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0174

Unofficial location criteria include those criteria arising from

the 19 face-to-face and email interviews conducted with REDD

stakeholders, as well as on-line media sources discussing the

locations of REDD activities.

Official criteria motivating investors’ involvement in

REDD activities within particular countries, or regions within

countries, were varied. The most often cited official criteria

were: biodiversity benefits (33), community benefits (13),

threat of deforestation (10), environmental value (8), dem-

onstration of user need (6), and climate benefits (6)

(Supplementary material Fig. 1). Additional categories cited

5 or fewer times are: business value, climate benefits,

cultural value, medical benefits, and water conservation

value. These official selection criteria alone, however, do not

explain the uneven distribution of REDD activities across the

developing world.

A total of 65 unofficial criteria for the location of REDD

activities were identified. These were sorted into 13 groups,

including: creating a net benefit, cultural value, financial

viability, good governance/institutional setting, high conser-

vation/biodiversity value, high level of deforestation, current

low level of deforestation but threat of future deforestation,

other parties interested (NGOs, Government), previous expe-

rience in related sectors/projects, prior relations with country/

region/stakeholders, technical capacity, technical interest,

and water resources protection. The most often cited unoffi-

cial criteria were: other parties interested such as NGOs or host

country government agencies (17), prior relations with

country/region/stakeholders (16), good governance/institu-

tional setting (10), and previous experience in related

sectors/projects (6) (Supplementary material Fig. 2).

Official motivations for choice of project sites did not vary

significantly between stakeholders: most stakeholders cited

biodiversity, user needs and co-benefits as key motivators.

REDD location decisions were often based on a combination of

official and unofficial criteria. The most-oft cited official

criterion for location selection was biodiversity benefits, while

the most-oft unofficial criterion was previous relationships in

the country.

3.2. Count model results of factors affecting country-levelREDD activities

As described in Section 2.2 above, a cross-country statistical

analysis was undertaken of factors affecting the number of

REDD activities. Two models were developed: one in which the

dependent variable was the number of REDD readiness

activities (REDDred) and one in which the dependent variable

was the number of REDD demonstration activities (REDDpr).

As noted in Section 3.1, government agencies tend to be more

involved in REDD readiness activities, while non-governmen-

tal organizations and private sector actors tend to be more

involved in REDD demonstration activities. Governments in

developing countries are allowed to volunteer to be involved in

many REDD readiness activities, for example, almost all

developing countries could apply to be involved in the REDD

readiness activities of the World Bank’s Forest Carbon

Partnership Facility. On the other hand, external agencies

are much more involved in the selection of countries and sites

for REDD demonstration activities. All of the REDD demon-

stration activities included in this inventory were at least

partially financed by external agencies.

Table 3 – Descriptive statistics for variables used for count model analysis (n = 64).

Variable Description Mean Std. Dev. Min. Max.

REDDpra Number of REDD demonstration activities 1.12 2.66 0 15

REDDreda Number of REDD readiness activities 0.67 1.18 0 6

ENDANGb Number of threatened species in 2009 272 360 15 2211

LANDAREAc Total land area (1000 ha) 70402 116739 527 845942

PERFORd % of land area forested (2005) 46.79 29.21 0.04 93.5

CDENSITYe Average carbon density (Mg C/ha) 58.17 40.62 5 185.5

STOCKf Forest carbon stock (1000 Mg C) (LANDAREA*PERFOR*CDENSITY) 2229.4 6556.5 0.198 47055

BEMISg Baseline forest emissions (10,000 Mg C/year) 14.05 37.6 0.0002 258.8

OPPCOSTg Average opportunity cost of agriculture (annual agric. revenue in

2000 US$/ha)

122.9 92.89 15.4 396.2

HDIh Human development index 0.61 0.17 0.35 0.94

PSI3i Government effectiveness index 2008 (values between �2.5 and +2.5) �0.53 0.66 �2.24 0.83

READa REDD readiness activities in the country = 1; no readiness activities = 0 0.344 0.479 0 1

AFRICAj Country is in Africa = 1; else = 0 0.484 0.504 0 1

ASIAj Country is in Asia; else = 0 0.25 0.436 0 1

AMERICAj Country is in America; else = 0 0.234 0.427 0 1

a Cerbu et al. (2009).b IUCN (2009).c FAO, 2005.d ESA (2008).e IPCC (2003).f Constructed variable.g Venter et al. (2009).h UNDP (2009).i Kaufmann et al. (2009).j UNFCCC (2010).

