REDD herring: Epistemic community control of the production, circulation and application of deforestation knowledge in Zambia

Forest Policy and Economics 46 (2014) 19–29

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REDD herring: Epistemic community control of the production, circulation and application of deforestation knowledge in Zambia Kewin Bach Friis Kamelarczyk ⁎, Carsten Smith-Hall University of Copenhagen, Faculty of Science, Department of Food and Resource Economics, Rolighedsvej 23, 1958 Frederiksberg C, Denmark

a r t i c l e

i n f o

Article history: Received 14 August 2013 Received in revised form 1 April 2014 Accepted 11 May 2014 Available online 12 June 2014 Keywords: Environmental science Environmental policy Environmental knowledge Forests REDD+

a b s t r a c t To enhance understanding of environmental science–policy interactions, this study analyses how environmental knowledge is produced, circulated, and applied in the Reduced Emissions from Deforestation and Degradation (REDD +) programme in Zambia. Data are drawn from interviews with key actors in the REDD + process and an extensive critical review of policy documents and deforestation estimates. We find that research over the past 50 years has not resulted in accurate estimates of forest cover and deforestation rates, nor have major deforestation drivers been convincingly documented. Estimates are difficult to compare due to inconsistent use of key terms, methodological pluralism and differences in social framing. We argue that an epistemic community is able to influence production, circulation, and application of deforestation related knowledge. Furthermore, in a situation of weak and contradictory empirical evidence, this community is arguably able to sustain a deforestation discourse centred on high forest loss and neo-Malthusian causal explanations. This is done through mechanisms making it difficult to separate facts from politics, e.g. by black boxing the origin and units of measure of deforestation estimates. We argue that this makes it more difficult to realise positive outcomes through REDD+ implementation. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Scientific knowledge about the environment is generally seen as an essential component in environmental policy making. The conventional perception amongst management practitioners, academia, and government authorities is that knowledge is produced in the “science sphere” and subsequently diffuses into the “sphere of politics,” where it is instrumental in forming the basis for evidence based decision-making (Pregernig, 2004). Political Ecology but also Science and Technology Studies (STS) have questioned this line of thinking. They argue that environmental science is also shaped by politics and social relationships. As a result, it may not be able to provide an unbiased and accurate image of “reality” (Forsyth, 2003; Goldman and Turner, 2011). Science and policy could be considered co-produced rather than occupying distinct domains of knowledge production and knowledge utilisation (Jasanoff, 2004). Thus, in order to understand science–policy interactions, it is inadequate to restrict analyses to the application of science in policy making. The analysis of the mechanisms by which politics and social framings influence the production and circulation of scientific knowledge is equally important (Goldman and Turner, 2011).

⁎ Corresponding author. Tel.: +45 51545922. E-mail addresses: [email protected] (K.B.F. Kamelarczyk), [email protected] (C. Smith-Hall).

http://dx.doi.org/10.1016/j.forpol.2014.05.006 1389-9341/© 2014 Elsevier B.V. All rights reserved.

This paper applies the concepts of knowledge production, circulation, and application (Goldman and Turner, 2011) to enhance the understanding of environmental science–policy interactions. Whilst this is relevant to most, if not all, environmental sub-disciplines, we centre our attention on interactions between forestry sciences and politics in developing countries. There are three main reasons for this. First, global interest in tropical forests has soared since 2005 (Santili et al., 2005). This interest stems from the fact that forests, whose conversion and degradation generate 12–17% of anthropogenic CO2 emissions (IPCC, 2007; van der Werf et al., 2009), are essential in combatting global climate change. The strategy for 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 (REDD+) aims at financial compensation to national and subnational actors that document reduced emissions and enhanced carbon stocks. Indeed, it has attracted billions of dollars in commitments (Agrawal et al., 2011). In extension, getting REDD+ design right will inter alia depend on understanding the relevant science–policy interactions as these determine what can realistically be implemented. More than 40 countries in Africa, Asia, and Latin America are currently being assisted by the UN-REDD Programme in preparing national REDD+ strategies (UN-REDD, 2012). Second, REDD+ policies are expected, through a process of rational decision-making, to be based on scientific knowledge. Herold and Skutsch (2009) for instance, emphasise that national policies to support

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REDD+ implementation must be based on understanding and targeting drivers and processes of deforestation. The UN-REDD (2008, p. 12) Programme states that “Establishing appropriate monitoring systems at the national level is a key REDD preparation action…[to] generate affordable and timely knowledge for national level decision-making and accounting”. Indeed, there appears to be a widespread perception in the forest sector that science and policies are situated in separable social spheres (e.g. Guldin, 2003; Janse, 2008; Klenk and Hickey, 2011). Whilst there are important examples from developing countries illustrating the opposite (e.g. Fairhead and Leach, 1996, 1997; Lambin et al., 2001; Vandergeest and Peluso, 2011), current REDD+ approaches generally do not acknowledge the political economy of states (Karsenty and Ongolo, 2012) and do not take into consideration challenges arising from the integration of environmental science and politics. Third, forest resources are important to the poor in developing countries. Recent studies from a range of developing countries have shown that the share of forest income in total rural household income ranges from 15 to 39% (Uberhuaga et al., 2012). Hence, getting REDD+ policies right is not only a matter of reducing GHG emissions, but also a matter of direct impacts on the agency and welfare of communities that are economically reliant on forests. Specifically this paper will analyse the case of environmental science–policy interactions relevant to the formation of the REDD + Programme in Zambia. The aim is to increase our understanding of how science–policy interactions influence REDD+ designs. We argue that the dominant framing (see e.g. Entman, 1993) of the deforestation problem in Zambia is based on weak and contradictory scientific evidence and that the current interaction of REDD+ relevant forest science and policy in Zambia is likely to undermine the outcomes of REDD + efforts and associated policies. We do not reject that deforestation is taking place in Zambia nor do we seek to minimalise the issue. In the following, we characterise the theory that informs our approach but also the case, reasons for case selection and methods applied. Subsequent sections analyse and discuss the interplay of scientific knowledge and politics related to deforestation and REDD+ in Zambia. 2. Theory Before moving on to analyse and discuss the case, we take a closer look at two key concepts that inform our theoretical approach: science– policy interactions and deforestation.

2.1. Science–policy interactions A common conceptualisation of how science and policy interact is described by the so-called knowledge-transfer, or rational model (Fig. 1a). This is an ideal type model, both with regard to how science influences policy making and how policies are formulated. It thus has a positivist perspective on science (Jasanoff and Wynne, 1998; Pregernig, 2005). It suggests that the production of scientific knowledge is demarcated from politics and is transferred into the policy process, where it makes direct contributions. Whilst being questioned widely for not representing empirical reality (e.g. Crewe and Young, 2002; Keeley and Scoones, 2003; Pregernig, 2004; Sutton, 1999), we argue that this conventional perception of science–policy interaction is common in the forest sector (e.g. Guldin, 2003; Janse, 2008; Klenk and Hickey, 2011; Pregernig, 2004), amongst practitioners, policy-makers and researchers. Understanding environmental policy through this lens implies that knowledge is treated as a product, which retains its form as it is circulated and applied (Goldman and Turner, 2011). An alternative perspective on the interaction of environmental science and formulating policy is offered by theory in the fields of Political Ecology and STS, Fig. 1b. The former has inter alia provided insights into the politics of environmental change and its representations and how knowledge claims are linked to politics, whilst STS has focused on the production of scientific knowledge and technologies within political and social contexts. In line with Forsyth (2003, 2011) and Goldman and Turner (2011), it is fruitful to combine Political Ecology and STS theory to achieve a more elaborate understanding of science–policy interactions. Three elements from this body of work are of particular relevance to this study. First, Political Ecology and STS literature challenges the idea that science is able to provide unbiased and accurate facts about nature and society, or the causal links that exist between them. The idea that environmental explanations can be based on universalistic statements and generalisations of causality, legitimised by reference to scientific knowledge, is problematised. Instead, Political Ecology and STS attain their ontological and epistemological perspectives from constructivism and critical realism, in which scientific explanations of environmental problems are viewed as a result of social and political framings and partial experiences of biophysical reality (Forsyth, 2003). A string of studies have called attention to the misinterpretation or exaggeration of environmental problems and argued that the use of environmental

Circulation of environmental knowledge

Science (facts)

Truth

Politics (power, values)

Co-production of science and politics

Production of environmental knowledge

(a)

Application of environmental knowledge

(b)

Fig. 1. Views on the relationship between environmental science and policy: (a) the linear, conventional view (Jasanoff and Wynne, 1998, p. 8) with separate production of science and politics, and (b) an alternative view (adapted from Goldman and Turner, 2011) where science and politics are co-produced through production, application and circulation of knowledge.

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science in decision-making should explicitly acknowledge social and political factors that potentially undermine the ability of institutions to address underlying biophysical causes of perceived environmental problems (e.g. Bassett and Zueli, 2000; Beymer-Farris and Basset, 2012; Fairhead and Leach, 1996, 1997; Forsyth, 2011). We extend this line of enquiry and use critical discourse analysis to understand the history and nature of deforestation in Zambia, where Hajer (1995) defines discourse as: “a specific ensemble of ideas, concepts, and categorizations that are produced, reproduced, and transformed in a particular set of practises and through which meaning is given to physical and social realities” (p. 44). According to Bryman (2008), critical discourse analysis is closely associated with the epistemology of critical realism and involves examination of social structures, such as power relationships, that are responsible for giving rise to the discourses. The notion of discourse is more broadly defined in critical discourse analysis than in anti-realist approaches to discourse analysis. Discourse analysis has only recently been applied to forest related issues (Arts, 2012). Second, it is argued that science and politics should be considered co-produced rather than separated. Co-production refers to the process by which knowledge (including scientific) is framed, collected and disseminated through social interaction and change (Jasanoff, 2004). In this view, science is an outcome of messy and situated practises shaped by particular historical, socioeconomic, political, and cultural practises (Forsyth, 2003). To analyse such practises, we apply the concepts of boundary organisations and objects. The former are “social organisations or collectives that sit in two different worlds such as science and policy, and can be accessed equally by members of each world without losing identity” (Forsyth, 2003: 141), whilst the latter are “objects which both inhabit several intersecting social worlds ….[and] are both plastic enough to adapt to local needs and the constraints of the several parties employing them, yet robust enough to maintain a common identity across sites” (Star and Griesemer, 1989: 393). Boundary organisations and objects can be used to translate environmental knowledge across social boundaries of science and politics (Forsyth, 2003) and suggest the demarcation of science and non-science, not as a sharp line, but a flexible and changing boundary that is managed by institutions of science in order to justify claims for authority or resources (Gieryn, 1983). Third, environmental politics run through the inseparable nexus of production, circulation, and application of environmental knowledge (Goldman and Turner, 2011). The way research agendas are established and funded and the process by which scale and definitions are selected and applied in scientific enquiry all shape the production of environmental knowledge. When scientific knowledge is circulated and applied as part of the policy making process this takes place within a discursive framework. To promote our understanding of these processes, we apply the concept of epistemic community (Cross, 2013), i.e. “a network of professionals with recognised expertise and competence in a particular domain and an authoritative claim to policy-relevant knowledge within that domain or issue area” (Haas, 1992: 3), to the formation of REDD + policies in Zambia. There are also other approaches to understanding environmental science–policy interactions. These include (i) realist approaches which in brief suggest that it is the interests and material power of the dominant actors which explain policy output (e.g. see Art and Jervis, 1996) and (ii) Advocacy Coalition Framework which sees policy change to happen only when otherwise stable and shared core beliefs in a dominating advocacy coalition alter (Sabatier, 2007). Both of these could also be applied to the present case; however, we find that the co-production of science and politics approach (Fig. 1b) allows a broader and more integrated analysis (Goldman and Turner, 2011) of REDD + politics, e.g. by moving beyond explaining outcomes as merely the result of actor power and interests. We do, however, in the Discussion, briefly look at our main findings from the realist approach and Advocacy Coalition Framework approach.

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2.2. Understanding deforestation Estimating rates and causes of deforestation is essential to designing and implementing REDD+ policies, e.g. in connection to establishing measurement, reporting, and verification (MRV) systems (Herold and Skutsch, 2009). However, definitions of “forest”, “deforestation”, “degradation”, and related terms are numerous, often vague, and subject to intense debate. Lund (2012) has for instance identified more than 1500 definitions of forest. Choice of definition directly impacts estimates of deforestation rates, analysis of its causes, and what constitutes appropriate policy responses. In this regard, Verchot et al. (2007) showed that thresholds for crown cover in forest definitions had financial implications for countries under the Clean Development Mechanism (CDM). Wunder (2000) categorises definitions of deforestation as broad or narrow, the former including more or less all disturbances to forests that negatively affect forest quality and widely used by actors with a conservationist point of view. The latter is narrow, in the sense that forests must be converted into other land uses and trees eliminated, and commonly used by foresters and economists. Whatever the definition applied, deforestation can be reported in gross (the sum of all negative forest area changes) or net (gross minus forest area gains, e.g. from afforestation) figures. Many studies do not report definitions or whether figures are gross or net, leaving room for misunderstanding (and political exploitation) and hampering comparisons. Regarding drivers of deforestation, there are a large number of studies that aim at uncovering the causes at play in different geographical regions (e.g. Rudel, 2005; Williams, 2003). At a meta-level, deforestation causes are often divided into direct and underlying explanatory factors (e.g. Geist and Lambin, 2002). This distinction can, however, be difficult to maintain as factors may be perceived as both direct and underlying, depending on the viewpoint of the analyst, e.g. an environmental lobbyist vs. a government official (Wunder, 2000). In other words, arguments are influenced by subjective perceptions and the discursive viewpoints to which actors subscribe. Whilst not referring to discourses as such, Wunder (2000) provides a useful systematic analysis of different causes of deforestation. He distinguishes between the “impoverishment school”, the “neo-classical school”, and the “political-ecology school”. In the first, a growing number of poor push smallholders to clear forests. This type of explanation largely overlaps with what has been referred to as the neo-Malthusian perspective (Adger et al., 2001; Leach and Fairhead, 2000). In the neo-classical school, open-access property rights and rent seeking behaviour are the main factors causing deforestation. In the political-ecology school, capitalist investors crowd out smallholders who are pushed to deforest. Deforestation is thus hard to quantify and understand because of the proliferation of definitions, lax reporting of applied definitions and units of measures, complex causal relationships that are difficult to determine and measure, and the involvement of a large number of actors (from individual farmers to national governments and international bodies). This may in turn lead to radically different explanations of what happens in a particular time span for some location. Fairhead and Leach (1996) argue, for instance, that local villagers increased forest cover in a study area in Guinea through creation and protection of forest islands. Indeed, the government portrayed these villagers as the principal agents of deforestation. This and a string of similar cases (e.g. Ali et al., 2005; Beymer-Farris and Basset, 2012; Forsyth and Walker, 2008; Ribot, 1999) illustrate the need to carefully assess biophysical uncertainties and explanations of deforestation. 3. Methods 3.1. Case Zambia was selected as case country for four main reasons: (i) there are substantial forest resources, apparently high rates of deforestation,