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 175

The explanatory variables are derived from the results of

the official and unofficial reasons guiding the location of REDD

activities, presented above. Table 3 presents a description of

all of the dependent and explanatory variables included in the

analysis, including the sources and mean values. The models

were estimated for 64 of the 145 non-Annex 1 countries of the

UNFCCC. We follow Venter et al. (2009) in including only those

countries that experienced deforestation over the previous 10

years and had complete data available. A list of the countries

included in the analysis is given in Table 4.

Table 5 presents data on the number of countries that had

received different types of REDD investment as of October 2009

compared to the total number of non-Annex I countries (145)

and the number of non-Annex I countries included in the

analysis (64). The data show that the 64 countries involved in

this analysis included almost all of the REDD investment, with

only 3 other countries hosting demonstration activities and 2

other countries involved in readiness activities. Of the 64

Table 4 – Lists of countries included in the count models of fa

Region List of countries

Africa Angola, Benin, Botswana, Burkina Faso, Buru

Republic of the Congo, Equatorial Guinea, Eth

Madagascar, Malawi, Mali, Mozambique, Nam

United Republic of Tanzania, Zambia, Zimbab

Asia Afghanistan, Bangladesh, Brunei, Darussalam

Lao People’s Democratic Republic, Malaysia,

Sri Lanka, Thailand, Timor-Leste

Americas Argentina, Bolivia, Brazil, Colombia, Costa Ri

Panama, Paraguay, Peru, Venezuela (Bolivaria

Oceania Papua New Guinea, Solomon Islands

countries involved in this analysis, 31 (48%) had no REDD

activity, while roughly equal numbers of countries were

involved in demonstration activities only (11 countries),

readiness activities only (10 countries) and both readiness

and demonstration activities (14 countries).

Table 6 shows that there are some systematic differences in

the explanatory variables across the regions considered in this

study. On average, African nations included in this analysis

have the lowest human development index score (HDI = 0.50)

and numbers of threatened species (ENDANG = 148), while

American countries – which include both North and South

America – tend to be highest in both HDI (0.79) and threatened

species (467) values. Baseline emissions are noticeably lower

in both Oceania (2820) and Africa (8480) than in Asia (12,600)

and America (28,600).

We estimated two count models, a Poisson and negative

binomial for the number of REDD readiness activities. As

noted as in the methods section above, the main differences

ctors affecting the location of REDD activities.

ndi, Cameroon, Central African Republic, Chad, Congo, Democratic

iopia, Gabon, Ghana, Guinea, Guinea-Bissau, Kenya, Liberia,

ibia, Nigeria, Senegal, Sierra Leone, Sudan, Togo, Uganda,

we

, Cambodia, Democratic People’s Republic of Korea, Indonesia,

Mongolia, Myanmar, Nepal, Pakistan, Philippines, Republic of Korea,

ca, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua,

n Republic of)

Table 5 – Frequency of REDD projects in non-Annex I countries.

Type of activities All non-Annex Icountries (n = 145)

Non-Annex I countriesincluded in analysis (n = 64)

Number Percentage (%) Number Percentage (%)

Demonstration only 14 9.7 11 17.2

Readiness only 12 8.3 10 15.6

Demonstration and readiness activities 14 9.7 14 21.9

No activities 105 72.4 31 48.4

Table 6 – Mean values of each variable used to determine REDD readiness and demonstration activity count by region.