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and widespread local level forest reliance; (ii) the government is committed to reducing emissions from deforestation and degradation; (iii) there has been an explicit national REDD + process underway, and (iv) it represents a dry forest case country located in an understudied region. Whilst the forests of Zambia have been used by humans for millennia (Lawton, 1978) and are thus subject to fluxes of deforestation and reforestation (Vail, 1977), the issue of deforestation has only entered national polices with colonial state-making and centralisation (Matose and Wily, 1996). An example of this can be found in the colonial administration perceiving the traditional “chitemene” system of small-scale shifting cultivation as a main cause of forest destruction, wherefore it was banned (Holden, 1997). Contemporary, nominal forest legislation is largely a continuation of the first forestry law, the Forest Ordinance from 1949, prepared by the colonial administration (IDLO, 2011). Indeed, the Forests Act No. 39 of 1973 has not been replaced, despite many new policy initiatives over the last three decades, such as the Zambia Forestry Action Plan of 1996 (GoZ, 1996). The process leading up to this Plan, however, resulted in the National Forestry Policy of 1998 (replacing the 1965 policy) that specifically addresses the issue of deforestation (GoZ, 1998). The fifth and sixth national development plans, from 2006 and 2011 respectively, also recognise deforestation as a central environmental issue (GoZ, 2006, 2011). The National Forestry Policy of 1998 led to the legislative proposal for the Forests Act No. 7 of 1999, which however, was never enacted due to internal government disagreements on the establishment of a forest commission to restructure the Zambian Forestry Department (ZFD) (IDLO, 2011). The Zambian REDD+ process began in 2008, where initial preparations for establishing a UN-REDD + Programme began. In 2009 Zambia was selected as one of nine pilot countries for a so-called UNREDD “quick-start initiative”. The 4.5 million USD project was approved by the UN-REDD Policy Board in 2010 and the “National Joint Programme Document” formulated (UN-REDD, 2010). Following the “quick-start initiative” that inter alia has as objective to identify rates and drivers of deforestation (UN-REDD, 2010), a 2010 National Forestry Policy draft was prepared (GoZ, 2010). In extension, a bill for a new Forestry Act is currently being drafted. Thus, although there is much functional forest related legislation in Zambia, relevant to the implementation of REDD +, such as the Lands Act of 1995 and the Decentralisation Policy of 2002 (Chundama, 2009), we focus on the science and politics related to the nominal forest legislation processes. This is arguably the most central to the Zambian REDD + initiatives as it defines how the resources subject to REDD + activities will be owned, used, and managed (IDLO, 2011).

Table 1 List of analysed documents related to the forestry policy and REDD+ policy process in Zambia. Action programme to combat deforestation (UNDP and FAO, 1993) Zambian Forestry Action Plan. Volume II – Challenges and opportunities (GoZ, 1996) National Forestry Policy 1998 (GoZ, 1998) Forestry Outlook Studies in Africa (FOSA) – Zambia. FAO and Government of Zambia (Chileshe, 2001) Zambian Forestry Department Annual Report 2005 (ZFD, 2005) Fifth national development plan 2006–2011 (GoZ, 2006) UN-REDD concept note Zambia (FAO-Zambia (undated)) Integrated Land Use Assessment (ILUA) 2005–2008 Republic of Zambia (ZFD and FAO, 2008a) Forestry Sector Situation Analysis Report (Mwitwa, 2009) Zambian Forestry Department Annual Report 2009 (ZFD, 2009) UN-REDD National Joint Programme document (UN-REDD, 2010) National Forestry Policy draft 2010 (GoZ, 2010) Sixth National Development Plan 2011-2016 (GoZ, 2011)

Finland). All interviewees were informed that data could be used in anonymous form, and that they could withdraw from the interview at any time. The interviews were recorded and verbatim transcribed, and then coded according to the following broad themes: i) interviewees' perceptions on the state of the forest and drivers of deforestation in Zambia; ii) the potential role of REDD+ in Zambia; and iii) the respondents' perception of the utilisation of forest-related scientific knowledge in Zambian forestry policy. Based on the analysed and coded policy documents and verbatim interview transcripts, a critical discourse analysis was carried out to search for emerging discursive patterns associated to the broad theme of deforestation and its stated causes. The critical discourse analysis also relied on experience obtained from attending various workshops and meetings in the Zambia forestry community during fieldwork as well as the primary author's work experience within FAO and the UN-REDD forest assessment initiatives in Zambia (2006– 2009). Third, an archival investigation was conducted by locating and reviewing studies with estimates of forest cover and/or rates of deforestation in Zambia from 1962 to present time. 4. Results and discussion In the following, we focus on science–policy interactions in relation to preparing REDD + policies in Zambia, paying particular attention to how knowledge on forest area estimates and changes are produced, circulated, and applied.

3.2. Data collection and analysis

4.1. Production of scientific claims of knowledge of forest cover, deforestation rates and causes

Information was collected during fieldwork in Zambia (2010–2011) and consists of three main sources. First, it included the collection of key documents from the forestry policy and REDD+ process (e.g. government documents, strategies and reports) (Table 1). The material was subsequently analysed by identifying and coding statements related to deforestation and REDD+ . Second, a series of semi-structured and open-ended interviews were conducted with national and sub-national key actors in the REDD + process to investigate their views and perceptions of deforestation and the REDD+ policy process. The interviewees included 13 respondents from government authorities at the national level (departments and bodies under the Ministry of Tourism, Environment and Natural Resources), seven respondents from government authorities at provincial and district levels (including Forestry Department offices), two NGO representatives (WWF and IUCN), six consultants and academics (CIFOR, Copperbelt University, independent researchers and consultants) and six representatives of international donors (Food and Agriculture Organization of the United Nations (FAO), United Nations Development Programme (UNDP), bi-lateral donors: Denmark and

The body of literature on forest cover and deforestation estimates for Zambia has not been critically reviewed previously. We found 19 national-level or regional studies published in the almost 50 years from 1962 to 2010, providing 21 forest cover estimates and 15 deforestation estimates (Table 2). National forest cover estimates range from approximately 29– 61 million ha with no discernible pattern across comparable time periods; low and high estimates are found in each decade, e.g. forest cover was estimated at 30.1 million ha in 2004 (Pohjonen, 2004) and 50.0 million ha in 2006/2007 (ZFD and FAO, 2008a). Comparison across sources is hampered by methodological differences: lack of definitions of key terms and specification of what forest types and categories are included in a particular survey. Early assessments were based on expert opinion, inventory data from district forest management books from 1952 to 1967 (de Backer et al., 1986), and the Vegetation Map of Zambia (Edmonds and Fanshawe, 1976). The two latter sources have been re-used in various ways, typically in connection with development of model estimates, to the present day. National inventory derived estimates are limited to two primary data sets: the district forest

Table 2 Forest cover and deforestation estimates for Zambia, 1962–2010. All figures are national-level estimates unless otherwise noted. Forest cover (‘000 ha)

Year

Annual deforestation (‘000 ha)

Annual relative decline (%)

Period

Type of data

Comments

Persson (1975) FAO (1963) de Backer et al. (1986)

34,000 37,024 61,200

1962 1963 1965

n.a. n.a. n.a.

n.a. n.a. n.a.

n.a. n.a. n.a.

Includes “open woodlands” Excludes “protected forests” Includes “woodlands and forest”

Millington and Townsend (1989)a

n.a.

n.a.

n.a.

n.a.

n.a.

Chakanga and de Backer (1986)

56,240

1974

n.a.

0.5b

1975–85

c

d

FAO (1981)

29,510

1980

40

0.1

1976–80

Country reporting to FAO's Yearbook 1971 Country reporting to FAO, and FAO expert appraisal Inventory data from district management books (1952–1967) Vegetation map of Zambia 1976 (Edmonds and Fanshawe, 1976) and satellite imagery Vegetation map of Zambia 1976 (Edmonds and Fanshawe, 1976) Country reporting to FAO, and satellite imagery

FAO (1988)

29,548

1980

70

0.2

1981–85

FAO experts' appraisal of data from FAO (1981)

de Backer et al. (1986) FAO (1992)

41,200-55,200 56,899

1985 1990

n.a. 287

0.5–1.0b 0.5

1965–85 1975–90

FAO (1993)

32,301

1990

363

1.1d

1981–90

Inventory data from district management books Satellite and aerial photos, and Chakanga and de Backer (1986) Satellite imagery

GoZ (1996)

44,550

1992

250-300

0.5

1965–1996

Chidumayo (1997) ZFD and FAO (2008a, b) Mukosha and Wamunyima (1998)

44,015 51,384 n.a.

1997 1990 n.a.

849 284 562

1.9d 0.62 5.3d

1990 1990–2005 1993

de Backer et al. (1986) and desk estimates by Zambia Forestry Department Chidumayo (1994)e and de Backer et al. (1986) Satellite imagery Satellite imagery and primary inventory data

FAO (1997) Alajärvi (1996)a Strid (1997) FAO (2001)

31,398 n.a. 29,400 31,246

1995 n.a. 1997 2000

264 n.a. n.a. 851

0.8 n.a. n.a. 2.4

1990–95 n.a. n.a. 1990–2000

Updates of FAO (1993) data de Backer et al. (1986) Not specified de Backer et al. (1986) and satellite imagery

ZFD and FAO (2008a, b) Pohjonen (2004) FAO (2005)

47,681 30,100 42,452

2000 2004 2005

284 540–980 445

0.62 1.7–3.3d 1.1d

1990–2005 1965–2003 1990–2000

ZFD and FAO (2008a, b) ZFD and FAO (2008a)

46,556 49,968

2005 2006/2007

284 n.a.

0.62 n.a.

1990–2000 n.a.

Satellite imagery de Backer et al. (1986) and satellite imagery Millington and Townsend (1989) and Chakanga and de Backer (1986) Satellite imagery Primary inventory data

FAO (2010)

49,468

2010

167

0.3d

1990–2000

a b c d e

Chakanga and de Backer (1986) and 2005 primary inventory data (ZFD and FAO, 2008a)

Includes “all wooded areas” Includes “wooded savannas and woodlands”. Excludes “fallow areas” and shrub formations. Forest: woody vegetation cover N10% Excludes “other wooded areas” and fallow areas. Forest: woody vegetation cover N10% Includes both “forests” and “woodlands” Includes “wooded areas” Excludes plantations. Forest: woody vegetation cover N20% Includes only forest “under good forest cover” Excludes “savanna woodland” Forest: woody vegetation cover N10% Only includes provinces of Copperbelt, Central and Luapula. Excludes plantations Coverage as in FAO (1993) Includes 15.9 million ha of “trees outside forests” Excludes “other wooded land”. Forest: woody vegetation cover N10% Forest: woody vegetation cover N10% Excludes “wooded land” Excludes “other wooded land” Forest: woody vegetation cover N10% Forest: vegetation cover N10% Excludes “other wooded land”. Forest: woody vegetation cover N10% Excludes “other wooded land”. Forest: woody vegetation cover N10%

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Author

The sources are commonly referred to in other deforestation related documents where they are cited for providing forest cover estimates; however, such estimates are not found in the sources. These annual relative decline estimates were derived arbitrarily, i.e. not empirically based. Does not include estimate of deforestation in wooded savannas and woodlands. No estimate of annual relative forest decline provided in studies. Estimate calculated by us using data in each source. It is not clear on what basis Chidumayo (1994) estimated forest area, but Edmonds and Fanshawe (1976) were included in list of references.