Variable Africa (n = 31) Asia (n = 16) America (n = 15) Oceania (n = 2)

Projects REDDpr 0.35 (0.71) 1.00 (3.74) 2.93 (3.31) 0.50 (0.71)

REDDred 0.45 (0.96) 0.69 (1.54) 1.07 (1.16) 1.00 (1.41)

Explanatory variables ENDANG (threatened species) 148 (158) 322 (377) 467 (547) 333 (163)

BEMIS (baseline emissions) 8,480 (16,400) 12,600 (34,700) 28,600 (64,600) 2,820 (2,620)

PSI3 (govt effect index) �0.68 (0.52) �0.48 (0.84) �0.19 (0.65) �0.95 (0.51)

HDI (human devp index) 0.50 (0.11) 0.67 (0.16) 0.79 (0.06) 0.58 (0.05)

OPPCOST (avg opp cost of agric) 105.3 (69.4) 153.1 (119.2) 135.9 (103.2) 55.9 (30.6)

STOCK (forest carbon stock) 1,450,000

(3,810,000)

876,000

(1,910,000)

5,800,000

(11,900,000)

497,000

(618,000)

1 Another model was constructed using REDDred, the presenceof readiness activities in the nation, as a binary variable. ThePoisson specification resulted in this as a significant variablehowever the significance on other national characteristic vari-ables decreased. Furthermore, as the McFadden R-squared valuewas similar to the primary model (0.585), we assume that variationdue to readiness activities captures characteristics that are al-ready determined within the model.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0176

between the Poisson and negative binomial models are the

way they handle different patterns of dispersion. The Poisson

model assumes that the variance of the dependent variable is

equal to the mean; however the mean of REDD readiness

projects for this dataset is 0.67 and the variance is 1.183

(variance/mean ratio of 1.766). The data thus appear to be

over-dispersed. As discussed by Faria et al. (2003), over-

dispersion may disappear when more explanatory variables

are included in the analysis, so a negative binomial model

could be more efficient than a Poisson model. However, in this

case the alpha parameter (variance parameter) is not signifi-

cant in the negative binomial model. This implies that the

Poisson model is sufficient and over-dispersion is not a major

concern.

The Poisson version of the model of REDDred has a

McFadden pseudo R-squared value of .140, indicating that

the chosen independent variables capture approximately 14%

of the variation in the number of REDD readiness activities

across the 64 countries included in the analysis. Only baseline

emission levels (BEMIS) and the numbers of threatened

species (ENDANG) are statistically significant in the Poisson

model, and only at the 10% level of confidence (Supplementary

material Table 1). These results imply that non-Annex I

nations with higher levels of threatened species and countries

with higher levels of baseline CO2 emissions are somewhat

more likely to implement REDD readiness activities. Holding

all else constant, the effect of increasing the number of

threatened species in a nation by 10% of the sample mean (27

species) is to increase the expected number of REDD readiness

activities by 1.8%. If baseline emissions increased by

1000 Mg C/year, REDD readiness activities would increase by

a factor of 1.01 (1.00%) in the nation, holding all other factors

constant.

Results of the count models for REDD demonstration

activities (REDDpr) also indicate that the Poisson model

provides the best fit for the data. While the variance/mean

ratio is 2.368, the value of alpha is not significant in the

negative binomial model. LIMDEP results on the number of

countries predicted to have zero counts also confirm that the

Poisson version of the model is most appropriate. Of the 64

countries included in the analysis, 39 had zero projects. The

Poisson model predicted 52.7 zeros, while the negative

binomial predicted 55.9 zeros1 (Supplementary material Table

2).