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management book inventory data collected in 1952–1967 and the Zambian Integrated Land Use Assessment (ILUA) with field data collected in 2005–2007 (ZFD and FAO, 2008a). Remotely sensed forest cover estimates have been generated in the last two decades. These estimates also display large differences and hence cannot serve to create consensus concerning the extent of forest cover in Zambia. Estimates of annual deforestation deviate by a factor of 25, from approximately 40,000–980,000 ha/year. Again, there is no discernible pattern within comparable time periods, e.g. deforestation in the period 1990–2000 has been estimated from 167,000 to 851,000 ha/year (FAO, 2001, 2010). Likewise, no particular method appears superior, e.g. the remote sensing derived estimates of deforestation of 562,000 ha in three provinces in 1993 (Mukosha and Wamunyima, 1998) and the national annual deforestation rate of 284,000 ha from 1990 to 2000 (ZFD and FAO, 2008a, b) do not compare well. Whilst some variation can be ascribed to differences in temporal coverage and errors associated with technical limitations, e.g. difficulties in accurately measuring tree cover in low density or degraded forests (FAO, 2011), the gap is noticeable. The purpose of this analysis is not to dispute that deforestation is taking place in Zambia, indeed the reviewed sources all agree that forest cover has been reduced. Nor is the purpose to assess the most probable levels of forest cover and change in the country. Rather, the analysis shows that there is a wealth of studies and estimates and that none are clearly preferably. The variation in available estimates, with large discrepancies in both directions over short periods of time, is clearly not an objective image of real changes on the ground – there are no alternative land uses that could explain fluctuations at this scale – but instead a result of inconsistent use of key terms, methodological pluralism and social framing. Here, based on Entman (1993), social framing refers to the selection of social aspects of a perceived reality so as to promote a particular problem definition, causal interpretations and recommendations. Hence researchers and officials arguably make a number of socially framed choices during data collection, analysis, and interpretation. These choices then construct “facts”. For instance, whilst computerised (and objective) interpretation methods have been applied by FAO and officials from the ZFD in their analysis of satellite imagery for Zambia, the automatic categorisation of image polygons were determined a priori through decisions about what qualifies as a forest and when a change can be classified as deforestation. An involved FAO expert noted that this “categorisation error” is an important source of uncertainty due to the difficulty of consistently classifying forest “… due to the prevailing pattern of small-holder agriculture and shifting cultivation”. A priori methodological decisions can thus be seen as socially framed as they can change the salience of selected information, e.g. the importance of small-holders as deforestation agents. This problem is more generally discussed by Forsyth (2003). Thus, the available evidence does not offer a univocal image of past and current deforestation levels in Zambia. Whilst some actors did express concern about available evidence, primary data sets are rarely subject to critical validation, evaluation, and debate by parties outside the epistemic community arguably constituted by FAO, the Zambian Forestry Department, and the associated network of professionals that control knowledge production related to deforestation (see below). The possibility for outside parties to comment and scrutinise data was first of all constrained by a general unwillingness of the ZFD (who holds ownership of most data) to grant access to the data and unresolved data sharing policies within FAO, “If we spend a lot of money on the data collection, should we just share it for free?” (a respondent from the ZFD). Secondly, it also appears that access to primary data was impeded by poor data management, “You will not find data for the previous inventories…I think the only data that exists now is the ILUA Integrated Land Use Assessment” (a respondent from Zambian academia). There are no large-scale comprehensive empirical investigations on uncovering deforestation drivers in Zambia (the recent nationallevel report by Vinya et al. (2012) appears to be based on a very limited empirical data set). This, in consequence, means that there is no

national-level documentation of what constitutes key drivers. The few available empirical studies are either geographically limited in scope, e.g. focusing on just one protected area (Eriksen, 2007), or included as sub-components in studies with other foci, e.g. on migration (Unruh et al., 2005) or health (Frank and Unruh, 2008). Holden (2001) convincingly argues that deforestation in Miombo woodlands in Northern Zambia has been driven by agricultural and technological change (introduction of new crops and varieties) but also subsequent population growth, migration, and government policies. Other studies indicate that subsistence (firewood) and commercial (charcoal) fuelwood production may also be important (Chidumayo, 1987; Kutsch et al., 2011). Other noticeable drivers are tenure arrangements and infrastructural developments, including railways, urban areas, and mines (Misana et al., 1996; Mwitwa et al., 2012). The scant evidence does not point towards domination by any of the three above mentioned deforestation schools. 4.2. Circulation of scientific knowledge claims and the REDD+ policy process Goldman and Turner (2011) note that circulation is difficult to distinguish from the production and application of knowledge, since it happens simultaneously and through highly interwoven processes. Whilst available studies do not give a clear indication of the extent of forest cover nor its change over time, high rates of deforestation have been emphasised in knowledge circulation (and application). For instance, the Forest Resources Assessment 2000 (FAO, 2001) estimated forest loss of 851,000 ha/year from 1990 to 2000 is widely quoted (and used to rank Zambia as one of the most rapidly deforesting countries in Africa). Likewise, though the evidence is weak and ambiguous, as also noted above, neo-Malthusian deforestation drivers (simple technology, lack of land, land degradation) are emphasised in knowledge circulation (Table 1). A specific example is the argument that wood energy needs amongst small-scale farmers drive them to clear forests (e.g. UNREDD, 2010) though the causal link between woodfuel production and deforestation is not well documented in Zambia (Chidumayo, 1993) or elsewhere (Wunder, 2000). We argue that, since colonial times, the dominant deforestation discourse has remained essentially unchanged (Vail, 1977; Lawton, 1978; Chidumayo, 1987, 1997; UNDP and FAO, 1993; Frost, 1996; GoZ, 1996, 1998, 2010; Holden, 2001; ZFD and FAO, 2008a; Makano, 2008; Mwitwa, 2009; UN-REDD, 2010; IDLO, 2011; Vinya et al., 2012). A crude summary of this discourse is: there is rapid deforestation, and increasing degradation caused by population growth and neo-Malthusian pressures that push small-scale farmers to clear forest for agricultural production and to meet energy needs. In the following, we will focus on the circulation of authoritative scientific knowledge claims in the REDD+ policy process. The Zambian National Joint Programme document on REDD + provides the primary policy framework for designing and implementing REDD + in the country. This document – including seven provincial forestry sector analysis reports, a synthesising national sector analysis, and a new draft Forestry Policy – was the main outcome of a national forestry policy consultation process. This process was developed and implemented by the ZFD supported by UNDP. It consisted of a number of national and provincial meetings and workshops, conducted in 2009, in which representatives from government institutions, NGOs, civil society, customary leaders, donors, and the private sector were invited to provide inputs towards what the forestry sector should look like and what a REDD+ programme should address. In practical terms the process was facilitated almost single-handedly by a consultant from Zambian academia, who was also the main author of most documents – including the draft Forestry Policy. The provincial sector analysis reports were authored by a number of contracted consultants. The process was therefore centrally planned, organised, and controlled. The knowledge and views contributed by participants were summarised through reports and revisions by the contracted consultants, government officials, and donor representatives, who made meaning out of