Table 7 presents results for the Poisson and negative

binomial models for the number of REDD demonstration

activities. The Poisson model has a McFadden pseudo R-

squared value of .567, implying that 57% of the variation in

REDD demonstration activities may be attributed to the

chosen independent variables. Significant variables for this

model, all at the 1% level of significance, are: baseline

emissions (BEMIS), political government effectiveness (PSI3),

the number of threatened species (ENDANG), forest carbon

stock (STOCK) and ASIA. The regional dummy variable,

AFRICA, is statistically significant at the 5% level. Variables

that are not statistically significant in the Poisson model are

Human Development Index (low levels of which indicate

greater human need) and the opportunity cost of land (high

levels of which indicate that it will be difficult to provide

farmers with high enough payments to adequately compen-

sate them for reducing deforestation).

The results can be interpreted in terms of the marginal

effects of changes in the explanatory variables. Results for the

regional dummy variables are striking. If a country is located

in Asia or Africa, we estimate that the number of REDD

Table 7 – Estimates for the Poisson and negative binomial REDD demonstration projects (n = 64).

Variable Poisson Negative binomial

Coefficient Factor Coefficient Factor

Constant 1.808 (1.086) – 1.808 (0.599) –

BEMIS (baseline emissions) 0.023 (4.293)*** 1.02 0.023 (2.872)*** 1.02

HDI (human devp index) �1.757 (0.816) 0.17 �1.757 (0.468) 0.17

PSI3 (govt effectiveness index) 1.074 (2.951)*** 2.93 1.074 (2.23)** 2.93

ENDANG (threatened species) 0.001 (4.423)*** 1.00 0.001 (2.353)** 1.00

STOCK (forest carbon stock) 0.001 (3.078)*** 1.00 0.000 (1.951)* 1.00

OPPCOST (avg opp.cost of agric) �0.003 (1.398) 1.00 �0.003 (1.223) 1.00

AFRICA �1.371 (2.282)** 0.25 �1.371 (1.338) 0.25

ASIA �1.509 (3.065)*** 0.22 �1.509 (1.646)* 0.22

OCEANIA �27.091 (0.000) 0.00 �27.091 (0.000) 0.00

alpha Not estimated 0.200 (0.499)

Log-likelihood �59.729 �68.867

AIC 2.179 2.429

BIC 2.516 2.801

McFadden R2 0.563 –

Absolute value of T-statistics reported.* Significant at p = 0.10.** Significant at p = 0.05.*** Significant at p = 0.01.

Fig. 3 – Baseline emissions for non-Annex I countries

included in the analysis compared to the number of REDD

demonstration activities recorded in each nation.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 177

demonstration activities would decrease by 77% and 75%

respectively, compared to a country located in the Americas. If

baseline emissions increased by 1000 Mg C/year (approxi-

mately the amount emitted by Costa Rican forests per year),

the number of REDD demonstration activities is estimated to

increase by 2.3%. If the level of forest carbon stock increased by

10% of the mean sample level within a country (229,000 Mg C,

or about the forest stock of Cambodia), REDD demonstration

activities would decrease by approximately 2.3%. If the

number of threatened species was to increase by 10% of the

mean level (27 species), the number of REDD demonstration

activities in the nation would increase by nearly 4%. Finally, an

increase in the government effectiveness index of 1 (a

substantial jump) would raise the number of REDD demon-

stration activities by 193%.

For the 64 countries included in the analysis, we plotted the

number of REDD demonstration activities against the levels of

the three variables that were the most statistically significant

determinants of the number of REDD demonstration projects.

These plots help to discern the overall pattern of the

relationships and indicate countries that may have fewer or

a greater number of projects than the model would suggest.

The plot of baseline emissions against the number of

activities illustrates the very high emissions from Brazil and

Indonesia, both of which have high numbers of demonstration

activities. However, the third largest emitter is Nigeria, which

has no demonstration activity and the fourth largest emitter is

the Democratic Republic of Congo, which has only one

demonstration activity (Fig. 3). The Democratic Republic of

Congo appears to be disadvantaged by its low government

effectiveness index and high forest carbon stock. The plot of

forest carbon stock against the number of REDD readiness

projects confirms the somewhat surprising result that forest

carbon stock has a negative effect on the number of REDD

demonstration projects (Supplementary material Fig. 3).