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(and translated) the gathered pool of knowledge using their subjective interpretations, values, and understandings. As noted by an interviewee “People have picked from science what they want to believe and what they don't want to believe. People have picked from local knowledge what is useful to them and they have discarded the rest” (a respondent from Zambian academia). To understand these processes, it is interesting to note what happened to the use of the concept of deforestation during the time of the consultation. The traditionally dominant view, that deforestation is rampant and problematic, was being questioned by actors outside the forestry sector and contested by new low deforestation estimates in the ILUA (ZFD and FAO, 2008b) and FAO's FRA 2010 Zambia country report (FAO, 2010). Now FAO and the ZFD, co-authors of the FRA 2010 Zambia country report, noted that: “There is a slight forest decrease, but the real problem is the forest degradation” (FAO, 2010, p. 10). This change in emphasis from deforestation to forest degradation (i.e. negative changes in forests that are not classified as deforestation, e.g. defaunation or disappearance of old trees) could denote an effort to move from the simplistic estimation of deforestation to a more differentiated account of forest change dynamics. Alternatively the emphasis on forest degradation as the real problem could be an attempt to “compensate” for the low deforestation estimate to maintain attention on the forestry sector and the central role of the ZFD in safeguarding forest resources. Whilst the process therefore arguably reframed the deforestation problem as a degradation problem, this did not go against the prevailing discourse prescribing Zambian forests to be under serious pressure. Also, it did not deflect the urgency of establishing REDD + policies. Respondents generally noted that there are few scientific deforestation studies developed independently by the government or its UN partners. Further, knowledge produced in dialogue with the government has a higher chance of being legitimised and incorporated into formal policymaking as compared to independent research: “When the government commissions the research, you know that the result findings will ultimately be owned by government and they will use it” (government official). This suggests that it becomes difficult to challenge the dominant deforestation discourse. Several interviewed actors also expressed concern that, despite the participation of a range of actors in policy processes, the government systematically excluded alternative truth claims. When commenting on the REDD+ process and the involvement of actors in shaping the debate, a researcher stated that: “it's only the government talking to itself!” Through their collaborative dominance of knowledge production on deforestation (see Table 1) and their ability to influence knowledge circulation, the epistemic community arguably constituted by the FAO, the ZFD and associated professionals holds a key role in defining and up-dating the prevailing deforestation discourse. Further, the FAO and the ZFD may also be considered boundary organisations that are able to transport knowledge across the boundaries of “science” and “politics”. In the words of Forsyth (2003), such organisations “…set the goalposts of environmental political debate by providing definitions or approaches to contested science that are then used as ‘facts’” (p. 142). Through the production of terminology and methods, forest statistics, consultancy reports and formal policy documents (hereafter “boundary documents”), deforestation knowledge is circulated amongst actors in the policy process and across social boundaries. Through boundary documents, the concept of deforestation enters a process of translation and discursive framing where perceived scientific facts about the forest are co-produced with values, orthodoxies, and subjective experiences of actors involved in the policy process. The boundary between “scientific evidence” and other knowledge types becomes blurred and the concept of deforestation becomes black boxed, i.e. actors take its nature to be objectively established and do not see any need to discuss its meaning (Forsyth, 2003). In our case, only few actors questioned how deforestation estimates were generated and how to interpret the figures. For instance, whilst the majority of interviewed respondents were acquainted with the above mentioned “close to 900,000 hectare deforestation estimate”, it was rarely recognised that this boundary object had root

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in the two distinct, and in many respects incomparable (different methods, different reported units), studies by Chidumayo (1997) and FAO (2001). Further, although some respondents did question whether such high deforestation levels could be correct, that did not impede the circulation of such deforestation “facts” in boundary documents as well as in media, general public debate, etc. In the process, these “facts”, become “real” and part of the discursive framing of reality as seen in statements such as: “Encroachments in forest reserves…and charcoaling were responsible for a high deforestation rate now reaching record high at 850,000 ha, per annum.” (ZFD, 2005, p. 3). For several of the boundary documents reviewed in this study, it was impossible to trace the original sources from which the circulated statistical figures were stated to originate. In addition, documents published by boundary organisations are seldom explicit about their sources of information, making it difficult to distinguish “facts” from “politics”. Whilst it could be argued that such boundary documents do not have to meet the same formalities as academic publications, a problem emerges when claimed “evidence” is used to legitimise the validity of statements, e.g. “Studies on the state and management of the forests in Zambia show that agricultural expansion and wood fuel harvesting have the strongest causal correlation to deforestation and forest degradation” (FAO-Zambia, undated: p. 3). However, as in many other reviewed boundary documents, no reference was subsequently made to the studies referred. According to our knowledge, no such country-level studies have been made for Zambia. Such statements do therefore not reflect scientific consensus, but rather the viewpoint of the dominating actors in policy formation. FAO and the ZFD arguably make up the institutional centres of a professional network able to convince others of their causal beliefs and policy goals by using their authoritative and policy-relevant expertise; hence constituting an epistemic community able to influence government and other non-state actors (translating knowledge into power). The short term consultancy study on the drivers of deforestation and the potentials for REDD+ in Zambia (Vinya et al., 2012) commissioned and funded by UN-REDD/FAO (in collaboration with the ZFD) exemplifies how knowledge production and circulation is linked to externally funded government programmes and the epistemic community. Using the criteria identified by Cross (2013) to describe when epistemic communities are likely to be most influential, this community is likely to have a high degree of policy influence in Zambia. This is because: deforestation is a complex issue characterised by high degrees of uncertainty (e.g. regarding rates of deforestation); the community has access to decision-makers and are able to influence the premises of the deforestation debate; community members share a high level of professional norms (background in forestry); and the policy field is coherent (focused on forests, with respect for quantitative data). 4.3. Application of authoritative scientific claims of knowledge in the REDD+ policy process The nominal forest policy process in Zambia has been influenced by science inputs. Estimates of forest cover and rates of deforestation became a basis for the deforestation discourse and the recent low deforestation estimate has been integrated into boundary documents (along with a focus on degradation). The application (and circulation) of science inputs, however, ignores the uncertainty associated with applied estimates as well as other possible causal explanations of deforestation. For instance, regarding the REDD+ negotiations between the Government of Zambia and the UN, a government official explained that the selection of Zambia as a UN-REDD pilot country was partly based on the renowned “fact” of 900,000 ha of annual deforestation. The identification of deforestation drivers in the REDD+ policy process is also closely aligned to the dominant deforestation discourse. For instance, the National Joint Programme document (UN-REDD, 2010, p. 9) states that deforestation is driven by “i) the overwhelming reliance of the largely poor rural population on natural resources for day to day

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survival; and ii) the lack of alternative energy sources in urban areas where much of the population also utilise charcoal and firewood for fuel”. The same arguments were put forward two decades earlier in connection with the Action Programmes to Combat Deforestation and Promote Rational Utilization of Forest Resources – Zambia (UNDP and FAO, 1993). Using the analytical schools of deforestation framework proposed by Wunder (2000), the deforestation discourse in Zambia seems closely related to the impoverishment school: due to population growth and neo-Malthusian pressures small-scale farmers are pushed into the forest that they clear to establish agricultural production and cover energy needs. From this follows that deforestation can be avoided through population policies, poverty alleviation, rural development, intensified agriculture, and closing resource gaps (Wunder, 2000) (e.g. by allowing households to cover their energy needs through nonwood sources). Such recommendations and activities are commonly found in REDD + policy documents, e.g. agricultural intensification through conservation agriculture and a switch from wood fuels through energy pilot projects in the National Joint Programme document (UNREDD, 2010). The REDD+ policy process also appears to simply ignore contradictory biophysical evidence; such evidence is not compiled and discussed in boundary documents like the National Joint Programme document and the 2010 draft National Forestry Policy. For instance, Miombo woodland is the most common forest type in Zambia with woodland cover being mainly affected by people, fire and elephants (Frost, 1996). These woodlands are not uniform or static and there are many examples of woodland clearing for agriculture, followed by abandonment and re-establishment of forest cover (e.g. Vail, 1977; Frost, 1996; Chidumayo, 2002). Yet, evidence and issues of woodland dynamics do not visibly enter the policy process. In particular, the ability of Miombo woodland to recover from shifting cultivation and charcoal production activities (Chidumayo, 2004; Syampungani, 2008, 2009) is ignored. Whilst some interviewed actors, including from the ZFD, acknowledged the potential importance of considering woodland dynamics in the deforestation debate, they also argued (in line with the impoverishment school) that disturbed forests often were not given time to regenerate due to increasing population pressure and consequently higher charcoal demand and shifting cultivation with shorter rotation period. We see three main reasons that the deforestation discourse in the REDD+ policy process, with the minor alteration to now also emphasise degradation, remains unchanged by the epistemic community. First, there are financial reasons. By positioning itself as a country with a large forest area with alarming rates of deforestation and degradation, Zambia is preparing to access future REDD + funding. In the words of a forestry official “money for REDD goes to the tropical high forested countries, not to a place like Zambia. We are just forcing ourselves to be in REDD”. Access to project specific funding also appears to be an important motivation for the ZFD: the annual budget is low (at approximately 3.35 million USD in 2011) and external funding made up almost 36% of the 2011 budget (GoZ, 2012). Interviews with government officials revealed that references to high deforestation estimates have previously been used deliberately to attract funding to the ZFD “You know people are not writing the truth. People are writing documents to please where the money has come from”. International organisations have also noted that “these generously funded inventory projects” (respondent from the FAO) are a strong financial incentive for the ZFD to call for more forest monitoring initiatives – indeed, the forest cover and deforestation uncertainties highlighted in this paper could be considered a truth claim that will subsequently be used by the epistemic community to raise funding for generating new estimates. It also appears that this modus operandi is accepted by allied boundary organisations. A UNDP representative for instance conceded that his organisation had referred to Zambia as a country with a very high rate of deforestation in various documents and presentations, despite being aware of the uncertainty of the claim. It could, however, be argued that building institutional capacity at the ZFD, previously identified in an institutional analysis as

a key factor limiting the effectiveness of the department (Makano, 2008), in order to enhance REDD + related decision-making is more important than generating new estimates. Second, the neo-Malthusian impoverishment school of deforestation appeals to major actors (the Government of Zambia, the ZFD, international donors and agencies) involved in the REDD + policy process. It allows them to reach an agreement on causes of deforestation in Zambia and how to address them; it does not require action on deforestation drivers associated with, e.g., clarification of land tenure or rent seeking. Third, REDD + provides an opportunity for the ZFD to protect and possibly expand its institutional mandate. During the initial phase of the Zambian REDD+ programme, it was discussed whether the institutional responsibility should be with the ZFD or a more cross sectorial government entity, as REDD + issues need to be tackled by involving actors in sectors such as energy and agriculture. However, ZFD officials successfully framed REDD+ as a forestry issue and now host the programme. It should be noted that these findings can also be explained using the realist and advocacy coalition framework approaches. The first would suggest that the key actors (the Zambian forest authorities and the FAO) have an interest in high rates of deforestation and the power to maintain such estimates in the face of criticism, allowing them to generate funding for REDD projects. The latter would suggest that the core deforestation beliefs, in terms of extent and driving factors, held by the dominant advocacy coalition have been very stable over time, hence leading to policy continuity. We note that these explanations are in line with the explanations generated by our analysis of the co-production of science and politics. 4.4. Consequences for REDD+ design REDD+ in Zambia is still being developed. Preparation for the UNREDD sponsored quick start programme started in 2008 and efforts have concentrated on reviewing causes of deforestation (Vinya et al., 2012), assessing forest management practises in relation to REDD + (Kokwe, 2012), and preparing a national MRV system. This is envisioned to provide more reliable and relevant data on changes in forest carbon stocks. The mode of production, application and circulation of deforestation knowledge in Zambia is likely to have significant implications for the country's REDD+ policy design and future on-the-ground implementation. First, deforestation knowledge production will likely continue to be dominated by the epistemic community. For instance, the on-going MRV initiative is oriented towards generating another (more accurate) estimate of forest cover, which is thus de facto prioritised over other types of knowledge production, such as research on woodland regeneration and dynamics. This dominance may also serve to maintain sectorial barriers, such as those between agriculture and forestry that obstruct the identification and implementation of alternative REDD + solutions able to contribute to meeting the diverse goals of curtailing deforestation and providing positive livelihood outcomes. As for instance noted by Kokwe (2012), intense maize monocropping is currently being “…substantially subsidized at the expense of other REDD+ compatible agricultural cropping systems, such as agroforestry systems” (p. 29). It thus appears unlikely that the important deforestation drivers are identified and addressed; this has otherwise been identified as a prerequisite for REDD + to succeed (Gupta, 2012). Second, there are no indications of a new influential coalition emerging, e.g. encompassing NGOs and local community organisations that will be able to challenge the epistemic community's authority to define the dominant deforestation discourse. This does not necessarily imply that the discourse cannot be changed, e.g. to include acknowledgement of the ability of woodlands to regenerate after slash-and-burn agriculture. However, the continued epistemic community domination may further make it difficult to address complex, locally grounded issues, such as forest tenure. It may also lead to long-run tensions over the design of benefit sharing mechanisms (Luttrell et al., 2012). The lack of consensus