Ecuador, Peru and Panama are interesting cases in the

Americas. Ecuador has a large number of projects relative to its

baseline emission levels (Fig. 3) and government effectiveness

index (Fig. 4), but it has a very high number of threatened

species (Fig. 5). Peru also has a large number of projects

relative to its emissions, but has a relatively high forest stock,

a high government effectiveness index and a large number of

threatened species. The tiny country of Panama appears to

have attracted its two REDD demonstration activities largely

because of its high government effectiveness index.

Based on the pattern of REDD investment — in both

readiness and demonstration activities — seen in this study,

an expanded investment approach targeting underinvested

countries and regions with high forest stock and/or defores-

tation rate, potential development benefits, and high govern-

ment effectiveness would increase REDD’s overall

effectiveness in reducing emissions.

Fig. 4 – Government effectiveness index value for non-Annex I countries included in the analysis compared to the number of

REDD demonstration activities recorded in each nation.

Fig. 5 – Number of threatened species for non-Annex I countries included in the analysis compared to the number of REDD

demonstration activities recorded in each nation.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0178

4. Conclusions

The inventory of REDD readiness and demonstration activi-

ties reported in this paper shows that at least 100 readiness

activities and 79 demonstration activities had been under-

taken by October 2009. Despite this level of investment, most

developing countries (105 of the 145 non-Annex 1 countries)

were not involved in any REDD readiness or REDD demon-

stration activities. Of the 40 countries that were involved, 14

were involved solely in demonstration activities, 12 solely in

readiness activities, and 14 in both. REDD readiness activities

are relatively evenly distributed across Africa, Latin America,

and the East Asia/Pacific region. Two countries, Indonesia

and Brazil, have by far the largest number of REDD activities.

This reflects, in part, the very high emissions associated with

deforestation in those countries. In the case of Indonesia, it

may also reflect the fact that the country was in the process

of opening up its forest governance to outside influence in

the months preceding and soon after COP13 was held in Bali.

The Indonesia Forest Carbon Alliance is generally viewed as

a very successful early attempt to develop a viable REDD

strategy.

The Poisson count model of REDD readiness activities did

not find any variables to have marked influence on the

location of REDD readiness activities. This might be largely due

to the influence of programs such as the World Bank Forest

Carbon Partnership Facility and UN-REDD, which were

established to enhance broad participation in REDD. For

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 4 ( 2 0 1 1 ) 1 6 8 – 1 8 0 179

example, national governments across the developing world

had the opportunity to apply for involvement in the Forest

Carbon Partnership Facility.

However, both the summary data from the inventory and

the Poisson count model indicate an uneven regional and

country-level distribution of REDD demonstration activities. A

relatively high proportion of REDD demonstration activities

have been located in Latin America and relatively low

proportions of REDD demonstration activities have been

located in Africa and South Asia.

Our study gleaned both official and unofficial reasons for

location choices; biodiversity benefits and community benefits

were most often cited official reasons, while prior experience

and quality of partnerships were the most frequently noted

unofficial reasons. These stated reasons were used to generate

hypotheses for the Poisson count model of country-level

conditions that affect the number of REDD readiness activities

undertaken in a particular country. The Poisson count model

was estimated for the 64 Annex 1 countries that had a baseline

of deforestation over the previous ten years and complete data

(including 35 of the 40 countries that have already received

some REDD activity). The Poisson count model confirms some

of the reasons from the survey, particularly: (1) the importance

of threats to forestry resources, rather than remaining stocks

of those resources, in influencing choices of the location for

REDD demonstration activities; (2) the level of baseline

emissions, a measure of recent deforestation, exerts a positive

influence on REDD demonstration activities, although the size

of the forest carbon stock exerts a negative influence; (3) the

importance of the threat to biodiversity (as measured by the

number of threatened species in the country), rather than

stock of biodiversity per se; and (4) the quality of governance

(as measured by the government effectiveness index). The

positive bias toward Latin America is further amplified by the

high levels of threat to biodiversity in many countries of the

region (as indicated by the number of threatened species).