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on benefit sharing, between the government and local communities, was a key reason that Joint Forest Management never achieved any notable success in Zambia (Kokwe, 2007; Phiri et al., 2012). If knowledge production and circulation, related to deforestation and REDD+, remain discursively framed and controlled by an epistemic community constituted by actors associated with state and international forestry authorities, it appears likely that the REDD + design will not include alternative goals and activities supported by less powerful actors, such as local communities. Recent research has noted that this is likely to undermine REDD+ implementation (Murdiyarso et al., 2012; VisserenHamakers et al., 2012). Third, the history of forest cover data management does not indicate that the dominant epistemic community is willing to disclose and share data. Such lack of transparency could impede the implementation of a performance-based payment system such as REDD+ if funding agencies do not trust the data upon which emission reductions are assessed and payments made. The lack of transparency may also undermine trust in the REDD + process and programme by non-epistemic community actors. In sum, these factors threaten to undermine REDD + efforts and associated policies in Zambia. There is a risk that the REDD+ programme, rather than leading to reduced emissions and co-benefits will simply become just another programme unable to change on-the-ground realities. The present study directly contributes to on-going international discussions on design and implementation of REDD + programmes. The described processes of framing deforestation illustrate how, even in the face of weak and contradictory evidence, a dominant deforestation discourse emphasising the role of local communities as deforestation agents can be maintained. This also indicates a lack of alignment between REDD + initiatives and the interests of local communities. Findings thus support critical views of REDD+ arguing that local communities are seen as a problem rather than participants in the process (e.g. Holmgren, 2013; Thompson et al., 2011). Findings also lend support to the argument that REDD+ may provide incentives to countries to estimate high rates of deforestation in order to attract additional financial support from the international community (e.g. Karsenty and Ongolo, 2012). 5. Conclusion To enhance the understanding of environmental science–policy interactions, and those specifically taking place in REDD+ policy processes, theoretical approaches found in Political Ecology, but also Science and Technology Studies (STS) are useful. In the case of Zambia, we found that attempts over the past 50 years to estimate forest cover and deforestation rates, and identifying deforestation causes, did not give a clear indication of the extent of forest cover nor its change over time. Available estimates are difficult to compare due to inconsistent use of key terms, methodological pluralism, and differences in social framing. Moreover, there is only limited empirical evidence on what constitutes the main causes of deforestation. This, however, has not prevented the establishment of a dominant deforestation discourse that has remained essentially constant since colonial times. This discourse holds that there is rapid deforestation and increasing degradation, caused by population growth and neo-Malthusian pressures that push small-scale farmers to clear forest for agricultural production and meeting energy needs. Based on our analysis of how knowledge of forest cover and deforestation are grounded and how such knowledge is circulated in the REDD+ policy process, we argue that the ZFD, the FAO, and associated professionals constitute an influential epistemic community, which is able to define, modify, and heavily influence the deforestation discourse and allied policy initiatives, such as the REDD+ policy process. Further, the ZFD and the FAO may also be considered boundary organisations that – through promoting terminology and production of forest statistics, consultancy reports and formal policy documents – are able to circulate deforestation “facts” amongst actors in the policy process and across the boundaries of “science” and “politics”. Along the way, biophysical evidence

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becomes black boxed and increasingly difficult to separate from politics, e.g. when the origin and units of measure of deforestation estimates are blurred, or the National Forestry Policy draft specifies causes of deforestation as a matter of fact without referring to sources of evidence. Further, the REDD + policy process is characterised by largely ignoring biophysical evidence that does not support the deforestation discourse. Consequently, this is likely to lead to a REDD+ policy process in Zambia characterised by exclusivity in terms of actor participation, uptake of knowledge and ideas for REDD + solutions, and knowledge sharing. In turn, realising positive ecological and livelihood outcomes through REDD+ implementation could become difficult. To the extent that REDD+ could be viewed as a fallacy, drawing attention from what is important – a REDD herring. Acknowledgements All respondents are thanked for their participation. We are grateful to the FAO Zambia country office and the Zambian Forestry Department for providing institutional and logistical support during fieldwork. Dr. Davidson Gumbo at the Zambian CIFOR (Center for International Forestry Research) office and Dr. Jacob Mwitwa at Copperbelt University are acknowledged for inspiring academic discussions and country specific information. Dr. Christian Gamborg and Dr. Iben Nathan at the Faculty of Science, University of Copenhagen, provided valuable comments to the draft manuscript. Two anonymous reviewers provided constructive and highly useful criticism. The research was supported by Faculty of Science at University of Copenhagen and the Danish Development Assistance (DANIDA). References Adger, W.N., Benjaminsen, T.A., Brown, K., Svarstad, H., 2001. Advancing a political ecology of global environmental discourse. Dev. Chang. 32, 681–715. Agrawal, A., Nepstad, D., Chhatre, A., 2011. Reducing emissions from deforestation and forest degradation. Annu. Rev. Environ. Resour. 36, 373–396. Alajärvi, P., 1996. Forest management planning and inventory, draft report. Zambia Forest Action PlanForest Department, Lusaka. Ali, J., Benjaminsen, T.A., Hammad, A.A., Dick, Ø.B., 2005. The road to deforestation: an assessment of forest loss and its causes in Basho Valley, Northern Pakistan. Glob. Environ. Chang. 15, 370–380. Art, R.J., Jervis, R., 1996. International Politics: Enduring Concepts and Contemporary Issues, 11th ed. Pearson, New York. Arts, B., 2012. Forests policy analysis and theory use: overview and trends. For. Policy Econ. 16, 7–13. Bassett, T.J., Zueli, K.B., 2000. Environmental discourses and the Ivorian Savanna. Ann. Assoc. Am. Geogr. 90 (1), 67–95. Beymer-Farris, B.A., Basset, T.J., 2012. The REDD menace: resurgent protectionism in Tanzania's mangrove forests. Glob. Environ. Chang. 22, 332–341. Bryman, A., 2008. Social Research Methods, Third edition. Oxford University Press, Oxford. Chakanga, M., de Backer, M., 1986. The forest vegetation of Zambia. Technical note no. 2: the forest areaFAO, Ndola. Chidumayo, E.N., 1987. Woodland structure, destruction and conservation in the Copperbelt Area of Zambia. Biol. Conserv. 40, 89–100. Chidumayo, E.N., 1993. Zambian charcoal production. Miombo woodland recovery. Energy Policy 21, 586–597. Chidumayo, E.N., 1994. Inventory of wood used in charcoal production in Zambia. A report for the Biodiversity Support ProgramWorld Wildlife Fund, Washington D.C. Chidumayo, E.N., 1997. Miombo ecology and management. An introduction. Intermediate Technology Development Group Publishing, London. Chidumayo, E.N., 2002. Changes in Miombo woodland structure under different land tenure and use systems in central Zambia. J. Biogeogr. 29, 1619–1626. Chidumayo, E.N., 2004. Development of Brachystegia-Julbernadia woodland after clearfelling in Central Zambia: evidence for high resilience. Appl. Veg. Sci. 7, 237–242. Chileshe, A., 2001. Forestry Outlook Studies in Africa (FOSA): Zambia. Forestry Department, Ministry of Environment and Natural Resources and Food and Agriculture Organization of the United Nations, Lusaka and Rome. Chundama, M., 2009. Preparing for REDD in dryland forests: investigating the options and potential synergy for REDD payments in the Miombo eco-region. Zambia country studyIIED, London. Crewe, E., Young, J., 2002. Bridging research and policy: context, evidence and links. ODI Working Paper 173. Overseas Development Institute, London. Cross, M.K.D., 2013. Rethinking epistemic communities twenty years later. Rev. Int. Stud. 39, 137–160. de Backer, M., Castro, P., Chakanga, M., Mazzoudi, E.H.E., Gane, M., 1986. Wood energy consumption and resource survey, Zambia. Technical findings and conclusions. UNDP, Ndola.

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