No statistical support was provided for the importance of

user need or community benefits. The Poisson count model

confirmed the regional bias: everything else equal, countries in

Africa and South Asia were involved in significantly fewer

REDD demonstration activities than countries in Latin

America.

This statistical analysis suggests that investments in REDD

are following a similar uptake pattern as the CDM, a worrying

trend if REDD is to fulfill the dual goals of generating emissions

reductions and creating sustainable development benefits for

those involved. This is particularly worrying for Africa’s REDD

participation, where currently Africa hosts 1.95% of all CDM

projects globally, and in CDM afforestation/reforestation (AR)

Africa’s record is even poorer (UNFCCC, 2010; Desanker, 2005).

On average, the countries of Africa have relatively high human

development needs, a variable that does not play a significant

role in influencing the location of REDD demonstration

activities. The African countries with greatest baseline

emissions – Nigeria and the Democratic Republic of Congo –

have poor quality governance structures (as indicated by the

government effectiveness index), which is a major disincen-

tive for the location of REDD demonstration activities.

REDD has the potential to tackle a good part of the 12–20%

of emissions generated by the forest sector in tropical

countries while simultaneously creating sustainable develop-

ment benefits for communities. Given the results of this

analysis, decisions surrounding the location of future REDD

demonstration activities warrant careful consideration in

order for REDD to avoid following in the CDM’s footsteps.

African governments can be proactive in influencing the

spatial pattern of activities, for example, using REDD readiness

activities to develop plausible strategies and stronger forest

governance institutions. Investors also play key roles; partic-

ularly in influencing REDD demonstration activities. Investors

that put special emphasis on human well-being and adapta-

tion should seek out viable activities in Africa, in part to

complement the biodiversity conservation focus of conserva-

tion organizations.

Acknowledgements

This paper builds an analysis of REDD activities undertaken for

the UK Department for Environment, Forestry and Rural

Affairs (DEFRA) (grant number CEOSA 0803) led by the World

Agroforestry Centre in Nairobi, Kenya, particularly the Global

Coordination Office of the ASB Partnership for the Tropical

Forest Margins. Direction for the analysis of REDD activities

was provided by team members from the Macaulay Institute

for Land Use Research in Aberdeen, Scotland. Special thanks

go to Peter Akong Minang, Vanessa Meadu, Robin Matthews,

Klaus Glenk, Jaichu Xu and Sandeep Mohapatra for useful

contributions and comments. Views stated in this paper do

not reflect those of the funding organizations.

Appendix A. Supplementary data

Supplementary data associated with this article can be

found, in the online version, at doi:10.1016/j.envsci.2010.

09.007.

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Gillian Cerbu is a PhD student at the Graduate School ‘‘environ-ment, society and global change,’’ Albert-Ludwigs University,Freiburg and the Forest Research Institute of Baden-Wurttemberg(FVA), Germany. She joined the FVA after working as a consultanton REDD for the ASB Partnership for the Tropical Forest Margins(ICRAF, Nairobi). She has a special interest in the intersectionbetween climate change and forestry, notably REDD+ and com-munity-based adaptation.

Brent M. Swallow is professor and department chair in the De-partment of Rural Economy, University of Alberta, Edmonton,Canada. His PhD in resource and development economics is fromthe University of Wisconsin. Between 1998 and 2009 he was aprincipal economist with the World Agroforestry Centre in Nair-obi, Kenya and from 2007 to 2009 he was the global coordinator ofthe Alternatives to Slash-and-Burn Partnership for the TropicalForest Margins.

Dara Yvette Thompson is a masters student within the Depart-ment of Rural Economy at the University of Alberta. She completedher BSc in environmental conservation science in 2008. Her cur-rent work focuses on economic modelling of land use changedecisions in the context of deforestation, climate change andinstitutional frameworks.